Prediction and optimization of process parameter of friction stir welded AA5083- H111 aluminum
starccm对流换热系数
starccm对流换热系数STAR-CCM+ is a powerful computational fluid dynamics (CFD) software that is widely used for simulating heat transfer and fluid flow in various engineering applications. One of the key parameters in these simulations is the convective heat transfer coefficient, which plays a crucial role in determining the rate of heat transfer between asolid surface and a fluid. However, accurately predicting the convective heat transfer coefficient can be a challenging task due to the complex nature of fluid flowand heat transfer phenomena. In this response, I will discuss the challenges associated with predictingconvective heat transfer coefficients in STAR-CCM+ simulations and explore potential strategies to improve the accuracy of these predictions.One of the main challenges in predicting convectiveheat transfer coefficients in STAR-CCM+ simulations is the accurate modeling of turbulent flow. Turbulent flow is characterized by chaotic and irregular motion of fluidparticles, which significantly influences the heat transfer characteristics of the flow. In many engineering applications, such as automotive aerodynamics or industrial heat exchangers, the flow is often turbulent, making it essential to accurately capture the turbulent effects on heat transfer. STAR-CCM+ offers various turbulence models, such as the k-epsilon and SST (Shear Stress Transport) models, to simulate turbulent flow and predict convective heat transfer coefficients. However, selecting the most appropriate turbulence model for a specific application and ensuring its accurate implementation can be a non-trivial task.Another challenge in predicting convective heattransfer coefficients is the accurate representation of the solid-fluid interface. In many heat transfer applications, such as cooling of electronic components or heat exchanger design, the heat transfer occurs at the interface between a solid surface and a fluid. The accurate prediction of convective heat transfer coefficients requires a precise representation of the thermal boundary layer at the solid-fluid interface, as well as the effects of surfaceroughness, wall curvature, and other geometric complexities. STAR-CCM+ provides advanced meshing capabilities and boundary condition settings to capture the solid-fluid interface with high fidelity, but achieving an accurate representation of the interface still requires careful attention to mesh quality and boundary condition specifications.Furthermore, the accuracy of convective heat transfer coefficient predictions in STAR-CCM+ simulations can be influenced by the choice of numerical discretization schemes and solution algorithms. The numericaldiscretization schemes, such as finite volume or finite element methods, and solution algorithms, such as pressure-velocity coupling and turbulence modeling, can have a significant impact on the accuracy and convergence of heat transfer simulations. Selecting appropriate discretization schemes and solution algorithms, as well as optimizingtheir settings for a specific problem, is crucial for obtaining reliable predictions of convective heat transfer coefficients.In addition to the technical challenges, theavailability and quality of experimental data forvalidating convective heat transfer coefficient predictions in STAR-CCM+ simulations can also pose difficulties. While there are