1980Phenomenological model of shock initiation in heterogeneous explosives

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平顶山24年小学O卷英语第四单元自测题

平顶山24年小学英语第四单元自测题考试时间：80分钟（总分:100）A卷一、综合题(共计100题)1、填空题：I like to ______ (与社区互动).2、Which animal is known for its ability to change colors?A. ChameleonB. FrogC. ParrotD. Snake3、填空题：I like to create ________ (幻影) with shadows using a flashlight. It’s a fun ________ (游戏).4、填空题：I like to practice ______ (瑜伽) to relax and stay healthy.5、听力题：Reactants are the starting materials in a _____.6、填空题：The __________ (农业) is important for our economy.7、填空题：A rabbit's fur can come in many different ______ (颜色).8、填空题：The _____ (小鸟) builds nests to raise its young.9、听力题：The first successful flight of a hot air balloon was in _______.10、What do we call the science of studying the stars and planets?A. BiologyB. ChemistryC. AstronomyD. Physics答案: C11、How many states are there in Australia?A. 5B. 6C. 7D. 812、Which season comes after spring?A. FallB. WinterC. SummerD. Rainy答案: C13、What do you call a young crocodile?A. HatchlingB. PupC. CalfD. Kit答案:A14、Which animal is known for its long neck?A. TigerB. GiraffeC. ElephantD. Kangaroo答案: B. Giraffe15、听力题：An endothermic reaction absorbs ______ from the surroundings.16、听力题：The ____ is a friendly creature that enjoys being around people.17、填空题：I like to ______ (参加) cooking competitions.18、填空题：My teacher teaches us __________. (英语)19、What do we call the time it takes for the Earth to complete one rotation?A. YearB. DayC. MonthD. Hour答案: B20、填空题：A _____ (63) is a place where animals live in the wild.21、听力题：We will _______ (camp) under the stars.22、What animal is known for its ability to mimic sounds?A. DogB. ParrotC. CatD. Snake23、What is the name of the famous ancient city in Egypt?A. CairoB. AlexandriaC. ThebesD. All of the above24、Which animal is known for its ability to mimic sounds?A. ParrotB. EagleC. OwlD. Sparrow答案：A25、听力题：The chemical symbol for silicon is ______.26、What is the primary color of a strawberry?A. BlueB. RedC. YellowD. Green答案: B27、What is the largest mammal in the ocean?A. SharkB. DolphinC. WhaleD. OctopusThe Earth's crust is made up of ______ (tectonic) plates.29、填空题：The ________ (栽培) of herbs is popular.30、填空题：My friend enjoys helping __________ (他人).31、听力题：The weather is _____ (nice/bad) today.32、听力题：The fish are ______ (swimming) in the pond.33、填空题：I love building with my ________ (乐高) sets every weekend.34、填空题：I usually call my aunt . (我通常称呼我的阿姨为)35、What do we call a person who studies the effects of space on human behavior?A. Space PsychologistB. SociologistC. AnthropologistD. Biologist答案: A36、听力题：A ____ is often found in the water and has a sleek body.37、What is 8 x 2?A. 14B. 16C. 18D. 20答案:B38、填空题：We went ______ (露营) last summer.39、听力题：I see a _____ butterfly in the garden. (beautiful)A rabbit's fur can be very ______ (温暖).41、What do you call a tool used to cut paper?A. KnifeB. ScissorsC. RulerD. Tape答案:B42、What do we call a young tortoise?A. HatchlingB. PupC. KitD. Chick答案:A. Hatchling43、听力题：The alligator swims in the _____.44、听力题：Acids taste ______ and can be sour.45、听力题：The process of crystallization purifies ______.46、填空题：I can create a _________ (玩具购物车) and pretend to go grocery shopping.47、听力题：The chemical formula for silicon dioxide is _____.48、What is the name of the famous British author who wrote "Pride and Prejudice"?A. Charlotte BrontëB. Jane AustenC. Emily DickinsonD. Mary Shelley答案:B49、What do you call the person who flies an airplane?A. PilotB. NavigatorC. EngineerD. StewardessThe __________ (历史的情感联系) enhance community.51、What do you call the tall grass that grows in water?A. TreeB. ShrubC. ReedD. Moss答案:C52、What is the process of changing a solid into a liquid called?A. MeltingB. FreezingC. CondensingD. Boiling答案:A53、填空题：This is my ______ friend. (这是我的______朋友。

For the Quantum Heisenberg Ferromagnet, Tao to the Proof of a Phase Transition

a r X i v :m a t h -p h /0202044v 1 27 F eb 2002For the Quantum Heisenberg Ferromagnet,Tao to the Proof of a Phase TransitionPaul Federbush Department of Mathematics University of Michigan Ann Arbor,MI 48109-1109(pfed@)Abstract1Introduction.This work does not depend on our previous poking at the Quantum Heisenberg system,[1],[2].We only learned from this previous study the surprisingly relevant relation between solutions of the heat equation and quantities in this model.There is the precise rigorous relation of Eq.(19)of[1];and the numerical approximations of[1], which[2]makes feeble eﬀort to justify.The key conjecture of this paper,as given in Section3,is so inspired.We work in d dimensions,on a cubical periodic lattice,Λ,of side L,so the total number of lattice sites is N=L d.The Hilbert space,H,splits into sectors H i,i= 0,...,N,where in H i there are i spins up.The Hamiltonian,H,is given asH=− i∼j(I ij−1)(1.1) I ij interchanges the spins of two neighboring sites i and j of the latticeΛ.We will sometimes view H as an operator on H,and sometimes as an element of the group algebra of the permutation group on the N vertices ofΛ,allowing I ij to interchange vertices i and j.In what follows most of the development is not precise and rigorous,hand waving in nature.The conjectures are not precise either.We are far from a mathematically rigorous treatment.However,the independence of the arguments on precise details also means that a mathematically honest proof of the phase transition along these lines will not depend on obtaining proofs of the conjectures in a very circumscribed form.I.e., estimates of theﬂavor of our conjectures should work.2Strategy for Proving a Phase Transition.In this Section all arguments are precise,and results proven or easy to prove.H is taken as an operator on H.We let T r(e−βH)L,i be the trace of e−βH restricted to H i,with L the edge size(which we will vary).LetFβ(L,n)=ni=0T r(e−βH)L,i.(2.1)We noteFβ(L,N)=T r(e−βH).(2.2)Theorem.2andβbeﬁxed.Then ifFβ(L,[rN])T r(e−βH)L(2.4) where the subscript,L,of course,indicates the edge size ofΛ.We argue for spontaneous magnetization by excluding the existence of a d(|i−j|)withlimx→∞d(x)=0(2.5) for which|ρL(i,j)|<d(|i−j|)(2.6) if|i−j|<LWe assume,by contradiction,the existence of such a d in the presence of Eq.(2.3) being satisﬁed.We let H′= i∈Λσzi and considerT r(e−βH−δH′)LA L(δ)=The approximation we now conjecture is˜Cα−→β→∞˜CNi=1gβ(i,iα).(3.4)But we are not going to be precise in what sense the right side approximates the left side(the type of convergence).We will in fact replace the left side by the right side in expressions we use from now on.What we desire in a rigorous form of(3.4)is a result that enables the remaining proof to proceed.We have ideas how to mathematically prove approximations similar to(3.4)and plan to work on them as theﬁrst step in rigorizing the present paper.4The PolymersEach permutation group element,G,has associated to it in a1-1way a partition of the|Λ|=N vertices,within each subset of the partition being given a speciﬁc cyclic ordering.If S is a subset of the partition with k vertices,then S may be given as{i,Gi,G2i,...,G k−1i}=S(4.1) for any i in S.S,of course,corresponds to a k-cycle of G.We label the vertices in S by α1,α2,...,αk with Gαi=αi+1,i<k,Gαk=α1.To this k-cycle S it is natural from (3.4)to associate an“activity”e S bye S= k−1 i=1gβ(αi,αi+1) gβ(α1,αk).(4.2) We have constructed a“polymer”with vertices,{αi},and activity,e S.This we call a “k-polymer”.We now consider the sum over all possible k-polymers through vertex i,each times its activity.This leads to a sumγ2,γ3,...,γk gβ(i,γ2)gβ(γ2,γ3)···gβ(γk−1,γk)gβ(i,γk)(4.3)where the vertices i,γ2,...,γk are restricted to be distinct.We estimate the sum in(4.3) to be∼1β)d·1k)d.(4.4) We argue this by viewing the sum in(4.3)to be a random walk in d dimensions,with√k steps,and step size∼√β1n s(n).(5.3)s(n)!·The factorial arises from the Boltzmann statistics.The L d factors arise from the choice of i in(4.3).The middle factors arise from(4.4)and theﬁnal factor accounts for the fact that any of the n vertices in a polymer may be theﬁrst vertex i in(4.3).We write the subsum of(5.1)we’ve approximated and expressed by eµand in standard style approximateµas follows(with s(n)written as s)µ= n sdℓn(L)−(sℓn(s)−s)− sd(µ+αsn)=0.(5.5)dsSolving(5.5)and(5.2)together one getsn12(√n(1+d/2)eαn.(5.7)We now restrict the above approximation to the trace on H k.This involves considering sequences r i(n)satisfyingn r i(n)n=k(5.8)with r i(n)satisfyingr i(n)≤s(n).(5.9) The i labels such a sequence of r i(n).ThenTr e−βH k∼= i n=1 s(n)r i(n) ·eµ.(5.10) Basically we are selecting ways of choosing which cycles have spin up,and making sure for each such choice(5.8)holds,so there are total k spins up.We again approximate the sum in(5.10)by its biggest term,using a Lagrange mul-tiplier to uphold(5.8).We letτbe the Lagrange multiplier.We geteτnr(n)=s(n)·√n d/2 eτnd=2.Case2d=3,andβ>>1.In cases1and2one has the equation(5.6)satisﬁed withα<0,s(n)as given by (5.7).Equations(5.6)and(5.7)are satisﬁed using only“ﬁnite”k-cycles,there are no “inﬁnite”k-cycles.There is not spontaneous magnetization.In case3,to satisfy(5.6)αmust be greater than zero.For given suchβ,as L gets large one has the following limiting situation:α=0+ε.(6.1) That is,α→0+as L→∞.s(n)=2 Lβ 31for“ﬁnite”n,and in addition a single“inﬁnite”k-cycle with k given byk=N−2N 1n3/2.(6.3)This single“inﬁnite”k-cycle is needed to complement in the sum of(5.6)the contributions of“ﬁnite”k-cycles.It is perhaps easy for the reader to believe,and even deduce at the level of our calculations that the presence of the“inﬁnite”k-cycle yields spontaneous magnetization. Alternatively we may use the arguement of Section2,picking r satisfying(forβ>>1)r>>1n3/2(6.4)We have attempted some improvements to the estimates used,particularly in Section 5,but the ones we have considered have not changed theﬂow of the argument and results in any meaningful way.We believe the picture we have presented herein is essentially correct,and that the key test and challenge to completing a rigorous presentation will be in proving a satisfactory form of(3.4),the central approximation.The remaining steps, not unremniscent of a Peierls’argument,may be easier to substantiate.AcknowledgementReferences。

