Storage device performance prediction with CART models

[收稿日期]2021-08-31[作者简介]邓孝林（1985-），男，工学学士，工程师，库存与计划管理办公室副主任，主要研究方向：核电站备件管理；谢宏志（1989-），男，工学硕士，高级工程师，主要研究方向：核电站热工仪表设备选项、库存管理等。

doi:10.3969/j.issn.1005-152X.2022.01.017核电站备件库存金额预测方法邓孝林，谢宏志（中广核核电运营有限公司备件中心，广东深圳518124）[摘要]设计了一种对入库金额和领用金额进行预测的方法，基于核电站的历史入库数据、维修领用数据，通过入库金额和领用金额对库存金额进行预测，并以某核电站为例进行仿真验证，当预测时长为12个月时，入库金额预测的平均相对误差为7.77%，领用金额预测的平均相对误差为7.83%，库存金额预测的平均相对误差为3.59%，基本满足核电备件管理中对库存金额预测精度的要求。

[关键词]核电站；备件储存与管理；入库金额；领用金额；库存金额；库存预测[中图分类号]TM623；F253[文献标识码]A [文章编号]1005-152X(2022)01-0086-06Prediction of Spare Parts Inventory Value of Nuclear Power PlantDENG Xiaolin,XIE Hongzhi(Spare Parts Center,China Nuclear Power Operations Co.,Ltd.,Shenzhen 518124,China)Abstract:In this paper,we designed a method to predict the storage amount and requisition amount of nuclear power plants.Based on the historical storage data and maintenance requisition data of a nuclear power plant,we predicted the inventory value of the plant by calculating its storage amount and requisition amount.Next,we had a numerical simulation analysis and found that with the prediction time set to 12months,the average relative error of the storage amount predicted was 7.77%,the average relative error of the requisition amount predicted was 7.83%,and the average relative error of the inventory value was 3.59%,which basically meets the accuracy requirements for the inventory value forecasting in the spare parts management of nuclear power plants.Keywords:nuclear power plant;spare parts storage and management;storage amount;requisition amount;inventory value;inventory forecasting0引言备品备件的保障对核电站的安全稳定运行具有重要意义，然而过高的备件库存会增加电厂的运营成本，因此，需要对备件库存进行管控，在保障供应和控制库存之间寻求一个合理的平衡点[1-2]。

第20卷第3期装备环境工程2023年3月EQUIPMENT ENVIRONMENTAL ENGINEERING·15·某机械陀螺长贮性能演变规律及性能退化模型研究张世艳，吴护林，赵方超，谭甜甜，杨小奎（西南技术工程研究所 弹药贮存环境效应重点实验室，重庆 400039）摘要：目的针对长期贮存后机械陀螺的性能退化会影响制导系统侧偏和射程的问题，提出一种性能退化预测模型的建立方法，用于掌握机械陀螺长贮性能退化规律。

方法首先，针对机械陀螺结构特性和贮存环境，确定敏感应力为温度，加速模型为阿仑尼乌斯模型，开展机械陀螺的加速贮存试验。

其次，对加速贮存试验过程中的机械陀螺进行周期性的参数检测，分析各性能参数随试验时间的性能退化演变规律，确定垂直漂移为其退化敏感参数。

最后，拟合垂直漂移参数在各温度应力下的性能退化曲线，建立性能退化轨迹模型。

结果采用实际自然环境贮存6、7、8、10 a的性能数据对模型进行验证，模型预测准确度分别为86.70%、96.28%、91.53%、85.92%。

结论建立的性能退化模型评估准确度在85%以上，该模型可应用于指定贮存时间下机械陀螺仪的性能退化行为预测。

关键词：机械陀螺；温度；性能退化；预测模型中图分类号：TJ04 文献标识码：A 文章编号：1672-9242(2023)03-0015-07DOI：10.7643/ issn.1672-9242.2023.03.002Performance Evolution Law and Degradation Model of MechanicalGyroscope during Long-term StorageZHANG Shi-yan, WU Hu-lin, ZHAO Fang-chao, TAN Tian-tian, YANG Xiao-kui(CSGC Key Laboratory of Ammunition Storage Environment Effects, Southwest Institute of Technology andEngineering, Chongqing 400039, China)ABSTRACT: The work aims to propose a method for establishing a performance degradation prediction model aiming at the problem that the performance degradation of mechanical gyroscope will affect the sideslip and range of guidance system after long-term storage, so as to master the performance degradation law of mechanical gyroscope during long-term storage. Firstly, according to the structure characteristic and storage environment of the mechanical gyroscope, the sensitive parameter was de-termined as temperature, the acceleration model was Arrhenius equation model, and the accelerated storage test was carried out to mechanical gyroscope. Secondly, the parameters of the mechanical gyroscope were periodically detected during the acceler-ated storage test and the performance degradation evolution law of the mechanical gyroscope under each performance parameter with the test time was analyzed, thus determining vertical drift as the degradation sensitive parameter. Finally, the performance收稿日期：2021–12–09；修订日期：2022–08–15Received：2021-12-09；Revised：2022-08-15作者简介：张世艳（1985—），女，硕士。

Adjustment Strategy of Inlet Guide Vane of MultistageCentrifugal Compressor Applied in CAES *Kai-xuan Wang 1,2Zhi-tao Zuo 1,2,3Qi Liang 1Wen-bin Guo 1Ji-xiang Chen 1,2Hai-sheng Chen 1,2,3,*(1.Institute of Engineering Thermophysics,Chinese Academy of Science;2.University of Chinese Academy of Science;3.National Energy Large Scale Physical Energy Storage Technology R&D Center (Bijie))Abstract：In the process of compressed air energy storage system working,the internal pressure of the gas storage device continues to rise,which requires the compressor to work in a wide pressure ratio range.High efficiency variable operating condition is the core requirement of compressors in compressed air energy storage system.In order to achieve this design goal,it is necessary to adopt appropriate adjustment methods and adjustment strategies.The adjustable inlet guide vane is simple in structure,can be operated in the working process,and can be automated by the servo device,which is one of the most suitable adjustment methods for the compressor in the compressed air energy storage system.This paper takes a 4-stage centrifugal compressor in compressed air energy storage system as the research object,and establishes a performance prediction method suitable for the multi-stage centrifugal compressor under varying working conditions.The performance curve of the single-stage centrifugal compressor is obtained by numerical simulation,and the performance superposition program of the stage is written to obtain the performance curve of the whole machine.The performance data of multistage centrifugal compressor is fitted to polynomial function by least square method,and the adjustable inlet guide vane adjustment strategy program is established by genetic algorithm with isentropic efficiency as optimization objective,inlet guide vane opening as optimization variable and outlet pressure or flow of the whole machine as constraint conditions.Keywords：Inlet Guide Vane Adjustment;Multistage Centrifugal Compressor;Adjustment Strategy摘要：压缩空气储能系统在储能过程中，储气装置内部压力不断升高，这要求压缩机在较大压比范围内工作。

第21卷第2期装备环境工程2024年2月EQUIPMENT ENVIRONMENTAL ENGINEERING·51·武器装备引信步进应力加速试验贮存寿命预测研究姚松涛1，崔洁1*，赵河明1，彭志凌1，孔德景2（1.中北大学 长治产业技术研究院，山西 长治 046012；2.中国船舶集团有限公司第七一四研究所，北京 100101）摘要：目的针对某机电引信加速寿命试验数据，采用传统统计分析方法存在计算量大、寿命预测精度难以保证的问题，开展与智能算法相结合的引信贮存寿命预测研究。

方法针对步进应力加速寿命试验数据，采用贝叶斯理论的环境因子法，对各级应力下的贮存时间进行折合计算。

利用进化策略对粒子群算法进行改进，进而对所建立的BP神经网络预测模型的全局参数进行调整和优化，突破传统方法的局限。

将折合后的试验时间、样本量、应力水平作为网络输入，失效数作为输出，来预测引信贮存寿命。

结果利用训练好的BP神经网络预测引信在正常应力水平下的失效数，计算其贮存可靠度。

在迭代402次后，模型找到最优解，且预测误差在1%以内。

结论步进应力加速寿命试验与智能算法相结合的方法计算过程简单，预测精度较高，可有效提高引信贮存寿命的预测精度。

关键词：步进应力加速寿命试验；BP神经网络；引信；改进粒子群优化算法；Bayes理论；环境因子中图分类号：TJ430 文献标志码：A 文章编号：1672-9242(2024)02-0051-08DOI：10.7643/ issn.1672-9242.2024.02.007Storage Life Prediction of Fuze under Step Stress Accelerated TestYAO Songtao1, CUI Jie1*, ZHAO Heming1, PENG Zhiling1, KONG Dejing2(1. Changzhi Industrial Technology Research Academy, North University of China, Shanxi Changzhi 046012, China;2. The 714th Research Institute of China Shipbuilding Industry Corporation, Beijing 100101, China)ABSTRACT: The work aims to study the storage life prediction of fuze combined with the intelligent algorithm against the problem that the traditional statistical analysis method adopted for accelerated test data of fuze in a certain motor has high computational complexity and cannot guarantee the storage life prediction accuracy. For the step stress accelerated life test data, the environmental factor method based on Bayesian theory was adopted to convert the storage time at different stress levels. The particle swarm algorithm was improved by evolutionary strategy to adjust and optimize the global parameters of the BP neural network, breaking through the limitations of the traditional method. The converted test time, sample size, and stress level were used as inputs to the network, and the failure count was used as the output to predict the fuze storage life.The trained BP neural network was used to predict the failure count of the fuze under normal stress levels, and then calculate its storage reliability. After 402 iterations, the model found the optimal solution with a prediction error within 1%. Therefore, the combination of step stress accelerated life test and intelligent algorithm can effectively improve the prediction accuracy of fuze storage life.收稿日期：2023-12-17；修订日期：2024-02-03Received：2023-12-17；Revised：2024-02-03引文格式：姚松涛, 崔洁, 赵河明, 等. 引信步进应力加速试验贮存寿命预测研究[J]. 装备环境工程, 2024, 21(2): 51-58.YAO Songtao, CUI Jie, ZHAO Heming, et al. Storage Life Prediction of Fuze under Step Stress Accelerated Test[J]. Equipment Environmental Engineering, 2024, 21(2): 51-58.*通信作者（Corresponding author）·52·装备环境工程 2024年2月KEY WORDS: step stress accelerated life test; BP neural network; fuze; improved particle swarm optimization algorithm;Bayes theory; environmental factor引信是自主探测识别目标、敏感发射环境，综合利用网络平台信息进行安全与解除保险控制，在高动态复杂弹目交会条件下，按预定策略引爆或引燃弹药战斗部装药的一次性使用长期贮存的控制系统。

