网格环境下的航空发动机集成设计与分布仿真研究

建立了一个开发航空发动机分布仿真的系统环境．该环境中子模型具有自治性,子模型的设计者可以根据自己的需要选择所使用的工具,定义变量以及他们与其他设计者之间的关系．应用新兴的网格技术建立了一个集成设计和分布仿真的环境框架,它可以灵活地建立和修改基于部件对象模型建立起来的航空发动机模型,并尝试解决仿真中存在的多学科耦合以及大计算量的问题．该框架具有图形化界面,可以方便地更改发动机的参数和结构,其所具有网格技术的易扩展性也为今后建立更复杂的发动机模型提供了良好的平台．研究人员可以应用该系统提供的可扩展设计和仿真环境,灵活地组建新的航空发动机模型并进行仿真．     

航空发动机的非线性模块化建模与仿真

摘要采用面向对象建模技术和模块化层次化的架构,在发动机非线性数学模型基础上,应用Dymola／Modelica仿真软件开发了航空发动机模块化仿真模型库．该模型库具有分层结构,以及可扩展、标准化的特点．以此模型库为基础,用户可以根据需要灵活地建立相应的发动机系统级模型,并进行仿真验证．文中采用NASA的数据建立了一个混合排气的航空发动机模型,经仿真并与国外仿真结果比较,证明达到了预期目标．     

基于Python的航空发动机仿真平台开发

航空发动机控制系统研制过程中,系统仿真是控制算法验证、边界条件模拟和故障注入等的有效手段,对提升航空发动机控制系统质量和降低验证成本具有重要作用.结合工程实际需要,基于Python程序设计语言,综合运用面向对象的发动机建模、数据库和人机界面等技术完成某航空发动机全数字仿真平台开发,并通过仿真实验验证了该平台的正确性和可靠性.该平台充分利用了Python在程序设计、数据库、数值分析和GUI等方面的强大优势,具有良好的可交互性和可扩展性.     

航空发动机LQG/LTR多变量控制器实验验证

针对航空发动机这一具有耦合和强非线性被控对象,引入了LQG/LTR多变量控制器设计方法,在介绍其原理的基础上利用该方法设计了某型航空涡扇发动机的LQG/LTR控制器;为了检验所设计控制器的性能,设计了硬件在回路仿真系统,从软件编程和硬件选型构建两方面介绍了仿真系统的设计方法,并利用构建的仿真系统验证了所设计的LQG/LTR控制器具有良好的鲁棒性和抗干扰性,表明LQG/LTR方法适合用于航空发动机多变量控制器设计,所设计的硬件仿真系统能较好的再现发动机及其控制系统的工作过程.     

应用框架法的化工过程系统通用模型

企业领域的面向对象应用框架研究是现代面向对象应用框架技术的重要趋势之一.应用框架可以增强大型应用软件代码的重用性和可扩充性.针对目前化工过程系统软件中存在的问题,采用面向对象应用框架方法,建立起化工过程系统的通用模型.结合使用本模型完成的国家教委项目--化工仿真系统来阐述该模型.     

面向对象的飞行器仿真平台的控制结构研究

随着仿真技术和软件技术的发展,仿真系统已经发展成为一个功能完善的仿真环境.面向对象仿真是当前仿真研究领域最为引人关注的研究方向之一.提出了一个基于面向对象技术的飞行器动态仿真环境的控制框架.该控制框架包括:输入接口控制、输出接口控制、实验数据控制、仿真实验框架控制、模型结构控制以及仿真逻辑控制.这个系统的理论基础是DEVS,系统可以有效地重用模型库中的模型和仿真数据库.     

航空发动机模糊神经网络控制研究

航空发动机是一个结构复杂、非线性强的多变量控制对象.随着航空发动机全权限数字式电子控制器的研制和应用,控制变量也随着发动机性能要求的不断提高而越来越多,发动机智能控制技术的应用式必然的趋势.本文将智能控制引入到航空发动机多变量控制中,将模糊控制和神经网络相结合,设计出了航空发动机模糊神经网络控制器.并以某型涡扇发动机为被控对象,进行了数字仿真研究,检验了该方法的适应性.     