FORNELL教授经典的顾客满意度论文1

TOTAL QUALITY MANAGEMENT, VOL. 11, NO. 7, 2000, S869-S882EUGENE W. ANDERSON & CLAES FORNELLNational Quality Research Center, University of Michigan Business School, Ann Arbor,MI 48109-1234, USAABSTRACT How do we know if an economy is performing well? How do we know if a company is performing well? The fact is that we have serious difficulty answering these questions today. The economy—for nations and for corporations—has changed much more than our theories and measurements. The development of national customer satisfaction indices (NCSIs) represents an important step towards addressing the gap between what we know and what we need to know. This paper describes the methodology underlying one such measure, the American Customer Satisfaction Index (ACSI). ACSI represents a uniform system for evaluating, comparing, and—ultimately- enhancing customer satisfaction across ifrms, industries and nations. Other nations are now adopting the same approach. It is argued that a global network of NCSIs based on a common methodology is not simply desirable, but imperative.IntroductionHow do we know if an economy is performing well? How do we know if a company is performing well? The fact is that we have serious difficulty answering these questions today. It is even more difficult to tell where we are going.Why is this? A good part of the explanation is that the economy—for nations and for corporations—has changed much more than our theories and measurements. One can easily make the case that the measures on which we rely for determining corporate and national economic performance have not kept pace. For example, the service sector and information technology play a dominant role in the modern economy. An implication of this change is that economic assets today reside heavily in intangibles—knowledge, systems, customer relationships, etc. (see Fig. 1). The building of shareholder wealth is no longer a matter of the management of ifnancial and physical assets. The same is true with the wealth of nations.As a result, one cannot continue to apply models of measurement and theory developed for a 'tangible' manufacturing economy to the economy we have today. How important is it to know about coal production, rail freight, textile mill or pig-iron production in the modern economy? Such measures are still collected in the US and reported in the media as if theyhad the same importance now as they did over 50 years ago.The problem gets worse when we take all these measures, add them up and draw conclusions. For example, in early 1999, the US stock market set an all time record highCorrespondence: E. W. Anderson, National Quality Research Center, University of Michigan Business School, Ann Arbor, MI 48109-1234, USA. Tel: (313) 763-1566; Fax: (313) 763-9768; E-mail: genea@ISSN 0954-4127 print/ISSN 1360-0613 online/00/07S869-14 0 2000 Taylor & Francis LtdS870 E. W. ANDERSON & C. FORNELLDow Jones Industrials:Price-to-Book Ratios11970 1999Source: Business Week, March 9, 1999Figure 1. Tangible versus intangible sources of value, 1970-99.with the Dow Jones Index passing 11 000 points, unemployment was at record lows, the economy expanded and inflation was almost non-existent. These statistics suggested a strong economy, which was also what was reported in the press and in most commentary by economists. As always, however, the real question is: Are we better off? How well are the actual experiences of people captured by the reported measures? Do the things economists and Governments choose to measure correspond with how people feel about their economic well-being? A closer inspection of the numbers and their underlying statistics reveals a somewhat different picture of the US economy than that typically held up as an example.?Corporate earnings growth for 1997 and 1998 were much lower than in the previous2 years, with a negative growth for 1998.?The major portion of the earnings growth in 1995 and 1996 was due to cost-cutting rather than revenue growth.?The trade deficit in 1999 was at a record high and growing.?Wages have been stagnant in the last 15 years (although there were small increases in 1997 and 1998).?The proportion of stock market capitalization versus GDP was about 150% of GDP in 1998 (the historical average is 48%; the proportion before the 1929 stock market crash was 82%).?Consumer and business debt were high and rising.?Even though many new jobs were created, 70% of those who lost their jobs got new jobs that paid less.?The number of bankruptcies was high and growing.?Worker absenteeism was at record highs.?Household savings were negative.Add the above to the fact that there is a great deal of worker anxiety over job security and lower levels of customer satisfaction than 5 years ago, and the question of whether we areyrFOUNDATIONS OF ACSI S871better off is cast in a different light. How much does it matter if we increase productivity,that the economy is growing or that the stock market is breaking records, if customers arenot satisifed? The basic idea behind a market economy is that businesses exist and competein order to create a satisifed customer. Investors will lfock to the companies that are expectedto do this well. It is not possible to increase economic prosperity without also increasingcustomer satisfaction. In a market economy, where suppliers compete for buyers, but buyersdo not compete for products, customer satisfaction defines the meaning of economic activity,because what matters in the final analysis is not how much we produce or consume, but howwell our economy satisfies its consumers.Together with other economic objectives—such as employment and growth—thequality of what is produced is a part of standard of living and a source of national competitiveness. Like other objectives, it should be subjected to systematic and uniform measurement. This is why there is a need for national indices of customer satisfaction. Anational index of customer satisfaction contributes to a more accurate picture of economicoutput, which in turn leads to better economic policy decisions and improvement of standard ofliving. Neither productivitymeasures nor price indices can be properly calibrated without taking quality into account.It is difficult to conduct economic policy without accurate and comprehensive measures. Customer satisfaction is of considerable value as a complement to the traditional measures.This is true for both macro and micro levels. Because it is derived from consumption data(as opposed to production) it is also a leading indicator of future proifts. Customer satisfactionleads to greater customer loyalty (Anderson & Sullivan, 1993; Bearden & Teel, 1983; Bolton& Drew, 1991; Boulding et al., 1993; Fornell, 1992; LaBarbera & Mazurski, 1983; Oliver,1980; Oliver & Swan, 1989; Yi, 1991). Through increasing loyalty, customer satisfactionsecures future revenues (Bolton, 1998; Fornell, 1992; Rust et al., 1994, 1995), reduces thecost of future transactions (Reichheld & Sasser, 1990), decreases price elasticities (Anderson,1996), and minimizes the likelihood customers will defect if quality falters (Anderson & Sullivan, 1993). Word-of-mouth from satisifed customers lowers the cost of attracting new customers and enhances the firm's overall reputation, while that of dissatisifed customersnaturally has the opposite effect (Anderson, 1998; Fornell, 1992). For all these reasons, it isnot surprising that empirical work indicates that ifrms providing superior quality enjoy higher economic returns (Aaker & Jacobson, 1994; Anderson et al., 1994, 1997; Bolton, 1998;Capon et al., 1990).Satisfied customers can therefore be considered an asset to the ifrm and should be acknowledged as such on the balance sheet. Current accounting-based measures are probablymore lagging than leading—they say more about past decisions than they do about tomorrow's performance (Kaplan & Norton, 1992). If corporations did incorporate customer satisfactionas a measurable asset, we would have a better accounting of the relationship between theenterprise's current condition and its future capacity to produce wealth.If customer satisfaction is so important, how should it be measured? It is too complicatedand too important to be casually implemented via standard market research surveys. The remainder of this article describes the methodology underlying the American Customer Satisfaction Index (ACSI) and discusses many of the key ifndings from this approach.Nature of the American Customer Satisfaction IndexACSI measures the quality of goods and services as experienced by those that consume them.An individual ifrm's customer satisfaction index (CSI) represents its served market's—its customers'—overall evaluation of total purchase and consumption experience, both actualand anticipated (Anderson et al., 1994; Fonrell, 1992; Johnson & Fornell, 1991).S872 E. W. ANDERSON & C. FORNELLThe basic premise of ACSI, a measure of overall customer satisfaction that is uniform and comparable, requires a methodology with two fundamental properties. (For a complete description of the ACSI methodology, please see the 'American Customer Staisfaction Index: Methodology Report' available from the American Society for Quailty Control, Milwaukee, WI.) First, the methodology must recognize that CSI is a customer evaluation that cannot be measured directly. Second, as an overall measure of customer satisfaction, CSI must be measured in a way that not only accounts for consumption experience, but is also forward-looking.Direct measurement of customer satisfaction: observability with errorEconomists have long expressed reservations about whether an individual's satisfaction or utility can be measured, compared, or aggregated (Hicks, 1934, 1939a,b, 1941; Pareto, 1906; Ricardo, 1817; Samuelson, 1947). Early economists who believed it was possible to produce a 'cardinal' measure of utility (Bentham, 1802; Marshall, 1890; Pigou, 1920) have been replaced by ordinalist economists who argue that the structure and implications of utility-maximizing economics can be retained while relaxing the cardinal assumption. How_ ever, cardinal or direct measurement of such judgements and evaluations is common in other social sciences. For example, in marketing, conjoint analysis is used to measure individual utilities (Green & Srinivasan, 1978, 1990; Green & Tull, 1975).Based on what Kenneth Boulding (1972) referred to as Katona's Law (the summation of ignorance can produce knowledge due to the self-canceling of random factors), the recent advances in latent variable modeling and the call from economists such as the late Jan Tinbergen (1991) for economic science to address better what is required for economic policy, scholars are once again focusing on the measurement of subjective (experience) utility. The challenge is not to arrive at a measurement system according to a universal system of axioms, but rather one where fallibility is recognized and error is admitted (Johnson & Fornell, 1991) .The ACSI draws upon considerable advances in measurement technology over the past 75 years. In the 1950s, formalized systems for prediction and explanation (in terms of accounting for variation around the mean of a variable) started to appear. Before then, research was essentially descriptive, although the single correlation was used to depict the degree of a relationship between two variables. Unfortunately, the correlation coefficient was otfen (and still is) misinterpreted and used to infer much more than what is permissible. Even though it provides very little information about the nature of a relationship (any given value of the correlation coefficient is consistent with an inifnite number of linear relationships), it was sometimes inferred as having both predictive and causal properties. The latter was not achieved until the 1980s with the advent of the second generation of multivariate analysisand associated sotfware (e.g. Lisrel).It was not until very recently, however, that causal networks could be applied to customer satisfaction data. What makes customer satisfaction data difficult to analyze via traditional methods is that they are associated with two aspects that play havoc with most statistical estimation techniques: (1) distributional skewness; and (2) multicollinearity. Both are extreme in this type of data. Fortunately, there has been methodological progress on both fronts particularly from the field of chemometrics, where the focus has been on robust estimation with small sample sizes and many variables.Not only is it now feasible to measure that which cannot be observed, it is also possible to incorporate these unobservables into systems of equations. The implication is that the conventional argument for limiting measurement to that which is numerical is no longer allFOUNDATIONS OF ACSI S873that compelling. Likewise, simply because consumer choice, as opposed to experience, is publicly observable does not mean that it must be the sole basis for utility measurement. Such reasoning only diminishes the influence of economic science in economic policy (Tinbergen 1991).Hence, even though experience may be a private matter, it does not follow that it is inaccessible to measurement or irrelevant for scientific inquiry, for cardinalist comparisons of utility are not mandatory for meaningful interpretation. For something to be 'meaningful,' it does not have to be 'flawless' or free of error. Even though (experience) utility or customer satisfaction cannot be directly observed, it is possible to employ proxies (fallible indicators) to capture empirically the construct. In the ifnal analysis, success or failure will depend on how well we explain and predict.Forward-looking measurement of customer satisfaction: explanation and predictionFor ACSI to be forward-looking, it must be embedded in a system of cause-and-effect relationships as shown in Fig. 2, making CSI the centerpiece in a chain of relationships running from the antecedents of customer satisfaction —expectations, perceived quality and value —to its consequences —voice and loyalty. The primary objective in estimating this system or model is to explain customer loyalty. It is through this design that ACSI captures the served market's evaluation of the ifrm's offering in a manner that is both backward- and forward-looking.Customer satisfaction (ACSI) has three antecedents: perceived quality, perceived value and customer expectations. Perceived quality or performance, the served market's evaluation of recent consumption experience, is expected to have a direct and positive effect on customer satisfaction. The second determinant of customer satisfaction is perceived value, or the perceived level of product quality relative to the price paid. Adding perceived value incorpo-rates price information into the model and increases the comparability of the results across ifrms, industries and sectors. The third determinant, the served market's expectations, represents both the served market's prior consumption experience with the firm's offeringCustomization Complaints to Complaints toinagement PersonnelPriceü GivenQualityQualityGivenPrice DelepurchasePrice Likelihood ToleranceCustomization Reliability O v e r a l l Figure 2. The American Customer Satisfaction Index model.S874 E. W. ANDERSON & C. FORNELLincluding non-experiential information available through sources such as advertising and word-of-mouth—and a forecast of the supplier's ability to deliver quality in the future.Following Hirschman's (1970) exit-voice theory, the immediate consequences of increased customer satisfaction are decreased customer complaints and increased customer loyalty (Fornell & Wemerfelt, 1988). When dissatisifed, customers have the option of exiting (e.g. going to a competitor) or voicing their complaints. An increase in satisfaction should decrease the incidence of complaints. Increased satisfaction should also increase customer loyalty. Loyalty is the ultimate dependent variable in the model because of its value as aproxy for profitability (Reichheld & Sasser, 1990).ACSI and the other constructs are latent variables that cannot be measured directly, each is assessed by multiple measures, as indicated in Fig. 1. To estimate the model requires data from recent customers on each of these 15 manifest variables (for an extended discussion of the survey design, see Fomell et al., 1996). Based on the survey data, ACSI is estimated as shown in Appendix B.Customer satisfaction index properties: the case of the American Customer Satisfaction IndexAt the most basic level the ACSI uses the only direct way to ifnd out how satisifed or dissatisifed customers are—that is, to ask them. Customers are asked to evaluate products and services that they have purchased and used. A straightforward summary of what customers say in their responses to the questions may have certain simplistic appeal, but such an approach will fall short on any other criterion. For the index to be useful, it must meet criteria related to its objectives. If the ACSI is to contribute to more accurate and comprehen-sive measurement of economic output, predict economic returns, provide useful information for economic policy and become an indicator of economic health, it must satisfy certain properties in measurement. These are: precision; validity; reliability; predictive power; coverage; simplicity; diagnostics; and comparability.PrecisionPrecision refers to the degree of certainty of the estimated value of the ACSI. ACSI results show that the 90% confidence interval (on a 0-100 scale) for the national index is ± 0.2 points throughout its first 4 years of measurement. For each of the six measured private sectors, it is an average ± 0.5 points and for the public administration/government sector, it is + 1.3 points. For industries, the conifdence interval is an average ±1.0 points for manufacturing industries, + 1.7 points for service industries and ± 2.5 points for government agencies. For the typical company, it is an average ± 2.0 points for manufacturing ifrms and 2.6 points for service companies and agencies. This level of precision is obtained as a result of great care in data collection, careful variable speciifcation and latent variable modeling. Latent variable modeling produces an average improvement of 22% in precision over use of responses from a single question, according to ACSI research.ValidityValidity refers to the ability of the individual measures to represent the underlying construct customer satisfaction (ACSI) and to relate effects and consequences in an expected manner. Discriminant validity, which is the degree to which a measured construct differs from other measured constructs, is also evidenced. For example, there is not only an importanto-FOUNDATIONS OF ACSI S875 conceptual distinction between perceived quality and customer satisfaction, but also anempirical distinction. That is, the covariance between the questions measuring the ACSI ishigher than the covariances between the ACSI and any other construct in the system.The nomological validity of the ACSI model can be checked by two measures: (1) latentvariable covariance explained; and (2) multiple correlations (R'). On average, 94% of thelatent variable covariance structure is explained by the structural model. The average R2ofthe customer satisfaction equation in the model is 0.75. In addition, all coefficients relatingthe variables of the model have the expected sign. All but a few are statistically signiifcant.In measures of customer satisfaction, there are several threats to validity. The most seriousof these is the skewness of the frequency distributions. Customers tend disproportionately touse the high scores on a scale to express satisfaction. Skewness is addressed by using a fairlyhigh number of scale categories (1-10) and by using a multiple indicator approach (Fornell,1992, 1995). It is a well established fact that vaildity typically increases with the use of more categories (Andrews, 1984), and it is particularly so when the respondent has good knowledgeabout the subject matter and when the distribution of responses is highly skewed. An indexof satisfaction is much to be preferred over a categorization of respondents as either 'satisfied'or 'dissatisfied'. Satisfaction is a matter of degree—it is not a binary concept. If measured asbinary, precision is low, validity is suspect and predictive power is poor.ReliabilityReliability of a measure is determined by its signal-to-noise ratio. That is, the extent to whichthe variation of the measure is due to the 'true' underlying phenomenon versus randomeffects. High reliability is evident if a measure is stable over time or equivalent with identicalmeasures (Fonrell, 1992). Signal-to-noise in the items that make up the index (in terms of variances) is about 4 to 1.Predictive power and financial implications of ACSIAn important part of the ACSI is its ability to predict economic returns. The model, ofwhich the ACSI is a part, uses two proxies for economic returns as criterion variables: (1)customer retention (estimated from a non-linear transformation of a measure of repurchase likelihood); and (2) price tolerance (reservation price). The items included in the index areweighted in such a way that the proxies and the ACSI are maximally correlated (subject tocertain constraints). Unless such weighting is done, the index is more likely to include mattersthat may be satisfying to the customer, but for which he or she is not willing to pay.The empirical evidence for predictive power is available from both the Swedish data andthe ACSI data. Using data from the Swedish Barometer, a one-point increase in the SCSBeach year over 5 years yields, on the average, a 6.6% increase in current return-on-investment (Anderson et al., 1994). Of the firms traded on the Stockholm Stock Market Exchange, it isalso evident that changes in the SCSB have been predictive of stock returns.A basic tenet underlying the ACSI is that satisifed customers represent a real, albeit intangible, economic asset to a ifrm. By deifnition, an economic asset generates future incomestreams to the owner of that asset. Therefore, if customer satisfaction is indeed an economicasset, it should be possible to use the ACSI for prediction of company ifnancial results. It is,of course, of considerable importance that the ifnancial consequences of the ACSI arespecified and documented. If it can be shown that the ACSI is related to ifnancial returns,then the index demonstrates external validity.The University of Michigan Business School faculty have done considerable research onS876 E. W. ANDERSON & C. FORNELLthe linkage between ACSI and economic returns, analyzing both accounting and stock market returns from measured companies. The pattern from all of these studies suggests a statistically strong and positive relationship. Speciifcally:?There is a positive and significant relationship between ACSI and accounting return_ on-assets (Fornell et al., 1995).?There is a positive and signiifcant relationship between the ACSI and the market valueof common equity (Ittner & Larcker, 1996). When controlling for accounting book values of total assets and liabilities, a one-unit change (on the 0-100-point scale used for the ACSI) is associated with an average of US$646 million increase in market value. There are also significant and positive relationships between ACSI and market-to-book values and price/earnings ratios. There is a negative relationship between ACSI and risk measures, implying that firms with high loyalty and customersatisfactionhave less variability and stronger financial positions.?There is a positive and significant relationship between the ACSI and the long-term adjusted financial performance of companies. Tobin's Q is generally accepted as the best measure of long-term performance. It is deifned as the ratio of a firm's present value of expected cash lfows to the replacement costs of its assets. Controlling for other factors, ACSI has a significant relationship to Tobin's Q (Mazvancheryl et al.,1999).?Since 1994, changes in the ACSI have correlated with the stock market (Martin,1998). The current market value of any security is the market's estimate of the discounted present value of the future income stream that the underlying asset will generate. If the most important asset is the satisfaction of the customer base, changes in ACSI should be related to changes in stock price. Until 1997, the stock market went up, whereas ACSI went down. However, in quarters following a sharp drop in ACSI, the stock market has slowed. Conversely, when the ACSI has gone down only slightly, the following quarter's stock market has gone up substantially. For the 6 years of ACSI measurement, the correlation between changes in the ACSI and changes in the Dow Jones industrial average has been quite strong. The interpretation of this relationship suggests that stock prices have responded to downsizing, cost cutting and productivity improvements, and that the deterioration in quality (particularly in the service sectors) has not been large enough to offset the positive effects. It also suggests that there is a limit beyond which it is unlikely that customers will tolerate further decreases in satisfaction. Once that limit is reached (which is now estimated to be approximately —1.4% quarterly decline in ACSI), the stock market will not go up further.ACSI scores of approximately 130 publicly traded companies display a statistically positive relationship with the traditional performance measures used by firms and security analysts (i.e. return-on-assets, return-on-equity, price—earnings ratio and the market-to-book ratio). In addition, the companies with the higher ACSI scores display stock price returns above the market adjusted average (Ittner & Larcker, 1996). The ACSI is also positively correlated with 'market value added'. This evidence indicates that the ACSI methodology produces a reliable and valid measure for customer satisfaction that is forward-looking and relevant to a company's economic performance.CoverageThe ACSI measures a substantial portion of the US economy. In terms of sales dollars, it is approximately 30% of the GDP. The measured companies produce over 40%, but the ACSIFOUNDATIONS OF ACSI S877measures only the sales of these companies to household consumers in the domestic market. The economic sectors and industries covered are discussed in Chapter III. Within each industry, the number of companies measured varies from 2 to 22.The national index and the indices for each industry and sector are relfective of the total value (quality times sales) of products and services provided by the ifrms at each respective level of aggregation. Relative sales are used to determine each company's or agency's contribution to its respective industry index. In turn, relative sales by each industry are used to determine each industry's contribution to its respective sector index. To calculate the national index, the percentage contributions of each sector to the GDP are used to top-weight the sector indices. Mathematically, this is deifned as:Index for industry i in sector s at time t = ES f i;If _S S ,, S I Index for sector s at time t =I g = E ,whereSr…, = sales by ifrm f, industry i, sector s at time t= index for firm f, industry i, sector s at time tandSit = E S,, = total sales for industry i at time tS, = E S i , = total sales for sector s at time t ,The index is updated on a quarterly basis. For each quarter, new indices are estimated for one or two sectors with total replacement of all data annually at the end of the third calendar quarter. The national index is comprised of the most recent estimate for each sectorT S National index at time t — ____________ E 4, V s9t t =T -3 s W,13where I s , = 0 for all t in which the index for a sector is not estimated, and I = I for all ,, quarters in which an index is estimated. In this way, the national index represents company, industry and sector indices for the prior year.SimplicityGiven the complexity of model estimation, the ACSI maintains reasonable simpilcity. It is calibrated on a 0-100 scale. Whereas the absolute values of the ACSI are of interest, much of the index's value, as with most other economic indicators, is found in changes over time, which can be expressed as percentages.DiagnosticsThe ACSI methodology estimates the relationships between customer satisfaction and its causes as seen by the customer: customer expectations, perceived quality and perceived value. Also estimated are the relationships between the ACSI, customer loyalty (as measured by customer retention and price tolerance (reservation prices)) and customer complaints. The。