The Orderly Charging and Discharging Dispatching ManagementSystem of Electric Vehicle under the InternetYuman ZhangSchool of North China Electric Power University, Beijing 102206, China****************Keywords: Internet; Electric vehicle; Charge-discharge; Dispatch; Network platformAbstract. The orderly charging and discharging management and dispatching system of electric vehicle is researched and designed based on internet in order to achieve orderly dispatching and management of electric vehicle in peak load regulation. Researching and analyzing the function of main components (power grid side, platform data center, platform management system, charging pile and client) of system and designing the hardware and software of the system. To optimize the data processing part of backstage network server, combining with the security and economy of grid and the maximization of owner benefit by using multi-objective and multi-constraint optimization model. System can realize bidirectional communication and real-time update regulation and control function among grid side, system backstage and client through internet and protect the liability and high efficiency of electric vehicle in grid-connected dispatching.IntroductionIn recent years, with the development of electric vehicle scale and the coming age of internet and big data, under the “Internet plus” pattern, the widespread use of electric vehicle has driven the development of global electric traffic internet and the global electric vehicle will also become the important basis of global traffic internet. In the internet, intelligent processing and high-efficient transfer in information can develop networking of charge-discharge technology for electric vehicles. In addition to realize cleaning and energy-saving transport, an increasing number of electric vehicles also show that they can be able to provide reliable and high-quality power supply to the power grid.For the technology development of electric vehicle in peak load regulation, the international society puts forward the policy of V2G （Vehicle-to-Grid ）in recent years which takes the electric vehicle as movable distributed energy storage device and feeds back the electric energy to power grid under the premise of satisfying the driving demand of the users of electric vehicle. The current studies show that it is a challenge to dispatch electric vehicles in grid connection. Domestic and overseas studies mainly concentrate on relevant applications of V2G, frequency modulation, optimal unit combination, as well as costs and benefits of the networked electric vehicles in economy or technology, and influences on the environment. However, reliability and operability of electric vehicles’ power connection can be affected by distribution uncontrollability of electric vehicles and subjectivity of owners to a great extent. As a result, it will be of great importance in studying the organic combination of the networked electric vehicles and the internet and orderly charge-discharge management strategy of electric vehicles in practical applications.Based on the existing grid-connected technology of electric vehicles, the author studied power grid parameters and user demands under different sub-regions and provided a kind of internet-oriented charge-discharge control and management strategy, which realizes peak load regulation of electric vehicles via two-way communication of the internet and webpage platform. System CompositionThe whole system consist of four parts which are network platform, charging pile, power grid side and client.7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017)Network platform as a network server is a integrated platform for data collection, storage, processing and maintenance of electric vehicle charging and discharging. And it consists of computer, network equipment, storage equipment, and other peripheral devices and platform application software. Network platform is the core of the whole system which can collect power grid information, release charging and discharging plan, calculate and operate regularly and also can operate and implement the workflow.Charging pile is a charging device for electric vehicle. The charging pile can monitor the state of electric vehicle real-timely and share the information with server and it also is a direct link between network platform and client.Client as user side is an interface for owner to participant in electricity management. Users can inquire the information of the power grid load demand, electricity price forecasting and the place and state of charging pile through web platform and also they can make confirmation to participant in grid connection from client and make a series of operations to make money, such as charge in low and discharge in peak.[1]Communication relationships among the four are as follows:Overall Design SchemeDuring the operational process of the whole system, the network platform will be divided into data center and management system specifically. The data of power grid real-time frequency, power grid load forecasting and the state prediction of V2G will be sent to platform data center from power grid side according to the peak load regulation demand information. Data center deals with these data through relevant algorithm strategy and on the premise of protecting the power grid security and users interest, makes a plan of V2G charging and discharging and sends the charging and discharging plan and each order to platform management system. After accepting the plan, management sends information to charging pile and client in order to release demand time, demand for electricity quantity and price forecasting and at the same time management system feeds back the electricity quantity of electric vehicle to platform through charging pile and feeds back the top and bottom limit of charging participation to the platform through users. Users as the direct connection between charging pile and data center feeds back the charging and discharging running condition of charging pile and electric vehicle to data center real-timely in order to update and adjust the plan and thus reach the bidirectional communication. Charging pile reasonably chooses vehicle distribution and the charging and discharging power in order to guarantee the execution of the charging and discharging. Users choose whether to participate in grid connection under the condition of ensuring the orderly discharging of vehicles and the information of the real-time electricity quantity also be verified by charging pile and thus get the permission of battery charging and discharging. The specific operation chart is shown in fig. 1.Figure 1. System run chartSystem Hardware Design. The main hardware of system basically consists of three parts. The background control center which consists of platform database and background server, the data transfer unit which consists of concentrator and wireless devices and field collection device which consists of charging pile as a main body. The specific schematic diagram as shown in fig. 2.[2]Figure 2. System hardware structure diagramThe unit circuit design of whole control system is as follows:(1)Master ControllerA master controller selects a PLC controller, which has a series of functions, including logical operation, timing, technology, displacement, self-diagnosis and monitoring, as well as a little analog quantity I/O, arithmetic operation, data transmission, remote I/O and communication. As a result, the controller was selected as the core control unit of the hardware system to launch, operate, monitor and close the charging process in real time [3].(2)Serial Interface CircuitsIn the system, four serial interfaces are connected with display screen, wireless module, RS232 interface’s card reader and RS485 interface’s electric energy meter. The display is RS232 level andcommunicates with MCU through level switch. The communication protocol of display screen and MCU is the Modbus RTU communication protocol. MCU is used as the master, while display screen is the slave. The card reader belongs to TTL level and it can be connected with MCU directly. Moreover, the protocol proposed by the card reader module is applied to communicate. TTL serial port is used in micropower wireless module. Level 2.0 electric energy meter is selected as the electric energy meter for battery charging measurement [4], which has the current specification of 5A. The processor PCL realizes information interaction between electric energy measuring modules through RS485 bus interface circuits, which are shown in Fig. 3. Energy metering module abides by the DL/T645-2007 communication specification. Electric energy value on the meter can be used as the electric energy measurement of the charging piles. At the same time, current and voltage on the ammeter can be used to judge whether there is overcurrent, overvoltage and undervoltage in the charge-discharge process. If so, relevant measures should be applied to deal with it[5].Figure 3. RS485 interface circuit diagram(3) Control Guiding CircuitsThe confirmative connection circuits between charging piles and charging cables, as well as control guiding circuits between charging piles and vehicle-mounted battery chargers are able to control the guiding circuits to finish connective confirmation of charging piles and electric vehicles before charging, power, identification of current-carrying capability for charge-discharge connecting devices, as well as monitoring of the charge-discharge process. The circuits are preconditions for charging piles, battery chargers and battery management system to realize information exchange. With the different voltage values on detection points, MCU is used to judge the state and the interface circuits are shown in Fig. 4. Generally speaking, the circuits should be equipped with stable output of +12V and bipolar PWM signal functions. In the PWM state, peak and valley should be +12V and -12V, respectively. By virtue of PWM pin of PLC, amplifying circuits Q1 and Q2 are amplified and outputted from Cp after 6N135 isolation, where R7 is the output matched resistance. According to GB/T20234.2[6], resistance should be 1000 Ω. R5 has the metering function and protects LED[7].Figure 4. Control Guiding Circuits diagramSystem Software Design. System program mainly consists of system initialization, system self-inspection, swiping card effective identification, user validation, system connection validation and the real-time monitoring during charging process. Realize reading user information and settlement at the same time in the part of user authentication. In the part of real-time monitor during charging process, mainly collecting real-timely and analyzing and processing the CP signal in order to ensure the connection status of charging cable, the situation of surplus electric quantity and device state. Charging pile completes interaction control through display screen. Charging and discharging model offers multiple choices. The charging and discharging can be set according to time, electric quantity and amount of money and also can be set to full directly or on the minimum value. Charging process is similar to discharging process. Now list the overall flow chart of the charging program which as shown in fig. 5.Figure 5. Software design flow chartRule Design. System internal procedure and external management execute the command according to certain rules which are divided into pricing rule, user participation rule, information processing rule and information release rule.1. Pricing RuleCharge capacity not only can execute tariff but also can participate in electric power transaction directly and buy low charge capacity. The electric quantity of charging can be used by themselves and also can be sold to power grid as distributed power. Release the price forecasting to users through web platform.2. User Participation RuleUsers participate in power grid peak shaving and valley filling through active selection and are encouraged to participate by the market mechanism which caused by the electricity price difference. Users can obtain the information of price, discharge capacity and charging pile distribution in web platform and also can choose the participation discharge capacity and participation time. Once the users confirm to participate and input data in web platform they cannot exit the charging and discharging plan. The users’ action will be regulated by the users credit line mechanism in the webplatform. The user flow chart is shown in fig. 6[8].Figure 6. The flow chart of user charging3. Information Processing RuleProcessing the information collected by power grid is the important gist to make charging and discharging plan in network data center. Multi-objective optimization model is adopted as the processing rule because of the randomness of charging and discharging behavior of the owner in order to make electric vehicle charge and discharge reasonably and satisfy the driving demand of electric vehicle and the economic interest of the owner at the same time of protecting the safe and economical operation of power grid. This paper only gives the confirmation of objective function and the making of constraints condition as a calculation basis for reference. [9](1) Objective FunctionSynthetically considering the common interests of power grid side and owner, four objective functions are established from the perspective of power grid side and owner.1) Minimal node voltage deviation and active loss of power gridBased on load prediction data of power grid, power flow equation is used to calculate.()122111min N N i t i U t f == =∆∑∑ (1) 121min ()N t f P t ==∑ (2)In the formula:()i U t ∆ is the voltage excursion of note i at t ; ()P t is the power grid loss at t ; 1N is the charge-discharge periods in a day; 2N is node number.2) Lowest networked service costsPower grid is used to provide V2G state prediction. A cost function is listed as follows []31min ()()v V f pri t P t t =∆∑ (3)In the formula:1()v pri t is electricity price to servicers paid by power grid as networking vehiclesat t ; ()V P t is the input power from electric vehicles to power grid at t ;t is the discharge time.3) Maximal benefits of ownersFrom the perspective of owners, predictive data of electricity price is used. Charging costs should be reduced, for the sake of ensuring maximal economic benefits for owners, namely the charge cost function is shown as follows:1421min ()P ()()P ()N g G v V t f pri t t t pri t t t = =∆−∆ ∑ (4)In the formula:()g pri t is the favorable charge price for owners given by power grid at t ;2()v pri t represents the electricity price from servicers of vehicle networking to owners at t ; P ()G t means the charge power of electric vehicles at t .In weight allocation, grid power supply quality should be prioritized. Secondly, owners’ participation should be encouraged. However, service costs of power grid shouldn’t be too high. Last but not least, it is necessary to reduce power loss, namely the target priority is ranked as 1432f f f f >>> (2) Constraint Conditions1) Equality constraintsPower flow constraint conditions must be satisfied. The power flow equation is applied to calculate node voltage and power grid loss, so the power flow constraint conditions must be satisfied.[10](cos sin )i i i j ij ij ij ij j i P P U U G B θθ∈∆=−+∑ (5)(sin cos )i i i j ij ij ij ij j i Q Q U U G B θθ∈∆=−−∑ (6)21,...,i N =In th formula:i P and i Q are active power and inactive power of the node i respectively;i U isthe voltage of node i , ij G and ij B stand for the conductivity susceptance between the nodes iand j ; ij δrepresents the voltage phase angle difference between node i and j .2) Inequality constraints①Limited by battery life protection, it is impossible to discharge all electricity in batteries, namely (1)E SOC DOD ≤−− (7) In the formula:E is the available electric quantity from electric vehicles; SOC is surplus capacity of batteries ，DOD stands for discharge depth of batteries.② When electric vehicles discharge, due to large loss in batteries, only to have the higher load rate in lines can electric vehicles discharge in power connection. After discharging, load rate of lines can’t be too low, namely()l h L L t L ≤≤ (8)In the formula:l L and h L stand for the lowest and highest discharge load rates, respectively.4. Information Release Rule(1)The information which is released in the network platform by the background has two main parts, the demand information and the basic information.(2)The specific information of electricity demand is charging and discharging plan. Distribute the electricity demand to client platform in fixed time. Collecting the users’ feedback and feeding back the adjustable capacity to the power grid side within the fixed time.(3)Release the electricity price forecasting information, the position of the charging pile and use condition to the platform real-timely. Guarantee the effectiveness of information as the basis of client choice.Work ProcessDeal with the data from power grid, such as loading forecasting, etc. Make charging and discharging plan.Release the demand of electric vehicle discharge capacity and price information in web platform in the early time.The owner decides whether they participate in discharging in the scheduled time.Judge whether the owners have ability to discharge by detecting the electric vehicle battery parameters.Feed back the charging and discharging plan to grid according to the participation situation which users choose.The owners park the car around the charging pile in advance and connect the charging wire and then verify the order.When time to, the platform will control the charging pile automatically and make the electric vehicle start charging.System Function(1)Power Bidirectional Exchange Function: Power can be transmitted to grid (electric vehicle discharge state) and also the power can be absorbed from grid (electric vehicle charge state).(2)Bidirectional Communication Function: Instructions can be accepted and the power information can be sent remotely. Peak regulation instruction can be sent to the electric vehicle by the dispatching of power grid through the communication network within charge station. The response of the electric vehicle is monitored and recorded and through communication network the response is fed back to the charging station (backstage management system).(3)Real-Time Control: It has the function of Real-time information feedback, real-time control, the real-time updated information and demand, the rational allocation of resources.ConclusionThis paper discusses the system structure of orderly charging and discharging management of electric vehicle and dispatching system based on the internet, analyzes the composition and function of four main units which are network platform, charging pile, power grid side and client and realize the function of bidirectional communication, bidirectional power exchange, real-time update and regulation and control. This system can allow users to use handheld terminal to realize the functions of battery capacity status query, charging pile position navigation, charging and discharging forecasting and set independently and provide a platform for the interaction between owner and power grid. At the same time, in network manager data processing section, the V2G proposed during making charging and discharging plan in multi-objective and multi-constraints model of distribution application can deal with the interest of power grid and owner very well and also it can ensure the safe and economic operation of power grid and guide owner to participate in the net sevice actively. This system puts forward effective solution scheme for the question of dispatchingand regulation of electric vehicle in peak load regulation and have a certain reference value of the implementation and popularization of V2G policy.References[1] X.R. Gong, R. Liu and X.M. Qin: Internet Oriented Design and Application of IntelligentCharging System of Electric Vehicle, Electricity power construction,(2015),No.7,p.222.[2] Z.M. Guo: The design and implementation of Electric vehicle charging stations real-timeInternet electric energy metering system, Electronic world,(2016), No.20,p.81.[3] J.Y. Yuan, X.D. Wang, D.Q. Wang and H.M. Wang: The Study of Electric Vehicle’s ACCharging Pile Control System Based on PLC, Journal of Qingdao university (engineering technology edition),(2015), No.2,p.38.[4] Q/CSG 11516.8-2010, Acceptance Specification of Electric Vehicle Charging Station andCharging Pile, Guangzhou: China southern power grid company ,2010:[5] G. Wen, H.J. Shang, C.B. Zhu and R.G. Lu: The System Design of Electric Vehicle ACCharging Pile, Modern Electronic Technology,(2012), No.21,p.124.[6] GB/T 20234.2—2011, The part 2 of Conduction Charging Connectioin Device of ElectricVehicle : AC charging interface, Beijing: China Standards Press, 2012:[7] K. Xu, Z.A. Zhou, D.Y. Wu, W.B. Geng and X.D. Li: The Control System Design of ElectricVehicle AC Charging Pile, Journal of Henan Science and Technology University (natural sciences edition),(2016), No.3,p.47.[8] T.R. Gong, T. Li and R. Liu: Internet Oriented Operation Analysis of Electric VehicleIntelligent Charging System, Power supply and consumption,(2015), No.12,p.11.[9] H.L. Li, X.M.Bai and W.Tan: The Study of Electric Vehicle Network Access Technology inthe Application of Distribution Network, Proceedings of the CSEE,(2012), No.S1, p.22. [10] G.Y. Li: Power System Analysis Basis (China Machine Press, China 2011.)。

