Prospects of using simulations to study the photospheres of brown dwarfs

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大学是否应该把游泳技能与学位挂钩英语作文

大学是否应该把游泳技能与学位挂钩英语作文

The Debate on Linking Swimming Skills toDegrees in UniversitiesIn the modern era of higher education, the discussion on whether universities should make swimming a requirement for graduation has sparked significant debate. This issue encapsulates a broader discussion about the role of extracurricular activities in academic institutions and the value they add to students' overall development. While some argue that swimming should be a prerequisite for earning a degree, others maintain that it is an unnecessary burden that does not align with the primary objectives of higher education.Proponents of making swimming mandatory argue that it promotes physical health and well-being, which is crucial for students' overall development. They emphasize that swimming is a low-impact exercise that can be enjoyed by people of all ages and physical abilities, making it an inclusive and accessible activity. By requiring students to master this skill, universities would foster a culture of active lifestyles and health consciousness, settingstudents on a path of healthy habits that could last a lifetime.Moreover, proponents suggest that swimming is an essential life skill that could save lives in emergency situations. They argue that having a basic understanding of swimming and water safety could help students avoid drowning accidents, a significant risk especially in areas where water activities are common. By equipping students with these skills, universities would be fulfilling their duty to prepare students for real-world challenges and emergencies.However, opponents of this proposal argue that强制将游泳与学位挂钩 may impose an unfair burden on students, particularly those who may have physical limitations or fears of water. They emphasize that not everyone is comfortable in the water, and forcing them to swim could lead to psychological distress and anxiety. Furthermore, they argue that the focus of higher education should be primarily on academic excellence and critical thinking, rather than on physical activities.Additionally, opponents suggest that universities already offer a wide range of extracurricular activitiesfor students to choose from, and making swimming mandatory would limit their freedom of choice. They argue that students should have the autonomy to decide whichactivities they want to participate in, rather than being forced to engage in one particular sport.In considering both sides of the argument, it is evident that there are valid reasons to support and oppose the idea of linking swimming skills to degrees. While swimming certainly has numerous benefits for physical health and emergency preparedness, it is also crucial to recognize that not everyone is comfortable with this activity. Universities, therefore, need to strike a balance between promoting physical well-being and respecting students' individual preferences and limitations.One approach that could potentially address this debate is to offer swimming as an elective course rather than a mandatory requirement. This way, students who areinterested in swimming can take the course and learn the skill, while those who are not comfortable with it canchoose other extracurricular activities that align with their interests and abilities. Such a flexible approach would allow universities to promote physical well-being without imposing an unfair burden on students.In conclusion, the debate on whether universities should make swimming a requirement for graduation is complex and multifaceted. While swimming has numerous benefits for physical health and emergency preparedness, it is crucial to balance these benefits with students' individual preferences and limitations. By offering swimming as an elective course, universities can foster a culture of physical well-being while respecting students' autonomy and choice.**大学是否应把游泳技能与学位挂钩的辩论**在高等教育现代化的今天,关于大学是否应该把游泳技能作为毕业要求的问题,已经引起了广泛的讨论。

Simplifying Mold Design with SolidWorks Plastics

Simplifying Mold Design with SolidWorks Plastics

Simplifying Mold Design withSolidWorks PlasticsWith the rapid advancement of technology, the manufacturing industry has seen significant improvements in recent years. One area in particular, mold design, has benefitted greatly from the integration of computer-aided design (CAD) software. SolidWorks Plastics, a powerful CAD tool, has emerged as a game-changer in simplifying the mold design process.SolidWorks Plastics is a simulation software specifically designed to analyze and optimize plastic injection molding processes. It provides engineers and designers with valuable insights into the flow of molten plastic, cooling, and part deformation. By accurately predicting potential challenges and areas for improvement, SolidWorks Plastics enables users to create optimized molds efficiently, saving time and resources.One of the main advantages of SolidWorks Plastics is its user-friendly interface. The software is intuitive and easy to navigate, even for individuals with minimal experience in CAD. This means that engineers and designers can quickly learn to use the program effectively, without the need for extensive training. With SolidWorks Plastics, mold design becomes more accessible to a wider range of professionals, promoting collaboration and efficiency within design teams.Another key feature of SolidWorks Plastics is its ability to simulate the injection molding process. By modeling the flow of molten plastic, the software can identify potential issues such as air traps, weld lines, and sink marks. With this knowledge, engineers can make informed design decisions, optimizing the mold for improved quality and reduced manufacturing defects. Moreover, the simulation capabilities allow users to experiment with different design iterations, comparing the results in real-time. This iterative process helps to achieve the desired part quality and performance, eliminating the need for costly physical prototypes.SolidWorks Plastics also aids in enhancing the cooling system design of molds. Efficient cooling is crucial for achieving consistent part quality and minimizing production cycle times. The software analyzes the cooling channels and provides insights on their effectiveness and potential hot spots. By visualizing the temperature distribution, engineers can identify areas with insufficient or excessive cooling and make necessary adjustments to ensure uniform cooling throughout the mold. This capability leads to improved part quality and reduced cycle times, ultimately enhancing the overall productivity of the manufacturing process.Furthermore, SolidWorks Plastics enables designers to predict and analyze part deformation during the cooling process. Plastic materials tend to shrink as they cool down, and this shrinkage can cause warpage or distortion in the final product. With SolidWorks Plastics, designers can simulate the cooling process and predict the amount and location of deformation. By adjusting the mold geometry or incorporating features such as ribs or gussets, engineers can counteract the effects of shrinkage and achieve dimensional accuracy in the finished product. This functionality ensures that the mold design effectively accounts for potential deformations, resulting in high-quality parts.In addition to its core features, SolidWorks Plastics offers valuable auxiliary tools to further streamline the mold design process. For instance, the software provides a database of material properties, enabling designers to select the most suitable plastic material for their specific application. Additionally, SolidWorks Plastics offers a gate location advisor, which helps determine the optimal gate location in the mold for improved flow and reduced fill time.Overall, SolidWorks Plastics revolutionizes mold design by simplifying the complex process and creating a more collaborative environment for engineers and designers. With its intuitive interface, simulation capabilities, cooling system analysis, part deformation prediction, and auxiliary tools, SolidWorks Plastics empowers users to optimize mold designs, minimize defects, and improve productivity. By adopting this innovative software, companies can stay ahead of the competition and deliver high-quality plastic parts to their customers.。

高三英语学术研究方法创新不断探索单选题30题

高三英语学术研究方法创新不断探索单选题30题

高三英语学术研究方法创新不断探索单选题30题1. In academic research, a hypothesis is a ______ that is tested through experiments and observations.A. predictionB. conclusionC. theoryD. assumption答案:D。

本题考查学术研究中“假说”相关的基本概念。

选项A“prediction”意为“预测”,通常是基于现有信息对未来的估计;选项B“conclusion”指“结论”,是在研究后得出的最终判断;选项C“theory”是“理论”,是经过大量研究和验证形成的体系;选项D“assumption”表示“假定、设想”,更符合“假说”的含义,即在研究初期未经充分验证的设想。

2. The main purpose of conducting academic research is to ______ new knowledge and understanding.A. discoverB. createC. inventD. produce答案:A。

此题考查学术研究目的相关的词汇。

选项A“discover”意思是“发现”,强调找到原本存在但未被知晓的事物;选项B“create”意为“创造”,侧重于从无到有地造出新的东西;选项C“invent”指“发明”,通常指创造出新的工具、设备等;选项D“produce”有“生产、产生”的意思,比较宽泛。

在学术研究中,主要是“发现”新知识和理解,所以选A。

3. A reliable academic research should be based on ______ data and methods.A. accurateB. preciseC. correctD. valid答案:D。

本题关于可靠学术研究的基础。

选项A“accurate”侧重于“准确无误”,强调与事实完全相符;选项B“precise”意为“精确的、明确的”,更强调细节的清晰和明确;选项C“correct”指“正确的”;选项D“valid”表示“有效的、有根据的”,强调数据和方法具有合理性和可靠性。

新目标英语九年级Unit1复习课件

新目标英语九年级Unit1复习课件
Writing tasks
Completing writing tasks that require the application of grammar rules, such as writing essays or stories, to improve writing skills and accuracy.
New Goal English 9th Grade Unit1 Review Courseware
contents
目录
• Unit Overview • vocabulary review • Grammar Review • Text review • Practice and consolidation • Summary and Outlook
对于文章中的长难句,需要进行详细的分析和解读,理 解其句型结构、语法点和含义,这对于提高阅读理解和 写作能力都非常有帮助。
Language points in the text
词汇辨析 语法点解析 句型转换
对于文章中出现的近义词或容易混淆的词汇,需要进行 辨析和比较,明确它们之间的细微差别和正确用法。
命题作文
给出题目和写作要求,要求学生撰写 一篇完整的议论文或记叙文。
写作修改
提供一篇学生已完成的作文,要求学 生对文章进行修改和润色,提高写作 水平。
06 Summary and Outlook
提前预习下一单元内容
建议学生在本单元结束前,提前预习下一单元的主题和词汇, 了解相关背景知识,为下一单元的学习做好准备。
句型转换:要求学生将句子从一种形式转换 为另一种形式,以熟悉不同句型的特点。
Reading comprehension exercises