Relating Communicating Processes with Different Interfaces

2
J.Burton, M.Koutny and G.Pappalardo
The process of reﬁning the target into the implementation also permits the control structure of the latter to be changed. In such a case, Q is said to implement P in the twofold sense that: (i) Q describes the internal structure of P in a more concrete and detailed manner; and still (ii), if this new structure is (conceptually) hidden, Q and P will exhibit the same behaviour at their external interface, which is assumed to be the same for both. Indeed, the standard notions of reﬁnement, such as those of [8, 16, 18], are interested only in the behaviour observable at the interface of processes, and require the interfaces of the speciﬁcation and implementation to be the same, so as to facilitate comparison. Yet in deriving an implementation from a speciﬁcation we will often wish to implement abstract, high-level interface actions at a lower level of detail and in a more concrete manner. For example, the channel connecting Q to another component process may be unreliable, and so may need to be replaced by a pair of channels, one for data and one for acknowledgments. Or Q itself may be liable to fail, so that its behaviour may have to be replicated, with each new component having its own communication channels to avoid a single channel becoming a bottleneck [13] (such a scenario was one of the major historical motivations behind the current work [11, 15]). Or it may simply be the case that a high-level action of P is rendered in a more concrete, and hence more implementable, form. As a result, the interface of an implementation process may exhibit a lower (and so diﬀerent) level of abstraction to a speciﬁcation process. In the process algebraic context, dealing with this phenomenon of interface diﬀerence necessitates the development of what Rensink and Gorrieri [17] have termed a vertical implementation relation. This should correctly capture the nature of the relationship holding between a speciﬁcation and an implementation whose interfaces diﬀer; and should collapse into the standard, horizontal one whenever the interfaces happen to coincide. Within the FD (failure-divergence) model of CSP [9], we have pioneered such an approach in works like [11, 12, 15]), on whose results the present one introduces major advances, as argued in the Conclusions. Our treatment deals with interface diﬀerence using the notion of extraction pattern. Such a device interprets the behaviour of a system at the level of communication traces, by relating behaviour on a set of channels in the implementation to behaviour on a speciﬁc channel in the speciﬁcation. In addition, it allows the behaviour of an implementation to be suitably constrained, in connection to, e.g., well-formedness of input traces and deadlock properties. The set of all extraction patterns relating the interface of the implementation process to that of the speciﬁcation appears as a formal parameter in the implementation relation we develop. We now consider the potential applications of the approach outlined to veriﬁcation, in order to deduce two light but natural restrictions which must be placed upon any sensible vertical implementation relation, and are indeed met by that presented in this work. Suppose the speciﬁcation system is in the form df P = (P1 P2 . . . Pn ) \ A, where A is the set of events on which synchronization among the Pi components takes place. Correspondingly, let the implemendf tation system be Q = (Q1 Q2 . . . Qn ) \ B . In general, the communication

students approach to learning(学习品质)