Huawei OceanStor Dorado 5000/6000are mid-range storage systems in the OceanStor Dorado all-flash series,and are designed to provide excellent data service experience for enterprises.Both products are equipped with innovative hardware platform,intelligent FlashLink®algorithms,and an end-to-end (E2E)NVMe architecture,ensuring the storage systems deliver a 30%higher performance than the previous generation,and achieve the latency down to just 0.05ms.The intelligent algorithms are built into the storage system to make storage more intelligent during the application operations.Furthermore,the five-level reliability design ensures the continuity of core business.Excelling in scenarios such as OLTP/OLAP databases,server virtualization,VDI,and resource consolidation,OceanStor Dorado 5000/6000all-flash systems are smart choices for medium and large enterprises,and have already been widely adopted in the finance,government,healthcare,education,energy,and manufacturing fields.The storage systems are ready to maximize your return on investment (ROI)and benefit diverse industries.OceanStor Dorado 5000/6000All-Flash Storage Systems 30%higher performance than theprevious generation E2E NVMe for 0.05ms of ultra-low latencyFlashLink®intelligent algorithmsSCM intelligent cache acceleration for 60%lower latencyDistributed file system with 30%higher performanceLeading Performance withInnovative Hardware✓The intelligent multi-protocol interface module hosts the protocol parsing previously performed by the general-purpose CPU, expediting the front-end access performance by 20%.✓The computing platform offers industry-leading performance with 25% higher computing power than the industry average.✓The intelligent accelerator module analyzes and understands I/O rules of multiple application models based 3-layer intelligent management:•365-day capacity trends prediction •60-day performance bottleneck prediction •14-day disk fault prediction •Immediate solutions for 93%ofproblemsSAN&NAS convergence,storage and computing convergence,and cross-gen device convergence for efficient resource utilizationFlashEver:No data migration over 10years for 3-gen systems Efficient O&M with IntelligentEdge-Cloud SynergyComponent reliability :Wear leveling and anti-wear levelingArchitecture and product reliability :0data loss in the event of failures of controllers,disk enclosures,or three disksSolution and cloud reliability :The industry's only A-A solution for SAN and NAS,geo-redundant 3DC solution,and gateway-free cloud backup Always-On Applications with5-Layer ReliabilityProduct Features Ever Fast Performance with Innovative Hardware Innovative hardware platform: The hardware platform of Huawei storage enables E2E data acceleration, improving the system performance by 30% compared to the previousgeneration.on machine learning frameworks to implement intelligentprefetching of memory space. This improves the read cache hit ratio by 50%.✓SmartCache+ SCM intelligent multi-tier caching identify whether or not the data is hot and uses different media tostore it, reducing the latency by 60% in OLTP (100% reads) scenarios.✓The intelligent SSD hosts the core Flash Translation Layer (FTL) algorithm, accelerating data access in SSDs andreducing the write latency by half.✓The intelligent hardware has a built-in Huawei storage fault library that accelerates component fault location anddiagnosis, and shortens the fault recovery time from 2hours to just 10 minutes.Intelligent algorithms: Most flash vendors lack E2E innate capabilities to ensure full performance from their SSDs. OceanStor Dorado 5000/6000 runs industry-leading FlashLink® intelligent algorithms based on self-developed controllers, disk enclosures, and operating systems.✓Many-core balancing algorithm: Taps into the many-core computing power of a controller to maximize the dataprocessing capability.✓Service splitting algorithm: Offloads reconstruction services from the controller enclosure to the smart SSD enclosure to ease the load pressure of the controller enclosure for moreefficient I/O processing.✓Cache acceleration algorithm: Accelerates batch processing with the intelligent module to bring intelligence to storagesystems during application operations.The data layout between SSDs and controllers is coordinated synchronously.✓Large-block sequential write algorithm: Aggregates multiple discrete data blocks into a unified big data blockfor disk flushing, reducing write amplification and ensuringstable performance.✓Independent metadata partitioning algorithm: Effectively controls the performance compromise caused by garbagecollection for stable performance.✓I/O priority adjustment algorithm: Ensures that read and write I/Os are always prioritized, shortening the accesslatency.FlashLink® intelligent algorithms give full play to all flash memory and help Huawei OceanStor Dorado achieve unparalleled performance for a smoother service experience.E2E NVMe architecture for full series: All-flash storage has been widely adopted by enterprises to upgrade existing ITsystems, but always-on service models continue to push IT system performance boundaries to a new level. Conventional SAS-based all-flash storage cannot break the bottleneck of 0.5 ms latency. NVMe all-flash storage, on the other hand, is a future-proof architecture that implements direct communication between the CPU and SSDs, shortening the transmission path. In addition, the quantity of concurrencies is increased by 65,536 times, and the protocol interaction is reduced from four times to two, which doubles the write request processing. Huawei is a pioneer in adopting end-to-end NVMe architecture across the entire series. OceanStor Dorado 5000/6000 all-flash systems use the industry-leading 32 Gb FC-NVMe/100 Gb RoCE protocols at the front end and adopt Huawei-developed link-layer protocols to implement failover within seconds and plug-and-play, thus improving the reliability and O&M. It also uses a 100 Gb RDMA protocol at the back end for E2E data acceleration. This enables latency as low as 0.05 ms and 10x faster transmission than SAS all-flash storage. Globally shared distributed file system: The OceanStor Dorado 5000/6000 all-flash storage systems support the NAS function and use the globally shared distributed file systems to ensure ever-fast NAS performance. To make full use of computing power, the many-core processors in a controller process services concurrently. In addition, intelligent data prefetching and layout further shorten the access latency, achieving over 30% higher NAS performance than the industry benchmark.Linear increase of performance and capacity: Unpredictable business growth requires storage to provide simple linear increases in performance as more capacity is added to keep up with ever-changing business needs. OceanStor Dorado5000/6000 support the scale-out up to 16 controllers, and IOPS increases linearly as the quantity of controller enclosures increases, matching the performance needs of the future business development.Efficient O&M with Intelligent Edge-Cloud Synergy Extreme convergence: Huawei OceanStor Dorado 5000/6000 all-flash storage systems provide multiple functions to meet diversified service requirements, improve storage resource utilization, and effectively reduce the TCO. The storage systems provide both SAN and NAS services and support parallel access, ensuring the optimal path for dual-service access. Built-in containers support storage and compute convergence, reducing IT construction costs, eliminating the latency between servers and storage, and improving performance. The convergence of cross-generation devices allows data to flow freely, simplifying O&M and reducing IT purchasing costs.On and off-cloud synergy: Huawei OceanStor Dorado5000/6000 all-flash systems combine general-purpose cloud intelligence with customized edge intelligence over a built-inintelligent hardware platform, providing incremental training and deep learning for a personalized customer experience. The eService intelligent O&M and management platform collects and analyzes over 190,000 device patterns on the live network in real time, extracts general rules, and enhances basic O&M. Intelligence throughout service lifecycle: Intelligent management covers resource planning, provisioning, system tuning, risk prediction, and fault location, and enables 60-day and 14-day predictions of performance bottleneck and disk faults respectively, and immediate solutions for 93% of problems detected.FlashEver: The intelligent flexible architecture implements component-based upgrades without the need for data migration within 10 years. Users can enjoy latest-generation software and hardware capabilities without investing again in the related storage software features.Always-On Applications with 5-Layer Reliability Industries such as finance, manufacturing, and carriers are upgrading to intelligent service systems to meet the strategy of sustainable development. This will likely lead to diverse services and data types that require better IT architecture. Huawei OceanStor Dorado all-flash storage is an ideal choice for customers who need robust IT systems that consolidate multiple types of services for stable, always on services. It ensures end-to-end reliability at all levels, from component, architecture, product, solution, all the way to cloud, supporting data consolidation scenarios with 99.9999% availability. Benchmark-Setting 5-Layer ReliabilityComponent –SSDs: Reliability has always been a top concern in the development of SSDs, and Huawei SSDs are a prime example of this. Leveraging global wear-leveling technology, Huawei SSDs can balance their loads for a longer lifespan of each SSD. In addition, Huawei's patented anti-wear leveling technology prevents simultaneous multi-SSD failures and improves the reliability of the entire system.Architecture –fully interconnected design: Huawei OceanStor Dorado 5000/6000 adopt the intelligent matrix architecture (multi-controller) within a fully symmetric active-active (A-A) design to eliminate single points of failure and achieve high system availability. Application servers can access LUNs through any controller, instead of just a single controller. Multiple controllers share workload pressure using the load balancing algorithm. If a controller fails, other controllers take over services smoothly without any service interruption. Product –enhanced hardware and software: Product design is a systematic process. Before a stable storage system is commercially released, it must ensure that it meets the demands from both software and hardware, and can faultlesslyhost key enterprise applications. The OceanStor Dorado5000/6000 are equipped with hardware that adopts a fully redundant architecture and supports dual-port NVMe and hot swap, preventing single points of failure. The innovative 9.5 mm palm-sized SSDs and biplanar orthogonal backplane design provide 44% higher capacity density and 25% improved heat dissipation capability, and ensure stable operations of 2U 36-slot SSD enclosures. The smart SSD enclosure is the first ever to feature built-in intelligent hardware that offloads reconstruction from the controller to the smart SSD enclosure. Backed up by RAID-TP technology, the smart SSD enclosure can tolerate simultaneous failures of three SSDs and reconstruct 1 TB of data within 25 minutes. In addition, the storage systems offer comprehensive enterprise-grade features, such as 3-second periodic snapshots, that set a new standard for storage product reliability.Solution –gateway-free active-active solution: Flash storage is designed for enterprise applications that require zero data loss or zero application interruption. OceanStor Dorado5000/6000 use a gateway-free A-A solution for SAN and NAS to prevent node failures, simplify deployment, and improve system reliability. In addition, the A-A solution implements A-A mirroring for load balancing and cross-site takeover without service interruption, ensuring that core applications are not affected by system breakdown. The all-flash systems provide the industry's only A-A solution for NAS, ensuring efficient, reliable NAS performance. They also offer the industry's firstall-IP active-active solution for SAN, which uses long-distance RoCE transmission to improve performance by 50% compared with traditional IP solutions. In addition, the solution can be smoothly upgraded to the geo-redundant 3DC solution for high-level data protection.Cloud –gateway-free cloud DR*: Traditional backup solutions are slow, expensive, and the backup data cannot be directly used. Huawei OceanStor Dorado 5000/6000 systems provide a converged data management solution. It improves the backup frequency 30-fold using industry-leading I/O-level backup technology, and allows backup copies to be directly used for development and testing. The disaster recovery (DR) and backup are integrated in the storage array, slashing TCO of DR construction by 50%. Working with HUAWEI CLOUD and Huawei jointly-operated clouds, the solution achieves gateway-free DR and DR in minutes on the cloud.Technical SpecificationsModel OceanStor Dorado 5000 OceanStor Dorado 6000 Hardware SpecificationsMaximum Number ofControllers3232Maximum Cache (DualControllers, Expanding withthe Number of Controllers)256 GB-8 TB 1 TB-16 TBSupported Storage Protocols FC, iSCSI, NFS*, CIFS*Front-End Port Types8/16/32 Gbit/s FC/FC-NVMe*, 10/25/40/100 GbE, 25/100 Gb NVMe over RoCE*Back-End Port Types SAS 3.0/ 100 Gb RDMAMaximum Number of Hot-Swappable I/O Modules perController Enclosure12Maximum Number of Front-End Ports per ControllerEnclosure48Maximum Number of SSDs3,2004,800SSDs 1.92 TB/3.84 TB/7.68 TB palm-sized NVMe SSD,960 GB/1.92 TB/3.84 TB/7.68 TB/15.36 TB SASSSDSCM Supported800 GB SCM*Software SpecificationsSupported RAID Levels RAID 5, RAID 6, RAID 10*, and RAID-TP (tolerates simultaneous failures of 3 SSDs)Number of LUNs16,38432,768Value-Added Features SmartDedupe, SmartVirtualization, SmartCompression, SmartMigration, SmartThin,SmartQoS(SAN&NAS), HyperSnap(SAN&NAS), HyperReplication(SAN&NAS),HyperClone(SAN&NAS), HyperMetro(SAN&NAS), HyperCDP(SAN&NAS), CloudBackup*,SmartTier*, SmartCache*, SmartQuota(NAS)*, SmartMulti-Tenant(NAS)*, SmartContainer* Storage ManagementSoftwareDeviceManager UltraPath eServicePhysical SpecificationsPower Supply SAS SSD enclosure: 100V–240V AC±10%,192V–288V DC,-48V to -60V DCController enclosure/Smart SAS diskenclosure/Smart NVMe SSD enclosure: 200V–240V AC±10%, 100–240V AC±10%,192V–288V DC, 260V–400V DC,-48V to -60V DC SAS SSD enclosure: 100V–240V AC±10%, 192V–288V DC, -48V to -60V DC Controller enclosure/Smart SAS SSD enclosure/Smart NVMe SSD enclosure: 200V–240V AC±10%, 192V–288V DC, 260V–400V DC,-48V to -60V DCTechnical SpecificationsModel OceanStor Dorado 5000 OceanStor Dorado 6000 Physical SpecificationsDimensions (H x W x D)SAS controller enclosure: 86.1 mm ×447mm ×820 mmNVMe controller enclosure: 86.1 mm ×447mm ×920 mmSAS SSD enclosure: 86.1 mm ×447 mm ×410 mmSmart SAS SSD enclosure: 86.1 mm x 447mm x 520 mmNVMe SSD enclosure: 86.1 mm x 447 mm x620 mmSAS controller enclosure: 86.1 mm ×447mm ×820 mmNVMe controller enclosure: 86.1 mm ×447mm ×920 mmSAS SSD enclosure: 86.1 mm ×447 mm ×410 mmSmart SAS SSD enclosure: 86.1 mm ×447mm ×520 mmNVMe SSD enclosure: 86.1 mm x 447 mm x620 mmWeight SAS controller enclosure: ≤ 45 kgNVMe controller enclosure: ≤ 50 kgSAS SSD enclosure: ≤ 20 kgSmart SAS SSD enclosure: ≤ 30 kgSmart NVMe SSD enclosure: ≤ 35 kg SAS controller enclosure: ≤ 45 kg NVMe controller enclosure: ≤ 50 kg SAS SSD enclosure: ≤ 20 kgSmart SAS SSD enclosure: ≤ 30 kg Smart NVMe SSD enclosure: ≤ 35 kgOperating Temperature–60 m to +1800 m altitude: 5°C to 35°C (bay) or 40°C (enclosure)1800 m to 3000 m altitude: The max. temperature threshold decreases by 1°C for everyaltitude increase of 220 mOperating Humidity10% RH to 90% RHCopyright © Huawei Technologies Co., Ltd. 2021. All rights reserved.No part of this document may be reproduced or transmitted in any form or by any means without the prior written consent of Huawei Technologies Co., Ltd.Trademarks and Permissions, HUAWEI, and are trademarks or registered trademarks of Huawei Technologies Co., Ltd. Other trademarks, product, service and company names mentioned are the property of their respective holders.Disclaimer THE CONTENTS OF THIS MANUAL ARE PROVIDED "AS IS". EXCEPT AS REQUIRED BY APPLICABLE LAWS, NO WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO, THE IMPLIEDWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, ARE MADE IN RELATION TOTHE ACCURACY, RELIABILITY OR CONTENTS OF THIS MANUAL.TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO CASE SHALL HUAWEI TECHNOLOGIESCO., LTD BE LIABLE FOR ANY SPECIAL, INCIDENTAL, INDIRECT, OR CONSEQUENTIAL DAMAGES, OR LOSTPROFITS, BUSINESS, REVENUE, DATA, GOODWILL OR ANTICIPATED SAVINGS ARISING OUT OF, OR INCONNECTION WITH, THE USE OF THIS MANUAL.Tel: + S h e n z h en 518129,P.R.C h i n aBantian Longgang DistrictHUAWEI TECHNOLOGIES CO.,LTD.To learn more about Huawei storage, please contact your local Huawei officeor visit the Huawei Enterprise website: .Huawei Enterprise APPHuawei IT。