航空发动机试车台推力仿真技术研究

航空发动机试车准确测量推力问题,航空发动机推力是衡量发动机性能的主要参数.航空发动机试车台推力测量系统复杂,测试环境恶劣,易出现推力测量不准确.为解决上述问题,提出贝叶斯正则化算法的BP神经网络技术和相关性分析建立航空发动机试车台推力模型,进行推力仿真.在发动机试验过程中可实时计算发动机推力,和试车台推力测量系统的测试结果进行对比,判别推力测量系统工作是否正常.仿真结果表明,设计的基于L-M贝叶斯正则化算法的BP神经网仿真模型可准确的计算不同大气条件下和各个工作状态的发动机实际推力.     

基于知识图谱的航空发动机PHM仿真验证平台设计

针对航空发动机PHM系统功能、性能验证要求高、验证内容多、验证业务复杂等实际问题,分析了国外先进航空发动机PHM系统开发验证情况及经验,提出了一种基于知识图谱技术的PHM仿真验证平台设计方法。该方法利用了知识图谱在具有的半结构化、高效性、直观等特点,实现了发动机PHM验证涉及到的故障模式、故障特征、算法模型及专家知识等大量信息知识的有效组织与管理。在此基础上,进一步面向发动机PHM验证数据量大、计算需求高等需求,采用大数据、数据挖掘、机器学习、云服务等技术,搭建了基于知识图谱的航空发动机PHM仿真验证平台,实现了数据挖掘与信息提取、专家知识获取、多层级融合诊断智能导向型推理等应用。最后,以某型发动机为对象,介绍了发动机PHM仿真验证情况。经实际验证结果分析,该文提出的PHM仿真验证平台能够有效解决航空发动机PHM系统可验证的历史故障样本少、验证功能单一等问题。     

航空发动机装配数字化系统研究

研究并开发了一个航空发动机装配数字化系统(assembly digitalization system,ADS),该系统采用面向对象系统分析和分层思想进行了系统框架设计和装配信息建模.对于装配数据处理、系统同ERP、CAPP系统的数据交互和接口实现,提出了一种统一和简化的方法.该系统实现了装配工艺的三维可视化、装配任务管理以及装配履历表管理.系统应用表明,ADS系统在航空发动机装配数字化管理中是成功和有效的.     

基于工作流技术的业务流程的仿真评估

该文基于工作流技术的基本原理,运用系统仿真的方法对工作流进行时间和成本评价,并基于评价结果,提出一种对系统组织结构和业务流程进行定量化评估的方法。     

GloMoSim中服务发现技术仿真框架的构建

网络仿真是无线自组网服务发现技术研究的基本方法,现有网络仿真软件均未提供服务发现技术的仿真框架。该文介绍在GloMoSim中构建服务发现技术仿真框架的方法,扩展了GloMoSim的节点层次模型、消息类型、全局统计数据等。分析GloMoSim无线网络仿真器体系结构,对GloMoSim进行功能扩展以支持用户特定用途的仿真。     

A robust optimisation framework in composite generation and transmission expansion planning considering inherent uncertainties

Abstract

This paper presents a robust optimisation framework for long-term composite generation and transmission expansion planning problem which considers inherent uncertainties such as load growth, fuel cost and renewable energy output uncertainties. In this paper, a bi-level robust optimisation framework is proposed to accommodate wind output uncertainty in line with the uncertain demanded loads and uncertain fuel cost. The addressed optimisation problem is modelled as a mixed-integer optimisation framework with the objective of providing a robust expansion plan while maintaining the minimum cost expansion. In order to evaluate the robustness of each plan, an off-line Lattice Monte Carlo simulation technique is adopted in this study. The validity of the proposed method is examined on a simple six-bus and modified IEEE 118-bus test system as a large-scale case study. The simulation results show that the presented method is both satisfactory and consistent with expectation.     

A Discrete-Event Neural Network Simulator for General Neuron Models

Efficient simulation techniques for a discrete-event pulsed neural network simulator are developed. In a discrete-event simulation framework, simulation of complex neural behaviours, such as phase precession and phase arbitration, demands the prediction of delayed firing times. The new technique, the incremental partitioning method, uses linear envelopes of the state variable of a neuron to partition the simulated time so that the delayed-firing time is reliably calculated by applying the bisection-combined Newton-Raphson method to every partition. The quick filtering technique is also proposed for reducing calculation cost of linear envelopes. The simulator developed, Punnets, has achieved efficiency and precision, but is still capable of simulating a complex behaviour of large-scale neural network models.     

