Numerical simulation of leakage effect for quantum NOT operation on three-Josephson-junctio
Effects of Leakage in Simulations of Positive Pressure Ventilation

Effects of Leakage in Simulationsof Positive Pressure VentilationC.M.Beal and M.Fakhreddine and O.A.Ezekoye,Department of MechanicalEngineering,University of Texas at Austin,Austin,TX 78712,USA;e-mail:dezekoye@Received:10December 2007/Accepted:14April 2008Abstract.The fire service uses a number of tactics to reduce hazards for fire-fighters and civilians within a structure on fire.One offensive fire-fighting tactic that has potential for rapidly improving or degrading conditions within the structure is venti-lating the structure.Positive pressure ventilation is a tactic in which a fan is used to push hot products of combustion out of a burning structure.While a recent body of work has been produced on the effects of positive pressure ventilation in a number of fire systems,there is still widespread uncertainty on how the tactic affects the fire putational tools will play an important role in exploring the impact of positive pressure ventilation in various fire scenarios.In many simulations of structure fires,the impact of leakage on the evolution of the fire is not addressed.We find in this study that ad hoc models of leakage have significant impact on the evolution of the fire.Several ad hoc leakage models are proposed and these are stud-ied in terms of their impact of the fire.We show that one particular leakage geometry is able to best model leakage effects in a series of fire simulations that are compared to experiments.Simple,first-order analysis is used to understand how these leakage flows affect the predictions.Keywords :ventilation ,positive pressure ventilation ,simulation ,firefighting ,leakage1.IntroductionComputational modeling of fire phenomena plays an increasingly important role in engineering and scientific endeavors.Fire protection engineers rely on simula-tions to improve their designs and provide justification for various engineered fire protection systems.Research scientists may design simulations to gain insight into fire related phenomena and generate new knowledge.Simulations can take the place of impractical or expensive experimental setups and can yield highly reliable data.When experiments are performed,the correlation of simulation results with experimental data can be particularly useful in gaining insight into the underlying physics of the fire.In this paper,we will discuss the results of our efforts to use simulation data to better understand experimental results.Specifically,data was collected during experimental fires in which a firefighting ventilation technique known as positive pressure ventilation (PPV)was applied.In the course of *Correspondence should be addressed to:O.A.Ezekoye,E-mail:dezekoye@Fire TechnologyÓ2008Springer Science+Business Media,LLC.Manufactured in The United StatesDOI:10.1007/s10694-008-0055-712Fire Technology2008 explaining the experimental data we determined that simulating thefire compart-ment as a leaky room had significant effect on agreement between simulations and experiments.This manuscript will discuss the development and testing of various simplified leaky room models.PPV is afirefighting tactic aimed at alleviating hazardous conditions created by thefire.The use of PPV has been documented to improve conditions for victims andfirefighters[1].PPV involves placing a large fan in front of an inlet vent(such as a door)of a structure that contains afire so that aflow can be established from the inlet vent,through a path within the structure,and out through an exit vent.The massflow from the inlet vent towards the exit vent carries heat and smoke out of the structure.The decrease in hazardous combustion products and increase in visibility are among the benefits of PPV.The effects of PPV are an active and important area of study since the opti-mal conditions for its use have not been characterized entirely.Most PPV stud-ies focus on residentialfires in which the conditions forfirefighters are likely to be improved by the removal of smoke and heat from the structure.However,it is possible that the conditions for a victim caught in the structure may deterio-rate because of unfavorable changes in the local environment associated with PPV[2].Svensson conducted tests using a370kWfire in a three room training facility and concluded that PPV increases the heat release rate(HRR)of the fire,reduces temperature on the upwind side of thefire,and increases the tem-perature on the downwind side[3].These results suggest that PPV may harm anyone trapped in the downwind side of thefire.In another related study, Svensson articulates the need for clarification on the use of PPV in order to avoid exacerbating hazardous conditions[4].Ezekoye et al.measured tempera-tures in a structure with an approximately2.4MWfire contained within[5].The effects of PPV on temperatures in afire room and a downwind‘‘victim’’room were quantified.Kerber and Walton have analyzed the fanflow associated with PPV as well as the effects of PPV on the heat release rate(HRR)[6].They found that after application of PPV there was an initially sharp rise in the HRR relative to natural ventilation conditions,but that this increase in HRR was of relatively short duration.After approximately200s,the HRR dropped below the level associated with natural ventilation.In recent years,there have been line-of-duty-death(LODD)investigations that have focused on the role that PPV may have played in either diminishing or promoting the environmental conditions that resulted in a LODD.For example,Vettori used the computa-tionalfluid dynamics code Fire Dynamics Simulator(FDS)to investigate the role that PPV may have had in afirefighter fatality in Texas[7].Further study of PPV is required to characterize its role in promoting extremefire behavior due to increased oxygen availability and to determine optimal conditions for its application.This study focuses on the effects of accounting for leakage in the simulation of PPV.Sinai presented one of the few studies that addressed the impacts of leakage on predictions of under-ventilated compartmentfire properties using computa-tionalfluid dynamics modeling.He shows that leakage has a significant effect on the temperature distributions in the compartments[8].We performed ourEffects of Leakage in Simulations of PPVsimulations with the computationalfluid dynamics software Fire Dynamics Simu-lator(FDS)version4[9,10].We compare the results of FDS simulations that include leakage and those that neglect leakage to experimental data collected from housefires.We define leakage as mass transport through a barrier,such as a wall, that results from sources other than open windows or doors.Initially we compare simulations