Prospects of Using m-Technologies

Prospects of Using m-Technologies

Prospects of Using m-TechnologiesforDisaster Information ManagementinBangladesh and other LDCsAbstract: This paper explores the prospects of using wireless mobile technologies for disasterinformation management in Bangladesh. The basic objective of the paper is to give specificrecommendations to relevant stakeholders, such as the government and the mobile phoneservice providers, as to how mobile technologies may be used effectively before, during andafter a disaster. The first section of the paper gives an overview of the nature of the naturaldisasters that affect Bangladesh almost every year in varying degrees of intensity. The secondsection identifies some of the information and communication gaps before and after a disasterthat make disaster management more challenging and somewhat ineffective. The third sectionintroduces some of the relevant mobile technologies that may be used in Bangladesh and othersimilar LDCs. The fourth section establishes how these mobile technologies may be effectivelyused to address the information and communication gaps. The concluding section gives somespecific recommendations and suggestions for the relevant stakeholders.Keywords: mobile, wireless, m-Government, LDC, Bangladesh, disaster, flood, cyclone,SMS, mobile Internet, 2G1. IntroductionWith over 1300 rivers, including three major rivers of South Asia, flowing through Bangladesh and into the Bay of Bengal in the south, the country is one of the most disaster-prone countries of the world. Its devastating calamities, particularly floods and cyclones, are continuing to claim the lives of hundreds to thousands and to damage billions of dollars worth of property almost every other year. Although the disaster management systems have improved in the past decades, the government still faces significant problem in disseminating early warnings and post-disaster instructions to the populace of a country, where only 20 percent of its geographical region have electricity. The cyclone that hit the coast of Bangladesh in April of 1991 claimed the lives of about 138,000 people, whereas a cyclone of similar intensity in the US only killed 18 people the next year.Besides major disasters, there are other more preventable accidents, such as sinking of boats and ferries caught in storm, which occur due to misinformation or lack of access to information about most recent weather warnings. Since the country is laden with rivers, travel by boat is a regular phenomenon for many people. There is currently no system by which passengers can check the current weather condition in a particular location, and have to rely on the boat owners, who are often indifferent towards bad weather conditions and subsequently overload boats. These lead to innumerable accidents almost every other month in Bangladesh killing hundreds at a time.Chowdhury G. Hossan Senior Lecturer East West University Dhaka, Bangladesh hossan@iuj.ac.jp Mridul Chowdhury e-Government Consultant Ministry of Planning Government of Bangladesh Dhaka, Bangladesh mridul@ Ibrahim Kushchu Associate Professor Int’l University of Japan, Niigata, Japan ik@iuj.ac.jp2. Disaster and Management of Information System in BangladeshThe location and climate together makes Bangladesh a disaster-prone country. Although Bangladesh is experiencing almost all kinds of disaster, cyclone and flood are considered as the most devastating and regular occurrence.2.1 CycloneCyclone is a disaster calamity which is a low pressure and depression originated in the Bay of Bengal. Basic source of cyclone is the action and reaction of hot and cold water and the motion of air and water (Khan, 2003). During cyclone whirling storm and tidal bore hits the coastal areas at a speed of 80 to 120 miles per hour and it pushes saline water in the approaching larger plain areas. Natural disaster of similar intensity is known as typhoon in the Pacific ocean and Hurricane in the Atlantic ocean.Need for information for safety measures- Coastal inhabitants are needed to be aware about the damaging nature of cyclone well in advance to be prepared.- Concurrent update of danger signals.- Fishermen should be warned about the cyclone well in advance so that they can be back in the safe area from fishing.- Preservation of food is necessary as there will be immediate unavailability of food in the disaster affected area in the post cyclone period.- Electrical infrastructures fetch additional hazards during cyclone causing fire. Early warning can result in disconnection of electricity during cyclone.Possible use of instant information- Concerned organizations should ensure their readiness to follow standing orders and directives of the state authorities.- Local shelters need to arrange for probable population.- Area and village wise voluntary team can be formed beforehand for necessary preparedness, awareness and rescue and relief moves.- Location of relief food can be notified.2.2 FloodsExcessive rain fall during the rainy season and on rush of upstream flow pressurizes the rivers to overflow their banks and disastrous flood occurs. During the flood of 1998 in Bangladesh, for instance, 25.5 percent of the population were affected and the area submerged was about 100,000 sq. km.(67.76 percent of Bangladesh’s total land area) (CPD, 2004). The duration of the 2004 flood was 65 days. The cost of total damage in the flood 2004 was estimated to US$ 7 billion by the World Food Program as of August 2004, 2004 or 12.81 percent of current GDP, while in 1998 the total damage was estimated to be US$ 1.7 billion or 4.66 percent of current GDP (CPD, 2004).Need for information for safety measures:- To make aware about the possible time and intensity of upcoming flood.- To take last moment possible steps to reduce damages in agriculture.- To Warn about possible outbreak of diseases in the specific area.Possible use of instant information- To circular standing orders and emergency messages among the disaster management related agencies.- To keep provision for shelter and food ready in the probable flood affected areas.- To circulate information about the availability of relief, food, drinking water and medical assistance.3. Current Disaster Information Management SystemThe responsibility for managing disasters in Bangladesh is entrusted with the Disaster Management Bureau (DMB), a government agency, under the ministry of Disaster Management and Relief. The functions of disaster management bureau are as follows (DMB, 2002):- To coordinate disaster management activities;- To organize training and public awareness activities;- To collect, preserve and analyses data on various disasters;- To operate an Emergency Operation Center (EOC);- To promote prevention and preparedness at all levels on various disasters;- To help line ministries, departments and agencies to develop contingency disaster management plan and arrange effective dissemination of disaster warning and- To organize logistics arrangement in connection with disaster management.Current information flow systemLocal disaster shelters play a central role during disaster. The local centers are basically two storied buildings located in the disaster-prone areas. The number of cyclone centers that provide shelter during cyclone in Bangladesh as of 1999 is 1841 and this number for flood shelters that provide shelter during flood in Bangladesh as of 1998 is 200 (DBM, 2002). Use of radio is only effective medium to communicate directly with disaster prone area and this device has got widest reach even to the people living below poverty line. Uses of television for disaster information are increasing but yet get effective as the number of television is not significant due to un-affordability and lack of electricity in rural areas. Use of flag in cyclone centers and local focus points is another old medium for communication of disaster information. Information flow through human chain, that is word of mouth, is still the only duplex medium of communication. Private wireless communication is in use in some district level areas.Selected quotes about disaster management situation in Bangladesh“While much of the inefficiency was due to the scale of logistics involved, planning with better informed guesses about the aerial distribution of damage and relief requirements could have produced a better response.” (Maniruzzaman et al, 2001)One journalist aboard a helicopter distributing relief reported, `. . . relief work was not systematic in any way. We simply flew around, and dropped bags wherever we thought necessary. . . . Those getting relief were simply lucky’” (Haider et al, 1991)“One cannot wait for an accurate response from the field, which will take a long time.” (BIDS, 1991) This led to the realization that a duplex real-time communication medium can mitigate the intensity of disaster to a great extent.4. Relevant Mobile Technology (Context Aware & Location Identification)The driving force for focusing on Mobile technology for disaster management arises from its inheriting features of Global System for Mobile Communications (GSM) wireless network technology. For the purpose of disaster management works, it is important to identify geographic location of the disaster prone area. Location of the mobile user can be identified by two basic approaches (Heikki, 2001). One is through the mobile network signal system where signals send by the mobile phone system to its base and another is through using integrated Global Positioning System (GPS) with the mobile phone receiver, an additional hardware that takes care of location functions. In this paper we will focus on the techniques that locate the geographical position using mobile network signal system which does not require any additional hardware. Although accuracy of location based on signal system over integrated GPS is still an issue of debate but in the context of less developed country like Bangladesh adding hardware at the user’s end will involves additional cost at user’s end which is not possible to effort for a lot of user.The purpose of presenting these technical concepts is to give a general idea how location is identified in the cellular networks to non-technical readers and to avoid any technical details. Some basic GSM based location identification techniques are discussed below (Marko and Pentti, 2003):• Cell Coverage• Received Signal Levels• Angle of Arrival• Timing Advance• Enhanced Observed Time Difference4.1 Cell coverageCell is the geographical area periphery for one base station. When a mobile phone generates any call, it contains its Cell location information. Cell coverage or cell ID or cell of origin (COO) is the simplest methood of calculating location of the mobile user based on the cell information. Since it is an inherent feature of any cellular system there is no need to change mobile handsets or network infrastructure and can be implemented in an existing infrastructure through minor software updates. The major drawback of this method is its dependency on the cell radious which is variable depending on the context. The cell size of a base station can be 50 meters in a city to 35 km to an rural area. However, for the purpose of managing natural disaster, which is generally covers a wide area, this drawback can be over looked. But this method requires user intiative to generate call to identify her/ his position which poses contraint.4.2 Received signal levelsReceived signal level method or Signal strength method is an easy and low-cost method to enhance the accuracy of pure cell ID based location. This method identifies the location by analyzing the mobile signals between mobile unit and three surrounding base stations. Mobile signal level is used to estimate a range from three base stations where the location is determined as the unique intersectionpoint of these three circles.Figure 1: Position based on call identification (Marko and Pentti, 2003)4.3 Angle of arrivalIn Angle of arrival, directional antennas are used in the base station to estimate the angle from whichthe signal arrives. Assuming two-dimensional geometry, angle of arrival measurement at two base stations is sufficient for unique location. But this method has two shortcomings in disaster management context. The first is it requires line of sight between mobile station and base station which may be available at rural place but impossible to get at urban place. And the second is high cost of antennas for base station. This method can be applied to sensitive areas like sea port.4.4 Timing advanceTiming advance is available at the network which is the time delay between mobile and serving based station. The formula for calculating Timing advance is d= (TA * c) /2, where distance of mobile from base station is d and c is the signal speed.Figure 2: Received signal levels (Marko