ISSN 1306-3065 Copyright © 2006-2013 by ESER, Eurasian Society of Educational Research. All Rights Reserved.Student Approaches to Learning in Physics – Validityand Exploration Using Adapted SPQManjula Devi Sharma*1, Chris Stewart 1, Rachel Wilson 1, and MuhammedSait Gökalp 2Received 22 December 2012; Accepted 9 March 2013Doi: 10.12973/ijese.2013.203aAbstract: The aim of this study was to investigate an adaptation of the Study ProcessesQuestionnaire for the discipline of physics. A total of 2030 first year physics students at an Australian metropolitan university completed the questionnaire over three different year cohorts. The resultant data has been used to explore whether the adaptation of the questionnaire is justifiable and if meaningful interpretations can be drawn for teaching and learning in the discipline. In extracting scales for deep and surface approaches to learning, we have excised several items, retaining an adequate subset. Reflecting trends in literature, our deep scale is very reliable while the surface scale is not so reliable. Our results show that the behaviour of the mean scale scores for students in different streams in first year physics is in agreement with expectations. Furthermore, different year cohort performance on the scales reflects changes in senior high school syllabus. Our experiences in adaptation, validation and checking for reliability is of potential use for others engaged in contextualising the Study Processes Questionnaire, and adds value to the use of the questionnaire for improving student learning in specific discipline areasKeywords: Student approaches to learning, learning in disciplines, university physics education1University of Sydney, Australia* Corresponding author. Sydney University Physics Education Research group, School of Physics, University of Sydney, NSW 2006, Australia. Email: sharma@.au 2Dumlupınar University, TurkeyIntroductionSince the mid 1960’s a series of inventories exploring student learning in highereducation have been developed based on learning theories, educational psychology and study strategies. For reviews of the six major inventories see Entwistle and McCune (2004)International Journal of Environmental & Science EducationVol. 8, No. 2, April 2013, 241-253242C o p y r i g h t © 2006-2013 b y E S E Rand Biggs (1993a). As can be seen from the reviews, these inventories have two common components. One of these components is related to study strategies and the other one is about cognitive processes. Moreover, these inventories usually have similar conceptual structures and include re-arrangement of the items (Christensen et al., 1991; Wilson et al., 1996).In the current study, as one of these inventories, The Study Processes Questionnaire (SPQ) has been selected to be adapted for use in physics. The SPQ is integrated with the presage-process-product model (3P model) of teaching and learning (Biggs, 1987). Several studies have successfully used the SPQ across different culture s and years to compare students’ approaches in different disciplines (Gow et al., 1994; Kember & Gow, 1990; Skogsberg & Clump, 2003; Quinnell et al., 2005; Zeegers, 2001). Moreover, several other researchers used modified version of the SPQ at their studies (Crawford et al. 1998a,b; Fox, McManus & Winder, 2001; Tooth, Tonge, & McManus, 1989; Volet, Renshaw, & Tietzel, 1994). For example, Volet et al (2001) used a shortened SPQ included 21 items to assess cross cultural differences. Fox et al (2001) modified the SPQ and tested its structure with confirmatory factor analysis. In their study the modified version of the SPQ had 18 items, and this shortened version had same factor structure as the original SPQ. In another study, Crawford et al. (1998a, b) adapted the SPQ for the discipline of mathematics. That adapted questionnaire was named as Approaches to Learning Mathematics Questionnaire.Three different approaches of the students to learning are represented in the SPQ: surface, deep, and achieving approaches. Idea of approaches to learning was presented by Marton and Säljö (1976) and further discussed by several other researchers (eg. Biggs, 1987; Entwistle & Waterston, 1988). Basically, surface approach indicates that the students’ motivation to learn is only for external consequences such as getting the appreciation of the teacher. More specifically, it is enough to fulfill course requirements for the students with surface approach. On theother hand, a deep approach to learning indicates that the motivation is intrinsic. This approach involves higher quality of learning outcomes (Marton & Säljö, 1976; Biggs, 1987). Students with deep approach to learning try to connect what they learn with daily life and they examine the content of the instruction more carefully. On the other hand, achieving approach is about excelling in a course by doing necessary things to have a good mark. However, current study is not focused on this approach. Only the first two approaches were included in the adapted SPQ.Inventories like the SPQ are used in higher education because of several reasons. Such inventories can help educators to evaluate teaching environments (Biggs, 1993b; Biggs, Kember, & Leung, 2001). Moreover, with the use of these inventories, university students often relate their intentions and study strategies for a learning context in a coherent manner. On the other hand, the SPQ is not a discipline specific inventory. It can be used across different disciplines. However, in a research study, if the research questions are related with the common features of learning and teaching within 3P model framework, the SPQ can be used satisfactorily for all disciplines. But, a discipline specific version of the SPQ is required if resolution of details specific to a discipline area is necessary for the research questions. Moreover, in order to reduce systematic error and bias that can be resulted from students in different discipline areas; a discipline specific version may be required. As a community of educators, we are aware that thinking, knowing, and learning processes can differ across discipline areas. A direct consequence of this acknowledgement is the need to understand and model learning in specific discipline areas, such as by adapting the SPQ. However, for the theoretical framework to be valid the conceptual integrity of the inventory must be maintained.This paper reports on how the SPQ has been adapted for physics. The teaching context is first year physics at a research focused Australian university where students are grouped according to differing senior243Student approaches to learning in physicsC o p y r i g h t © 2006-2013 b y E S E Rhigh school experiences into Advanced, Regular, and Fundamentals streams.We report on the selection of items for the deep and surface scales and reliability and validity analyses. A comparison of the Advanced, Regular and Fundamentals streams is carried out to ensure that interpretations associated with the deep and surface scales are meaningful. This is a stage of a large-scale project. The project aims to understand and improve student learning based on the deep and surface approaches to learning inherent in the 3P model (Marton & Säljö, 1976; Biggs, 1987).The studyAs mentioned before, The SPQ has been designed for higher education; however, this questionnaire is not discipline specific. Therefore, in this study, we adapted the SPQ to physics for the following reasons: (1) The first year students have confusions about university studies when they come to university (White et al., 1995). This can lead to misinterpretation of the items. However, specific items related to physics can reduce these misinterpretations. For example students enrolled in a general science degree would view questions related to employment differently to those in professional degrees, and the students we have surveyed are from a range of degree programs. (2) In order to compare the students from the different discipline areas, we need discipline specific inventories. (3) We believe that there are contentious items in the original SPQ and aspects that are specific to physics. For example the use of “truth” in the following item was strongly challenged by a group of physicists validating the questionnaire. While I realize that truth is forever changing as knowledge is increasing, I feel compelled to discover what appears to me to be the truth at this time (Biggs, 1987, p. 132).The item was changed to the following, more in line with the post-positivist paradigm and agreeable to physicists.While I realize that ideas are always changing as knowledge is increasing, I feel a need to discover for myselfwhat is understood about the physical world at this time.One could argue that this is an issue of clarifying the item rather than being specific to physics. However, to our knowledge the clarity of this item has not been debated in literature.Just after we commenced this study in 2001, we became aware that Biggs et al (2001) had produced a revised Study Processes Questionnaire (R-SPQ- 2F). However, it was too late for our study and we did not switch midway. There are four main differences between the SPQ and the R-SPQ-2F; first, the removal of all items on employment after graduation; second, increased emphasis on examination; third, removal of words that imply specificity; and fourth exclusion of the contentious achieving factor identified by Christensen et al., 1991. We focus on the deep and surface approaches and not on the strategy and motive sub-scales as these are not pertinent to our larger study. The SPQ deep and surface scales, in particular, have been shown to be robust (see for example Burnett & Dart, 2000).The participant of the current study was from a university in New South Wales, Australia. Students are provided three basic physics units in the School during their first semester of university: Fundamentals, Regular or Advanced. Students are divided into these three groups of physics units based on their senior high school physics backgrounds. The students from the Fundamentals unit have done no physics in senior high school or have done poorly. On the other hand, in the Regular unit, there were the students had scored high grades in senior high school physics. The last unit, the Advanced unit, is suitable for those who have done extremely well overall in physics during all their years in senior high school.The three physics units that students can register in are for the degree programs in Engineering, Medical Science and Arts. Students who intend to major in physics as well as postgraduate physics students are selected from those enrolled in all three basic physics course in their first semester at university. The largest proportion of students of physics major is from the Advanced244C o p y r i g h t © 2006-2013 b y E S E Rstream, followed by those in the Regular stream, and finally the Fundamentals stream. The data was collected from these streams from 2001 to 2004. From 2001 to 2004, the high school physics syllabi and assessment system was changed in the state of New South Wales in Australia. The details of the changes can be seen in Binnie (2004). Due to these changes, the 2004 cohort of students in this study were instructed using a different curriculum.Within the above context, we have adapted the SPQ to generate a Study Processes Questionnaire for Physics (SPQP). The research questions addressed in this paper are as follows.(a) How do the factor solutions for the SPQP compare with those of the SPQ? (b) Is the SPQP reliable and valid?(c) Are the scales robust enough to reflect detail in senior high school syllabus change? The answers to the research questions will determine if the SPQP is a reliable and valid measure of student approaches to learning physics in our context.MethodRevising the items for the SPQPWe have adapted the SPQ by simply inserting the word “physics” in some items and making substantial changes to others. The adaptations are based on our experiences of student responses to open-ended questions and discipline knowledge, and have been extensively discussed amongst a group of physics educators. The adaptations are of the types listed below. (See appendix A for all items and the types of adaptations.) Type 0: No changeType 1: A simple insertion of terms such as “physics”, “studying physics”.I find that at times studying gives me afeeling of deep personal satisfaction. I find that at times studying physics gives me a feeling of deep personal satisfaction.Type 2: A substantial change in wording that can change the meaning, without intending to.I usually become increasingly absorbed in my work the more I do. When studying physics, I become increasingly absorbed in my work the more I do.Type 3: An intentional change in meaning.My studies have changed my views about such things as politics, my religion, and my philosophy of life. My studies in physics have challenged my views about the way the world works.The number of items corresponding to each Type of change is displayed in Table 1, as are the number of items selected from each Type for inclusion in the SPQP. Type 1 items were more useful in generating the items used in the SPQP.Administering the SPQPThe SPQP was administered at the beginning of the first semester to students in the Advanced, Regular and Fundamentals streams in 2001, 2002 and 2004, respectively. On the questionnaire, the students were requested to indicate their level of agreement with each item on a Likert scale with the options of Strongly Disagree, Disagree, Neutral, Agree, and Strongly Agree . Response rates of 2001, 2002, and 2004 cohorts was 95%, 65%, and 85%, respectively. Except 2002 cohort, the response rates were satisfactory. The main reasons of the lower response rate of 2002 cohort were the changes in class organization and questionnaire administration. Over these245Student approaches to learning in physicsC o p y r i g h t © 2006-2013 b y E S E Rthree years, a total of 2030 first year physics student were responded the SPQP: 63 percent of students in the Fundamentals stream was female, and about 30 percent of them was females in the Regular and Advanced streams. Nevertheless, the three streams are similar in other respects. The sample size of 2030 is large enough to access the natural variance within the diverse population. However, due to missing answers some of the cases were excluded from the analysis. These exclusions were only about 3% of the whole sample. Therefore, we can say that this missing data did not affect the overall results. Data Analysis MethodsFollowing analyses were carried out to answer research questions.(a) Both exploratory and confirmatory factor analyses were performed to validate the two-factor solution: the deep and surface scales. (b) Cronbach’s alpha coefficients were calculated to determine the reliability for the deep and surface scales for the complete data set and for each stream.(c) ANOVA and boxplots were used to determine if the SPQP is able to differentiate between the three streams and changes in syllabus.ResultsFactor analysisIn order to gain construct related evidence for the validity of the SPQP, exploratory and confirmatory factor analysis were conducted. Exploratory factor analysis (EFA) was carried out by using principal components as factor extraction method with quartimax, an orthogonal rotation. The complete data set was included in this analysis. Before proceeding to interpret the results, each item was checked for normality and sphericity. In order to check multicollinearity, the correlation matrix was examined. In terms of multicollinearity, we expect the items to be intercorrelated; however, these correlations should not be so high (0.90 or higher), whichcauses to multicollinearity and singularity. The intercorrelation was checked by Bartlett’s test of sphericity. This test showed that the correlation matrix is not an identity matrix. Moreover, multicollinearity was checked with the determinant of the correlation matrix. The determinant was more than 0. This showed that there is no multicollinearity (Field, 2000). Extraction of factors was based on two criteria: Scree test and Kaiser criterion (eigen values). Based on eigen values and the Scree test, two factors, which accounts for 48% of the variance, were extracted. The items with factor loadings of less than .4 were excluded from the further analyses (Field, 2000). Appendix A shows the two-factor solution for all items including loadings. Those that were retained for the SPQP are starred – 10 items form the deep scale and 6 items the surface scale.According to the results of the EFA, we note that the deep scale is in better agreement with Biggs ’s deep scale than the surface scale - there are more “usable” items on the deep scale than on the surface scale.After having results of the EFA, confirmatory factor analysis (CFA) was performed. This second step of the factor analysis helped us to ensure the factor structure of the SPQP (see Figure 1). Maximum likelihood (ML) was used as the method of estimation at the CFA. The results of the study showed that relative chi-square, which is the “chi square/degree of freedom” ratio is 3.1. Moreover, RMSEA and CFI were found to be 0.07 and 0.69 respectively. According to Browne and Cudeck (1992), RMSEA values less than 0.05 indicate close fit and models with values greater than 0.10 should not be employed. Here, RMSEA indicates moderate fit of the model whereas relative chi-square indicates good fit. However, the CFI should be over 0.90 to have good fit. Nonetheless, we can say that the first two indices support this two-factor model of the SPQP and indicate moderate fit.246C o p y r i g h t © 2006-2013 b y E S E RFigure 1. Validated two-factor structure of the SPQP.Reliability of the SPQPCronbach alpha coefficients of each scale were calculated for each stream and whole data. The results are shown in Table 2. It is apparent that the surface scale has the lowest Cronbach alpha coefficients at each stream. Similar findings were also reported at other studies (Biggs, 1987; Biggs et al, 2001; Wilson and Fowler, 2005). The foundational efficacy of these scales, given such low reliability, is questionable. However, in ourstudy higher levels of internal consistency were apparent (lowest α =.61).Comparing reliabilities across streams, students who have less experience with physics report surface approaches more reliably than students with more experience. On the other hand, students who have more experience with physics report deep approaches more reliably than those who have less experience. Considering reliabilities within streams, the Fundamentals students report deep approaches as reliably as surface247Student approaches to learning in physicsC o p y r i g h t © 2006-2013 b y E S E Rapproaches with values greater than 0.80, while the Advanced students report very different reliabilities for the two scales. The trends are not surprising since Advanced students would tend to be more confident in content and study strategies.The above trends also raise the question: Are the persistently low reliabilities noted for the su rface scale due to student ‘behaviours’ or poor items on the inventory? An adequate reliability measure for the surface scale for the Fundamentals stream α > .80, one that is similar in magnitude to that of the deep scale, implies that there is internal consistency amongst the items for each scale for this group of students. We note that the Fundamentals students have experienced junior high school physics, and are doing concurrent science and mathematics subjects at university. University students tend to have high internal coherence among learning components, intentions and study strategies and are able to adapt their ideas of knowledge and study methods to their expectations of studying in a particular context. The internal coherence is demonstrated in the reliability scores. So why are the reliabilities for the surface scale as low as 0.61 for the Advanced stream? Is it because the nature of the surface approach is different for the Advanced and Fundamentals streams, requiring possibly different items? Or is it because the Advanced students adapt their surface approaches in diverse ways, hence report this scale less reliably? Answers to such questions will indeed add to our understanding of student learning.ANOVA and BoxplotsTo determine if the SPQP is able to differentiate between the three streams, item and scale means were compared using one-way ANOVA.When comparing the means of the three streams for each item on the SPQP, the assumption of homogeneity of variances underpinning ANOVA was tested using Mulachy’s test of Sphericity. Items A5, A13 and A25 were excluded from ANOVA because they violated the assumption of sphericity. This does not affect their use on the SPQP scales. The results of ANOVA showed that there is a significant differenceamong the SPQP scores of the students from Fundamentals, Regular, and Advanced streams for both surface and deep scales (p < .05).There is a debate among the researchers to use ANOVA with the ordinal data mainly because of the normality concern. As stated in Glass, Peckham, and Sanders (1972), violation of normality is not fatal to the ANOVA. Moreover, performing ANOVA with the ordinal data, likert type items in this case, is a controversial issue among the researchers. Here, we have a big sample and this surely increases the power of the test. Therefore, failure to meet normality assumption will not affect the results of the ANOVA. Moreover, we performed Kruskal Wallis test as non-parametric alternative of the ANOVA. The results of that test supported the results of the ANOVA given above.Moreover, in order to investigate if SPQP is robust enough to be able to differentiate changes in syllabus even when sum of the items is used instead of factor scores, boxplots were checked (see Figure 2). The boxplots show a sequence for each scale with the first panel representing the factor scores, the second panel the simple sums of the items scores for the SPQP and the third panel the simple sums of all 16 item scores that should have loaded on each scale. We note two important features. First, the three panels representing the deep scale are sufficiently similar implying that if an adaptation such as that in this study is made, the sums of the 10 SPQP item scores, and indeed all 16 items scores provide a reasonable measure of deep approaches to learning. However, this is not so for the surface scale, while the sum of the 6 SPQP item scores provides a reasonable measure of surface approaches to learning, the sum of all 16 items does not do so. This raises concerns regarding the surface scale and is a reflection of the low reliabilities for the scale.DiscussionAs we are particularly interested in issues to do with learning physics, the rationale and manner in which items were modified and the SPQ adapted are discussed in detail. The advantages of adapting an established, well248C o p y r i g h t © 2006-2013 b y E S E RFigure 2. A comparison by stream and year of the deep and surface scales. The boxplots show a vertical sequence for each scale with panel (a) representing the factor scores for the SPQP deep scale and panel (d) those for the SPQP surface scale. Panel (b) represents the simple sum of item scores for the SPQP deep scale and panel (e) those for the SPQP surface scale. Panel (c) represents the simple sum of all 14 item scores that were intended to load on the deep scale and panel (f) those for the surface scale.implemented inventory with a sound theoretical framework, both for itsdevelopment and for its practical use in a teaching environment, are evident in the249Student approaches to learning in physicsC o p y r i g h t © 2006-2013 b y E S E Rmeaningful interpretations of our results summarized below.1. The SPQ items were modified for our context based on our experiences and any changes were extensively discussed amongst a group of physics educators. Ten items were retained for the deep scale and six for the surface. The rejection of items that had anomalous factor loadings could be conceptually justified. This two-factor solution of the SPQP confirmed with the EFA and CFA and supported the factor structure of the original SPQ (Biggs, 1987).2. The trends in reliabilities according to streams are as expected, with students with less experience in physics reporting less reliably on the deep scale and more reliably on the surface scale and vice versa. The issue of low reliabilities of the surface scale for the Advanced stream raises the question of whether Advanced students have more diverse forms of exhibiting surface approaches to learning. Moreover, the issue with the surface scale coincides with the previous studies (Biggs, 1987; Biggs et al, 2001; Wilson and Fowler, 2005).3. Comparisons of deep factor scores, simple sums of the 10 SPQP items and all 16 items suggest that the deep scale is reliable and particularly robust, see Figure 2. The surface factor scores compare well with the simple sums of the 6 SPQP items, but not with all 16 items, suggesting that reliability and validity checks are particularly important for the surface scale. The implication is twofold: first the SPQ is robust when contextualised as shown by reliability scores; and second, the contextualisation did not alter the overall coherency of the inventory as shown by the meaningful interpretations across streams and years. This, together with the conceptual meanings associated with the items, provides confidence that the SPQP is consistent with the theoretical framework of the SPQ.4. Changes in senior high school physics syllabus have impacted on approaches to study in the cohorts sampled in this study. The SPQP can illustrate differences between streams and years. From our study we are confident that the SPQP is a reliable andvalid measure of approaches to learning physics in our context.5. The adaptation of the SPQ into physics adds value to our project findings as it allows us to illustrate physics’ specific detail between the streams. We are confident that features that could have systematically biased the findings have been minimized. Lastly the ways of thinking, learning and knowing in physics are embedded in the larger context of intentions and study methods in higher education.ConclusionWe have adapted the Study Processes Questionnaire into physics and confirmed that a two-factor solution provides two subsets of selected items representing deep and surface approaches to learning. The resulting inventory is called the Study Processes Questionnaire for Physics, or SPQP. Further reliability and validation checks demonstrate that the two-scale SPQP is a useable inventory for our context. Reliabilities for the Advanced, Regular and Fundamentals streams are adequate and the behaviour of the mean scale scores for the three streams is not contradictory to expected student behaviours.The process of adapting the SPQ has provided useful insights into the way physicists interpret the items, and how deep and surface approaches can be conceptualised in physics. The sound theoretical framework and research underpinning the SPQ has added value to the use of questionnaires for understanding student learning in our project. Such contextualised inventories have the potential to provide context-specific understandings of teaching and learning issues and for improving student learning.AcknowledgementsThe authors acknowledge Science Faculty Education Research (SciFER) grants, University of Sydney, and support from staff and students. The authors are grateful to Professor David Boud for his constructive feedback on this paper.。