with and without leakage to experimental data collected from a house fire without PPV.The leakage configuration that best agrees with experimental results for the test cases without PPV is then used to study two PPV cases.In the two PPV cases,we compare simulations with the selected leakage configuration and simulations that neglect leakage to experimental data collected during differ-ent PPV applications.Wefind thatfires in which PPV is applied can be strongly influenced by leak-age.There are various sources for leakage in any given house and this study is not aimed at devising a method for defining the appropriate leakage model,but rather indicating the importance of including the presence of leakage when it is antici-pated that leakage might occur.Our results emphasize the surprising importance of including some leakage in order to produce simulations that predict reality bet-ter.2.Fire ScenariosThe experimental burns that we simulated were originally conducted to study the effects of PPV on thermal conditions on the leeward side of thefire[5].Sev-eral controlled burns were performed in a single-level,fire-hardened house on the outskirts of Austin,Texas.Figure1shows a schematic of the house used for burns;letters in the schematic denote the location of thermocouple trees.The room containing thefire(‘‘fire room’’)has a window(wd1,Figure1)which can be opened from outside the house.The downwind room(‘‘victim room’’)has a window(wd2,Figure1)that can be opened as well.Doors in Figure1are denoted with the label‘‘dr.’’Table1shows the elevation of the thermocouples in Figure1that will be used in this report.The simulations used an identical house geometry.Thefire was started by igniting polyurethane foam fuel arranged in a burner. The burner has slots for12foam elements.A total mass of22kg of polyurethane foam was used for each burn and an average of1.5kg of foam was left behind after the burn as residue.Figure2is a photograph showing the burner and foam elements.Thefirst setup we simulated involves a burn without PPV and with only thefire room window(wd1)being vented.The second setup also involves the venting of thefire room window and includes the use of PPV(‘‘fire room PPV’’).The third setup involves the use of PPV but with only the victim room window(wd2)being vented(‘‘victim room PPV’’).In both PPV cases,a belt-driven fan was turned on 100s after ignition with a volumetric output of3.1m3/s.All doors in the house were opened at all times.Fire Technology2008Table1Thermocouple Names and ElevationsThermocouple Elevation(m) A7 2.13A4 1.22B7 2.13B4 1.22D8 2.29D4 1.07E8 2.29E4 1.07F8 2.29F4 1.07For example,‘‘A4’’refers to a thermocouple located in thermocouple treeÔAÕat a height of approximately1.22m abovefloor level3.Model Characteristics and Boundary ConditionsThe fan used in the simulations is modeled as aÔventÕin the plane of the front door(dr1)and is located on thefloor.The fan vent is0.29m2and produces a uniform velocity of13.72m/s over that area which produces aflow rate of4m3/s.However,the fan model does not consider more complex characteristics of the fan flow such as the swirl component.In all simulations discussed in this paper the mesh used in FDS was a spatially uniform grid with 10cm cubic cells.Tests were also conducted on 20cm cubic cells.Interestingly,there was very little difference in temperature results between the 20cm and 10cm cells up to simulation times of approximately 50s.After 50s,the temperature differences are smaller at the higher elevation temperature measurement locations (e.g.,A7)than at the lower elevations (e.g.,A1and A4).Specifically,the maximum error between the two grids at the A7location was roughly 7%while at the A4location we find peak errors between the two grids of approximately 20%.These maximum errors corresponded to peak temperatures which occurred soon after 100s of simulation time.We recognize that decreasing the grid size below 10cm would likely improve accuracy even further,but the computational resources made this infeasible.The computational domain extends beyond the walls by 1m and is specified as Ôopen Õenabling free interaction with the atmosphere.However,the bottom domain boundary,which models the ground,was specified as closed.In order to develop accurate simulations,initial effort was aimed at validating a fire model.FDS-v4provides several methods to model a fire.In the simplest case,the user may specify the heat release rate per unit area (HRRPUA)of a particular surface and the corresponding surface area.This method is a direct way to apply a desired heat release rate (HRR).The user may specify any HRR profile that is desired and can model events such as extinguishing the fire based on the defined profile.It is important to note that although the user inputs the desired HRR,FDS converts the HRR to a pyrolysis rate.Thus,the HRR that is specified by the user in a given compartment may not actually occur there depending on the oxy-gen available during the FDS simulation.The issue with this method is that the physics of the pyrolysis,and thus the burning progression of the fuel,is not directly modeled.Rather,FDS maintains the pyrolysis rate regardless of fire con-ditions which could potentially result in unrealistic pyrolysis of fuel.A more phys-ically based model can be used to predict the burning progression based ontheFigure 2.Photograph of the burner and foam elements.Effects of Leakage in Simulations of PPVFire Technology2008 fuel materialÕs heat of vaporization(HoV).In the HoV method,the user specifies a fuel material,its heat of vaporization,and ignition temperature.Based on these physical properties the burning progression is simulated directly.However,the drawback of the HoV method is that it does not allow the user to modelfire extinguishment.We thus chose the HRRPUA method for the simulations in this report to enable extinguishing thefire.However,we also wanted to have a physically accu-rate HRR profile for early times that is more effectively determined with the HoV method.It is important that the HRRPUA that is specified in the simulations resembles the physical time evolution of the burning process.So,we conducted simulations with the HoV method and compared them to simulations with the HRR profile used for the simulations in this study.We determined the HRR profile by considering the fuel that was used during the experiments and selecting a HRR profile shape common in largefires.For the experimental burns,we calculated an average2.4MW HRR using a heat of com-bustion of26MJ/kg and an average burning rate of25g/m2s for polyurethane foam[11].We chose a triangular HRR profile since many furniturefires can be modeled as triangular.The triangular profile that produces an average HRR of 2.4MW was selected for the simulations and is shown