dynamic device capacity机制
动态设备容量机制 (Dynamic Device Capacity Mechanism) 是一
种管理设备资源的机制，用于根据实际需求分配和管理设备的处理能力和存储容量。

该机制可以根据系统负载和用户需求的变化，动态调整设备资源的分配，以提高系统性能和资源利用率。

动态设备容量机制通常涉及以下几个方面：
1. 自适应调整：根据设备的工作负载情况，动态调整设备的处理能力和存储容量。

例如，当系统负载较高时，可以增加设备的处理能力，以提高系统的响应能力；当系统负载较低时，可以减少设备的处理能力，以降低功耗和资源浪费。

2. 资源分配优化：根据用户需求和系统优先级，优化设备资源的分配。

例如，对于响应时间要求较高的任务，可以优先分配更多的处理能力和存储容量；对于批处理任务或低优先级任务，可以降低设备的资源分配。

3. 预测和动态调节：通过对设备资源使用情况的预测和实时监测，及时调整设备的处理能力和存储容量。

例如，根据过去的使用模式和趋势预测将来的负载情况，并根据实时监测结果调整设备的资源分配。

4. 弹性扩展：根据需要进行设备的扩展和收缩。

例如，当负载超过设备的容量时，可以扩展设备资源以满足需求；当负载较低时，可以收缩设备资源以节省成本。

通过使用动态设备容量机制，可以更有效地管理和利用设备资源，提高系统的性能和灵活性，同时降低能耗和资源浪费。

这对于需要处理大量数据或具有高度动态性的应用场景尤为重要，例如云计算、大数据处理和物联网等领域。

专利名称：PERFORMANCE PREDICTION ANDSURVEILLANCE DEVICE AND PREDICTIONMETHOD FOR REACTOR CORE USING MIXEDOXIDE FUEL发明人：KOKUBU TAKEHIKO,國分 毅彦,MOGITOSHIHIKO,茂木 俊彦,SASAGAWA MASARU,笹川 勝申请号：JP特願平8-21356申请日：19960207公开号：JP特開平9-211177A公开日：19970815专利内容由知识产权出版社提供专利附图：摘要：PROBLEM TO BE SOLVED: To predict core performance with high accuracy by considering the accumulation effects of Am in storage period until loading in a reactor and conducting prediction calculation of core power distribution and core life. SOLUTION: A reactor state parameter input device 3 inputs present data of core and fuel from the reactor 9 such as core flow in a present core state calculation device 4. The calculation device 4 calculates the core state such as power distribution using contained physical models based on the past operation history and the data from the state amount input device 3 and inputs in a core state prediction device 6. The prediction device 6 predicts the accumulated amount of Am-241 in the mixed oxide fuel which is a mixture of uranium fuel and plutonium fuel, based on the fuel state parameters from the calculation device 4 and the data from an operator demand input device 7, produces new nuclear constants, substitutes these nuclear constants in the physical models for calculating the fuel characteristics, predicts new fuel characteristics, and performs prediction calculation of core power distribution and that of core life. The results of the prediction calculation isoutput on a prediction result indication device 8.申请人：HITACHI LTD,株式会社日立製作所,HITACHI ENG CO LTD,日立エンジニアリング株式会社地址：東京都千代田区神田駿河台四丁目6番地,茨城県日立市幸町3丁目2番1号国籍：JP,JP代理人：秋本 正実更多信息请下载全文后查看。

cgroup测试存储设备IOPS分配1 使⽤：创建树并且attach⼦系统⾸先要创建⽂件系统的挂载点作为树的根 mkdir /cgroup/name mkdir /cgroup/cpu_and_memMount这个挂载点到⼀个或者多个⼦系统 mount -t cgroup -o subsystems name /cgroup/name mount -t cgroup -o cpu,cpuset,memory cpu_and_mem /cgroup/cpu_and_mem这个时候查看⼦系统 ~]# lssubsys -am cpu,cpuset,memory /cgroup/cpu_and_mem net_cls ns cpuacct devices freezer blkio重新mount mount -t cgroup -o remount,cpu,cpuset,cpuacct,memory cpu_and_mem /cgroup/cpu_and_mem查看⼦系统~]# lssubsys -amcpu,cpuacct,cpuset,memory /cgroup/cpu_and_memnet_clsnsdevicesfreezerblkio创建⼦group: mkdir /cgroup/hierarchy/name/child_namemkdir /cgroup/cpuset/lab1/group1使⽤：Process Behavior in the Root Control Group对于blkio和cpu⼦系统来说，在root cgroup下的进程和在⼦cgroup下的进程，对资源的分配不同例如有⼀个root cgroup，⽂件夹为/rootgroup，有两个⼦cgroup，/rootgroup/red/ and /rootgroup/blue/在这三个cgroup下⾯都创建cpu.shares，并且值设为1 如果在三个cgroup下⾯各创建⼀个进程，则每个进程CPU占有率为三分之⼀然⽽当⼦cgroup⾥⾯添加更多的进程，则整个⼦cgroup还是占有三分之⼀的CPU如果在root cgroup⾥⾯再创建两个进程，则变成了按照进程数来分，也即每个进程五分之⼀所以在使⽤blkio和cpu的时候，尽量使⽤⼦cgroup⼦系统：blkio⼦系统控制并监控cgroup中的任务对块设备的I/O访问。

工艺流程数字化平台核心技术点1.工艺流程数字化平台需要具备数据采集和传输的能力。

The digital platform for process flow needs to have the capability of data collection and transmission.2.它需要支持多种数据格式的输入和输出。

It needs to support input and output in various data formats.3.平台必须具备数据处理和分析的功能。

The platform must be capable of data processing and analysis.4.它需要具备实时监控和预警功能。

It needs to have real-time monitoring and alerting capabilities.5.平台需支持大数据存储和管理。

The platform should support big data storage and management.6.它需要具备数据可视化和报表输出的能力。

It needs to have the capability of data visualization and report generation.7.该平台需具备智能优化和决策支持的功能。

The platform should have intelligent optimization and decision support capabilities.8.它需要支持多用户协同工作和权限管理。

It needs to support multi-user collaboration and access control.9.该平台需具备开放式接口和标准化数据格式。

The platform needs to have open interfaces and standardized data formats.10.它需要具备安全性和稳定性保障的能力。

锂离子电池和金属锂离子电池的能量密度计算吴娇杨，刘品，胡勇胜，李泓（中国科学院物理研究所，北京，100190）摘要：锂电池是理论能量密度最高的化学储能体系，估算各类锂电池电芯和单体能达到的能量密度，对于确定锂电池的发展方向和研发目标，具有积极的意义。

本文根据主要正负极材料的比容量、电压，同时考虑非活性物质集流体、导电添加剂、粘结剂、隔膜、电解液、封装材料占比，计算了不同材料体系组成的锂离子电池和采用金属锂负极、嵌入类化合物正极的金属锂离子电池电芯的预期能量密度，并计算了18650型小型圆柱电池单体的能量密度，为电池发展路线的选择和能量密度所能达到的数值提供参考依据。

同时指出，电池能量密度只是电池应用考虑的一个重要指标，面向实际应用，需要兼顾其它技术指标的实现。

关键词：锂离子电池；金属锂离子电池；能量密度；18650电池；电芯中图分类号：O 文献标志码：A 文章编号：Calculation on energy densities of lithium ion batteries and metalliclithium ion batteriesWU Jiaoyang，Liu pin, HU Yongsheng, LI Hong(Institute of Physics, Chinese Academy of Science, Beijing 100190, China)Abstract: Lithium batteries have the highest theoretical energy densities among all electrochemical energy storage devices. Prediction of the energy density of the different lithium ion batteries (LIB) and metallic lithium ion batteries (MLIB) is valuable for understanding the limitation of the batteries and determine the directions of R&D. In this research paper, the energy densities of LIB and MLIB have been calculated. Our calculation includes the active electrode materials and inactive materials inside the cell. For practical applications, energy density is essential but not the only factor to be considered, other requirements on the performances have to be satisfied in a balanced way.Key words：lithium ion batteries; metal lithium ion batteries; energy density calculation; 18650 cell; batteries core收稿日期：；修改稿日期：。

摘 要牛乳中富含脂肪，而脂肪中的不饱和脂肪酸易氧化而产生酸败。

鉴于UHT 乳在储存、销售过程中无不受到温度、光照等的影响而使其品质下降，通过分析温度、和光照对UHT 乳储存期内脂氧化程度和感官品质及黏度的影响，建立这两个因素对UHT 奶的货架期预测模型。

同时建立温度和光照双因素交叉试验下UHT 奶的货架期预测模型,研究得出如下结论：1.结合脂氧化机理，分析了不同储存温度对UHT 奶脂氧化程度的影响，同时做感官分析及黏度测定。

得出随着储存温度的提高脂氧化速率逐渐增大，在37℃下，UHT 乳的脂氧化速率可达到0.0398abs/d ，并推导出了温度影响下产品的货架期预测模型。

t=ln(TBA/TBA 0)× (32.855-0.173C)。

并通过感官品质及黏度的测定更有效的验证了该货架期预测模型。

2.分别对UHT 奶在不同光源照射储存条件下进行脂氧化程度测定及感官评定及黏度测定，得出光照是影响乳脂氧化的重要因素，同时不同光源照射对UHT 乳脂氧化程度影响不同。