钝感炸药点火增长模型的欧拉数值模拟

钝感炸药点火增长模型的欧拉数值模拟郝鹏程;冯其京;洪滔;王言金【摘要】The ignition-growth reactive model and a desensitizing model were introduced into the self-developed two-dimensional multicomponent Eulerian elastic-plastic hydrodynamics code (MEPH2Y),respectively.Numerical studies were carried out on some detonative phenomena including the shock ignition, the diameter effect and the formation of the dead zone, with the aid of the adaptive mesh refinement technique.The numerical results show that the computation can simulate the major characteristics of the planar detonation waves, such as the detonation wave speed, CJ state, von Neumann spike state and so on.And the diameter effects of the explosives can be simulated correctly.Under the consideration of the desensitizing reactive model, the dead zone formation of the insensitive high explosives can also be reproduced.%在自主研发的二维多介质欧拉弹塑性流体力学程序中,通过引入点火增长的反应率模型以及炸药减敏模型,借助网格自适应技术,研究钝感炸药的冲击点火、直径效应以及死区形成等爆轰现象.数值模拟结果表明,该程序能够正确模拟平面爆轰波的爆速、CJ状态、von Neumann尖点等爆轰参数；并能够较好模拟炸药的直径效应.另外,通过引入考虑减敏效应的反应率模型,能较好地模拟钝感炸药的死区形成过程.【期刊名称】《爆炸与冲击》【年(卷),期】2012(032)003【总页数】8页(P243-250)【关键词】爆炸力学;多介质欧拉弹塑性流体力学程序;网格自适应加密;钝感炸药;爆轰;点火增长模型;直径效应;死区【作者】郝鹏程;冯其京;洪滔;王言金【作者单位】北京应用物理与计算数学研究所,北京100094;中国工程物理研究院研究生部,北京100088;北京应用物理与计算数学研究所,北京100094;北京应用物理与计算数学研究所,北京100094;北京应用物理与计算数学研究所,北京100094【正文语种】中文【中图分类】O381塑性炸药的爆轰机理很复杂，对爆轰波结构的研究，早在一个多世纪以前就提出了平面、定常的CJ爆轰模型，即认为爆轰波由前导冲击波及紧跟的稀疏波构成，能量在冲击波后瞬间释放；ZND模型则在前导冲击波后引入化学反应区［1］。