relative to the HRR of a bunk bed from a NIST experiment in Figure3[12].The bunk bed HRR profile was selected for this comparison because it displays a similar HRR peak as the one used in our simulations.Furthermore,the resultingfire progression and decline is similar in trend to the HRR profile specified in our simulations.We also ran preliminary simulations with the HoV method in an effort to directly simulate the actual HRR and compare it to our chosen triangular profile. These simulations modeled the burner and foam elements but included only the geometry of thefire room,allowing for faster run times.Twelve polyurethane foam pads were specified in the modelfire room to replicate the experimental fuel as shown in Figure2.Each pad has the same dimensions(0.15m·0.61m·0.61m)Effects of Leakage in Simulations of PPVas the pads used in the full scale experiment.The mesh used for the HoV simula-tions was a spatially uniform10cm grid.Due to the grid size,the pads are mod-eled as being separated by10cm in the FDS simulations.In the full scale experiment the pads were actually separated by2or3cm.The added separation may affect heat and mass transfer between the pads.For the HRRPUA method the fuel was modeled as a solid block with equal overall dimensions as the polyure-thane foam burner used in the experiments.In each method thefire room door was open from the beginning of the simulation and thefire was naturally ventilated at 100s by opening the window in thefire room(wd1).Figure4shows a comparison of HRR profiles from a simulation based on the HoV method and the HRRPUA method.Note that although the HRRPUA was specified to be triangular,oxygen depletion constrains thefire resulting in the HRR profile shown below.The HoV trend reasonably approximates the HRRPUA trend with some devia-tion,especially at the end of the burn.This deviation can be attributed to the fact that the HRRPUA method contains a ramp down function beginning at100s, causing the simulation to run out of unused pyrolyzed fuel and to effectively ‘‘turn off’’.In contrast,the simulation using the HoV method has no means of extinguishing thefire and thus levels offat approximately5MW.The reasonable agreement between the HoV method and the HRRPUA method up too approxi-mately125s suggests that our selected triangular HRR profile is a plausible repre-sentation of the actual burning process.The triangular HRR profile was the one used for the full scale experimental housefire simulations discussed in the remain-der of this paper.4.Modeling Leakage Using the Non-PPV CaseWe define leakage to be mass transport through a barrier such as a wall due to passages other than open windows and mon sources of leakage include clearances or cracks at the edges of window and door frames.Because theFire Technology2008 amount of oxygen available is a critical variable in the burning process,leakage may drastically influence afireÕs behavior.Indeed,visual observations of the exte-rior of the house during the experimental burns showed smoke leaking at the frame of the closed window in thefire room.Figure5shows thefire room win-dow(wd1)and the victim room window(wd2)during a victim room PPV burn. Victim room PPV utilizes wd2as the exit vent andfire room PPV utilizes wd1as the exit vent when PPV is applied.Thefire room window,wd1,is the window on the right.In imageÔaÕof Figure5,which is taken approximately15s after ignition,the fire begins to burn but smoke is not visibly leaking to the exterior of the house. ImageÔbÕ,taken after the housefills with smoke(approximately60s after igni-tion),shows smoke leaking out of thefire room window(wd1)despite it being closed.ImageÔcÕwas taken at approximately110s after ignition and shows the victim room window(wd2)opened and a large plume of smoke billowing out-ward.A comparison of the closed window leakage in imageÔbÕto the venting of the nearby window in imageÔcÕshows that the closed window leakage is signifi-cant even when compared to the effects of a completely open window.It appears that FDS version5now provides a method to simulate leakage. However,in our FDS version4,this method is only capable of incorporating leakage for very select cases.Our application prevents the use of the FDS leakage method.As an alternative,we defined holes through an exterior wall to compen-sate for leakage.Because smoke was observed to leak out from the closed window of thefire room,defining leakage holes may be a reasonable method to model the physics of leakage.Recognize also that theflow through such computationally narrow slits is actually‘‘sub-grid’’.As such,these features are not grid indepen-dent.Rather,the actual venting provided is a function of the slit length scale and the grid length scale.The holes are often only as narrow as the grid size,a con-straint which limits the application of this leakage method.The difficulty in this method lies in defining a proper leakage area and geometry.We defined the leak-age through a trial and error process by comparing the experimental thermocou-ple data with the simulation temperatures for the non-PPV experiment.Once agreement was achieved in the non-PPV case between the simulated and experi-geometrymental temperature data for a particular leakage geometry,the same Array Figure5.Thefire room and victim room windows are shown during a victim room PPV burn.Smoke appears in(b)although the window had not been opened.was applied for the two PPV cases.Figure 6shows three of the many different leakage geometries that were used to simulate the non-PPV case.The total leak-age area was between 0.25and 0.3m 2for each of these simulations as shown in the figure below.The leakage geometries shown in Figure 6are present in the fire room and allow mass transport from the fire room to the exterior of the house.The dark block represents the burner.Because fire room leakage directly affects oxygen availability to the fire,we have deemed it the most important leakage and have neglected leakage in other rooms.Several different leakage geometries were simu-lated in the non-PPV case and the graphs in Figure 7show the most important results.Each graph title begins with a letter and a number denoting the thermo-couple location.The letter corresponds to a location in Figure 1and the number corresponds to an elevation in Table 1.For example,the label A7refers to a ther-mocouple of elevation 2.13m (7¢)on the A thermocouple tree.The no leakage simulated fire room temperatures,shown in graphs Ôa Õand Ôb Õin Figure 7,produce trends that considerably underestimate temperatures between 50and 100s after ignition.These trends most likely result from the no leakage simu-lation producing an oxygen constrained burn while the experimental fire was less oxygen constrained.Graphs Ôc Õand Ôd Õin Figure 7show results from the leakage trial 