and Pentti, 2003) Figure 3: Positioning with angle of arrival measurements (Marko and Pentti, 2003).4.5 Enhanced observed time differenceE-OTD is included in GSM location standards where mobile device measures the time differences of signals received from a pair of base stations in known locations. This method is particularly useful for the user to identify his/her location in an unknown place. This method has higher accuracy and no capacity limitation as mobile device calculates its position. This technique needs for software modifications to the handsets and the need for additional receivers (Heikki et al, 2001).5. Mobile Technology and Its Spreads in Disaster Prone Areas in Bangladesh5.1 Mobile penetration in BangladeshOn a brighter note, Bangladesh is one of the first countries in the world to have exemplified a model for rural access to mobile phones through widely acclaimed initiatives of Grameen Phone followed by other mobile service providers. The growth rate for mobile phone market has been 200 percent over the last two years. Currently, there are approximately 3.5 million mobile phone subscribers, compare to only seven thousands fixed phone subscribers. Almost every single village in Bangladesh has been brought under the coverage of mobile network system. And due to the wide prevalence of low cost pre-paid cards, there are now many who can afford to keep a mobile phone since the minimum bill payable per month is about US$ 5.Figure 4: Timing Advance (Marko and Pentti, 2003) Figure 5: E-OTD (Marko and Pentti, 2003)Company Subscribers2002 2003 20042005 Grameen Phone Number(Appox)100000015000002000000 2700000 Growth Rate(Appox)150% 133% 135% Aktel Number500000 750000 1300000 Growth Rate 150% 173%Table 1: Number of subscribers of two major mobile phone operators Source: Authors compilation from various sources5.1 M-Tech availability in disaster- prone areasIn this section we will examine the availability of mobile technology in disaster-prone areas. For the simplicity of understanding two maps of Bangladesh are presented below. Map 1shows cumulative network coverage of all mobile service providers in Bangladesh in 2004. Map 2 shows the areas that were affected during the flood of 2004 in Bangladesh. By comparing following two maps, we can see that the Network coverage of cellular phones has reached a fairly distributed geographical area covering most of the disaster-prone vicinityMap 1: Existing Coverage of Grameen Phone (Grameen Phone, 2005) Map 2: Damage Intensity ofFlood 2004(CPD, 2004).6. Model for Disaster Information Management Using M-Tech6.1 AssumptionsThe basic assumption is that 2nd Generation level technology for mobile communication is available in Bangladesh and that mobile phones have reached a critical mass. Moreover, the Network coverage of cellular phones has reached a fairly distributed geographical region covering most of the disaster prone areas.6.2 The proposed modelIn the proposed model, the Disaster Management Bureau (DMB) will play the central role of coordination for implementing mobile technology for disaster management. This DMB has a line of communication with other weather forecasting agencies. The weather forecasting agencies will forecast the disaster, cyclone for example, and pitch this information to the DMB. Dissemination of disaster warning, rescue and recovery information will be disseminated through two separate but complementary approaches. One is through the formal channel of communication like local authority and local disaster shelters. To implement this channel, the prerequisite is that all local centers will have at least one mobile phone. It is also possible to select a local representative who owns a mobile phone to keep communication with the centers that don’t have mobile phone. The central coordinator (DMB) will send updated information to the local centers which in turn will be distributed using both online and offline media. This weather information will be highly specific depending upon the cell of the mobile phone. The prevailing system of communication is through radio which is not much targeted.Another approach, which is the focus of proposed model, is disseminating disaster warning, rescue and recovery information directly to the affected people using mobile phones. The central coordinator (DMB) will collect weather information from the weather information department. There will be line of communication between the DMB and the mobile phone operators. After receiving location based weather report, the central coordinator will write a Short Message Service (SMS) describing the weather report and necessary steps to be taken and then send it to the mobile phone operators. Mobile operators then disseminate this short message to all mobile phones in a specific geographic cell. This service will be push service which will not require users’ active participation.6.3 Pull services and reverse informationThe key motivators of using mobile technology are the opportunities to use pull service and reverse information. One of the limitations of disaster management systems developed so far is that it has been considered the user/ beneficiary as a dormant payer in information receiving. By using mobile technology, a person can query and be informed by sending request to a specific number at any time. This request can be in the form of SMS and/or call. Also depending on his/her current position, she/he can get specific weather forecast, including disaster warning. Moreover, disaster recovery activities like providing relief can also be informed to the people of specific location using mobile based context aware feature.Most of the relief work did achieve its level of success due to lack of response from the field where the affected people were stayed. There is yet to have any medium of effective real-time communication between the relief team and the field. As a result, relief is not properly distributed among suffered people. Using mobile technology concurrent information can be gathered from the field which in turn ensures proper distribution and minimizes losses.Figure 6: Proposed Model for Disaster Information ManagementIn figure 6, proposed model has exposed. Disaster Management Bureau is playing the central role and coordinating the information flow from prediction to dissemination. DBM will collect Weather information from several forecasting agency through collected through ground satellite. DMB then interpret the information and create a SMS message based on pre determine template. This SMS warning will then send to the mobile operator for broadcasting in the specific area. Another SMS will also be sent to the local disaster shelter center for showing flags and relay through other existing media. Two way line between the mobile user and the operator implies that user can call or SMS to a specific number for weather forecasting information.7. Lessons Learnt, Policy Implications and Recommendations7.1 Infrastructure and cost- Government should provide at least one mobile phone to all disaster management agencies like local center for communication. Partnership with the mobile operators could be an effective way to reduce the operating cost.- Mobile operators should reduce/ subsidize the call charge in disaster affected areas which can be identify using location identification techniques.programs7.2 Awareness- Awareness programs need to be organized at grass-roots level in order to promote and make easy to understand mobile messages containing disaster information all available media.- Awareness programs should also be targeted towards local government, NGOs and other civil society organizations.7.3 Coordination- Disaster Management Bureau (DMB) may play the central role of coordination with private public partnership model basis that is maintaining partnership with private companies like mobile operators for disaster information management.- Role of the private sector, particularly the mobile telephony providers, should be encouraged as part of promotion of corporate social responsibility.- Grass-roots NGOs should prepare themselves to make effective use of mobile technologies during pre-disaster and post-disaster.7.4 Technology- Technology needs to be developed for messaging in local language like Bengli. Example could be Nokia’s recent innovation in India that is messaging in Hindi language.8. Concluding remarksIn conclusion, mobile technology, in disaster management, may be used in the following waysTo disseminate pre-disaster warningsMobile phones may be used to disseminate information about impending disasters. Since only 30 percent of the population of Bangladesh has access to electricity, they do not always have access to other media such as TV or radio, and if they have, they may not have it turned on during emergency. But mobile phones are widely prevalent and are ‘always on’.To disseminate post-disaster announcementsThe government and NGOs can send relevant announcements such as transferring to specific shelters or information about relief distribution after a disaster. Immediately after a disaster, it is found that many are left homeless and always on the move. During these situations, sending out announcements through mobile phones can be an effective means to keep people organized and run post-disaster operations smoothly.To receive information about relief needsThe mobile phone can also be an effective means for the affected people to send out information about relief needs, and notify relevant bodies about unequal or undesired relief distribution strategies. This can empower the affected people and enable them to find a voice during a helpless time.To exchange information about health hazardThe mobile technologies can also be used to send emergency information about health hazards, both from the side of the government and also from the side of the disaster-affected people. The government can send warnings about possible hazards and preventive measures, and likewise the affected people can send information about the situation on the ground and notify relevant bodies about medication needs.ReferencesBIDS (1991) The April Cyclone and Economic Loss: An Informed Guess from Secondary Data. Dhaka: Bangladesh Institute of Development Studies.Disaster Management Bureau (DMB), (2002) Disaster management Training Handbook, the ministry of Disaster Management and Relief.Grammen Phone corporate website. / dated March 11, 2005Haider, R., Rahman, A.A. & Huq, S., Eds (1991) Cyclone ’91: An Environmental and Perceptional Study. Dhaka: Bangladesh Centre for Advanced Studies.Heikki Laitinen and at el, (2001) “Cellular network optimisation based on mobile location”, 2001 CELLO Consortium.Khan Alauddin, (2003) “Safety Measures for Disasters”, Jelly Khan.Maniruzzaman K. M., Okabe Atsuyuki & Asami Yasushi, (2001) “GIS for Cyclone Disaster Management in Bangladesh”, Geographical & Environmental Modelling., Vol. 5, No. 2, 123-131.Palola Marko & Tarvainen Pentti, (2003) “Location Technologies in the Mobile Domain”, VTT Electronics, Oulu.Centre for Policy Dialogue (CPD), (2004), Rapid assessment of flood 2004, Dhaka: University press limited.About the authors:Chowdhury Golam Hossan is teaching as a Senior Lecturer at the Department of Business Administration, East West University, Dhaka, Bangladesh. He has received his Masters degree in E-business Management from International University of Japan. He did his MBA in MIS from the University of Dhaka, Bangladesh. He is currently He is also teaching as a part-time faculty at the Department of Business Administration, Jahangirnagar University, Dhaka, Bangladesh where he teaches project management to the final semester students of BBA. He is being affiliated with , a premier web based resource for mobile commerce, since its inception. He has several publications in national journals on Mobile government issues, economics and business application of IT.Mridul Chowdhury is currently an e-Government consultant for the Ministry of Planning, Government of Bangladesh and is also a Research Affiliate of Harvard University’s IT Group based at the Berkman Center for Internet and Society. He was the Executive Director-in-charge of , an NGO that promotes the use of ICTs for social and economic development. He has several publications in international journals on ICT policy issues and on computational economics.Ibrahim Kushchu, PhD is an Associate Professor and Director of CODI and at International University of Japan. His broad research efforts aim to apply evolutionary learning models to simulate behaviors of Adaptive Virtual Business Organizations using Complexity and Evolutionary Artificial Intelligence techniques (i.e. genetic algorithms and programming). In particular, he is interested in using these methods to build intelligent wireless and voice applications for web based e-business solutions.253。