为延缓UHT 奶脂肪氧化和感官品质，在（23±1）℃条件贮存时，应采用白炽灯照明。

3.结合脂氧化机理，分析了不同光照强度条件下UHT 奶脂氧化程度测定及感官评定及黏度测定，得出随着光照度的增大，会明显加深脂肪氧化程度，在光照强度为950lx 以上时，光照时间一周后其TBA 值就达到0.2以上， UHT 奶应陈列于光照度低于950lx 的光照条件下。

避光存放可以大大降低UHT 奶中脂氧化的进程，其光照条件下的氧化速率约为无光照条件下的1.5倍。

同时得出基于脂氧化的光照强度对产品的货架寿命模型：t L = ln(TBA/TBA 0)/ (0.0328+8.6736×10-6L ·T L )。

并通过感官品质及黏度的测定更有效的验证了该货架期预测模型。

4.在不同温度及不同光照强度条件下对UHT 奶进行脂氧化程度交叉试验测定。

装备环境工程第21卷第4期·24·EQUIPMENT ENVIRONMENTAL ENGINEERING2024年4月长期立贮固体发动机药柱变形预估方法研究彭湃1,2,3，陈家兴4，樊自建2,3*，邓旷威2,3（1.长沙理工大学 土木工程学院，长沙 410015；2.国防科技大学 空天科学学院，长沙 410073；3.空天任务智能规划与仿真湖南省重点实验室，长沙 410073；4.内蒙动力机械研究所，呼和浩特 010010）摘要：目的研究老化与颠振影响下长期立式贮存固体发动机药柱的蠕变特性，基于广义Kelvin模型，构建一种考虑老化和损伤因素的蠕变本构模型。

方法通过开展温度与应力加速蠕变试验、高温加速老化试验以及往复拉伸损伤试验，获得老化与损伤对蠕变的影响规律，建立蠕变预测模型，获取模型参数，并将其嵌入有限元软件。

结果利用所得蠕变本构模型计算了某型发动机立式贮存15 a后药柱蠕变的应力应变，结果表明，考虑老化因素后，发动机药柱的最大等效应力增大了96.84%，最大等效应变减小了4.07%，在同时考虑老化与损伤因素后，最大等效应力增加了82.77%，最大等效应变减小了3.62%。

结论对比不同条件下的仿真结果，老化和损伤对发动机蠕变状态的影响较大。

使用该模型能够较好地体现出固体发动机药柱的老化硬化和损伤软化。

关键词：固体发动机；立式贮存；时间-温度-应力等效原理；蠕变预测模型；有限元中图分类号：V435 文献标志码：A 文章编号：1672-9242(2024)04-0024-11DOI：10.7643/ issn.1672-9242.2024.04.004Deformation Prediction Method of Engine Grain under Long-term Vertical StoragePENG Pai1,2,3, CHEN Jiaxing4, F AN Zijian2,3*, DENG Kuangwei2,3(1. College of Civil Engineering, Changsha University of Science and Technology, Changsha 410015, China; 2. College ofAerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China; 3. Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions, Changsha 410073, China; 4. InnerMongolia Power Machinery Research Institute, Hohhot 010010, China)ABSTRACT: The work aims to study the creep characteristics of long-term vertically stored solid engine grain under the influ-ence of aging and knocking, and establish a creep constitutive model considering aging and damage factors based on the gener-alized Kelvin model. The creep test accelerated by temperature and stress, high temperature accelerated aging test and recipro-cating tensile damage test were carried out to obtain the influence law of aging and damage on creep. The creep prediction model was established, the model parameters were obtained and embedded into the finite element software. The stress and strain of the engine grain creep after 15 years of vertical storage were calculated by the obtained creep constitutive model. The simula-收稿日期：2024-02-26；修订日期：2024-04-07Received：2024-02-26；Revised：2024-04-07基金项目：湖南省研究生科研创新项目（CX20230059）Fund：Research Innovation Project of Hunan Province（CX20230059）引文格式：彭湃, 陈家兴, 樊自建, 等. 长期立贮固体发动机药柱变形预估方法研究[J]. 装备环境工程, 2024, 21(4): 24-34.PENG Pai, CHEN Jiaxing, FAN Zijian, et al.Deformation Prediction Method of engine grain under Long-term Vertical Storage[J]. Equipment Environmental Engineering, 2024, 21(4): 24-34.*通信作者（Corresponding author）第21卷第4期彭湃，等：长期立贮固体发动机药柱变形预估方法研究·25·tion results showed that the maximum effective stress of the engine grain increased by 96.84% and the maximum effective strain decreased by 4.07% after considering the aging factor. After considering both the aging and damage factors, the maximum ef-fective stress increased by 82.77% and the maximum effective strain decreased by 3.62%. Compared with the simulation results under different conditions, aging and damage have great influence on the creep state of the engine. The use of this model can better reflect the aging hardening and damage softening of solid engine grain. The established model and the method used can provide reference for the structural integrity analysis of long-term vertically stored engines.KEY WORDS: solid engine; vertical storage; time-temperature-stress equivalence principle; creep prediction model; finite element analysis固体发动机具有战勤简单、可靠性高、成本低等优点，其可靠性一直是各行各业研究和关注的重点。

Data SheetFujitsu PRAID EP400i / EP420iRAID Controller SAS 12Gbit/s 1GB or 2GB cache based on LSI MegaRAID® for internal storage devices The RAID architecture (Redundant Array of Independent Disks) combines multiple storage devices, including hard drives and NVMe devices, into a single logical unit. Redundancy data is generated from data segments (barring RAID 0) and distributed across the devices. Consequently, operating systems interact with this collective array rather than the individual devices. The core purpose of RAID is to enhance data availability, reducing potential disruptions from storage devices failures. The effectiveness of a RAID setup largely depends on the RAID controller in use.Choose Fujitsu RAID controllers for a blend of modern technology and proven experience,providing the data protection that businesses need today.PRAID EP400i / EP420iThe Fujitsu RAID ControllerPRAID EP400i with 8ports sets new speed and data security standards for internal storage drives. The RAID stack isbased on the LSI MegaRAID® and offers high data throughput, a comprehensive fault tolerancefunction and user-friendly management options.Moreover, the Controller management is integrated seamlessly into the Fujitsu server managementconcept. All controller functions are supported by the Fujitsu ServerView RAID Manager. The PRAIDEP400i is designed for backward compatibility with 3Gbit/s SAS as well as with 6Gbit/s and 3Gbit/sSATA hard drives. Regardless of the drive speed, itdelivers significant performance improvements in both read and write applications. Due to the support of 64-bit addressing, a robust set of RAID features and demanding error tolerance functions the controller provides high RAID performance and data availability. Powerful online management service programs (Fujitsu ServerView RAIDManager), which are simple to operate and quick to install, provide the administrator with unparalleled flexibility and access to the arrays. The RAIDcontroller supports all of the important RAID levels including RAID 6 and 60. The optional flash battery backup (FBU) combined with TFM module ensures the integrity of the data stored in the cache on the RAID controller in case of a power outage. In this event, the data will be copied to a non-volatile flash memory (TFM). The FBU provides a low-priced alternative to an uninterruptible power supply (UPS) and, when compared to battery backup up units (BBU), enables a long-term, secure store ofdata and better serviceability. Always select FBU and TFM module in combination.The Advanced Software Options in combination with Solid State Disks in front of HDD volumes can create high-capacity,high-performance controllercache pools, depending on the load profile. A free of charge test version is available at PRIMERGY-PM.Link: /dl.aspx?id=c816a64f-8b6d-47df-ba31-836874f08c07Technical detailsTechnical detailsController Silicon RoC (RAID on Chip) LSI SAS3108Adapter Type RAID 5/6 Ctrl.Operating system pre-installed Information to released operating systems can be found in the server datasheets. Details can be found in thereleased drivers list on the support portal.Released drivers list link /Download/Index.aspNumber of ports8 ports int.Connector internal2x SFF8643 (Mini-SAS HD)Data transfer rate up to12 Gbit/sBus type PCIe 3.0Bus width x8RAID Management ServerView RAID ManagerStorCLI (command-line interface)BIOS Configuration UtilityKey RAID Data Protection Feature- RAID levels 0, 1, 5 and 6- RAID spans 10, 50 and 60- Maximum number of spans is 8- Online Capacity Expansion (OCE)- Online RAID level Migration (RLM)- Auto resume after loss of system power during array rebuild or reconstruction (RLM)- Fast initialization for quick array setup- Single controller multipathing (failover)- Load Balancing- Configurable stripe size up to 1MB- Fast initialisation for quick array setup- Check consistency for background data integrity - Make Data Consistent (MDC)- Patrol read for media scanning and repairing- Up to 64 logical drives per controller- S.M.A.R.T support- Global and dedicated Hot Spare with Revertible Hot Spare support- Automatic rebuild- Enclosure affinity- Enclosure management- SES (inband)- SGPIO (outband)RAID level0, 1, 10, 5, 50, 6, 60RAID cache backup unit Optional FBURAID controller notes based on LSI SAS3108Interface technology SAS/SATAOrder code Product Name Height of bracket RAID controller cache size Number of Connectors S26361-F5243-E11PRAID EP400i Matching to system 1 GB2S26361-F5243-E12PRAID EP420i Matching to system 2 GB2S26361-F5243-E14PRAID EP420i for SafeStore Matching to system 2 GB2S26361-F5243-L11PRAID EP400i Full Height / Low Profile 1 GB2S26361-F5243-L12PRAID EP420i Full Height / Low Profile 2 GB2S26361-F5243-L14PRAID EP420i for SafeStore Full Height / Low Profile 2 GB2S26361-F5243-L1PRAID EP400i Full Height / Low Profile 1 GB2S26361-F5243-L2PRAID EP420i Full Height / Low Profile 2 GB2S26361-F5243-L4PRAID EP420i for SafeStore Full Height / Low Profile 2 GB2Order code Product Name NotesS26361-F5243-E100PRAID EP400i TFM installed - Transportable Flash Module - contains flash memoryand control logic for Flash Backup Unit (FBU) – required for FBUoptionS26361-F5243-E200TFM PRAID EP420i/e installed - Transportable Flash Module - contains flash memoryand control logic for Flash Backup Unit (FBU) – required for FBUoptionS26361-F5243-E125RAID Ctrl FBU option for PRAID EP4xx with 25cm cable installed - Super-capacitor incl. cableinstalled - Super-capacitor incl. cableS26361-F5243-L110RAID Ctrl FBU option for PRAID EP4xx with 25cm, 55cm, 70cmcableComplianceCompliance notes According to the corresponding systemCompliance link https:///sites/certificatesContactFujitsu LimitedWebsite: /primergy2023-11-27 WW-ENworldwide project for reducing burdens on the environment.Using our global know-how, we aim to contribute to the creation of a sustainable environment for future generations through IT.Please find further information at http://www./global/about/environmenttechnical specification with the maximum selection of components for the named system and not the detailed scope ofdelivery. The scope of delivery is defined by the selection of components at the time of ordering. The product was developed for normal business use.Technical data is subject to modification and delivery subject to availability. Any liability that the data and illustrations are complete, actual or correct is excluded. Designations may be trademarks and/or copyrights of the respective owner, the use of which by third parties for their own purposes may infringe the rights of such owner.More informationAll rights reserved, including intellectual property rights. Designations may be trademarks and/or copyrights of therespective owner, the use of which by third parties for their own purposes may infringe the rights of such owner. For further information see https:///global/about/resources/terms/ Copyright 2023 Fujitsu LIMITED。