28simulation;the corresponding leakage geometry is presented in screenshot Ôa Õof Figure 6.The A7thermocouple readings show intervals of overestimating the experimental temperatures,likely the result of too much oxygen available in the simulation.It seems that the experimental fire was moderately constrained,enough to produce higher temperatures at thermocouple A7than the no leakage trial,but not temperatures as high as the leakage trial 28.Trial 34has a leakage vent that was designed to mimic leaks that occur around the edges of the fire room window (wd1).The temperatures produced by this simulation demonstrate an improvement from those of trial 28when compared to the experimental results.However,the temperatures from trial 34also overestimate the tempera-tures for times between 40and 100s after ignition.We continued to iterate the selection of a leakage geometry until we arrived at trial 36.Graphs Ôg Õand Ôh Õfrom Figure 7show the temperature results for trial 36;the corresponding leakage geometry is presented in screenshot Ôc Õin Figure 6.The leakage area for trial 36isFigure 6.Smokeview screenshots showing different leakage geometries in the fire room.Effects of Leakage in Simulations of PPVFire Technology2008slightly less than the area for trail28and slightly larger than the area for trial34. Its location coincides with the center of thefire room window that is created at 100s after ignition.Trial36produces temperature trends at thermocouples A4 and A7that are remarkably accurate when compared to the experimental data trends.The data presented in Figure7is representative of the simulation improve-ment produced by the leakage geometry of trial36at other thermocouples.The agreement between the temperature profiles of trial36and the experiment suggest that the trial36simulation is a good representation of the physics associ-ated with the experimentalfire progression.It is important to understand the physical phenomena associated with implementing leakage in simulations of com-partmentfires.The following section presents an analysis of the effects of leakage on compartmentfires that is obtained by comparing the physical processes of trial 36with the less accurate leakage trials and the experimental results.These com-parisons,in conjunction withfirst principles,provide clarity of how leakage physi-cally affects compartmentfires.This understanding is then used in the analysis of the effects of implementing leakage on simulations of PPV.5.Analyzing the Effects of Leakage on CompartmentFiresThe results presented above demonstrate the effects of leakage on a compartment fiparing these simulations to each other and to the experimental results will provide a deeper understanding of thefirst principles associated with includ-ing leakage in compartmentfire simulations.We see that the experimentalfire temperatures level offaround40s after ignition(Figure7).This indicates that at roughly40s the HRR diverges from the expected triangular profile that is dis-played in Figure3.We hypothesize that the HRR is limited because the neutral plane descends in thefire room and thefire becomes oxygen -partmentfires with a single exterior vent(such as a door)establish a neutral plane which separatesflow entering the compartment fromflow exiting the compart-ment.The elevation of the neutral plane has a significant effect on thefire because it dictates the amount of oxygen that is provided to thefipartments with additional exterior vents(including leakage)will establish neutral planes at greater elevations than compartments with a single exterior vent.We hypothesize that the addition of leakage is therefore responsible for producing greaterfire room tem-peratures by increasing the neutral plane elevation.It is reasonably intuitive that the addition of leakage vents will promote entrainment,thus producing a higher HRR and greater temperatures.Figure8presents a visualization of ourhypothesis in the form of a comparison between a compartment fire with and without secondary vents.Another limiting factor is the presence of a second compartment,such as the hallway in this study.The secondary compartment also limits the oxygen available to the fire by circulating combustion products back into the fire room.For com-partments without leakage this effect is compounded by a lower neutral plane.As the secondary compartment becomes saturated with smoke,the oxygen concentra-tion of the flow into the fire compartment decreases.Including leakage in the fire room enables the combustion products to exit the structure,and more fresh air to be available to the fire.Using simplified analysis of compartment fires with a single exterior vent we can estimate the entrainment rate into the fire room in the simulations with no leakage as_m¼k 0A 0ffiffiffiffiffiffiH 0p ð1Þwhere k 0is a proportionality constant,A 0is the fire room door area,and H 0isthe fire room door height [13].Using k 0¼0:5kgs m 5=2,A 0=1.79m 2,and H 0=2.1m we expect a mass flow rate of 1.29kg/s.FDS can calculate the mass flow rate and oxygen mass fractions at specific locations.With these tools the overall mass flow rate and the corresponding oxygen mass flow rate into the fire room were calculated for the simulation without leakage and are displayed in Figure 9.For times between 40and 100s (i.e.after the fire becomes oxygen con-strained and prior to opening wd1,the fire room window)the average mass flow rate was 0.91kg/s and the average oxygen mass flow rate was 0.18kg/s.The theo-retical value of 1.29kg/s is quite reasonable compared to the computational value of 0.91kg/ing 13kJ/g as the heat of combustion for oxygen,this oxygen mass flow rate corresponds to an average HRR of 2.3MW.The triangular HRRthat was specified(Figure3)has an average HRR of3.5MW over this time per-iod,indicating that thefire was oxygen constrained.When leakage is included we expect that the neutral plane elevation will increase and,as a result,the overall massflow rate and the oxygen massflow rate into thefire room will also increase.The FDS results show that theflow rates do increase and are displayed in Figure10.The average massflow rate was1.11kg/s and the average oxygen massflow rate was0.23kg/s for times between40and 100s after ignition.Based on oxygenÕs heat of combustion,this oxygen massflow rate corresponds to an average HRR of3.0MW during this time period.3MW is greater than the average HRR that occurred during the simulation without leak-age,but still less than the average prescribed value from Figure3.Although the leakage increases the amount of oxygen available to thefire,thefire is still oxygen constrained.Figure11displays the normal velocity out of the leakage vent at t=72s.At this time the neutral plane has descended below the bottom of the leakage vent and as a resultflow is exiting through the entire leakage area.The average veloc-ity out of the leakage vent for trial36at72s is2.3m/s.With this in mind we can estimate the increase in massflow rate into thefire room by continuity with _m¼AV qð2Þwhere A is the leakage area(0.25m2),q is the density(0.41kg/m3for air around 575°C)and V is the average velocity.With this rough estimate we expect the mass flow rate to increase by approximately0.24kg/s.This estimate is consistent with the increasedflow rate as calculated by FDS,recalling that the averageflow rate for the simulation with leakage was1.11kg/s and for the simulation without leak-age was0.91kg/s.Theflow out of the leakage vent enables combustion products to exit the struc-ture.As discussed above,this results in greater oxygen availability tofire.The。