Using the SimOS machine simulator to study complex computer systems

Using the SimOS machine simulator to study complex computer systems

Using the SimOS Machine Simulator to Study Complex Computer SystemsMENDEL ROSENBLUM,EDOUARD BUGNION,SCOTT DEVINE,and STEPHEN A.HERRODComputer Systems Laboratory,Stanford UniversitySimOS is an environment for studying the hardware and software of computer systems. SimOS simulates the hardware of a computer system in enough detail to boot a commercial operating system and run realistic workloads on top of it.This paper identifies two challenges that machine simulators such as SimOS must overcome in order to effectively analyze large complex workloads:handling long workload execution times and collecting data effectively.To study long-running workloads,SimOS includes multiple interchangeable simulation models for each hardware component.By selecting the appropriate combination of simulation models, the user can explicitly control the tradeoff between simulation speed and simulation detail.To handle the large amount of low-level data generated by the hardware simulation models, SimOS contains flexible annotation and event classification mechanisms that map the data back to concepts meaningful to the user.SimOS has been extensively used to study new computer hardware designs,to analyze application performance,and to study operating systems.We include two case studies that demonstrate how a low-level machine simulator such as SimOS can be used to study large and complex workloads.Categories and Subject Descriptors:C.4[Computer Systems Organization]:Performance of Systems;B.3.3[Hardware]:Memory Structures—performance analysis,and design aids Additional Key Words and Phrases:computer architecture,computer simulation,computer system performance analysis,operating systems1.INTRODUCTIONSimOS is a machine simulation environment designed to study large complex computer systems.SimOS differs from most simulation tools in that it simulates the complete hardware of the computer system.In contrast,most other environments only simulate portions of the hardware. As a result,they must also simulate portions of the system software.SimOS was developed as part of the Stanford FLASH project,funded by ARPA grant DABT63-94-C-0054.Mendel Rosenblum is partially supported by an NSF Young Investigator Award.E.Bugnion and Steve Herrod are supported in part by NSF Graduate Fellowships Awards.Steve Herrod currently holds an Intel Foundation Graduate Fellowship.Authors’addresses:Computer Systems Laboratory,Stanford University,Stanford,CA,94305; /SimOS;e-mail͗{mendel,bugnion,devine,herrod}@͘Permission to make digital/hard copy of part or all of this work for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage,the copyright notice,the title of the publication,and its date appear, and notice is given that copying is by permission of the ACM,Inc.To copy otherwise,to republish,to post on servers,or to redistribute to lists,requires prior specific permission and/or a fee.©1997ACM1049-3301/97/0100–0078$03.50ACM Transactions on Modeling and Computer Simulation,Vol.7,No.1,January1997,Pages78–103.SimOS Machine Simulator•79 SimOS simulates the computer hardware with sufficient speed and detail to run existing system software and application programs.For example,the current version of SimOS simulates the hardware of multiprocessor com-puter systems in enough detail to boot,run,and study Silicon Graphics’IRIX operating system as well as any application that runs on it,such as parallel compilation and commercial relational database systems. Simulating machines at the hardware level has allowed SimOS to be used for a wide range of puter architects can evaluate the impact of new hardware designs on the performance of complex workloads by modifying the configuration and timing model of the simulated hard-ware components.Operating system programmers can develop their soft-ware in an environment that provides the same interface as the target hardware,while taking advantage of the system visibility and repeatability offered by a simulation environment.Application programmers can also use SimOS to gain insight into the dynamic execution behavior of complex workloads.The user can nonintrusively collect detailed performance-analy-sis metrics such as instruction execution,memory-system stall,and inter-processor communication time.Although machine simulation is a well-established technique,it has traditionally been limited to small system configurations.SimOS enables the study of complex workloads by addressing some particularly difficult challenges.The first challenge is to achieve the simulation speed needed to execute long-running workloads.Given sufficient speed,machine simula-tors produce voluminous performance data.The second challenge is to effectively organize these raw data in ways meaningful to the user.To address the first challenge,SimOS includes both high-speed machine emulation techniques and more accurate machine simulation techniques. Using emulation techniques based on binary translation,SimOS can exe-cute workloads less than10times slower than the underlying hardware. This allows the user to position the workload to an interesting execution state before switching to a more detailed model to collect statistics.For example,emulation can be used to boot the operating system and run a database server until it reaches a steady execution state.SimOS can dynamically switch between the emulation and simulation techniques, allowing the user to study portions of long running workloads in detail.To address the second challenge,SimOS includes novel mechanisms for mapping the data collected by the hardware models back to concepts that are meaningful to the user.Just as the hardware of a computer system has little knowledge of what process,user,or transaction it is executing,the hardware simulation models of SimOS are unable to attribute the execu-tion behavior back to these concepts.SimOS uses a flexible mechanism called annotations to build knowledge about the state of the software being executed.Annotations are user-defined scripts that are executed when hardware events of particular interest occur.The scripts have nonintrusive access to the entire state of the machine,and can control the classification of simulation statistics.For example,an annotation put on the context switching routine of the operating system allows the user to determine the ACM Transactions on Modeling and Computer Simulation,Vol.7,No.1,January1997.80•M.Rosenblum et al.currently scheduled process and to separate the execution behavior of the different processes of the workload.This article describes our solution to the two challenges.We begin with an overview of SimOS in Section2.Section3describes the use of inter-changeable hardware simulation models to simulate complex workloads. Section4describes the data collection and classification system.In Section 5,we describe our experiences with SimOS in two case studies.In Section 6,we discuss related techniques used for studying complex systems.We conclude in Section7.2.THE SIMOS ENVIRONMENTThe SimOS project started in1992as an attempt to build a software simulation environment capable of studying the execution behavior of operating systems.Many of SimOS’s features follow directly from this goal. To study the behavior of an operating system,SimOS was designed as a complete machine simulator where the hardware of the machine is simu-lated in enough detail to run the actual operating system and application workloads.Furthermore,the large and complex nature of operating sys-tems required SimOS to include multiple interchangeable simulation mod-els of each hardware component that can be dynamically selected at any time during the simulation.In the rest of this section,we present a brief overview of these features of SimOS.Readers interested in the implementation details should refer to the previous papers on SimOS[Rosenblum et al.1995]and Embra[Witchel and Rosenblum1996]for a much more thorough discussion of the simula-tion techniques.The use of interchangeable simulation models for complete machine simulation,as discussed in Section2.2,is introduced in Rosen-blum et al.[1995].That paper also describes in detail the implementation of SimOS’s original approach to high-speed emulation based on direct execution.Embra,SimOS’s current approach to high-speed emulation based on binary translation,is described in detail in Witchel and Rosen-blum[1996].2.1Complete Machine SimulationDespite its name,SimOS does not model an operating system or any application software,but rather models the hardware components of the target machine.As shown in Figure1,SimOS contains software simulation of all the hardware components of modern computer systems:processors, memory management units(MMU),caches,memory systems,as well as I/O devices such as SCSI disks,Ethernets,hardware clocks,and consoles. SimOS currently simulates the hardware of MIPS-based multiprocessors in enough detail to boot and run an essentially unmodified version of a commercial operating system,Silicon Graphics’IRIX.In order to run the operating system and application programs,SimOS must simulate the hardware functionality visible to the software.For example,the simulation model of a CPU must be capable of simulating the ACM Transactions on Modeling and Computer Simulation,Vol.7,No.1,January1997.execution of all MIPS CPU instructions including the privileged instruc-tions.It must also provide the virtual address to physical address transla-tions done by the memory management unit (MMU).For the MIPS archi-tecture this means implementing the associative lookup of the translation lookaside buffer (TLB),including raising the relevant exceptions if the translation fails.SimOS models the behavior of I/O devices by responding to uncached accesses from the CPU,interrupting the CPU when an I/O request has completed,and performing direct memory access (DMA).For some devices,useful emulation requires