well-established correlations and empirical relationships for convective heat transfer in simple geometries and flow conditions, many engineering applications involve complex geometries and flow regimesfor which experimental data may be limited or non-existent. Validating the accuracy of convective heat transfer coefficient predictions in such cases can be challenging, and may require additional efforts such as conducting targeted experiments or comparing with similar validated simulations.Despite these challenges, there are several strategies that can be employed to improve the accuracy of convective heat transfer coefficient predictions in STAR-CCM+ simulations. First, conducting sensitivity analyses to assess the impact of turbulence models, mesh resolution, and boundary conditions on the predicted heat transfer coefficients can help identify the most influential factorsand guide the selection of appropriate modeling approaches. Additionally, leveraging the capabilities of STAR-CCM+ for uncertainty quantification and optimization can enable the exploration of a wide range of input parameters and model settings to identify the most accurate and robust predictions of convective heat transfer coefficients.Moreover, utilizing advanced post-processing and visualization tools in STAR-CCM+ can facilitate the interpretation and analysis of simulation results, allowing for a deeper understanding of the underlying flow and heat transfer physics. Visualizing the flow field, temperature distribution, and heat transfer coefficients in 2D and 3D representations can provide valuable insights into the behavior of convective heat transfer and help identify areas for improvement in the simulation setup or modeling assumptions. Furthermore, leveraging the capabilities of STAR-CCM+ for coupled simulations, such as fluid-structure interaction or conjugate heat transfer, can enable a more comprehensive and realistic representation of heat transfer phenomena, leading to more accurate predictions of convective heat transfer coefficients.In conclusion, predicting convective heat transfer coefficients in STAR-CCM+ simulations presents several challenges related to turbulent flow modeling, solid-fluid interface representation, numerical discretization and solution algorithms, as well as the availability of experimental validation data. However, by carefully addressing these challenges and leveraging the advanced capabilities of STAR-CCM+ for sensitivity analysis, uncertainty quantification, advanced visualization, and coupled simulations, it is possible to improve the accuracy of convective heat transfer coefficient predictions and obtain reliable insights into the heat transfer behavior in complex engineering applications.。
基于蝙蝠算法优化反向传播神经网络模型的无线网络流量预测
DOI：10. 11772/j. issn. 1001-9081. 2020101679
基于蝙蝠算法优化反向传播神经网络模型的无线网络流量预测
戴宏亮*，罗裕达
（广州大学 经济与统计学院，广州 510006） （ ∗ 通信作者电子邮箱 hldai618@gzhu. edu. cn）
摘 要：针对无线网络流量数据预测精度不高问题，提出一种基于蝙蝠算法（BA）优化的反向传播（BP）神经网络 的分类预测模型——BABP。通过采用蝙蝠算法对 BP 神经网络模型的初始权值与阈值进行全局寻优，构建崭新的基 于蝙蝠算法优化的神经网络模型。通过与基于传统寻优算法遗传算法（GA）与粒子群优化（PSO）算法的反向传播 （BP）神经网络模型比较，在无线网络流量数据的分类预测和稳定性方面，提出的 BABP 模型要优于 GABP 模型、 PSOBP 模型；同时，无论迭代次数的多与少，BABP 均比 GABP、PSOBP 算法更快地收敛。实验结果表明，BABP 模型在 预测精度、寻优速度以及模型稳定性等方面均比 GABP、PSOBP 模型更具优势。
向牛顿法的方向，从而提升了在接近最优解时的寻优速度［9］。
模型如下： S ( X(k) ) = -( H(k) + λ(k) I )-1 ∇f ( X(k) )
（3）
其中：S ( X(k) )为搜索方向；H(k) 为海森矩阵。H(k) 是一个多元函
数 二 阶 偏 导 数 构 成 的 方 阵 ，描 述 了 函 数 的 局 部 曲 率 ，可 用 H ≈ JT J 求得，其中：J 为雅克比矩阵；∇f ( X(k) ) 为二维梯度。λ