FLUENT软件专业英语词汇表

kinetics 动力学
Lagrangian approach 拉格朗日法
laminarization 层流化的
Laminar 层流
Laminar Flamelet Concept 层流小火焰概念
large-eddy simulation (LES) 大涡模拟
tortuosity 扭转, 曲折, 弯曲
toxic 有毒的,毒的
trajectory 轨迹,弹道
tracer 追踪者, 描图者, (铁笔等)绘图工具
translatory 平移的
transport coefficients 输运系数
transverse 横向,横线
triatomic 三原子的
viscosity 粘度
visualization 可视化
volatile 易挥发性的
volume fraction 体积分数, 体积分率, 容积率
volume heat 容积热
vortex burner 旋流式燃烧器
vorticity 旋量
wall-function method 壁面函数法
interface 接触面
intermediate 中间的,介质
intermediate species 中间组分
intermittency model of turbulence 湍流间歇模型
intermixing 混合
intersect 横断,相交
interval 间隔
tangentially 切线
tilting 摆动
the heat power of furnace 热负荷
fluent常见专业英语词汇

closure (模型的)封闭
cloud of particles 颗粒云
cluster 颗粒团
coal off-gas 煤的挥发气体
coarse 粗糙的
coarse grid 疏网格,粗网格
coaxial 同轴的
considerably 相当地
consume 消耗
contact angle 接触角
contamination 污染
contingency 偶然, 可能性, 意外事故, 可能发生的附带事件
continuum 连续体
converged 收敛的
conveyer 输运机
convolve 卷
composition 成分
cone shape 圆锥体形状
configuration 布置,构造
confined flames 有界燃烧
confirmation 证实, 确认, 批准
conservation 守恒不灭
conservation equation 守恒方程
conserved scalars 守恒标量
dissociate thermally 热分解
dissociation 分裂
dissipation 消散, 分散, 挥霍, 浪费, 消遣, 放荡, 狂饮
distribution of air 布风
divide 除以
dot line 虚线
drag coefficient 牵引系数, 阻力系数
drag and drop 拖放
drag force 曳力
有限元软件和 FLUENT 软件在海底管道研究中的应用

有限元软件和 FLUENT 软件在海底管道研究中的应用安利姣;马贵阳;关越;常方圆【摘要】有限元分析是一种现代计算的方法,其发展历程可追溯结构力学这一学科,管道受力的分析方面具有广泛应用。
FLUENT 软件广泛应用在工业工程中,在石油领域有较强的市场竞争力,在管道内流体流动、热传递方面具有广泛应用。
介绍有限元软件和 FLUENT 软件的基本原理,总结其在海底管道中的应用,例如海底管道管-土之间相互作用力分析,管-土-波作用力分析,海底石油管道和天然气管道泄漏的数值模拟,渗流对海底管道非稳态传热的影响,管道要停止输送时,研究其热传递。
提出未来发展趋势。
%Finite Element Analysis is a modern calculation method,its development course can trace the discipline of structural mechanics analysis,which is widely used in pipeline stress analysis. The FLUENT software is widely used in industrial engineering,it has strong market competitiveness ,thus its application in research on fluid flow and heat transfer in the pipe is wide. In this article, the basic principles of Finite Element Analysis and FLUENT softwares were introduced, and their application in the submarine pipeline was summarized, such as submarine pipeline of pipe - soil interaction force analysis ,pipe - soil - wave force analysis, the numerical simulation of the submarine oil pipeline and gas pipeline leakage, the influence of seepage on submarine pipeline’s unsteady heat transfer, and so on .Then their development trend in the future was put forward.【期刊名称】《当代化工》【年(卷),期】2015(000)007【总页数】4页(P1664-1666,1670)【关键词】有限元软件;FLUENT 软件;海底管道;数值模拟【作者】安利姣;马贵阳;关越;常方圆【作者单位】辽宁石油化工大学,石油天然气工程学院,辽宁抚顺 113001;辽宁石油化工大学,石油天然气工程学院,辽宁抚顺 113001;中国石油集团东南亚管道有限公司马德岛管理处,北京 100000;辽宁石油化工大学,石油天然气工程学院,辽宁抚顺 113001【正文语种】中文【中图分类】TE8321947年开始,美国首先在墨西哥开采海洋中的石油,紧接着其他国家也加入到开采海洋石油的行列当中,海洋石油的兴起在我国较晚,海底管道作为一种输送介质,主要负责运送海底开采出的海洋石油和天然气。
大处理量紧凑型气浮装置的数值模拟

2016年第35卷第3期CHEMICAL INDUSTRY AND ENGINEERING PROGRESS ·733·化工进展大处理量紧凑型气浮装置的数值模拟孔祥功1,陈家庆1,姬宜朋1,王春升2,张明2,尚超2,蔡小垒1,刘美丽1(1北京石油化工学院机械工程学院,北京 102617;2中海油研究总院技术研发中心,北京 100027)摘要:目前鲜有关于大处理量气浮装置结构设计研究方面的报道,气浮装置国产化研究进程缓慢。
为了解决这一问题,本文以自主研发的处理量为120m3/h紧凑型气浮装置为计算模型,采用Eulerian模型和RNG k-ε湍流模型,运用Fluent对其三维流场进行了数值模拟研究。
分别研究了内筒高度、半径间隙及入口管径等结构参数和含油量、处理量等操作参数的影响,以便考察和优化气浮装置的分离性能。
结构参数影响的数值模拟结果表明:随着半径间隙的减小,除油率先增大后减小;随着入口管径的减小,除油率先减小后增大;改变内筒高度对除油率的影响较小。
操作参数影响的数值模拟结果表明,装置的操作弹性相对较大,对水质水量一定程度的波动具有良好的适应性。
关键词:紧凑型气浮装置;含油污水;分离效率;数值模拟;工程放大中图分类号:X 74 文献标志码:A 文章编号:1000–6613(2016)03–0733–08DOI:10.16085/j.issn.1000-6613.2016.03.013Numerical simulation of flow field and structural and operational parameters in a large capacity compact flotation unit(CFU) KONG Xianggong1,CHEN Jiaqing1,JI Yipeng1,WANG Chunsheng2,ZHANG Ming2,SHANG Chao2,CAI Xiaolei1,LIU Meili1(1School of Mechanical Engineering,Beijing Institute of Petrochemical Technology,Beijing 102617,China;2 CNOOCResearch Center,Beijing 100027,China)Abstract:There were few reports about the structure design of large capacity floating device at present.The pace for domestication of flotation units was slow. In this paper,the self-developed compact flotation unit(CFU) with 120m3/h capacity was chosen as a simulation model. 3D flow fields were analyzed using Eulerian model and RNG k-εturbulence model. The structural parameters such as inner cylinder height,radial clearance,inlet tube diameter were examined,as well as the influences of operational parameters including oil wastewater treatment capacity and inlet oil content,in order to investigate and optimize de-oiling performance of the unit. Results of structural parameters analysis showed that the inner cylinder height has less influence on separation efficiency. As radial clearance decreases,oil-water separation efficiency raises then declines. As the inlet tube diameter decreases,de-oil efficiency decreases first then increases. Results of operational parameters simulations indicated that CFU has a relatively large operational flexibility and good adaptability to a certain degree of fluctuations on water quality and quantity.Key words:compact flotation unit;oily water;separation efficiency;numerical simulation;engineering scale-up收稿日期:2015-09-11;修改稿日期:2015-10-18。
基于计算流体方法对建筑物风场分布