面向对象仿真的基本概念框架研究

在分析了面向对象技术和仿真技术的基础上,提出了面向对象仿真的基本概念框架,并给出了这一基本概念框架的内涵。通过对仿真基本概念、仿真三要素、面向对象建模、面向对象仿真基本概念和系统发展需求等方面的深入分析,证明文中提出的面向对象仿真的基本概念框架是面向对象仿真的客观反映。     

Fast update of elastic body simulation under impact load by efficient reduction method

A large number of eigenmodes are required for the realistic simulation of the object under arbitrary dynamic load such as impact load while only a few lower modes are adequate for the simulation of free vibration with initial deformation. For the accurate dynamic simulation under impact, a large amount of computer resources are required to obtain considerable number of eigenpairs. The present study presents an efficient dynamic simulation algorithm and simulation results of elastically deformable body under impact. We employ a modal analysis technique that pre-computes the eigenvalues and the corresponding eigenvectors in the framework of finite element method. For low latency time of real-time simulation, eigenmodes are computed from a reduced system constructed from the original one. To construct reduced system, energy estimation technique in the element level is proposed in the present study. Through a number of examples, it is demonstrated that the proposed method saves computational cost effectively and provides reliable real time results of the deformable solids under impact.     

DYVERSO: A Versatile Multi‐Phase Position‐Based Fluids Solution for VFX

Many impressive fluid simulation methods have been presented in research papers before. These papers typically focus on demonstrating particular innovative features, but they do not meet in a comprehensive manner the production demands of actual VFX pipelines. VFX artists seek methods that are flexible, efficient, robust and scalable, and these goals often conflict with each other. In this paper, we present a multi‐phase particle‐based fluid simulation framework, based on the well‐known Position‐Based Fluids (PBF) method, designed to address VFX production demands. Our simulation framework handles multi‐phase interactions robustly thanks to a modified constraint formulation for density contrast PBF. And, it also supports the interaction of fluids sampled at different resolutions. We put special care on data structure design and implementation details. Our framework highlights cache‐efficient GPU‐friendly data structures, an improved spatial voxelization technique based on Z‐index sorting, tuned‐up simulation algorithms and two‐way‐coupled collision handling based on VDB fields. Altogether, our fluid simulation framework empowers artists with the efficiency, scalability and versatility needed for simulating very diverse scenes and effects.     

Parallel‐optimizing SPH fluid simulation for realistic VR environments

In virtual environments, real‐time simulation and rendering of dynamic fluids have always been the pursuit for virtual reality research. In this paper, we present a real‐time framework for realistic fluid simulation and rendering on graphics processing unit. Because of the high demand for interactive fluids with larger particle set, the computational need is becoming higher. The proposed framework can effectively reduce the computational burden through avoiding the computation in inactive areas, where many particles with similar properties and low local pressure cluster together. While in active areas, the computation is fully carried out; thus, the fluid dynamics are largely preserved. Here, a robust particle classification technique is introduced to classify particles into either active or inactive. The test results have shown that the technique improves the time performance of fluid simulation largely. We then incorporate parallel surface reconstruction technique using marching cubes to extract the surfaces of the fluid. The introduced histogram pyramid‐based marching cubes technique is fast and memory efficiency. As a result, we are able to produce plausible and interactive fluids with the proposed framework for large‐scale virtual environments. Copyright © 2013 John Wiley & Sons, Ltd.     

一种集成智能故障诊断方法研究

该文提出了一种新的故障诊断方法,该方法把基于观测器的诊断和基于知识的诊断方法相结合而成为一种集成智能故障诊断方法。在研究了基于多观测器故障诊断方法的基础上,对该集成诊断方法进行了较为详细的讨论。仿真例子表明了该方法的有效性和优越性。     

Modeling stochastic live load for long-span bridge based on microscopic traffic flow simulation

A general framework of modeling the stochastic live load from traffic for a long-span bridge is developed. The cellular automaton (CA) traffic flow simulation technique is adopted to develop the analytical basis of the framework. Based on the traffic flow simulation results, the live load on a long-span bridge from the stochastic traffic is studied with a focus on the static component. Parametric studies of major variables of the framework, such as the length of the connecting roadways, the speed limit, and the vehicle combination, are conducted.