服务器BIOS设置解析服务器BIOS设置解析DELL服务器BIOS设置解析BIOS：是一种系统设置程序，通过它可以管理系统硬件和指定BIOS级的选项。

包括：>在添加或删除硬件后更改NVRAM设置>查看系统硬件配置>启用或禁用电源管理阀值>管理系统安全进入系统设置程序：>打开或重新启动系统>系统显示以下信息时按<F2>键<F2> = System Setup （<F2> = 系统设置）如果按<F2>之前已经开始载入操作系统，请让系统完整引导过程然后重启再试一次。

System Stetup Program ：系统设置程序System Time ：系统时间Memory Settings ：内存设置System Memory Size ：显示系统内存容量System Memory Type ：显示系统内存类型System Memory Speed ：显示系统内存速度Video Memory ：显示视频内存容量System Memory ：指定系统内存检测是否在系统引导时运行。

选项为Enable（已启用）Testing：系统内存检测默认EnableMemory Operating Mode ：内存运行模，如果系统安装了有效内存配置，则此字段将显示内存运行类型。

设置为Optimizer Mode （优化器模式）时，内存控制器彼此独立运行，以提高内存性能。

如果此字段设置为Mirror Mode （镜像模式），则启用内存镜像—Advance ECC ModeNode Interleaving ：如果此字段为Enable，则安装对称内存配置时支持内存交错，如果为Disable，系统支持非一体化内存体系结构NUMA（非对称）内存配置。

Processor Settings ：处理器设置-显示有关微处理器的信息（速率和高速存缓大小等）64-bit ：指定处理器是否支持64位拓展Clock Speed ：显示处理器时钟速率Bus Speed ：显示处理器总线速率Logical Processor （逻辑处理器）：默认Enable，对于支持多线程并行处理（SMT）技术的处理器，每个处理器内核可支持最多两个逻辑处理器。

第20卷 第10期 装 备 环 境 工 程2023年10月EQUIPMENT ENVIRONMENTAL ENGINEERING ·101·收稿日期：2023-08-27；修订日期：2023-10-07 Received ：2023-08-27；Revised ：2023-10-07引文格式：许春, 张冬梅, 张鹏, 等. 某RDX 基浇注PBX 的加速老化规律与寿命评估[J]. 装备环境工程, 2023, 20(10): 101-107.XU Chun, ZHANG Dong-mei, ZHANG Peng, et al. Accelerated Aging Regulation and Life Evaluation of RDX Based Poured PBX[J]. Equipment Environmental Engineering, 2023, 20(10): 101-107.*通信作者（Corresponding author ）某RDX 基浇注PBX 的加速老化规律与寿命评估许春1，张冬梅2，张鹏1，郝晓飞1，肖茜1，睢贺良1﹡（1.中国工程物理研究院化工材料研究所，四川 绵阳 621900；2.西安近代化学研究所，西安 710065） 摘要：目的 掌握环三亚甲基三硝胺（RDX ）基浇注高聚物黏结炸药（PBX ）的加速老化规律，分析老化机理和关键敏感参量，并探讨加速老化寿命评估方法。

方法 针对RDX 基浇注PBX 开展60、70、80 ℃等恒定温度下的加速贮存老化试验，采用微焦点X 射线计算机断层扫描仪（微焦点CT ）、核磁共振、气相色谱等方法，分析浇注炸药在老化过程中微孔隙率、交联密度以及增塑剂含量等结构参量的变化规律，通过对这几种参量的对比，分析其老化机理，并进一步对加速老化寿命评估方法进行初步探讨。