基于计算流体方法对建筑物风场分布芦红莉【摘要】为了对建筑物整体风场进行定量定性分析,为建筑物设计提供参考和依据,利用计算流体力学构建了建筑物的风场模型,并对行人舒适性进行了评估.建立了单一建筑、多建筑及城市综合体风场的CFD模型,并将模型计算结果与风洞试验进行对比,验证模型的正确性和可行性.对比不同湍流模型计算结果,得出了不同情况下最优湍流模型.试验研究发现,标准RSM模型适用于计算单一建筑和小群建筑风场分布,而对于城市综合体风场的计算则以RSM-壁面反射模型为最优.本研究为通过模拟计算定性、定量评估建筑物周围风场提供了依据.【期刊名称】《沈阳工业大学学报》【年(卷),期】2019(041)002【总页数】5页(P236-240)【关键词】计算流体力学;建筑物风场;数值风场模型;湍流模型;k-ε模型;可实现的k-ε模型;雷诺应力模型;城市综合体【作者】芦红莉【作者单位】天津科技大学艺术设计学院, 天津300450【正文语种】中文【中图分类】TM343随着现代建筑外形越来越复杂,高度越来越高,建筑风场的研究逐渐得到各方的关注[1].其研究手段包括现场实测、实验室模拟(主要是风洞模拟)和理论分析(包括数值计算)[2].现场实测作为对其他两种研究手段模拟结果的检测是必不可少的,也是最直接的研究手段,但其成本高、时间长且不易测量,只有在建成后才可以测量,无法为拟建建筑提供预测,因此具有一定的局限性[3].风洞试验作为一种实验室模拟手段,应用较为广泛,对于体型复杂的建筑物,如大跨度体育场屋盖结构,或高层建筑,都宜进行风洞试验.风洞试验能够得出相关结果,并验证新方法的有效性.通过建立物理模型能够研究一些具体位置的风场,从而检测各种假设.风洞的缺陷是其具有稀缺性,尤其是用于研究城市的模型,风洞需要足够大,且在模型中的测点是有限的[4].近年来,计算流体动力学(CFD)在建筑物风场的研究中得到了越来越多的应用,已有研究用该方法从定性和定量方面较准确地预测了风场的影响[5].与前两种手段相比,其人力和物力的消耗小,且能够对研究对象整体进行定量和定性分析,而不仅是仪器测量的几个点.但是,需要证明计算结果的正确性和可行性,尤其是建筑物周围风场涉及到复杂的湍流流动[6].因此,利用CFD方法研究风场模型的准确性验证需要更深入的研究.本文介绍了使用FLUENT计算结果与风洞试验结果进行对比验证所建立的CFD模型方法,并优化了模型参数,为今后城市风场的研究提供一定的依据.1 基本原理CFD是利用高速计算机求解流体流动的偏微分方程组,目的是为了更好定性和定量地了解流体流动的物理现象.CFD计算包括前处理、求解和后处理三部分[7-8].FLUENT是常见的CFD软件之一,其本身所带的物理模型可以准确地预测层流、过渡流和湍流多种复杂现象.其中湍流模型理论(简称湍流模型),就是以雷诺平均运动方程与脉动运动方程为基础,依照理论与经验的结合,引进一系列模型假设,而建立起的一组描写湍流平均量的封闭方程组.本文选取FLUENT软件提供的k-ε模型,雷诺应力模型(RSM)即可实现k-ε模型进行计算分析.k-ε模型自从被提出就成为工程流场计算中的主要工具,其湍动能输运方程是通过精确的方程推导得到的,耗散率方程是通过物理推理,数学上模拟相似原型方程得到的[9].可实现的k-ε模型是近期才出现的,比起标准k-ε模型有两个主要的不同点:1)为湍流粘性增加了一个公式;2)为耗散率增加了新的传输方程,这个方程来源于一个为层流速度波动而做的精确方程[10].RSM在FLUENT中是最精细的模型.放弃等方性边界速度假设,使雷诺平均N-S方程封闭,解决了关于方程中的雷诺压力,还有耗散速率.这意味着在二维流动中加入了四个方程,而在三维流动中加入了七个方程[11].由于RSM比单方程和双方程模型更加严格地考虑了流线型弯曲、漩涡、旋转和张力快速变化,对复杂流动有更高的精度预测潜力.2 建立数值模型2.1 物理模型本文模拟计算了单一建筑,两栋建筑和城市建筑群的风场分布.图1为单一建筑模型参数,建筑长80 m,宽12 m,高18 m,中间为通道,宽6 m,高4 m.图1 单一建筑模型参数Fig.1 Model parameters for single building图2为两栋建筑的分布图,两栋建筑高为18 m,中间通道宽b为4 m,风向如图2所示.图2 两栋建筑模型参数Fig.2 Model parameters for two buildings图3为城市中复杂建筑群分布图,图4为风洞试验中各种测量点的位置[12].模型为不同高度的建筑群,中央建筑高度(76 m)为周围建筑高度的四倍(19 m),街道宽度为25 m.图3 城市综合体结构图Fig.3 Structure of urban complex图4 风洞试验测点Fig.4 Measurement points in wind tunnel test所建立模型需要满足以下条件:1)阻塞率不应超过3%;2)模型截面(宽度×高度)形状应优先遵循暴露于风中的建筑物表面的形状;3)模型长度在建筑物的上游延伸量大于10H,下游延伸量大于16H,H为较高建筑物的高度;4)满足对称条件,模拟一半模型的风场.2.2 边界条件模型边界和上表面对称,风入口处设为VELOCITY-INLET,风离开模型的表面设为OUTFLOW,其余边界设为WALL,假设地面和建筑物表面光滑(即粗糙度为0),墙体附近采用近壁面函数.3 结果分析3.1 单个建筑风场数值计算图5为单个建筑(80 m×12 m×18 m)的模拟结果,通过FLUENT软件计算各种湍流模型的U/U0值,并与Wiren的风洞试验结果进行比较[13].其中,U/U0为有建筑物时2 m处的风速与无建筑物时相同高度风速的比值[13].图5 不同FLUENT湍流模型计算结果与风洞试验对比Fig.5 Comparison between calculated results of differentFLUENT turbulence models andwind tunnel test由图5可知,雷诺应力模型(RSM)模拟结果与风洞试验结果吻合最好,可用于评估单个建筑物通道中行人对风的舒适度.该湍流模型不仅能识别关键区域,而且可以准确地确定最关键位置和风的最大不适值.因此,RSM湍流模型能够应用于建筑物风力舒适性的研究.3.2 两栋建筑物风场数值计算通过单个建筑的研究得出,RSM湍流模型与风洞试验吻合结果最好,因此,两建筑计算模型采用RSM湍流模型与风洞试验进行对比.图6为FLUENT RSM湍流模型计算结果与风洞试验对比.两栋楼通道风速强度与Wiren风洞测试结果[13]吻合,与风洞试验的测量值误差小于等于10%,因此,FLUENT软件的RSM湍流模型可用于预测建筑物风场的研究,以确定行人的舒适性和最大风速值.图6 FLUENT RSM湍流模型计算结果与风洞试验对比Fig.6 Comparison between calculated results of different FLUENT RSM turbulence models and wind tunnel test通过以上分析得出,FLUENT是评估建筑物风场影响的有效工具.FLUENT定性和定量两方面评估两座建筑物周围的风速.RSM模型是研究小群建筑物风力的理想湍流模型.3.3 城市建筑群风场数值计算图7为FLUENT各模型对各街区U/U0比值平均值的计算结果,与Stathopoulos & Wu风洞试验[12]进行对比,并选取风洞试验风速为8 m/s.五个街区点组为:A1~A7,B1~B8,C1~C7,D1~D8,E1~E7.图7 2 m高度处不同FLUENT湍流模型计算结果与风洞试验对比Fig.7 Comparison between calculated results of different FLUENT turbulence models and wind tunnel test at height of 2 m标准k-ε模型计算结果与试验结果偏差最大,标准RSM模型所计算的整个城市地区的平均值与风洞试验结果接近,相对误差为5%.而RSM-壁面反射模型精度更高,五个街区和整个城区的计算结果都与风洞试验测量值吻合较好.因此,RSM湍流模型结合壁面反射适用于研究密集城区建筑物风场,并可用于研究不同高度的建筑群. 图8为中间三排风速变化较大的九栋建筑物2 m高处水平面风速分布图,图9为垂直面的速度分布图.图8 高度2 m处水平面风速分布Fig.8 Wind speed distribution in horizontalplane at height of 2 m通过以上分析,城区中具有不均匀高度的建筑群可采用RSM-壁面反射模型进行计算.但本文方法可以预测几区或者整个城区的平均风速,无法确定每个点上的精确风速,能够预测最高风速的区域,但是准确位置可能会局部偏移.图9 通过B8和D8的垂直面风速分布Fig.9 Wind speed distribution in verticalplane through B8 and D8然而,实际应用中并不需要特定点的确切风速,只需要知道防风区,即给行人带来不舒适性的区域.因此,本文模型能够评估风的不舒适区域,以辅助建筑师或城市规划师设计出舒适的公共空间.4 结论本文建立了建筑物风场模型,并验证了模型的正确性.通过研究发现,从定性的角度来看,FLUENT提出的RANs湍流模型能够计算单个建筑物或小群建筑物以及城区非均匀高度建筑群的平均风速.从定量角度考量,RSM湍流模型更加适用于研究建筑物的风场,较好地预测三类建筑的平均风速分布.所建立的模型能够识别关键区域并量化风的不适程度.对于单一建筑和小群建筑,标准RSM模型计算结果最好.在城区建筑群的模拟中,RSM-壁面反射模型最好.本文研究为通过模拟计算评估建筑物周围行人的风力舒适性提供依据.【相关文献】[1]李梦雯.南京居住区建筑群体空间形态对风环境的影响研究 [D].南京:东南大学,2016.(LI Meng-wen.The study on effect of architecture space form on wind environment in Nanjing residential districts [D].Nanjing:Southeast University,2016.)[2]胡一东,谭洪卫,邓丰.上海地区住宅建筑布局对室外风环境的影响分析 [J].建筑热能通风空调,2017,36(1):32-37.(HU Yi-dong,TAN Hong-wei,DENG Feng.Analysis of building layout influences to residential outdoor wind environment in Shanghai [J].Building Energy & Environment,2017,36(1):32-37.)[3]李正农,耿燕.建筑物对屋顶风场的影响 [J].广西大学学报(自然科学版),2016,41(4):930-938.(LI Zheng-nong,GENG Yan.Influence of building roof on its wind field [J].Journal of Guangxi University(Natural Science Edition),2016,41(4):930-938.)[4]石银超.西安市小区室外风环境模拟分析研究 [D].西安:长安大学,2015.(SHI Yin-chao.Analysis of the Xi’an city residential outdoor 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[13]Wiren B G.A wind tunnel study of wind velocities in passages between and through buildings [C]//Proceedings of the 4th International Conference on Wind Effects on Buildings and Structures.Harlington,UK,1975:467-473.。
供热管道泄漏流场声源特性及其变化规律
消防理论研究供热管道泄漏流场/声源特性及其变化规律张曼\张立申%王随林\李仲博,王海鸿%张威\穆连波;(1.北京建筑大学环境与能源工程学院,北京100044; 2.北京市热力集团有限责任公司,北京100022)摘要:以供热管道泄漏喷射为研究对象,建立管道泄漏致噪的计算流体力学C'F D/计算声学C A混合数值模型,采用Flu—e n t与b A coustics搞合数值模拟,分析管内压力、温 度等因素对热水管道泄漏流场及声源特性的影响结果表明,泄 漏源附近流场呈非对称扇形结构,热水管道泄漏声能主要集中在低频段(0〜20 H z),平均声压级随管内压力的升高而增大,随水 温的升高而有所降低模拟结果与文献实验结果相对偏差小于7.99%,可为基于声波法的供热管道泄漏检测提供理论基础关键词:热水管道;泄漏声源;C F D/C A;流场特性;声源特性中图分类号:X956;T U995.3 文献标志码:A文章编号:1009—0029(2021)03-0312-07随着城市集中供热的发展,供热管网规模和规格不断 增大。
2018年我国城市集中供热面积达到87.8亿集中供热管道长度为371120 km。
由于管路老化、腐蚀、焊 缝缺陷及其他自然或人为损坏等,管道泄漏事故频繁发生。
直埋管线非开挖泄漏检测与定位困难,抢修时间长,影响正常供热和城市基础设施安全,势必会造成能源浪费、经济损失及不良社会影响。
为了保障城市供热管网安 全高效运行,开展供热直埋管道非开挖泄漏检测与精准定位的研究具有重要意义。
管道泄漏检测方法包括基于硬件和软件的方法。
声 波检测法具有动态无损检测的优点,是一种很有前景的管 道泄漏检测方法。
国内外学者基于声波检测原理对管道 泄漏检测与定位进行了研究。
Mostafapour等基于管壁泄 漏声发射技术,结合小波变换、数字滤波和互相关分析开 发了一种适用于城市埋地高压燃气管道的泄漏检测与定位算法,以布置在各向同性、均匀弹性介质的圆柱壳体中 的埋地输气管道泄漏声源理论模型进行了实验验证。
泄压阀对乘客舱通风流量和舱内压强影响的评估
J Automotive Safety and Energy, Vol. 11 No. 3, 2020322—328泄压阀对乘客舱通风流量和舱内压强影响的评估王夫亮(泛亚汽车技术中心有限公司,上海 201206,中国)摘要:评估了汽车乘客舱泄压阀(PRV)设计对舱内通风流量、舱内压强和舒适性的影响。
对乘客舱内流场进行数值模拟,根据数值模拟结果拟合泄压阀开口面积、舱内通风流量和舱内压强的相互影响规律,结合舱内流量和压强试验数据建立了可控泄漏和非可控泄漏等效方案和评估流程,根据数值模拟结果分析了舱内气流状态和途径的过程及其对舒适性的影响。
结果表明:泄漏开口面积和乘客舱压强、乘客舱压强和通风流量之间按照二次多项式规律变化;结合舱内通风流量、压强试验数据和乘客舱内流场数值模拟,可以获得较准确的可控泄漏和非可控泄漏开口等效方案。
从而,本方法可用于泄压阀设计参考以及乘客舱流动状态和舒适性评估。
关键词:汽车乘客舱;乘客舱空气质量;乘客舱舒适性;泄压阀(PRV);可控泄漏;非可控泄漏中图分类号: U 463.82+1 文献标识码: A DOI: 10.3969/j.issn.1674-8484.2020.03.007Evaluation of the influences of pressure relief valve on theventilation air flow and pressure in a passenger cabinWANG Fuliang(Pan Asia Technical Automotive Center Co., Ltd., Shanghai 201206, China)Abstract: The influences of the pressure relief valve (PRV) on the ventilation flow and the pressure in avehicle passenger compartment were evaluated. Some numerical simulation of the flow field in the passenger compartment were carried out. According to the calculation results, the relationship between the opening area ofthe relief valve, the ventilation flow and the pressure in the passenger compartment was established. Combinedwith the passenger cabin inside air flow and pressure testing results, the equivalent openings and evaluation process of controlled leakage and uncontrolled leakage were established, the dynamic flow status and path incabin and its influence on passenger comfort were analyzed based on the numerical simulation results. Theresults show that the leakage opening area and the pressure inside the passenger compartment, the pressureinside the passenger compartment and the ventilation flow vary according to the quadratic polynomial law. Combined with ventilation flow, pressure testing data and numerical simulation of flow field inside passenger compartment, accurate equivalent controlled and uncontrolled leakage openings can be obtained. Therefore, this method can be used to design relief valve and to evaluate the flow state and comfort of passenger compartment.Key words:v ehicle passenger cabin; passenger cabin air quality; passenger cabin comfort; pressure relief valve (PRV); controlled leakage; uncontrolled leakage收稿日期 / Received :2020-03-17。