186
计算机应用
第 41 卷
法，已被证明具有比传统寻优算法更为优良的特性。本文对 神经网络分类模型中的相关参数设定采用蝙蝠算法进行全局 寻 优 ，提 出 了 基 于 蝙 蝠 算 法 优 化 的 神 经 网 络 模 型 ——BABP （Bat Algorithm optimized Back Propagation）。并且，通过实验 验证 BABP 模型在寻优精度、寻优速度、稳定性等方面均具有 比 遗 传 算 法 优 化 的 反 向 传 播 神 经 网 络 模 型 GABP（Genetic Algorithm BP）和粒子群优化的反向传播神经网络模型 PSOBP （Particle Swarm Optimization BP）更为优良的性能。
dft计算在燃料电池中的应用
dft计算在燃料电池中的应用英文回答：DFT (Density Functional Theory) is a powerful computational method used in various fields of science and engineering, including the study of fuel cells. Fuel cells are electrochemical devices that convert the chemical energy of a fuel, such as hydrogen, into electrical energy. Understanding the behavior of fuel cell materials at the atomic level is crucial for improving their efficiency and performance.One of the key applications of DFT in fuel cell research is the prediction and optimization of catalyst materials. Catalysts play a critical role in fuel cell reactions by facilitating the electrochemical reactionsthat occur at the electrodes. DFT calculations can be used to investigate the electronic structure and reactivity of different catalyst materials, providing insights into their catalytic activity and selectivity. By analyzing the energyprofiles of the reaction pathways, researchers can identify the most promising catalysts for specific fuel cell reactions.Another important application of DFT in fuel cell research is the study of fuel cell membranes. Membranes are essential components of fuel cells as they separate thefuel and oxidant streams while allowing the transport ofions necessary for the electrochemical reactions. DFT calculations can be used to understand the transport properties of different membrane materials, such as proton conductivity and oxygen permeability. This information can guide the development of new membrane materials with improved performance and durability.Furthermore, DFT can also be used to investigate the interactions between fuel cell materials and impurities or contaminants. For example, DFT calculations can be employed to study the adsorption of carbon monoxide (CO) on the catalyst surface, a common impurity in fuel cell feedstocks. By understanding the adsorption behavior of CO, researchers can design catalyst materials that are more resistant topoisoning and improve the overall stability and longevity of the fuel cell system.In summary, DFT calculations have a wide range of applications in fuel cell research, including catalyst design, membrane optimization, and understanding material interactions. By providing atomic-level insights into the properties and behavior of fuel cell materials, DFT can contribute to the development of more efficient and durable fuel cell systems.中文回答：DFT（密度泛函理论）是一种在科学和工程的各个领域中广泛应用的强大计算方法，包括燃料电池的研究。
电驱系统设计流程
电驱系统设计流程1.电驱系统设计的第一步是确定系统的性能指标和需求。
1. The first step in the design of the electric drive system is to determine the performance indicators and requirements of the system.2.确定系统用途和工作环境，例如车辆、机械设备或工业生产线。
2. Determine the purpose and working environment of the system, such as vehicles, mechanical equipment, or industrial production lines.3.对于不同的应用领域，需要选择不同类型的电机和控制器。
3. Different types of motors and controllers need to be selected for different applications.4.根据功率需求和效率要求选择合适的电机类型，比如直流电机、异步电机或同步电机。
4. Choose the appropriate type of motor, such as DC motor, asynchronous motor, or synchronous motor, according to the power requirements and efficiency requirements.5.确定驱动系统的控制方式，可以是开环控制、闭环控制或者矢量控制。