Modeling Diesel Spray Flame Liftoff

Song-Charng Kong1 e-mail:kong@Yong SunRolf D.Rietz2Engine Research Center,University of Wisconsin,1500Engineering Drive,Madison,WI53706Modeling Diesel Spray Flame Liftoff,Sooting Tendency,and NO x Emissions Using Detailed Chemistry With Phenomenological Soot Model A detailed chemistry-based CFD model was developed to simulate the diesel spray com-bustion and emission process.A reaction mechanism of n-heptane is coupled with a reduced NO x mechanism to simulate diesel fuel oxidation and NO x formation.The soot emission process is simulated by a phenomenological soot model that uses a competing formation and oxidation rate formulation.The model is applied to predict the diesel spray lift-off length and its sooting tendency under high temperature and pressure con-ditions with good agreement with experiments of Sandia.Various nozzle diameters and chamber conditions were investigated.The model successfully predicts that the sooting tendency is reduced as the nozzle diameter is reduced and/or the initial chamber gas temperature is decreased,as observed by the experiments.The model is also applied to simulate diesel engine combustion under premixed charge compression ignition(PCCI) conditions.Trends of heat release rate,NO x,and soot emissions with respect to EGR levels and start-of-injection timings are also well predicted.Both experiments and models reveal that soot emissions peak when the start of injection(SOI)occurs close to TDC. The model indicates that low soot emission at early SOI is due to better oxidation while low soot emission at late SOI is due to less formation.Since NO x emissions decrease monotonically with injection retardation,a late injection scheme can be utilized for simultaneous soot and NO x reduction for the engine conditions investigated in this study.͓DOI:10.1115/1.2181596͔1IntroductionA better understanding of the diesel spray combustion process is crucial to help design low emission diesel engines.This is im-portant because diesel engine manufacturers are facing stringent emission regulations.Motivated by the need to better understand the soot and NO x formation processes in diesel sprays,researchers have made direct optical measurements in both engines͓1–5͔and high-temperature,high-pressure combustion chambers͓6–8͔. These investigations have provided new insights into diesel spray combustion and emission formation processes and have also helped numerical model development͓9–12͔.Experimental data have been used to construct a conceptual diesel spray combustion image that depicts theﬂame structure and soot and NO x distributions͓1͔.It has been shown that the details of theﬂame structure are crucial to the soot formation process during the mixing-controlled combustion phase͓7,8͔.The lifted ﬂame consists of a diffusionﬂame at the periphery of the fuel jet ͑where NO x is formed͒and a rich reaction zone located down-stream of the lift-off length in the central region of the fuel jet ͑where soot is formed͒.The lift-off length determines the time forfuel-air mixing prior to ignition and entering the reacting zone, and thus will affect the sooting tendency of diesel fuel jet.As a complement to optical soot and NO diagnostics,predictive numerical models can also help understand the diesel spray com-bustion process and provide insights to the details ofﬂame struc-ture.Development and applications of engine CFD models havebecome increasingly important and effective in analyzing thecomplex diesel combustion process͓9–12͔.The use of detailedchemistry is also essential to better predict fuel oxidation andemission formation,especially for the low-temperature HCCIcombustion process which is of much interest͓11,12͔.This study develops a numerical model that uses detailedchemical kinetics to simulate the diesel lift-offﬂame and its com-bustion and emission formation.The model is validated using ex-perimental combustion and emission data from a combustion ves-sel and from a heavy-duty diesel engine under various operatingconditions.2Model Formulation2.1Engine CFD Code.The CFD code is a version of KIV A-3V͓13͔with improvements in various physical and chem-istry models developed at the Engine Research Center,Universityof Wisconsin—Madison.The major model improvements includethe spray atomization,drop-wall impingement,wall heat transfer,piston-ring creviceﬂow,and soot formation and oxidation models ͓14,15͔.The RNG k-⑀turbulence model was used for in-cylinder ﬂow simulations using the standard values for turbulence param-eters as those derived originally͓16͔.Since detailed reaction mechanisms for n-heptane were used tosimulate diesel fuel chemistry,the CHEMKIN chemistry solver ͓17͔was integrated into KIV A-3V for solving the chemistry dur-ing multi-dimensional engine simulations.The chemistry andﬂow solutions were then coupled.Details of the model can be found in the original literature in which various PCCI engines have been1Currently at the Department of Mechanical Engineering,Iowa State University,Ames,IA50011.2To whom correspondence should be addressed.Contributed by the Internal Combustion Engine Division of ASME for publicationin the J OURNAL OF E NGINEERING FOR G AS T URBINES AND P OWER.Manuscript receivedMay16,2005;ﬁnal manuscript received December15,2005.Review conducted byM.Wooldridge.Journal of Engineering for Gas Turbines and Power JANUARY2007,Vol.129/245Copyright©2007by ASMEsimulated,including premixed and direct-injection conditions ͓11,12͔.It should be noted that the chemistry andﬂow turbulence are already coupled using the present model via diffusion trans-port,and a subgrid scale turbulence-chemistry interaction model is not used in this study.The turbulence affects the combustion by property transport,wall heatﬂux,etc.2.2Fuel Oxidation Chemistry.A skeletal reaction mecha-nism for n-heptane͓18͔was used to simulate diesel fuel chemistry due to their similar ignition characteristics and cetane number. This mechanism is obtained from a larger mechanism͓19͔using an interactive reduction scheme that utilizes SENKIN, XSENKPLOT,and genetic algorithm optimization.The resulting mechanism retains the main features of the detailed mechanism and includes reactions of polycyclic aromatic hydrocarbons.The mechanism was validated using constant-volume ignition delay data in a shock tube and also from engine combustion experiments.In the present study,the physical properties of the fuel use those of tetradecane͑C14H30͒,based on which the spray atomization model was developed͓15͔.However,due to the availability of the reaction mechanism and the similar cetane number͑56for n-heptane͒,the reaction chemistry of n-heptane is used to simulate that of diesel fuel.2.3Reduced NO Reaction Mechanism.A new NO mecha-nism was obtained by reducing the Gas Research Institute͑GRI͒NO mechanism͓20͔,which contains an additional22species and 101reactions pertaining to the formation of nitric oxides,in ad-dition to the fuel oxidation mechanism.The GRI NO mechanism wasﬁrst integrated with the fuel oxidation mechanism to be used in the SENKIN simulations.Both constant volume ignition delay and zero-dimensional HCCI engine combustion simulations were performed using SENKIN.The SENKIN solutionﬁles were then analyzed by XSENKPLOT to help construct the reduced NO mechanism.The choices of the resulting species and reactions are based on theirﬂux values,which are an indication of the relative importance in the reaction pathway.The resulting NO mechanism contains only four additional species͑N,NO,NO2,N2O͒and nine reactions that describe the formation of nitric oxides as listed be-low.All the rate constants remain the same as in the original GRI NO mechanism͓20͔.Note that the sum of NO and NO2in this study is compared with the engine-out NO x emissions measure-ments in this study.The original GRI NO mechanism includes both thermal and prompt NO.As a result of mechanism reduction,it was found that the prompt mechanism reaction͑such as CH+N2͒is not signiﬁ-cant under the present conditions studied partly due to the lack of fuel-bound nitrogen.N+NO⇔N2+O͑1͒N+O2⇔NO+O͑2͒N2O+O⇔2NO͑3͒N2O+OH⇔N2+HO2͑4͒N2O+m⇔N2+O+m͑5͒HO2+NO⇔NO2+OH͑6͒NO+O+m⇔NO2+m͑7͒NO2+O⇔NO+O2͑8͒NO2+H⇔NO+OH͑9͒2.4Phenomenological Soot Model.Soot emissions are pre-dicted using a phenomenological soot model͓14͔that was incor-porated into the KIV A/CHEMKIN code.Two competing pro-cesses are considered in this model,namely soot formation and oxidation.The rate of change of soot mass M˙s within a computa-tional cell is determined from the soot formation rate M˙sf and soot oxidation rate M˙so.dM sdt=dM sfdt−dM sodt͑10͒The formation rate uses an Arrhenius expression and the oxida-tion rate is based on a carbon oxidation model,described asdM sfdt=A sf M C2H2P n expͩ−E sf RTͪ͑11͒dM sodt=6Mw c␳s D s M s R Total.͑12͒The original formation rate calculation used a characteristic-time combustion model in which only seven major combustion species were considered͓14͔,and fuel was used as the soot incep-tion species in Eq.͑11͒.However,when a detailed reaction mechanism is used for combustion simulation,fuel is depleted quickly to form intermediate hydrocarbon species once reactions start.Thus,it is no longer useful to use fuel as the inception species for soot formation.Therefore,based on the previous lit-erature and available species in the reaction mechanism used in this study,it was decided to use acetylene͑C2H2͒as the inception species for soot formation,i.e.,M C2H2in Eq.͑11͒.This is because acetylene is the most relevant species pertaining to soot formation in hydrocarbon fuels.The preexponential constant Asf was ad-justed accordingly for the present implementation and also to ac-count for the fuel effects.On the other hand,the soot oxidation rate is determined by the Nagle-Strickland-Constable model that considers carbon oxidation by two reaction pathways whose rates depend on surface chemistry of two different reactive sites,as in the original model͓14͔.In the present model,Esf=12,500cal/mol,Asf=150,soot den-sity␳s=2g/cm3,and Ds=2.5E-6cm.In the calculation,acety-lene is consumed to form soot particles,which,in turn,will be converted to CO,CO2,and HC as a result of oxidation.3Experiments3.1Sandia Combustion Chamber.Experiments conducted in an optically accessible,constant-volume combustion chamber under simulated quiescent diesel engine conditions were used for model validations͓7,8͔.The vessel has a cubical combustion chamber,108mm on a side.The fuel injector is centrally mounted in one side of the chamber.Optical access is provided by sapphire windows that permit line-of-sight and orthogonal optical access to the injected fuel jet.The high-temperature and high-pressure environments are cre-ated by burning a speciﬁed premixed mixture before the start of fuel injection.Optical diagnostics of diesel spray combustion are performed under ambient conditions similar to those in typical diesel engines at the time of injection.A wide range of pressure, temperature,and density conditions were considered in the Sandia experiments and some of the cases were used to validate the present KIV A/CHEMKIN/soot model.Theﬂame lift-off experiments are valuable for model valida-tions because they were under well-controlled environments and well characterized.Low-temperature combustion characteristics of theﬂame lift-off experiments can be related to those in diesel engines.The fuel injectors and ambient conditions that result in low emissions can be utilized to help achieve low emissions in diesel engines͓7,8͔.3.2Caterpillar Diesel Engine.Engine experiments per-formed on a Caterpillar heavy-duty diesel engine were also used for model validations͓21͔.The engine is a single-cylinder engine246/Vol.129,JANUARY2007Transactions of the ASMEwhosespeciﬁcations are listed in Table1.The gaseous emissionsrecorded in the experiments include NO x,intake CO2,exhaust CO2,carbon monoxide,and hydrocarbons.The particulate was measured using a full dilution tunnel and an A VL DPL439par-ticulate analyzer.The EGR level was varied by changing the ex-haust back pressure.The intake and exhaust pressures were con-trolled by two Omega PID controllers,and the intake airﬂow rates were measured using criticalﬂow oriﬁces.Experimental data obtained using a high-pressure injector͓21͔were simulated by the model.The engine operating conditions were optimized to achieve low NO x and particulate emissions and fuel consumption.The parameters that were varied included start-of-injection timing and EGR.The fuel injector was a production style Caterpillar electronic unit injector͑EUI͒.The experimental results indicated that low emissions could be achieved by optimiz-ing the operating conditions to allow an optimal time interval between the end of fuel injection and the start of combustion.This is to allow a longer time for better mixing to produce a more homogeneous mixture.The experimental conditions used for model validation are also listed in Table1and include three EGR levels.4Results4.1Sandia Combustion Chamber.Experimental results of the Sandia combustion chamber͓7,8͔were used to validate the present models.The baseline experimental conditions for model validations are listed in Table2.The computations used a0.5deg sector mesh with2mm grid size in both radial and axial directions.The computational domain was12.6cm in diameter and10cm in height such that the total volume is the same as that of the combustion chamber in the experiment.Uniform chamber temperature,pressure,and species concentration based on experimental data were assumed initially without considering combustion radicals.A typical image of predicted fuel spray and gas temperature distributions of a free diesel lift-offﬂame is shown in Fig.1.The injector is located at the top of the image.It