开放式厨房与闭式厨房燃气泄漏模拟对比研究
第36卷第3期山东建筑大学学报Vol.36 No.32021 年 6 月JOURNAL OF SHANDONG JIANZHU UNIVERSITYJun. 2021D0I :10.12077/sdjz.2021.03.004开放式厨房与闭式厨房燃气泄漏模拟对比研究张增刚*,商铭恒,陈云丽(山东建筑大学热能工程学院,山东济南250101)摘要:开放式厨房中的燃气泄漏后,研究室内不同时间段的危险程度和燃气扩散规律,可以为相关建筑规范的制定及开放式厨房的推广提供理论依据。
文章基于仿真模拟软件,数值模拟了采用开放式厨房和闭式厨房的同一房屋的燃气泄漏,得到了室内燃气泄漏扩散的一般规律。
结果表明:发生燃气泄漏后,120 min 时闭式厨房内就会达到爆炸下限;开放式厨房之外区域燃气体积分数较高,扩散的区域更大;240 min 内,开放式厨房比闭 式厨房安全,但如果长时间泄漏,开放式厨房发生爆炸的可能性将会更大。
关键词:开放式厨房;闭式厨房;室内燃气泄漏;体积分数分布规律中图分类号:TU996 文献标识码:A 文章编号:1673-7644( 2021) 03-0025-07Comparative study of gas leakage simulation betweenopen kitchen and closed kitchenZHANG Zenggang * , SHANG Mingheng , CHEN Yunli( School of Thermal Engineering, Shandong Jianzhu University, Jinan 250101, China )Abstract : After gas leakage occurs in open kitchens , research on the degree of danger and gasdiffusion laws in the room at different time periods will help provide a theoretical basis for theformulation of regulations and the promotion of modern kitchens. Based on the Fluent simulationsoftware , the paper performs numerical simulation of gas leakage in the same house with open kitchenand closed kitchen respectively, and obtains the general law of indoor gas leakage and diffusion. The results show that when a gas leak occurs, the lower explosive limit will be reached at 120 minutes inthe closed kitchen. The gas concentration inside and outside the open kitchen is higher, and the diffusion area is larger. Within 240 minutes, the open kitchen is safer than the closed kitchen. But ifit leaks for a long time, the possibility of explosion in the open kitchen will be greater.Key words : open kitchen ; closed kitchen ; indoor gas leakage ; concentration distribution law0引言开放式厨房来自西方国家,将厨房和餐厅、起居室合而为一[1]。
天然气在大气中扩散规律的数值模拟研究
随着国家对环境保护力度的不断加大,发展天 然气行业成为减轻环境污染的重要举措。2019 年
DOI:10.3969/j.issn.1006-6896.2021.07.007
Hale Waihona Puke 12 月初,中俄东线北段投产,我国天然气正逐步 实现管道气与 LNG 接收站、储气库互联,进口气
油气田地面工程
36
油气田地面工程
第 40 卷第 07 期 (2021-07)
天然气集输处理
的线积分与积分长度的比值,计算公式为
图 2 甲烷在地面的浓度分布云图( t =2 min)
Fig.2 Cloud chart of methane concentration distribution on the
架空管道泄漏的出口边界为小孔,可以近似为 一个点,且浓度和压力等参数相同,并以小孔射流 的方式进入大气。天然气离开土壤在大气中初始扩 散过程是在土壤中进行的,扩散到地面后天然气的 分布近似为圆形区域,该区域内天然气的浓度和速 度都不相同,架空管道的泄漏流速较大。而天然气 从地面向大气中的扩散速度较小,且当天然气在土 壤中泄漏扩散趋于稳定后,管道泄漏孔处的质量流 量与扩散出地面的质量流量相等,所以选择泄漏扩 散趋于稳定后地面甲烷的质量流量作为大气扩散模 型 的 入 口 边 界 条 件 , 边 界 类 型 为 mass-flow-inlet, 方向沿 y 轴方向,地面为 wall 边界类型,其余面为 pressure-outlet 边界类型,流动模型选择 Laminar 层 流模型。
35
天然气集输处理
赵学俭:天然气在大气中扩散规律的数值模拟研究
与国产气互通,已建成“西气东输、北气南下、海 气登陆、就近供应”的供气格局,形成布局合理、 覆盖全国、调运灵活、安全高效的天然气管网。我 国天然气市场已经进入快速发展阶段,天然气泄漏 问题越发凸显。因腐蚀穿孔等因素引起的小孔泄漏 产生的信号很弱,泄漏初期很难被发现和定位,一 旦天然气泄漏到大气中达到爆炸极限,可能会造成 非常严重的后果。因此,研究输气管道小孔泄漏在 大气中的扩散特性,对管道的日常维护及应急救援 具有重要的意义。
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Numerical simulation of leakage effect for quantum NOT operation on three-Josephson-junction flux qubit1Tao Wu,1Jianshe Liu, 2Zheng Li1Institute of Microelectronics, Tsinghua University, Beijing 100084 2Department of Electronic Engineering, Tsinghua University, Beijing 100084Superconducting flux qubits with three Josephson junctions are promising candidates for the building blocks of a quantum computer. We have applied the imaginary time evolution method to study the model of this qubit accurately by calculating its wave functions and eigenenergies. Because such qubits are manipulated with magnetic flux microwave pulses they might be irradiated into non-computational states which is called the leakage effect. Through the evolution of the density matrix of the qubit under either hard-shaped π-pulse or Gaussian-shaped π-pulse to carry out quantum NOT operation,it has been demonstrated that the leakage effect for a flux qubit is very small even for hard-shaped microwave pulses while Gaussian-shaped pulses may suppress the leakage effect to a negligible level.PACS number(s): 74.50.+r, 03.67.Lx, 85.25.CpSuperconducting qubits[1, 2] are solid-state macroscopic systems conforming to the principles of quantum mechanics at ultra low temperatures. They can be compatibly fabricated in a microelectronic process line and easily manipulated by on-chip microwave currents or flux pulses, which make them promising candidates for the building blocks of a quantum computer.[3] For superconducting flux qubits,[4-9] the magnetic flux is the convenient parameter to mark and control their eigenstates. Flux qubits are insensitive to background charge fluctuations but relatively fragile to magnetic flux noise compared to superconducting charge[10, 11] and phase[12, 13] qubits. Quantum superposition in the spectroscopy[6] and Rabi oscillations of a flux qubit in the time domain[7] have been observed.Leakage effect[13-15] of a flux qubit during quantum operations means that the qubit escapes into non-computational subspaces. This effect of phase qubits has been discussed,[13, 14] and inspired by Lin in Ref. [15] we numerically study it for a flux qubit in this letter.This work is based upon the calculation of the eigenstates of a single flux qubit using the imaginary time evolution method[16-19] and the evolution of its density matrix under magnetic flux pulse perturbation. It has been revealed that the populations irradiated to the second and third excited states are extremely small for Gaussian-shaped pulses and also unimportant for hard-shaped pulses, thus there is no need to consider other excited states because the leakage to higher energy levels are much smaller and therefore negligible.A three-Josephson-junction (3JJ) flux qubit is composed of a superconducting loop interrupted by three Josephson junctions [4-6] as shown in Fig.1s. Junctions 1 and 2 have equal areas while junction 3 is α (0<α<1) times smaller. The critical currents for them are I c , I c and αI c , respectively. The loop is biased by a magnetic flux f Φ0, where f is the flux frustration, Φ0=h /(2e ) is the superconducting flux quantum, e is the electron charge, and h is Planck costant. The qubit can be reduced to a two-level system when f is in the vicinity of n +1/2 with n an integer. Neglecting the loop inductance, the Hamiltonian of the system can be written as [5]112121[2cos()cos()cos(2)]2T J H p M p E f αϕϕαπϕϕ−=••++−−−+−r r , (1) where 211j M C ααααπ0+−⎛⎞Φ⎛⎞=⎜⎟⎜⎟−+2⎝⎠⎝⎠, p M ϕ=r r &, i.e.,()()1212T T p p M ϕϕ=&& and0/(2)J c E I π=Φ is the Josephson energy for