communication with the nonsimulated host devices.For example,the SCSI disk simulator reads and writes blocks from a file in the host machine’s file system,making it possible to transfer large amounts of data into the simulated machine by creating the appropriate disk image in a file.Similarly,the console and network devices can be connected to real terminals or networks to allow the user to interactively configure the workloads that run on the simulator.The complete machine simulation approach differs from the approach generally used in simulation systems for studying application programs.Because application-level simulators are not designed to run an operating system,they only need to simulate the portion of the hardware interface visible to user-level programs.For example,the MMU is not visible to application programs and is not modeled by application-level simulators.However,complete machine simulators must perform an MMU lookup on every instruction.Although this requires additional work,complete ma-chine simulators have many advantages as described in Section 3.1.2.2Interchangeable Simulation ModelsBecause of the additional work needed for complete machine simulation,SimOS includes a set of interchangeable simulation models for each hard-ware component of the system.Each of these models is a self-contained software implementation of the device’s functional behavior.Although all models implement the behavior of the hardware components in sufficient detail to correctly run the operating system and application programs,theFig.1.The SimOS environment.SimOS Machine Simulator •81ACM Transactions on Modeling and Computer Simulation,Vol.7,No.1,January 1997.82•M.Rosenblum et al.models differ greatly in their timing accuracy,interleaving of multiproces-sor execution,statistics collected,and simulation speed.Furthermore,the user can dynamically select which model of a hardware component is used at any time during the simulation.Each model supports the ability to transfer its state to the other models of the same hardware component.For example,the different MIPS CPU models transfer the contents of the register file and the translation-lookaside buffer.As we show in Section3,the ability to switch between models with different simulation speed and detail is critical when studying large and complex workloads.A complete machine simulation system must model all components of a computer system.However,only a few components play a determining factor in the speed of the simulation.The processors,MMUs,and memory hierarchy account for the bulk of the simulation costs.In the rest of this section,we summarize the implementation techniques used by SimOS to model these critical components.2.2.1High-Speed Machine Emulation Models.To support high-speed emulation of a MIPS processor and memory system,SimOS includes Embra [Witchel and Rosenblum1996].Embra uses the dynamic binary translation approach pioneered by the Shade system[Cmelik and Keppel1994]. Dynamic binary translators translate blocks of instructions into code sequences that implement the effects of the original instructions on the simulated machine state.The translated code is then executed directly on the host hardware.Sophisticated caching of translations and other optimi-zations results in executing workloads with a slowdown of less than a factor of10.This is two to three orders of magnitude faster than more conventional simulation techniques.Embra extends the techniques of Shade to support complete machine simulation.The extensions include modeling the effects of the memory-management unit(MMU),privileged instructions,and the trap architec-ture of the machine.The approach used in Embra is to handle all of these extensions with additional code incorporated into the translations.For example,Embra generates code that implements the associative lookup done by the MMU on every memory reference.Embra also extends the techniques of Shade to efficiently simulate multiprocessors.Besides its speed,a second advantage of using dynamic binary transla-tion is the flexibility to customize the translations for more accurate modeling of the machine.For example,Embra can augment the emitted translations to check whether instruction and data accesses hit in the simulated cache.The result is that SimOS can generate good estimates of the instruction execution and memory stall time of a workload at a slowdown of less than a factor of25.2.2.2Detailed Machine Simulation Models.Although Embra’s use of self-generated and self-modifying code allows it to simulate at high speeds, the techniques cannot be easily extended to build more detailed and accurate models.To build such models,we use more conventional software ACM Transactions on Modeling and Computer Simulation,Vol.7,No.1,January1997.SimOS Machine Simulator•83 engineering techniques that value clean well-defined interfaces and ease of programming over simulation speed.SimOS contains interfaces for sup-porting different processor,memory system,and I/O device models. SimOS contains accurate models of two different processor pipelines.The first,called Mipsy,is a simple pipeline with blocking caches such as used in the MIPS R4000.The second,called MXS[Bennett and Flynn1995],is a superscalar,dynamically scheduled pipeline with nonblocking caches such as used in the MIPS R10000.The two models vary greatly in speed and detail.For example,MXS is an order of magnitude slower than Mipsy, because the R10000has a significantly more complex pipeline.Mipsy and MXS can both drive arbitrarily accurate memory system models.SimOS currently supports memory system models for a bus-based memory system with uniform memory access time,a simple cache-coherent nonuniform memory architecture(CC-NUMA)memory system,and a cycle accurate simulation of the Stanford FLASH memory system[Kuskin et al. 1994].For I/O device simulation,SimOS includes detailed timing models for common devices such as SCSI disks and interconnection networks such as Ethernet.For example,SimOS includes a validated timing model of the HP 97560SCSI disk[Kotz et al.1994].ING SIMOS TO SIMULATE COMPLEX WORKLOADSThe use of complete machine simulation in SimOS would appear to be at odds with the goal of studying large and complex workloads.After all, simulating at the hardware level means that SimOS must do a significant amount of work to study a complex workload.Our experience is that complete machine simulation can actually simplify the study of complex workloads.Furthermore,by exploiting the speed/detail tradeoff through the use of interchangeable hardware models,SimOS can run these work-loads without excessively long simulation time.In the rest of this section we describe how these two features of SimOS support the study of complex workloads.3.1Benefits of Complete Machine SimulationAlthough complete machine simulation is resource intensive,it has advan-tages in ease of use,flexibility,and visibility when studying complex workloads.Because SimOS provides the same interface as the target hardware,we can run an essentially unmodified version of a commercial operating system.This operating system can then in turn run the exact same applications that would run on the target system.Setting up a workload is therefore straightforward.We simply boot the operating system on top of SimOS,copy all the files needed for the workload into the simulated system,and then start the execution of the workload.In contrast,applica-tion-level simulators are generally not used to study complex workloads since they would need to emulate a significant fraction of the operating ACM Transactions on Modeling and Computer Simulation,Vol.7,No.1,January1997.84•M.Rosenblum et al.system’s functionality to simply run the workload.This emulation task is likely to be more complex than a complete machine simulation approach. For example,we have used SimOS to study complex multiprogrammed workloads such as parallel compilation and a client-server transaction processing database.We were able to simply copy the necessary programs and data files from an existing machine that runs the workloads.No changes to the application software or the simulator were required. SimOS is flexible enough that it can be configured to model entire distributed systems.For example,we have studied file servers by simulat-ing at the same time the machine running the file server software,the client machines,and the local area network connecting them.This made it possible to study the entire distributed system under realistic request patterns generated by the clients.Although there are certainly limits as to how far this approach will scale,we have been able to simulate tens of machines and should be able to study hundreds of machines.Using software simulation has a number of additional advantages for SimOS.First,software simulation models are significantly easier to change than the real hardware of a machine.This makes it possible to study the effects of changes to the hardware.Secondly,simulating the entire machine at a low level provides SimOS excellent visibility into the system behavior. It“sees”all events that occur in the system,including cache misses, exceptions,and system calls,regardless of which part of the system caused the events.3.2Exploiting the Speed/Detail TradeoffSimOS’s use of complete machine simulation tends to consume large amounts of resources.Furthermore,complex workloads tend to run for long periods of time.Fast simulation technology is required to study such workloads.For example,the study of a commercial data processing work-load described in Section5.1required the execution of many tens of billions of simulated instructions to boot the operating system and start up the database server and client programs.Executing these instructions using the simulator with the level of detail needed for the study would have taken weeks of simulation time to reach an interesting execution state. SimOS addresses this problem by exploiting the inherent tradeoff be-tween simulation speed