5. Determine the control mode of the drive system, which can be open-loop control, closed-loop control, or vector control.6.设计电机的机械部分，包括轴承、结构和散热系统。
感兴趣的可靠性书籍
感兴趣的可靠性书籍已有1590次阅读2015-9-2210:27|个人分类:可靠性技术|系统分类:科研笔记|关键词:可靠性分析环境试验设备可靠性书籍一直从事可靠性方面的工作，看过几十本关于环境试验中文版本的标准，也参与起草过2个国标的编写。
近2年稍微时间比较充裕，打算把以下书籍浏览一遍，任务可不轻，有很多书可能都买不到或者借不到。
如果大家有好的可靠性图书也欢迎推荐给我。
1.Reliability Engineering Handbook(Volume1)–Dimitri Kececioglu2.Reliability Engineering Handbook(Volume2)–Dimitri Kececioglu3.Reliability&Life Testing Handbook,Volume1–Dimitri Kececioglu4.Reliability&Life Testing Handbook,Volume2–Dimitri Kececioglu5.Robust Engineering Design-By-Reliability with EMphasis on MEchanical Components and Structural Reliability,Vol.1–Dimitri Kececioglu6.Environmental Stress Screening:Its Quantification,Optimization and Management–Dimitri Kececioglu7.The New Weibull Handbook Fifth Edition,Reliability and Statistical Analysis for Predicting Life,Safety,Supportability,Risk,Cost and Warranty Claims8.Maintenance and Reliability Best Practices9.Software Reliability:Measurement,Prediction,Application10.Software Reliability Engineering:More Reliable Software Faster and Cheaper2nd Edition11.Automotive Electronics Reliability(Progress in Technology)12.Applied Reliability–Third edition13.Achieving System Reliability Growth Through Robust Design and Test14.电子元器件应用手册（参考书）15.轨道列车可靠性、可用性、维修性和安全性16.动车组结构可靠性与动力学17.可靠性工程与管理实践–怎样提高产品可靠性18.疲劳强度设计19.统计学–科学与工程应用20.概率统计21.可靠性设计大全22.风力机可靠性工程23.耐热钢持久性能的统计分析及可靠性预测24.故障诊断、预测与系统健康管理（培训课时看过）25.现代机械工程设计–全寿命周期性能与可靠性26.系统可靠性设计与分析27.可靠性与维修性工程概论28.可靠性工程数学29.结构可靠性理论与应用30.电子元器件可靠性设计31.产品可靠性、维修性及保障性手册32.数控机床性能分析及可靠性设计技术33.液压系统可靠性工程34.可靠性数据分析教程（看过）35.漫画玩转统计学36.软件可靠性工程37.高可靠性航空产品试验技术38.系统可靠性评定方法研究39.空间运载器的可靠性保证40.机械系统设计初期的可靠性模糊预计与分配41.MEMS可靠性42.高加速寿命试验与高加速应力筛选（此书翻译质量较差，建议大家不要购买）43.Accelerated Reliability Engineering—HALT and HASS44.Contributions to Hardware and Software Reliability45.Sensor Performance and Reliability46.Reliability Toolkit:Commercial Practices Edition–A Practical Guide for Commercial Products and Military Systems Under Acquisition Reform（已阅读）47.Engineering Design Reliability Handbook48.Reliability Improvement with Design of Experiment,Second Edition49.Design for Reliability(Quality and Reliability Engineering Series)50.Reliability Data Analysis With Excel and Minitab51.Effective FMEAs:Achieving Safe,Reliable,and Economical Products and Processes using Failure Mode and Effects Analysis52.Global Vehicle Reliability:Prediction and Optimization Techniques（已阅读）53.电子封装技术丛书：电子封装技术与可靠性54.大功率电站汽轮机寿命预测与可靠性设计55.汽车可靠性工程基础56.航天器机构及其可靠性57.腐蚀试验方法及监测技术（已阅读）58.机械可靠性：理论·方法·应用59.大容量电站锅炉可靠性与寿命的设计及评定60.普通高等教育十五国家级规划教材：汽车可靠性技术61.Accelerated Reliability and Durability Testing Technology62.Design and Analysis of Accelerated Tests for Mission Critical Reliabilitypressors:How to Achieve High Reliability&Availability64.Product Warranty Handbook65.Electronic Derating for Optimum Performance66.Automotive Electronics Reliability,Volume267.Vibration Spectrum Analysis68.可靠性工程（第2版）69.现代机械设计手册·单行本：疲劳强度与可靠性设计70.电子组装工艺可靠性71.机械可靠性工程（已阅读）72.Warranty Cost Analysis73.Reliability of Electronic Components:A Practical Guide to Electronic Systems Manufacturing74.Long-Term Non-Operating Reliability of Electronic Productsponent Reliability for Electronic Systems76.THE RELIABILITY HANDBOOK VOLUME1(NATIONAL SEMICONDUCTOR CORPORATION)77.At&t Reliability Manual78.Reliability of Large Systems79.Understanding Measurement:Reliability(Understanding Statistics)80.15Most Common Obstacles to World-Class Reliability:A Roadmap for Managers81.Reliability Theory With Applications to Preventive Maintenance82.Early Prediction models for software reliability83.Reliability Assurance for Medical Devices,Equipment and Software84.Reliability Assessment:A Guide to Aligning Expectations,Practices,and Performance85.Introduction to the Physics of Materials86.