结果 浇注PBX 在加速老化过程中会出现明显的孔隙率逐渐增加、交联密度逐渐增加以及增塑剂逐渐降低等问题，且表现为温度越高，相关性能参量变化得越快。

计算机网络英文缩写词ACK (ACKnowledgement) 确认ADSL (Asymmetric Digital Subscriber Line) 非对称数字用户线AF PHB (Assured Forwarding Per-Hop Behavior) 确保转发每跳行为AH (Authentication Header) 鉴别首部ALOHA (Additive Link On-line HAwaii system) 一种随机接入系统AN (Access Network ）接入网ANSI (American National Standards Institute) 美国国家标准协会AP (Access Point) 接入点API (Application Programming Interface) 应用编程接口APNIC (Asia Pacific Network Information Center) 亚太网络信息中心ARP （Address Resolution Protocol ）地址解析协议ARPA (Advanced Research Project Agency)美国国防部远景研究规划局（高级研究计划署）ARQ (Automatic Repeat reQuest) 自动请求重发AS (autonomous system) 自治系统ASN.1(Abstract Syntax Notation One ）抽象语法记法1ATM (Asynchronous Transfer Mode) 异步传递方式ATU (Access Termination Unit) 接入端接单元ATU-C (Access Termination Unit Central Office ）端局接入端接单元ATU-R (Access Termination Unit Remote) 远端接入端接单元AUI (Attachment Unit Interface ）连接接口单元AWT （Abstract Window Toolkit ）抽象窗口工具箱BECN (Backward Explicit Congestion Notification) 反向显式拥塞通知BER (Basic Encoding Rule) 基本编码规则BGP (Border Gateway Protocol) 边界网关协议B-ISDN (Broadband-ISDN) 宽带综合业务数字网BLAM (Binary Logarithmic Arbitration Method) 二进制对数仲裁方法BOOTP (BOOTstrap Protocol) 引导程序协议BSA (Basic Service Area) 基本服务区BSS (Basic Service Set) 基本服务集CA (Certification Authority) 认证中心CAC (Call Admission Control ）呼叫接纳控制CAC (Connection Admission Control) 连接准许控制CAP (Carrierless Amplitude Phase) 无载波振幅相位调制CATV (Community Antenna TV, CAble TV) 有线电视CBR （Constant Bit Rate ）恒定比特率CCIR (Consultative Committee ，International Radio) 国际无线电咨询委员会CCITT (Consultative Committee, International Telegraph and Telephone)国际电报电话咨询委员会CCS (Common-Channel Signaling) 共路信令CDM (Code Division Multiplexing) 码分复用CDMA (Code Division Multiplex Access) 码分多址CDV (Cell Del 盯Variation ）信元时延偏差CDVT (Cell Delay Variation Tolerance) 容许的信元时延偏差CFI (Canonical Format Indicator) 规范格式指示符CGI (Common Gateway Interface) 通用网关接口CIDR (Classless InterDomain Routing) 无分类域间路由选择CIR (Committed Information Rate ）承诺的信息速率CLP (Cell Loss Priority) 信元丢失优先级CLR （Cell Loss Ratio ）信元丢失率CMIP (Common Management Information Protocol) 公共管理信息协议CMOT (Common Management information service and protocol Over TCP/IP)在TCP/IP 上的公共管理信息服务与协议CNNIC (Network Information Center of China) 中国互联网络信息中心CPE (Customer Premises Equipment) 用户屋内设备CRC (Cyclic Redundancy Check) 循环冗余检验CS (Convergence Sublayer) 汇聚子层CS-ACELP (Conjugate-Structure Algebraic-Code-Excited Linear Prediction)共轭结构代数码激励线性预测（声码器）CSMA/CD (Carrier Sense Multiple Access ／Collision Detection)载波监听多点接刀冲突检测CSMA/CA (Carrier Sense Multiple Access ／Collision Avoidance) 载波监听多点接A/ 冲突避免CSRC (Contributing SouRCe identifier) 参与源标识符CSU/DSU （Channel Service Unit/Data Service Unit) 信道服务单元／数据服务单元CTD (Cell Transfer Delay) 信元传送时延CTS (Clear To Send) 允许发送DACS (Digital Access and Cross-connect System) 数字交接系统DARPA (Defense Advanced Research Project Agency) 美国国防部远景规划局（高级研究署）DAVIC (Digital Audio Visual Council) 数字视听协会DCE (Data Circuit-terminating Equipment) 数据电路端接设备DCF (Distributed Coordination Function ）分布协调功能DDoS (Distributed Denial of Service ）分布式拒绝服务DE (Discard Eligibility) 丢弃指示DES (Data Encryption Standard) 数据加密标准DHCP (Dynamic Host Configuration Protocol) 动态主机配置协议DiffServ (Differentiated Services ）区分服务DIES (Distributed Coordination Function IFS ）分布协调功能帧间间隔DLCI (Data Link Connection Identifier) 数据链路连接标识符DMT (Discrete Multi-Tone) 离散多音（调制）DNS (Domain Name System) 域名系统DOCSIS (Data Over Cable Service Interface Specifications)电缆数据服务接口规约DoS (Denial of Service) 拒绝服务DS (Distribution System) 分配系统DS (Differentiated Services) 区分服务（也写作DiffServ ）DSCP (Differentiated Services CodePoint) 区分服务码点DSL (Digital Subscriber Line) 数字用户线DSLAM (DSL Access Multiplexer) 数字用户线接入复用器DSSS (Direct Sequence Spread Spectrum) 直接序列扩频DTE (Data Terminal Equipment) 数据终端设备DVMRP (Distance Vector Multicast Routing Protocol) 距离向量多播路由选择协议DWDM (Dense WDM) 密集波分复用EBCDIC (Extended Binary-Coded Decimal Interchange Code) 扩充的二/十进制交换码EDFA (Erbium Doped Fiber Amplifier) 掺饵光纤放大器EF PHB (Expedited Forwarding Per-Hop Behavior) 迅速转发每跳行为EGP (External Gateway Protocol) 外部网关协议EIA (Electronic Industries Association ）美国电子工业协会ESP (Encapsulating Security Payload) 封装安全有效载荷ESS 伍xtended Service Set) 扩展的服务集EUI （Extended Unique Identifier) 扩展的惟一标识符FCS (Frame Check Sequence) 帧检验序列FDDI (Fiber Distributed Data Interface ）光纤分布式数据接口FDM (Frequency Division Multiplexing) 频分复用FEC (Forwarding Equivalence Class) 转发等价类FEC (Forward Error Correction) 前向纠错FECN (Forward Explicit Congestion Notification) 前向显式拥塞通知FHSS (Frequency Hopping Spread Spectrum) 跳频扩频FIFO （First In First Out) 先进先出FOIRL (Fiber Optic Inter-Repeater Link) 转发器间的光纤链路FQ (Fair Queuing) 公平排队FR (Frame Relay) 帧中继FSK (Frequency Shift Keying) 移频键控FTP (File Transfer Protocol ）文件传送协议FTTB (Fiber To The Building) 光纤到大楼FTTC (Fiber To The Curb ）光纤到路边FTTH (Fiber To The Home) 光纤到家GCRA (Generic Cell Rate Algorithm) 一般信元速率算法GIF (Graphics Interchange Format) 图形交换格式GII (Global Information Infrastructure) 全球信息基础结构，全球信息基础设施GFC （Generic Flow Control) 通用流量控制GSM (Group Special Mobile) 群组专用移动通信体制HDLC (High-level Data Link Control) 高级数据链路控制HDSL (High speed DSL) 高速数字用户线HEC (Header Error Control) 首部差错控制HFC (Hybrid Fiber Coax) 光纤同轴混合（网）HH'PI (HIgh-Performance Parallel Interface) 高性能并行接口HSSG (High Speed Study Group) 高速研究组HTML (HyperText Markup Language) 超文本置标语言HTTP (HyperText Transfer Protocol) 超文本传送协议IAB (Internet Architecture Board) 因特网体系结构委员会IAC （Interpret As Command ）作为命令解释IAHC (Internet International Ad Hoc Committee ）因特网国际特别委员会IANA (Internet Assigned Numbers Authority) 因特网号码指派管理局ICANN (Internet Corporation for Assigned Names and Numbers) 因特网名字与号码指派公司ICMP （Internet Control Message Protocol ）因特网控制报文协议IDEA (International Data Encryption Algorithm) 国际数据加密算法IESG (Internet Engineering Steering Group) 因特网工程指导小组IETF (Internet Engineering Task Force) 因特网工程部IFS (InterFrame Space) 帧间间隔IGMP (Internet Group Management Protocol) 因特网组管理协议IGP (Interior Gateway Protocol) 内部网关协议IM (Instant Messaging) 即时传信IMAP (Internet Message Access Protocol) 因特网报文存取协议IMP （Interface Message Processor) 接口报文处理机IntServ (Integrated Services) 综合服务IP (Internet Protocol ）网际协议IPOA (IP Over ATM ）IP 在ATM 上运行IPsec (IP security) IP 安全协议IPX (Internet Packet Exchange) Novell 公司的一种连网协议IR (InfraRed ）红外技术IRTF （Internet Research Task Force ）因特网研究部ISDN (Integrated Services Digital Network) 综合业务数字网ISO （International Organization for Standardization ）国际标准化组织ISOC (Internet Society) 因特网协会ISP （Internet Service Provider) 因特网服务提供者ITU （International Telecommunication Union ）国际电信联盟ITU-T （ITU Telecommunication Standardization Sector) 国际电信联盟电信标准化部门JPEG (Joint Photographic Expert Group) 联合图像专家组标准KDC (Key Distribution Center) 密钥分配中心KDC (Key Distribution Center) 密钥分配中心LAN (Local Area Network ）局域网LANE (LAN Emulation ）局域网仿真LAPB (Link Access Procedure Balanced) 链路接入规程（平衡型）LCP (Link Control Protocol) 链路控制协议LDP (Label Distribution Protocol) 标记分配协议LLC (Logical Link Control) 逻辑链路控制LSP (Label Switched Path) 标记交换路径LSR (Label Switching Router) 标记交换路由器MAC (Medium Access Control) 媒体接入控制MAN (Metropolitan Area