and simulation detail.Each of SimOS’s inter-changeable simulation models chooses a different tradeoff between simula-tion speed and detail.We have found the combination of three models to be of particular use:emulation mode,rough characterization mode,and accu-rate mode.3.2.1Emulation Mode.As indicated,positioning a complex workload usually requires simulating large amounts of uninteresting execution such as booting the operating system,reading data from a disk,and initializing the workload.Furthermore,issues such as memory fragmentation and file system buffer caches can have a large effect on the workload’s execution. Many of these effects are not present in a freshly booted operating system; ACM Transactions on Modeling and Computer Simulation,Vol.7,No.1,January1997.SimOS Machine Simulator•85 they only appear after prolonged use of the system.Realistic studies require executing past these“cold start”effects and into a steady-state representative of the real system.To position a workload,SimOS can be configured to run the workload as fast as possible.We refer to this configuration as emulation mode because its implementation shares more in common with emulation techniques than with simulation techniques.The only requirement is to correctly execute the workload;no statistics on workload execution are required. Emulation mode uses Embra configured to model only the hardware components of the system that are necessary to correctly execute the workload.No attempt is made to keep accurate timing or to model hard-ware features invisible to the software,such as the cache hierarchy and processor pipelines.I/O devices such as disks are configured to instanta-neously satisfy all requests,avoiding the time that would be required to simulate the execution of the operating system’s idle loop.To enable the high speed emulation of multiprocessors,Embra can run as multiple parallel processes where each process simulates a disjoint set of processors.Embra can make highly effective use of the additional proces-sors,achieving linear and sometimes superlinear speedups for the simula-tion[Witchel and Rosenblum1996].Embra is able to make such an optimization because it is only used in emulation mode,and that does not require the separate simulated processors to have their notions of time closely synchronized.In parallel Embra,the scheduler of the host multiprocessor has an impact on the interleaving of the simulated processors.The final machine state is guaranteed to be one of a set of possibilities that are feasible if no timing assumptions are made about code execution.Note,however,that the simulation is not deterministic and that different simulation executions will result in different final machine states.In practice,the operating system and application software execute correctly independently of the actual interleaving of the instructions executed.As a result,all reached machine states,although temporally inaccurate,are functionally plausible, and can be used as the starting point for more accurate simulation models. Early versions of SimOS contained an emulation mode based on direct execution of the operating system and the applications[Rosenblum et al. 1995].Direct execution was frequently used to position the workloads,but was removed in1996in favor of the binary translation approach.Specifi-cally,binary translation is more amenable to cross-platform support than direct execution mode.3.2.2Rough Characterization Mode.The speed of emulation mode is useful for positioning and setup of workloads,but the lack of a timing model makes it unsuitable for many uses.To gain more insight into the workload’s behavior,SimOS supports a rough characterization mode that maintains high simulation speed yet provides timing estimates that ap-proximate the behavior of the machine.For example,rough characteriza-tion mode includes timing models that can track instruction execution, ACM Transactions on Modeling and Computer Simulation,Vol.7,No.1,January1997.86•M.Rosenblum et al.memory stall,and I/O device behavior,yet it is only two or three times slower than emulation mode.Rough characterization mode is commonly used in the following ways. First,it is used to perform a high-level characterization of the workload in order to determine first-order bottlenecks.For example,it can determine if the workload is paging,I/O bound on a disk,or suffering large amounts of memory system stall.Since the simulation speed of rough characterization speed is similar to that in emulation mode,it can be used to examine the workload over relatively long periods of simulated time.The second com-mon use of rough characterization is to determine the interesting parts of the workload that should be further studied in greater detail with the accurate simulators.The rough characterization provides enough informa-tion to determine the interesting points to focus on in the more detailed modes.An example of the use of the rough characterization mode can be found in Bugnion et al.[1996].The benchmark programs used in that study re-quired far too much execution time to run the entire program in the accurate simulation modes.Instead we used the rough characterization mode to run the program to completion.We observed that all the bench-marks in this study had a regular execution behavior.This allowed us to study limited representative execution windows.Having the rough charac-terization of the entire benchmark gave us confidence that the selected window of the workload,studied in the accurate mode,would be represen-tative of the benchmark as a whole.The rough characterization mode uses Embra configured to model a simple CPU pipeline and a large unified instruction and data cache much like the second-level cache of the MIPS R4000.The memory system models a fixed delay for each cache miss.I/O devices use a realistic timing model. For example,the SCSI disk models seek,rotation,and transfer times.This mode gives estimates of the instruction,memory system behavior,and I/O behavior of the workload.3.2.3Accurate Mode.The accurate simulation mode tries to model a given hardware configuration as accurately as possible.Because of their detail,these configurations lead to simulation speeds that are too slow to use for workload positioning.The goal of accurate mode is to collect detailed statistics on the workload’s execution behavior.Essentially all results reported in studies that use SimOS were generated in the accurate mode.In this mode,we use either the Mipsy or MXS processor models to study the performance of a simple processor pipeline or of a dynamically sched-uled processor.Because of the complexity of the dynamically scheduled processor it models,MXS can only simulate on the order of20,000instruc-tions per second when run on current machines.Because of this slow simulation speed,it takes a long time for the simulation to warm up the state of the cache hierarchy.We therefore generally use Mipsy,which is an ACM Transactions on Modeling and Computer Simulation,Vol.7,No.1,January1997.SimOS Machine Simulator•87 order of magnitude faster,to warm up the caches before switching into MXS.SimOS includes even more detailed gate-level models of some hardware components of the FLASH machine.Unfortunately,these models are so detailed that the simulation speed is limited to a few simulated cycles per second.With such slowdowns,simulating even a single transaction of a database workload is infeasible.To study such workloads,we use random sampling techniques that switch between different levels of detail.This allows us to use statistical analysis to estimate the behavior of the most detailed models during the execution of the workload.Sampling is also used to switch between the Mipsy and MXS processor models.For example,two architectural studies sampled10%of the executed cycles using MXS,running the remainder in the faster Mipsy[Nayfeh et al.1996;Wilson et al.1996].4.DATA COLLECTION MECHANISMSLow-level machine simulators such as SimOS have a great advantage in that they see all events that happen on the simulated system.These events include the execution of instructions,MMU exceptions,cache misses,CPU pipeline stalls,and so on.The accurate simulation models of SimOS are heavily instrumented to collect both event counts and timing information describing the simulated machine’s execution behavior.Unfortunately, when studying complex systems,collecting these data presents two prob-lems.The data are generated at a low hardware level and at a high rate. The low hardware level of data collected is problematic because the user needs to assign costs to higher-level concepts such as processes or transac-tions that are not known to the hardware.For example,tracking memory stalls by physical memory address is not useful if the mapping from the physical address back to the virtual address of the process being studied is not known.Even if the memory stalls are recorded by virtual address,it is often difficult to determine to which process they correspond in a multipro-grammed workload.Mapping events to higher-level concepts is also important when studying the behavior of the hardware.For example,the cycles per instruction(CPI) of a processor is a useful metric to computer architects only if the instruc-tions are factored out that are executed while in the operating system’s idle loop.The classification of events in SimOS is further complicated by the high rate at which the data are generated.Unless the classification and record-ing mechanism is efficient,the overall performance of the simulation will suffer.To address these challenges,SimOS’s data classification mechanisms need to be customized for the structure of the workload being studied as well as the exact classification desired.A Tcl scripting language interpreter [Ousterhout1994]embedded in SimOS accomplishes this in a simple and flexible ers of SimOS can write Tcl scripts that interact closely with ACM Transactions on Modeling and Computer Simulation,Vol.7,No.1,January1997.。