振动信号的现代分析技术与应用87.振动冲击及噪声测试技术(第二版)88.Vibration Spectrum Analysis89.Random Vibration in Perspective90.Reliability-Based Design91.冲击与振动手册（第5版）92.Ensuring Software Reliability93.Handbook of Reliability Engineering and Management2/E94.Reliability:Modeling,Prediction,and Optimization95.云计算实战:可靠性与可用性设计96.可靠性物理与工程:失效时间模型97.功率半导体器件:原理、特性和可靠性98.A Minimal-Mathematics Introduction to the Fundamentals of Random Vibration& Shock Testing:Measurement,Analysis and Calibration as Applied to Halt99.Reliability and Degradation of Semiconductor Lasers and LEDs100.Reliability and Fault Tree Analysis:Theoretical and Applied Aspects of System Reliability and Safety Assessment101.Fault Tree Analysis Primer Chinese Edition102.Reliability:For Technology,Engineering,and Management103.Methods for Statistical Analysis of Reliability and Life Data104.Reliability Engineering for Electronic Design105.Reliability physics(Volume6)106.Reliability Modelling:A Statistical Approach107.Introduction to Machinery Reliability Assessment108.Reliability and Validity in Qualitative Research109.Resistor Theory and Technology110.The Inductor Handbook:A Comprehensive Guide For Correct Component Selection In All Circuit Applications.Know What To Use When And Where.111.The Capacitor Handbook:A Comprehensive Guide For Correct Component Selection In All Circuit Applications.Know What To Use When And Where.112.THe Diode Handbook113.The Transistor Handbook114.The Resistor Handbook115.电容器手册116.Electronic Packaging:Design,Materials,Process,and Reliability117.Probability,Statistics,and Reliability for Engineers and Scientists,Second Edition 118.Reliability:For Technology,Engineering,and Management119.Reliability Engineering and Risk Assessment120.Reliability of RoHS-Compliant2D and3D IC Interconnects121.Reliability-Based Design in Civil Engineering122.Integrated Circuit Quality and Reliability,Second Edition123.Hydrosystems Engineering Reliability Assessment and Risk Analysis124.Digital Switching Systems:System Reliability and Analysis125.Reliability in Procurement and Use:From Specification to Replacement。
T型三电平并网逆变器有限集模型预测控制快速寻优方法
2021年4月电工技术学报Vol.36 No. 8 第36卷第8期TRANSACTIONS OF CHINA ELECTROTECHNICAL SOCIETY Apr. 2021DOI: 10.19595/ki.1000-6753.tces.200083T型三电平并网逆变器有限集模型预测控制快速寻优方法辛业春王延旭李国庆王朝斌王尉（东北电力大学现代电力系统仿真控制与绿色电能新技术教育部重点实验室吉林 132012）摘要三电平变流器控制系统采用有限集模型预测控制（FCS-MPC），滚动优化需要遍历所有开关状态，针对其导致处理器运算量增加、处理时间长的问题，提出一种T型三电平并网逆变器优化计算量的FCS-MPC方法。
通过构建基于电压预测的单目标代价函数，避免设计权重系数问题，减化单次寻优的步骤；根据直流母线电位分布选择冗余小矢量，实现中点电位平衡，使每个控制周期的预测次数减小至3次，提高寻优效率。
有限控制集在预测过程中将所包含矢量的加权误差二次方最小作为依据划分，并利用矢量角补偿系统延迟，提高预测精度，使并网电流质量得到改善。
搭建基于RT-Lab的功率硬件在环仿真系统和物理装置验证所提控制策略，实验结果验证了所提理论分析的正确性和控制策略的有效性。