junction 1 and 2. Eq. (1) can be expressed explicitly as222121202(12()2(12J p p H p p C ααπαα⎛⎞+)=++⎜⎟+)Φ1+⎝⎠ +1212[2cos()cos()cos(2)]J E f αϕϕαπϕϕ+−−−+−. (2)It is different from the usual one as in Ref. 5 because the Hamiltonian remains in the original frame without transformation.We have utilized the fourth order imaginary time evolution method [16-19] to calculate the above Hamiltonian. Table Ⅰshows the eigen-energies of the qubit for f =0.50 and f = 0.495. Figs. 2A-B illustrate the ground state 0and first excited state 1for f =0.50 , Figs. 2C-D show 0and 1 forf =0.495, and Figs. 2E-F show the second excited state 2and third excited state 3 for f =0.495. We choose in the computation α=0.80, E J =198.9437 GHz and C J =7.765 fF.Table Ⅰ. The energies E i for the lowest four eigenstates with two values of f . The energies are in units of GHz.f E 0 E 1 E 2 E 30.50 0 0.3313 28.9984 35.6331 0.495 0 8.7854 31.9617 40.9154 The wave functions of 0 and 1 for f =0.50 are symmetric and anti-symmetric, respectively, and they quickly lose the symmetry when f deviates from this degenerate point. To achieve larger supercurrents for readout, we bias the qubit at f =0.495, where the wave functions of 0 and 1 appear localized in two separate wells as shown in Figs. 2C and 2D.Based on these results, we have studied the leakage effect during a quantum NOT operation upon one flux qubit with microwave magnetic flux pulse operations. The interaction term W (t ) between the microwave field and the qubit can be considered as a perturbation on the original Hamiltonian in Eq. (2), or an external microwave magnetic flux perturbation ()00()cos f t t µωΦon the flux bias f Φ0 threading the loop,i.e.,()W t ()()()0122cos sin 2J E f t t f µπαωπϕϕ≅+−, (3)where ()f t f µ<< and ()010/E E ω=−h . From now on, we denote by 0ωh the energyunit. In the Hilbert space spaned by the eigenstates 0, 1, 2and 3, the interaction W (t ) can be written as a 4×4 matrix: ()()cos W F M ττ=, where 0/(2)t τωπ=, ()()2J F E f µτπατ=−, and M is a 4×4 matrix with (),121sin 21i j M i f j πϕϕ=−+−− for i , j =1,2,3,4. Note that F (τ) is controllable in experiments. In the computational space, the population inversion between the ground state and the first excited state depends on the effective matrix element M 1,2 and M 2,1, i.e., 0.0339. A quantum NOT operation can be achieved by a microwave π-pulse with duration p τ, which requires()1,201/2pF M d τττ=∫. (4)The hard-shaped pulse has constant microwave amplitude, and Eq. (4) yields ()1,21/(2)p F M ττ=for 0p ττ≤≤.For the Gaussian-shaped pulse,[15,16] one has ()()21,2(1/2exp((/2)/2)w p w F M ττττ)=−− for 0p ττ≤≤,where w τis thecharacteristic width of the Gaussian pulse chosen between 0.167p τ and0.100p τ.[20] The evolution of the qubit under irradiation can be described in the interaction picture by the Liouville equation:[21]()1/2[,)]i H ρττπτρτ∂()∂=(. (5) Here ()1H τ is a matrix with ()()1,,exp 2()i j i j i j H i k k W τπτ=−, where()()100/i i k E E ω−=−h . Choosing a time step small enough (e.g.,310τ−∆≈), we can integrate the equation to obtain the density matrix for a given time. The element ,i i ρat the end denotes the probability P i of the system in state i , i =1, 2, 3, 4. Table Ⅱshows the final values of the leaking populations for three time durations of pulses. Fig. 3 illustrates the leakage effect during the operations for τp =400 (i.e., 45.5ns ). Table Ⅱ. The populations (P i ) of the lowest four levels after the microwavemagnetic flux irradiations with Hard-shaped pulses (on the left of “/”) and with Gaussian-shaped pulses (on the right of “/”).p τ=100 p τ=200 p τ=400P 2 1.3e-5/4.6e-8 2.5e-6/1.4e-8 1.5e-6/4.2e-9P 3 2.9e-6/5.0e-8 1.0e-6/1.0e-8 3.3e-7/1.8e-9It is revealed that Gaussian-shaped π-pulse inhibit leakage effects 100 better than hard-shaped π-pulse, which is as remarkable as that in the case of phase qubits.[13] Also, longer durations reduce the leakage. However, leakage is very small during thisoperation even for hard-shaped pulses.In conclusion, we have solved the eigen-functions and eigen-energies of a 3JJ flux qubit by the imaginary time evolution method and studied the leakage effect during the quantum NOT operation through the evolution of its density matrix. It has been demonstrated that the leakage effect for a flux qubit is very small even for hard-shaped microwave pulses while Gaussian-shaped pulses may suppress the leakage effect to a negligible level.We gratefully appreciate Johnson P R and Strauch F W in NIST for their instructions about the imaginary time evolution method. Discussions with Wang Ji-lin, Li Tie-fu, Chen Pei-yi, Yu Zhi-ping and Li Zhi-jian are acknowledged. This work is supported by the 211 Program of Nanoelectronics of Tsinghua University (210605001).References[1] Makhlin Yu, Schön G and Shnirman A 2001 Rev. Mod. Phys. 73, 357[2] Devoret M H and Martinis J M 2004 Quantum Information Processing 3, 163.[3] Nielsen M A and Chuang I L 2000 Quantum Computation and QuantumInformation (Cambridge: Cambridge University Press, UK)[4] Mooij J E et al. 1999 Science 285, 1036[5] Orlando T P et al. 1999 Phys. Rev. B 60, 15398[6] van der Wal C H et al. 2000 Science 290, 773[7] Chiorescu I, Nakamura Y, Harmans C J and Mooij J E 2003 Science 299, 1869[8] Greenberg Y S et al. 2002 Phys. Rev. B 66, 214525; 2002 ibid 66, 224511; 2003 ibid 68, 224517[9] You J Q, Nakamura Y and Nori Franco 2005 Phys. Rev. B 71, 024532[10] Nakamura Y , Pashkin Y A and Tsai J S 1999 Nature 398, 768[11] Vion D et al. 2002 Science 296, 886[12] Martinis J M, Nam S, Aumentado J and Urbina C 2002 Phys. Rev. Lett . 89,117901[13] Steffen M, Martinis J M and Chuang I L 2003 Phys. Rev. B 68, 224518[14] Amin M H, cond-mat /0407080[15] Tian L, Ph. D. thesis 2002 MIT[16] Johnson P R et al. 2003 Phys. Rev. B 67, 020509[17] Auer J, Krotscheck E and Chin S A 2001 J. Chem. Phys. 115, 6841; Krotscheck Eet al. 2003 International Journal of Modern Physics B 17, 5459[18] Takahashi K and Ikeda K 1993 J. Chem. Phys. 99, 8680[19] Feit M D, Flek J A, Jr. and Steiger A 1982 J. Comput. Phys. 47, 412[20] This choice is based upon the approximation ()220exp (/2)/2pp w w d τττττ−−≈∫with p w k ττ= and 610k ≈ .[21] Blum Karl 1981 Density Matrix Theory and Applications (NewYork: PlenumPress)Figure CaptionsFig. 1. One flux qubit with crosses representing Josephson junctions. C J1, C J2 and C J3 are the equivalent capacitances of the junctions with C J1=C J2=C J and C J3=αC J .Fig. 2. (A-B)Ground state 0and first excited state 1for f =0.50. (C-F)0, 1 , second excited state 2and third excited state 3for f =0.495.Fig. 3 Population of 0 (P 0, solid line), 1 (P 1,dashed line) , 2 (P 2, solid line) and 3 (P 3, dashed line) after (A-B) the hard-shaped π-pulse and (C-D) the Gaussian-shaped π-pulse. The pulse duration is τp =400.Fig. 1Fig. 2Fig. 3。