使用类比法的英文作文

使用类比法的英文作文

使用类比法的英文作文Title: The Power of Analogies: Learning Through Comparison。

In the vast realm of human cognition, analogies stand as powerful tools for comprehension and learning. Analogies serve as bridges between the known and the unknown, allowing individuals to grasp complex concepts by drawing parallels with familiar situations or objects. Just as a skilled artist uses a palette of colors to create a masterpiece, analogies enrich our understanding and facilitate deeper insights into various subjects.One area where analogies prove invaluable is in the realm of education. When faced with abstract or intricate concepts, students often struggle to grasp their significance. However, by presenting these concepts through analogies, educators can make them more accessible and relatable. For instance, explaining the concept of photosynthesis by likening it to a factory where rawmaterials are transformed into finished products resonates far more effectively with students than a dry, technical explanation.Moreover, analogies transcend the boundaries of disciplines, offering interdisciplinary insights that foster holistic understanding. Consider the analogy of the human body as a machine. By comparing the heart to a pump and the nervous system to electrical wiring, students studying biology gain a deeper appreciation for the intricate functioning of living organisms while simultaneously drawing parallels to principles of physics and engineering.Analogies also play a pivotal role in problem-solving and decision-making. When confronted with complex challenges, individuals often rely on analogical reasoning to identify solutions. By recognizing similarities between the current problem and past experiences, they can adapt strategies that have proven effective in analogous situations. Just as a chess player may draw upon previous games to inform their moves, professionals in variousfields leverage analogies to navigate uncertainty and achieve optimal outcomes.Furthermore, analogies serve as powerful rhetorical devices, enhancing communication and persuasion. Whether in literature, politics, or advertising, compelling analogies captivate audiences and evoke visceral responses. Martin Luther King Jr.'s famous "I Have a Dream" speech, for instance, abounds with analogies that vividly illustrate the urgency of racial equality, resonating with listeners on a profound emotional level.In the realm of science and innovation, analogies serve as catalysts for creativity and breakthroughs. When scientists encounter enigmatic phenomena, they often draw analogies from disparate domains to formulate hypotheses and theories. This cross-pollination of ideas fosters innovation by encouraging novel perspectives and unconventional approaches. Just as Isaac Newton drew inspiration from falling apples to conceive the law of universal gravitation, contemporary scientists harness analogies to unravel the mysteries of the cosmos andadvance human knowledge.However, despite their undeniable utility, analogies are not without limitations. Misleading or faulty analogies can lead to erroneous conclusions and misconceptions. Thus, it is essential to exercise discernment and critical thinking when employing analogical reasoning. Moreover, analogies are inherently subjective, shaped by individual perspectives and experiences. What resonates with one person may not necessarily resonate with another, underscoring the importance of context and audience in crafting effective analogies.In conclusion, analogies serve as potent instrumentsfor learning, problem-solving, communication, and innovation. By illuminating connections between seemingly disparate concepts, analogies facilitate comprehension and stimulate creativity. Whether in the classroom, the laboratory, or the public sphere, analogies enrich our understanding of the world and empower us to navigate its complexities with clarity and insight. As we harness the power of analogies, we embark on a journey of discovery andenlightenment, guided by the timeless wisdom that lies within the art of comparison.。

三维解析仿真的英语作文

三维解析仿真的英语作文

三维解析仿真的英语作文Three-Dimensional Computational Modeling.Three-dimensional (3D) computational modeling is the process of creating a mathematical representation of a three-dimensional object. This representation can be used to simulate the behavior of the object under different conditions. 3D computational modeling is used in a wide variety of fields, including engineering, medicine, and manufacturing.In engineering, 3D computational modeling is used to simulate the behavior of structures and machines. This information can be used to design structures that are safe and efficient. In medicine, 3D computational modeling is used to simulate the behavior of organs and tissues. This information can be used to diagnose diseases and develop new treatments. In manufacturing, 3D computational modeling is used to simulate the behavior of products during the manufacturing process. This information can be used tooptimize the manufacturing process and reduce product defects.There are many different types of 3D computational modeling software available. The type of software used will depend on the specific application. Some of the most popular 3D computational modeling software programs include ANSYS, COMSOL, and Siemens NX.3D computational modeling is a powerful tool that can be used to simulate the behavior of objects in a variety of different fields. This information can be used to design safer and more efficient structures, diagnose and treat diseases, and optimize the manufacturing process.Benefits of 3D Computational Modeling.There are many benefits to using 3D computational modeling. Some of the most notable benefits include:Increased accuracy: 3D computational models are more accurate than traditional 2D models. This is because 3Dmodels can take into account the effects of all three dimensions of space.Reduced time and cost: 3D computational modeling can save time and cost by reducing the need for physical testing. Physical testing can be expensive and time-consuming, and it is not always possible to test all possible scenarios.Improved communication: 3D computational models can be used to communicate complex designs and concepts more easily. This can help to reduce errors and improve collaboration between different teams.Applications of 3D Computational Modeling.3D computational modeling is used in a wide variety of applications, including:Engineering: 3D computational modeling is used to simulate the behavior of structures and machines. This information can be used to design structures that are safeand efficient.Medicine: 3D computational modeling is used to simulate the behavior of organs and tissues. This information can be used to diagnose diseases and develop new treatments.Manufacturing: 3D computational modeling is used to simulate the behavior of products during the manufacturing process. This information can be used to optimize the manufacturing process and reduce product defects.Future of 3D Computational Modeling.The future of 3D computational modeling is bright. As computer hardware and software continue to improve, 3D computational models will become even more accurate and sophisticated. This will open up new possibilities for using 3D computational modeling in a wide variety of applications.One of the most exciting developments in 3Dcomputational modeling is the use of artificialintelligence (AI). AI can be used to automate the process of creating and running 3D computational models. This will make it easier for engineers, scientists, and other professionals to use 3D computational modeling in their work.Another exciting development in 3D computational modeling is the use of virtual reality (VR). VR can be used to create immersive 3D environments that allow users to interact with 3D computational models. This can make it easier to understand complex designs and concepts.3D computational modeling is a powerful tool that is transforming the way we design, build, and heal. As computer hardware and software continue to improve, 3D computational modeling will become even more powerful and versatile. This will open up new possibilities for using 3D computational modeling in a wide variety of applications.。