关键词：有限集模型预测控制T型三电平中点电位平衡快速寻优中图分类号：TM464Finite Control Set Model Predictive Control Method withFast Optimization Based on T-Type Three-Level Grid-Connected Inverter Xin Yechun Wang Yanxu Li Guoqing Wang Chaobin Wang Wei （Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China）Abstract Rolling optimization of Finite Control Set model predictive control (Finite Control Set-MPC, FCS-MPC) needs to traverse all the switch states in the three-level converter control system, which will cause the problems of increased processor calculation and long processing time. For this reason, this paper proposes a FCS-MPC method with optimized calculation amount of T-type three-level grid-connected inverter. By constructing a single objective cost function based on voltage prediction, the design of weighting factor is avoided and the steps of single optimization are reduced.For improving the efficiency of optimization, the redundant small vector is selected according to the DC bus potential distribution to balance the neutral-point potential and reduce the number of predictions per control cycle to 3 times. The finite control set is divided according to the minimum weighted error square of the included vectors in the prediction process, and the vector angle is used to compensate the system delay, thereby improving the prediction accuracy and the grid-connected current quality. A power hardware-in-the-loop simulation system based on RT-Lab and a physical device are established to verify the proposed control strategy. The results show that the proposed theoretical analysis is correct and the control strategy is effective.国家自然科学基金资助项目（U2066208）。
211065653_线性规划法预估单质炸药爆热研究
科学研究创线性规划法预估单质炸药爆热研究李翊（海军装备部驻西安地区军事代表局陕西西安710025）摘 要：为了进行单质炸药爆热参数的预估，本文采用9参数的自由能计算方法，并通过线性规划法进行自由能最小时组分的优化，获得了单质炸药爆轰过程产物组成，计算了多种单质炸药爆热，并与试验测试结果进行了对比分析。
结果显示，采用线性规划法进行产物组成的优化过程简单，与试验结果对比可以看出，预估结果与爆热实测性能误差在5%以内，可作为单质炸药爆热预估的简易方法。
关键词：单质炸药爆热线性规划最小自由能中图分类号：V43文献标识码：A文章编号：1674-098X(2022)09(a)-0013-04 Study on the Prediction of Detonation Heat of Simple Explosiveby Linear Programming MethodLI Yi( Naval Equipment Department, Xi'an, Shaanxi Province, 710025 China ) Abstract: In order to predict the heat of explosion of simple explosive, this paper adopts the free energy calculation method of 9 parameters, and optimizes the minimum component of free energy by linear programming method.The composition of detonation products of single explosives was obtained, and the heat of various single explosives was calculated and compared with the test results. The results show that the optimization process of product compo-sition by linear programming method is simple. Compared with the experimental results, the error between the pre-dicted results and the measured explosive heat is less than 5%, which can be used as a simple method to predict the explosive heat.Key Words: Simple explosive; Detonation heat; Linear programming; Minimum free energy爆热是反映单质炸药能量性能，也是单质炸药在混合炸药应用性能计算的关键参数。
成型仿真操作流程的归纳
成型仿真操作流程的归纳Molding simulation is an essential process in manufacturing to predict the flow and behavior of molten material during the molding process. 成型仿真是制造过程中的一个必要步骤，用于预测在成型过程中熔融材料的流动和行为。
The operation flow of molding simulation can be summarized into several steps. 成型仿真操作流程可以概括为几个步骤。
Firstly, the materials and parameters need to be input into the simulation software. 首先，需要将材料和参数输入到仿真软件中。
Next, the model of the product and the mold is created in the software based on the design. 接下来，根据设计，在软件中创建产品和模具的模型。
Once the model is set up, the simulation is run to analyze the flow, cooling, and any potential defects in the final product. 一旦模型设置完成，就运行仿真来分析流动、冷却和最终产品中可能存在的任何缺陷。
After the simulation, the results are analyzed to make any necessary adjustments to the design or process. 仿真之后，需要分析结果，并对设计或工艺进行必要的调整。
Finally, the optimized design and process are implemented for actual production. 最后，优化后的设计和工艺被用于实际生产。