can be seen that the liquid fuel undergoes atomization,vaporization,and mixing with entrained air before the lift-off location and then enters the reac-tion zones.Put into the context of a transient injection process, chemical reactions take place once the fuel is injected and mixed with air,and lead to autoignition at a certain location as seen in Fig.1͑a͒.Note that in Fig.1͑a͒,the light colors seen between the spray tip and the ignition location indicate a continuous tempera-ture rise as a result of preignition chemical reactions.The ignition location is approximately where the steady-stateﬂame is stabi-lized in most cases,i.e.,the lift-off location.The lift-off length is thus determined by bothﬂuid mechanics and chemical kinetics that take place during the fuel/air mixing process prior to the lift-off location.It is believed that chemical reactions prior to the lift-off loca-tion play an important role such that theﬂame is stabilized due to successive ignition events of the incoming fuel-air mixture.For example,it has been demonstrated that after theﬂame is estab-lished,if the chemical reactions before the lift-off location are suddenly deactivated,theﬂame is blown downstream and extin-guished.It is noted that the high gas velocity of the injected diesel fuel jet would require an unreasonably high turbulentﬂame speed to balance the incoming reactive mixture in order to stabilize a free standing dieselﬂame.Nonetheless,more research is needed to study the physics of dieselﬂame lift-off under engine conditions.4.1.1Spatial Soot Distributions.Planar laser-induced incan-descence͑PLII͒images of soot along a thin plane of the fuel jet were compared with model predictions,as shown in Fig.2.The injector oriﬁce is located at the far left center of each image,with fuel being injected to the right.Theﬂame lift-off length was de-termined from the OH chemiluminescence images in the experi-ments͓7,8͔.It is deﬁned as the axial distance between the oriﬁce and the location where the OH chemiluminescence intensity is approximately50%of that just downstream of the initial rapid rise in the OH chemiluminescence.The cross-sectional average equivalence ratio at theﬂame lift-off length was estimated and is given on the left of the PLII images.The present simulated soot mass fraction distributions are given in Fig.2͑b͒to compare with the PLII images.The predicted lift-off length was determined from the contour of OH species using aTable1Caterpillar3410E engine speciﬁcations†21‡BoreϫStroke137.2mmϫ165.1mm Compression ratio16.1:1 Displacement 2.44L Connecting rod length261.6mmSquish height 1.57mmCombustion chamber geometry In-piston Mexican hat with sharp-edgedcraterPiston Articulated Charge mixture motion Quiescent Maximum injection pressure190MPa Number of nozzle holes6Nozzle hole diameter0.214mm Included spray angle145deg Injection rate shape RisingExperimental conditions for model validationCase group SOI͑ATDC͒A͑8%EGR͒−20,−15,−10,−5,0,+5B͑27%EGR͒−20,−15,−10,−5,0,+5C͑40%EGR͒−20,−15,−10,−5,0,+5Table2Experimental conditions for model validations Fuel#2Diesel Injection system Common-rail Injection proﬁle Top-hat Injector oriﬁce diameter50,100,180␮m Oriﬁce pressure drop138MPa Discharge coefﬁcient0.80,0.80,0.77 Fuel temperature436K Ambient temperature850–1300K Ambient density7.3,14.8,30.0kg/m3 O2concentration21%Fig.1Sample images of the predicted fuel spray and gas tem-perature distributions for d nozz=100␮m,T amb=900K,P amb =138MPa,␳amb=14.8kg/m3.Color scale:900to2600K.Journal of Engineering for Gas Turbines and Power JANUARY2007,Vol.129/247similar method as in the experiments.The predicted equivalence ratio at the lift-off length is also given on the left of the images.The color scale of the predicted soot mass fraction is also shown in Fig.2.It can be seen that the predicted soot distributions agree ex-tremely well with the experiments.Both experiments and simula-tions show that as the ambient gas temperature decreases,lift-off length increases,soot concentration decreases,and the equiva-lence ratio at the lift-off length also decreases.The conditions with ambient temperatures 1000and 900K are found to be soot-ing cases while no soot production is observed for the 850K case,as revealed by both the experiments and simulations.In the simu-lations,the two sooting cases are found to have a soot mass frac-tion of the order of 1.0E−5while the predicted maximum soot mass fraction is only about 1.0E−8for the 850K case.Other comparisons between model results and experimental images sug-gest that a soot mass fraction of 1.0E−5can be used as the crite-rion to specify sooting and nonsooting conditions in the simula-tions.This criterion is used later in this paper to determine the sooting limit of injectors with different oriﬁce diameters.The temporal evolution of a typical injection and combustion event is illustrated in Fig.3.Time after start of injection ͑ASI ͒for each image is given on the left.The distance from the injector is shown at the bottom.The dashed vertical line shows the lift-off length ͑18.3mm ͒and the solid line shows the x =50mm position,which was found in the experiments to have the peak soot emis-sions at 3.2ms ASI.It can be seen from the ﬁgure that the evolution of the soot emissions predicted by the model agrees well with the experimen-tal results,especially after 2.0ms.It was found that the model predicts a slightly longer ignition delay which can explain the lower soot formation at the early stages,e.g.,at 1.3ms ASI.Nu-merical results indicate that soot does not appear upstream of the lift-off length,which is consistent with the conclusion drawn from the experiments ͓8͔.4.1.2Axial Soot Distributions .The axial distributions of soot along the centerline of the fuel jet were also compared.Figure 4shows comparisons of measured time-averaged KL factors and predicted soot mass fraction at 3.2ms ASI.The KL factor is an indication of optical thickness derived from laser-extinction soot measurements ͓8͔.The KL factor is proportional to the mass of soot along the line of sight of the extinction measurement,so it can be compared with the predicted soot mass that isintegratedFig.2Comparisons between PLII images and predicted soot mass fractions at the central plane of the fuel jet at 3.2ms ASI.The equivalence ratios were estimated at the lift-off length.Relative PLII camera gain is given in brackets.d nozz =100␮m,P inj =138MPa,␳amb =14.8kg/m 3.Fig.3Time sequence …ASI in ms …of PLII images and predicted soot mass fraction contours.The lift-off length and x =50mm positions are shown on the images with vertical dashed and solid lines,respectively.d nozz =100␮m,P inj =138MPa,T amb =1000K,␳amb =14.8kg/m 3.Fig.4Comparisons of measured time-averaged KL factors and predicted soot mass along the central axis of the fuel jet for the same conditions as in Fig.3.Both measured and pre-dicted data were normalized to allow qualitative comparison.Results were acquired at 3.2ms ASI for d nozz =100␮m,T amb =1000K,P inj =138MPa,␳amb =14.8kg/m 3.248/Vol.129,JANUARY 2007Transactions of the ASMEalong the same line.Optical thickness data were acquired at multiple axial locations along the centerline of the fuel jet at a certain time after start of injection.Due to the different nature of the KL factor from mea-surements and the integrated soot mass from the simulations,only qualitative comparisons can be made to assess the model perfor-mance.Thus,both measured KL factors and predicted soot mass are normalized to allow qualitative comparisons,as shown in Fig.4.Comparisons of the normalized curves show good agreement in the general trend of the soot distribution along the jet central axis.Although the positions of the peak value of soot emissions differ slightly between the simulation and experiments ͑50mm in ex-periments and ϳ55mm in simulation ͒,the agreement in the shape of the curves indicates that the transient features of soot formation and oxidation processes are captured by the present model.Comparisons between the measured KL factors and predicted soot mass along the central axis at 3.2ms ASI were also presented in Fig.5for other ambient gas temperature conditions of 950,1100,1200,and 1300K.It can be seen that the predicted axial soot distributions agree with measurements very well.It can be seen that as the ambient temperature increases,the peak of the soot curve moves upstream toward the fuel jet.The early soot formation is consistent with the observation that lift-off length decreases at high ambient temperatures as in Fig.2.4.1.3Radial Soot Distribution .The measured radial soot dis-tribution 50mm downstream of the injector ͑location indicated by the vertical solid lines in Fig.3͒was also compared with simula-tions in Fig.6.Optical thickness data were acquired at multiple radial locations 50mm from the injector at 3.2ms ASI for the same conditions as in Fig.3.Note that a 3-D cubic mesh with 2mm grid size was used for the calculation such that it would be easier to integrate the soot mass in the radial direction.As before,both measured KL factors and predicted soot mass were normal-ized to allow qualitative comparisons.Figure 6also indicates that the simulation results match the experiments very well even for such a small length scale ͑note that the length scale is 20mm in the radial direction while it is 100mm for the axial direction ͒.4.1.4Sooting Tendency of Diesel Spray .The ultimate goal of the numerical model is to predict the sooting tendency of a diesel injector under different operating conditions.Figure 7shows com-parisons of the measured and predicted sooting tendency of diesel injectors with different oriﬁce diameters in an ambient density-temperature domain.To the left of each curve are the nonsooting regimes and to the right are sooting regimes.In the experiments,the sooting limit is determined by the visibility of soot in the PLII images.In the simulations,the maximum soot mass fraction of 1.0E−5is used as the criterion,as discussed earlier.To determine the sooting limit in the simulation,cases of dif-ferent temperatures with a 25K interval were simulated at a ﬁxed ambient density.For example,nonsooting and sooting cases are marked with open and closed symbols,respectively,as shown in Fig.7.The average temperature between adjacent nonsooting and sooting cases is regarded as the sooting limit for a speciﬁc injector at the corresponding ambient density condition.As can be seen in Fig.7,although there are discrepancies be-tween the exact locations of the measured and predicted sooting curves,especially for the small oriﬁce ͑50␮m ͒injector,the trends are well predicted.As ambient density and temperatureincrease,Fig.5Comparisons of measured time-averaged KL factors and predicted soot mass for various ambient temperatures 950,1100,1200,and 1300K.Both measured and predicted data were normalized to allow qualitative comparison.Results were acquired at 3.2ms ASI for d nozz =100␮m,P inj =138MPa,␳amb =14.8kg/m 3.Fig.6Comparisons of measured time-averaged KL factors and predicted soot mass as a function of radial distance from the jet centerline at an axial distance of 50mm from the oriﬁce …vertical solid line in Fig.3….Both measured and predicted data were normalized to allow qualitative comparison.Results were acquired at 3.2ms ASI for the same conditions as in Fig.3.Fig.7Measured …solid lines …and predicted …dashed …sooting and nonsooting regimes as a function of ambient gas tempera-ture and density for P inj =138MPa.For the conditions of each curve,nonsooting combustion occurs to the left and sooting combustion to the right of each curve.Journal of Engineering for Gas Turbines and PowerJANUARY 2007,Vol.129/249or as oriﬁce diameter increases,the sooting tendency increases.The discrepancy between measurements and predictions for the small oriﬁce is probably due to the signiﬁcant difference in the spray atomization and mixing processes between oriﬁces with a conventional size and a small size which may not be well captured by the present spray model.4.2Caterpillar Diesel Engine.The present models were fur-ther applied to simulate combustion and emission processes in a heavy-duty diesel engine.Figures 8and 9show the measured and computed cylinder pressure and heat release rate data for selected cases.The model is seen to perform well over a wide range of engine conditions.The heat release rate data do not exhibit thedistinct premixed and diffusion burn characteristics of conven-tional diesel engines.The highly premixed burned features of the present PCCI experiments are captured well by the model.The predicted soot and NO x ͑i.e.,sum of NO and NO 2͒emis-sions were also compared with the measurements,as shown in Figs.10and 11.It can be seen that the overall trends of soot and NO x are captured with respect to the start-of-injection timing.Dis-crepancies in soot emissions at early injection timings may be due to the details of the spray/wall interactions.It is of interest to note that engine-out soot emissions reach a peak value when fuel is injected near top-dead-center.The present model also predicts cor-rectly the soot reduction seen at further retarded injection timing ͑e.g.,SOI=+5ATDC ͒for all different EGR levels.The numerical model can explain the soot emissionreductionFig.8Comparisons of measured …solid line …and predicted …dotted …cylinder pressure and heat release rate data for 8%EGR cases …SOI=−20,−10,and +5ATDC…Fig.9Comparisons of measured …solid line …and predicted …dotted …cylinder pressure and heat release rate data for 40%EGR cases …SOI=−20,−10,and +5ATDC…Fig.10Measured and predicted engine-out NO x emissions for cases listed in Table1Fig.11Measured and predicted engine-out soot emissions for cases listed in Table 1250/Vol.129,JANUARY 2007Transactions of the ASME。