Network) 城域网MAU (Medium Attachment Unit) 媒体连接单元MBONE (Multicast Backbone On the InterNEt ）多播主干网MBS (Maximum Burst Size ）最大突发长度MCR (Minimum Cell Rate ）最小信元速率MCU (Multipoint Control Unit)多点控制单元MD (Message Digest) 报文摘要MDI (Medium Dependent Interface ）媒体相关接口MIB (Management Information Base) 管理信息库MIME (Multipurpose Internet Mail Extensions) 通用因特网邮件扩充MIPS （Million Instructions Per Second ）百万指令每秒MOTIF (Message Oriented Text Interchange System) 面向报文的电文交换系统MPEG (Motion Picture Experts Group) 活动图像专家组标准MPOA (MultiProtocol Over ATM) 多协议在ATM 上运行MPLS (MultiProtocol Label Switching) 多协议标记交换MRU (Maximum Receive Unit) 最大接收单元MSS (Maximum Segment Size) 最长报文段MTU (Maximum Transfer Unit) 最大传送单元NAK (Negative AcKnowlegement) 否认帧NAP （Network Access Point) 网络接入点N.ISDN (Narrowband-ISDN) 窄带综合业务数字网NAT (Network Address Translation ）网络地址转换NAV (Network Allocation&nbsp;Vector) 网络分配向量NCP (Network Control Protocol) 网络控制协议NFS (Network File System) 网络文件系统NIC (Network Interface Card) 网络接口卡、网卡NII (National Information Infrastructure) 国家信息基础结构，国家信息基础设施NLA ID (Next-Level Aggregation Identifier) 下一级聚合标识符NLRI (Network Layer Reachability Information) 网络层可达性信息NNI (Network-Node Interface) 网络结点接口NSF (National Science Foundation) （美国）国家科学基金会NVT (Network Virtual Terminal ）网络虚拟终端CPU：Central Processing Unit，中央处理单元，又叫中央处理器或微处理器，被喻为电脑的心脏。

P3U1architecture n. 体系结构instruction set 指令集binary-coded adj. 二进制编码的central processing unit (CPU) 中央处理器processor n. 处理器location n. （存储）单元word length 字长access v. 存取，接近fetch v., n. 取来field n. 域，字段opcode n. 操作码operand n. 操作数address n. 寻址single-precision adj. 单精度的floating-point adj. 浮点的terminal n. 终端complement v. 补充，求补decode v. 解码，译码request n. 请求inactive n. 不活动，停止I/O-mapped adj. 输入/输出映射的（单独编址）memory-mapped adj. 存储器映射的（统一编址）难句翻译[1] …how the instruction execution cycle is broken down into its various components.……指令执行周期怎样分解成不同的部分。

[2] One way to achieve meaningful patterns is to divide up the bits into fields…一种得到（指令）有效形式的方法是将（这些）位分成段……[3] The majority of computer tasks involve the ALU, but a great amount of data movement is required in order to make use of the ALU instructions.计算机的大多数工作涉及到ALU(逻辑运算单元)，但为了使用ALU指令，需要传送大量的数据。

框架畸变应变能英文Title: Framework Distortion Strain Energy: A Technical Exploration.Abstract:The study of framework distortion strain energy is crucial in the field of structural engineering, as it provides insights into the stability, durability, and performance of structures. This article delves into the concept of framework distortion, its implications on strain energy, and the various factors that affect it. The objective is to provide a comprehensive understanding of this complex topic, highlighting the need for accurate analysis and modeling in structural design.Introduction:In the realm of structural engineering, frameworks play a pivotal role in supporting and transferring loads.However, these frameworks are subject to various forces and deformations that can lead to distortions. Framework distortion not only affects the aesthetics of a structure but also its integrity and functionality. Strain energy, a measure of the internal energy stored within a material due to deformation, is a key parameter in understanding and predicting framework distortions.Framework Distortion:Framework distortion refers to the deviation of a structural framework from its original, undeformed shape. This distortion can be caused by various factors such as external loads, temperature changes, material fatigue, and more. Distortion can lead to a reduction in structural stiffness, increased stress concentrations, and ultimately, a compromised ability to perform its designated function.Strain Energy:Strain energy, also known as elastic strain energy, is the energy stored in a material due to deformation. When amaterial is deformed, whether elastically or inelastically, internal forces develop within the material, resulting in the storage of energy. This stored energy is released when the deformation is reversed, driving the material back towards its original shape. In the context of framework distortion, strain energy provides a measure of the deformation within the framework and the associatedinternal forces.Factors Affecting Framework Distortion Strain Energy:1. Material Properties: The mechanical properties of the materials used in the framework significantly influence its ability to resist distortion. Materials with high elastic moduli and strength-to-weight ratios generally exhibit lower strain energy and better resistance to distortion.2. Geometric Configurations: The geometry of the framework, including its shape, size, and member configurations, plays a crucial role in determining strain energy. Complex geometric configurations can lead to stressconcentrations and increased strain energy.3. Boundary Conditions: The support and restraint provided by the foundation or supports affect thedistribution of loads and deformations within the framework. Adequate boundary conditions can reduce distortion andstrain energy.4. Loading Conditions: The type, magnitude, and distribution of loads applied to the frameworksignificantly affect its deformation and strain energy. Dynamic loads, such as seismic or wind loads, can lead to more significant distortions and higher strain energy.5. Temperature and Environmental Factors: Changes in temperature can affect the material properties of the framework, leading to thermal expansion or contraction.This can, in turn, lead to distortions and changes instrain energy. Other environmental factors, such as corrosion or exposure to chemicals, can also affect the mechanical properties of the materials.Analysis and Modeling:Accurate analysis and modeling of framework distortion strain energy are crucial for structural design and performance prediction. Finite element analysis (FEA) is a widely used method for simulating and analyzing the behavior of structures under various loading conditions. FEA allows engineers to model the framework, apply loads, and analyze the resulting deformations and strain energy distributions. This information can then be used to identify critical areas, optimize design, and ensure the structural integrity of the framework.Conclusion:Framework distortion strain energy is a fundamental concept in structural engineering, providing insights into the stability, durability, and performance of structures. Understanding the factors that affect framework distortion and strain energy is essential for effective structural design. By leveraging advanced analysis techniques such as finite element analysis, engineers can accurately predictand mitigate framework distortions, ensuring the safety and reliability of structures.(Note: This article provides a concise overview of the topic, focusing on the key concepts and factors affecting framework distortion strain energy. However, it is recommended to consult more detailed technical resources and research papers for a comprehensive understanding of this complex topic.)。