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a rXiv:074.1296v1[astro-ph]1Apr27Convection in Astrophysics Proceedings IAU Symposium No.239,2007F.Kupka,I.W.Roxburgh &K.L.Chan,eds.c 2007International Astronomical Union DOI:00.0000/X000000000000000X Prospects of using simulations to study the photospheres of brown dwarfs Hans-G¨u nter Ludwig 11CIFIST,GEPI,Observatoire de Paris-Meudon,92195Meudon,France email:Hans.Ludwig@obspm.fr Abstract.We discuss prospects of using multi-dimensional time-dependent simulations to study the atmospheres of brown dwarfs and extrasolar giant planets,including the processes of convec-tion,radiation,dust formation,and rotation.We argue that reasonably realistic simulations are feasible,however,separated into two classes of local and global models.Numerical challenges are related to potentially large dynamic ranges,and the treatment of scattering of radiation in multi-D geometries.Keywords.hydrodynamics,convection,radiative transfer,methods:numerical,stars:atmo-spheres,stars:low-mass,brown dwarfs2H.-G.Ludwig“standard”models)for L-and T-type objects(e.g.,Burrows et al.2006,Tsuji2002, Allard et al.2001,Ackerman&Marley2001),and we draw from this work.Simulations will augment our understanding of BD and EGP atmospheres by adding information about the detailed cloud meteorology on the local scale of convective cells,as well as on the scale of the global wind circulation pattern.This includes further characterization of the effects of irradiation in close-in EGPs.The simulation of the atmospheric dynamics might also add to our knowledge about local dynamo action in substellar objects,and acoustic activity contributing to the heating of chromospheres.2.Micro-physical inputIn order to perform realistic simulations micro-physical input data must be available–radiative opacities,equation-of-state(EOS),and a kinetic model describing the formation of dust grains.The requirements are similar to those for standard models,and conse-quently in simulation work one can usually take recourse to the descriptions developed for1D models–largely on the same level of sophistication.In all three before-mentioned areas substantial progress has been made over the last decade,spawned by the discov-ery of thefirst brown dwarf and EGP in1995.In particular,since the early work of Rossow1978kinetic models describing the nucleation,growth,and evaporation of dust grains under conditions characteristic of brown dwarf atmospheres have been developed, see Helling et al.2004and references therein.Hence,the present input data allow to set-up simulations on a sufficiently realistic level.3.Time scales:convection,radiation,dust,rotation,&numericsTo obtain insight into potential challenges one faces in simulations of the dynamics of brown dwarf and EGP atmospheres it is illuminating to take a look at the characteristic time scales of the governing physical processes.Figure1depicts these time scales in a representative brown dwarf model atmosphere at T eff=1800K and log g=5.0of solar chemical composition.The model comprises the stellar photosphere and the uppermost layers of the convective stellar envelope.Since it is expected that the cloud decks are located in vicinity of the boundary of the convective envelope(in this model located at a geometrical height of23km)it also contains the layers in which the dust harboring layers are expected.We emphasize that the model structure is taken from an experimental hydrodynamical simulation in which dust formation was not taken into account.Since here we are interested in order of magnitude estimates only this is not a critical issue. Infigure1the line labeled“C-F-L”computed as the sound crossing time over a pres-sure scale height depicts the upper limit of the time step which is allowed in an explicit hydrodynamical scheme due to the Courant-Friedrichs-Levy stability criterion.Depend-ing on the actual resolution of the numerical grid this number may be one to two orders of magnitude smaller than indicated.The line labeled“convection”depicts the modulus of the Brunt-Vai¨a s¨a l¨a period providing a measure of the time scale on which the convective flow evolves.Two lines labeled“radiation”indicate the time scale on which radiation changes the thermal energy content of the gas.The dashed-dotted line is computed using Rosseland mean opacities which give the correct behavior in the optical thick layers, the solid line is based on Planck mean opacities which give a better representation in the optically thin layers.For the rotational period depicted by the dashed line labeled “rotation”we took a representative value close to one day.For three different dust grain diameters of1,10,and100µm we plotted the sedimentation(“rain out”)time scale taking as drifting time over a pressure scale height.The drift velocities were taken fromProspects for simulations of brown dwarf photospheres 3020406080Geometrical height [km]100101102103104105T i m e s c a l e [s ] 2 1 0−1−2−3radiation convection rotation 1µm 10µm 100µmr a i n o u t C−F−L Figure 1.Characteristic time scales of various processes in a brown dwarf atmosphere of T eff=1800K as a function of geometrical height.The tick marks close to the abscissa indicate the (log Rosseland)optical depth.For details see text.the work of Woitke &Helling 2003.We did not depict the formation time scale of the dust grains in the figure:for grains of 100µm diameter it is of the same order as the sed-imentation time scale.Consequently,it is unlikely that larger grains can stay in brown dwarf atmospheres.The formation time scale becomes rapidly shorter for smaller grains so that they can be considered being essentially formed in quasi-static phase-equilibrium (see also Helling 2005.Computing resources available today typically allow to simulate a dynamical range in time of 104...105,and 102...103per dimension (for 3D models)in space.From figure 1we conclude that it should be feasible to include convection,radiative transfer effects,and dust formation in a simulation of a BD/EGP atmosphere.The simultaneous inclusion of rotation is beyond reach,in particular if one takes into consideration that one would like to simulate many rotational periods to obtain a statistically relaxed state.However,the substantial difference between the time scales on which rotation and convection operate moreover indicates that rotation is dynamically not relevant for the surface granulation pattern in BD/EGPs.A rather strong modeling limitation comes about by the large spatial scale separation between the typical size of a convective cell and the global scale of a BD or EGP of about 104,at best reduced to 103for the case of young,low mass EGPs.Hence,typical BD/EGP conditions are hardly within reach with 3D models,and the steep increase of the computational cost with spatial resolution (for explicite numerical schemes with (∆x )4)makes it likely that this situation prevails during the nearer future.We expect that 3D simulations will for some time be either tailored to simulate the global meteorology,or will be restricted to local models simulating the convective flow in detail.Figure 2illustrates the kinematics of the flow in a local BD simulation analogous to figure 1.The horizontal root-mean-square of the vertical velocity component is depicted by the diamond symbols.The key-point to note is that the convective motions proper are largely confined to the convectively unstable layers.The velocities in the convectively stable layers with log τ<0are almost exclusively related to sound waves.As essentially oscillatory motions they are ineffective for mixing so that they provide little updraft to keep dust grains aloft in the atmosphere.The green line is illustrating an estimate4H.-G.Ludwig 020406080Geometrical height [km]0.11.010.0100.01000.0V e l o c i t y [m s −1]1µm 10µm 100µm g r a i n v e l o c i t y RMS vertical hydro velocityV m i x h y d r o convective boundary 2 1 0−1−2−3Figure 2.Characteristic velocities in brown dwarf atmosphere analogous to figure 1.of the effective mixing velocity provided by the convective motions.The decline of the amplitude is rather steep –in the test model with a scale height of about 1/3of the local pressure scale height.Comparing the mixing velocities with typical grain sedimentation velocities indicates that the kinematics could support cloud decks in the convective zone and a thin adjacent overshooting layer at its top boundary.Fitting observed spectra Burrows et al.2006find a preference for a rather large grain size of ≈100µm in BD atmospheres.This would make the grain sedimentation velocities comparable to convective velocities which is numerically uncritical.More demanding would be small grain sizes.The distribution of small grains would hinge on the capability of a numerical code to deal with large velocity ranges.Any non-physical diffusivity in a code can artificially extend the region over which clouds of small grains could exist.4.The multi-D story so farA number of simulations of BD/EGP atmospheres have been already conducted in 2D and 3D geometry.Here,the problem of the circulation between the day-and night-side of close-in EGPs (“hot Jupiters”)achieved particular attention.However,to our knowledge none of the studies has addressed the coupled problem of hydrodynamics,dust formation,radiation,and rotation,but rather have focused on different parts of the overall problem.We would like to refer the interested reader to Showman &Guillot 2002,Cho et al.2003,Burkert et al.2005,including the follow-up works of these groups.5.SerendipityIn the previous sections we summarized expectations about the insights one might gain,and challenges one might face when trying to construct multi-D models for BD/EGP atmospheres.We added figure 3as a reminder that of course the unforeseen results are the most interesting ones.Figure 3illustrates a slight but distinct change of the granulation pattern between the familiar solar granulation and granulation in an M-dwarf.E.g.,similarly and perhaps more drastically the formation of dust in BD/EGP atmospheres might modulate the convective dynamics in unexpected ways –who knows.Prospects for simulations of brown dwarf photospheres5Figure3.Grey-scale images of the vertical velocity component of a solar hydrodynamical model (top row)and a M-dwarf model(bottom row).From left to right,the velocities are depicted at(Rosseland)optical depth unity as well as one and two pressure scale heights below that level in the respective models.The absolute image scales are5.6×5.6Mm2for the solar and 0.25×0.25Mm2for the M-dwarf model.6.ConclusionsReasonably realistic local or global models of brown dwarf and extrasolar giant planet atmospheres coupling hydrodynamics,radiation,dust formation,and rotation are numer-ically in reach at present.However,“unified”models spanning all spatial scales from the global scale down to scales resolving theflow in individual convective cells are stretch-ing the computational demands beyond normally available capacities.Hence,we expect that a separation between local and global models will prevail during the nearer future. Whether this will turn out to be a severe limitation remains to be seen.While appar-ently subtle we would like to point to the solar dynamo problem where the still not fully satisfactory state of affairs might be related to the lack of the inclusion of small enough scales when modeling the global dynamo action.In BD/EGP atmospheres it is perceiv-able that the local transport of momentum by convective and acoustic motions might alter the globalflow dynamics–in the simplest case by adding turbulent viscosity.If the sizes of dust grains in BD/EGP atmospheres turn out to be small,and the grains consequently exhibit low sedimentation speeds,numerical simulations must have the ability to accurately represent the large dynamic range between grain and convec-tive/acoustic velocities.Overly large numerical diffusivities artificially enlarge the height range over which cloud decks can persist.Standard model atmospheres are treating the wavelength-dependence of the radiation field commonly in great detail which is not possible in the more demanding multi-D geometry of simulation models.An approximate multi-group treatment of the radiative transfer has been developed for simulations of stellar atmospheres which has also been6H.-G.Ludwigproven to provide reasonable accuracy at acceptable computational cost in cooler(M-type)atmospheres.We expect that the scheme also works for even cooler atmospheres. However,one simplification usually made is treating scattering as true absorption.De-pending on the specific dust grain properties this approximation might need to be re-placed by a more accurate treatment of scattering.Hence,another challenge a modeler might face is to device a computationally economic scheme to treat scattering in the time-dependent multi-D case.ReferencesAckerman,A.S.,Marley,M.S,2001,ApJ556,872Allard,F.,Hauschildt,P.H.,Alexander,D.R.,Tamanai,A.,Schweitzer,A.,2001,ApJ556,357 Burkert,A.,Lin,D.N.C.,Bodenheimer,P.H.,Jones,C.A.,Yorke,H.W.,2005,ApJ618,512 Burrows,A.,Sudarsky,D.,Hubeny,I.,2006,ApJ640,1063Cho,J.Y.-K.,Menou,K.,Hansen,B.M.S.,Seager,S.,2003,ApJ587,117Helling,Ch.,2005,in:Proceeding of he workshop on Interdisciplinary Aspects of Turbulence, Garching:Max-Planck-Institut f¨u r Astrophysik,eds.:F.Kupka,W.Hillebrandt,p.152 Helling,Ch.,Klein,R.,Woitke,P.,Nowak,U.,Sedlmayr,E.,2004,A&A423,657Rossow,R.W.,1978,Icarus,36,1Showman,A.P.,Guillot,T.,2002,A&A385,166Tsuji,T.,2002,ApJ575,264Woitke,P.,Helling,Ch.,2003,A&A399,297DiscussionC.Helling:Your wish list implies that no progress has been made in the brown dwarf modeling.Additionally,I am convinced that we will need to work on both sides:on1D models which are fast and applicable,not only on3D models though they will play an important role.Ludwig:My wish list was intended as overall collection of things we would like to understand about brown dwarf atmospheres.Progress related to the various points has indeed already been made.Concerning the mutual role of1D and3D models,I fully agree.3D models should address crucial aspects that are in principle not accessible in 1D.Insight emerging from3D models should then be transferred to1D models.F.Kupka:Considering the complexity of molecular opacity I am actually surprised how robust the opacity binning seems to be.Ludwig:Tests have been performed for M-type stars where the effect of many millions of–primarily molecular–lines is captured quite well.As for brown dwarfs:the dust opacity has a rather smooth functional dependence on wavelength.Hence,it should be easy,but scattering is a problem.I.W.Roxburgh:You said overshooting was small,could you quantify this in terms of local scale height?Ludwig:The velocity amplitude declines exponentially with a scale height of about 1/3of the local pressure scale height.For comparison:in solar models the scale height of decline is about six times larger.However,keep in mind that the hydrodynamical model presented here is experimental,in particular does not include any effects of dust formation.。

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