when is the student t-statistic asymptotically standard normal

The Annals of Probability 1997, Vol. 25, No. 3, 1514–1531
WHEN IS THE STUDENT t-STATISTIC ASYMPTOTICALLY STANDARD NORMAL?
2 ¨ ´ 1 Friedrich Gotze By Evarist Gine, and David M. Mason3
1 Research
1514
ASYMPTOTICS OF THE STUDENT t-STATISTIC
1515
a consequence of our second result, we obtain that if the sequence Sn /Vn is stochastically bounded then all its subsequential limits in law are (uniformly) subgaussian and there is convergence of the moment generating function of Sn /Vn along each convergent (in distribution) subsequence to the moment generating function of the limit, for all t ∈ R (there is even convergence of some square exponential moments). If X is symmetric then the self-normalized sums are always stochastically bounded as a consequence of Khinchin’s inequality. But if X is not symmetric, there are even centered random variables with good moment conditions for which Sn /Vn fails to be shift tight. We present examples of this type and give as well a criterion for stochastic boundedness of self-normalized sums. The line of research leading to our results starts perhaps with Efron (1969), who studied the limiting behavior of the Student t-statistic and, equivalently, of self-normalized sums in some nonstandard cases. In a more strict sense, it actually begins with the conjecture of Logan, Mallows, Rice and Shepp (1973) stating that “Sn /Vn is asymptotically normal if [and perhaps only if] X is in the domain of attraction of the normal law” (and X is centered). The “if ” part of this conjecture follows rather easily from basic principles (Raikov’s theorem), as was noticed, among others, by Maller (1981), but the parenthetical “only if ” part has remained open until now for the general case of not necessarily symmetric random variables. For X symmetric, Grifﬁn and Mason (1991) attribute to Roy Erickson a beautiful proof of the fact that convergence in distribution of the self-normalized sums Sn /Vn to the standard normal law does indeed imply that X ∈ DAN. This result and its method of proof directly inspired ours. As in the symmetric case [Grifﬁn and Mason (1991)], proving that X ∈ D AN under the assumption that the self-normalized sums are asymptotically standard normal, ultimately reduces—via O’Brien’s (1980) observation that X∈D AN is equivalent to maxi≤n Xi /Vn → 0 in probability—to the analysis of the terms in the development of E Sn /Vn 4 . Most of these terms vanish in the symmetric case. The main difﬁculty in the absence of symmetry consists in showing that the terms which are zero under symmetry are indeed asymptotically negligible in the general case. Control of these terms is achieved by means of certain estimates (Lemma 3.1) of the moments 13

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Phenomenological model of shock initiation in heterogeneous explosives
E. L. Lee and C. M. Tarver Citation: Phys. Fluids 23, 2362 (1980); doi: 10.1063/1.862940 View online: /10.1063/1.862940 View Table of Contents: /resource/1/PFLDAS/v23/i12 Published by the American Institute of Physics.
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