Jetcloud collision, 3D gasdynamic simulations of HH 110
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华为创通大飞机仿真案例一:飞行器系统半实物仿真平台提供通用化飞行器组件模型库,飞行器组件模型库可供用户直接调用,并支持用户二次开发。
为用户飞行器数学仿真、快速原型开发、硬件在回路、飞控系统半物理仿真试验等提供强有力的技术支持。
华力智飞是华力创通全资子公司。
以航电总线技术、复杂电子系统仿真、测试技术为核心,致力于打造针对武器装备和高端制造业的通用化仿真和测试平台,为客户提供一流的解决方案和服务。
华力智飞服务于航空、航天、兵器、电子、轨道交通等行业,产品广泛应于产品论证、设计开发、生产服务的各个阶段,为C919、ARJ21、大运等多项国家重点型号提供产品服务,以及新装备的研制提供系统仿真产品、集成测试产品和解决方案。
案例二:飞行器系统半实物仿真平台是集教学与科研目的为一体的飞控系统多功能地面仿真试验平台,其在满足日常培训与展示的同时,兼顾飞行器飞行控制及飞行动力学等学科专业的科学研究。
本平台基于华力智飞公司自主研发的半实物仿真系统,采用基于模型的设计(MBD)开发思路,可支撑飞行器数学仿真试验(MIL)、飞控算法快速控制原型开发(RCP)、飞控系统硬件在环(HIL)以及飞控系统半物理或全物理仿真试验。
平台提供飞行仿真综合控制、模型编译及下载管理、仿真数据处理等功能,为用户提供一个高易用性、高可靠性、强实时性的设计、开发、仿真、验证平台。
飞行器系统半实物仿真平台提供全机飞行器、飞控系统、惯性导航、大气机、起落架系统等仿真模型,并模拟与机载设备交联的外部环境,结合自主研发的半实物仿真平台,完成对物理效应模拟器(转台、大气物理效应仿真系统、导航模拟器等)的接联和驱动,构建完整的飞控闭环仿真系统,为机载设备的控制算法验证、设计开发和验证提供支持。
同时提供飞行仿真三维可视化视景,直观显示飞行位置、姿态及舱内环境等,并与VR技术结合实现沉浸式仿真。
航空发动机是一个高度复杂的系统,涉及时变性、非线性、耦合性和多变量等问题,这些特点使得其控制成为一项极具挑战的任务,主要包括控制策略优化、燃油效率提升、故障检测与诊断、环境自适应等方面。
传统控制方法主要是在特定飞行状态下基于线性模型设计控制器,但这种方法对于在广泛飞行包线内运行的发动机效果不佳。
此外,由于发动机部件长期运行导致的性能下降,传统控制器将难以适应新的控制环境。
随着人工智能技术的发展,特别是深度强化学习的进步,国内外研究人员开始将其与航空发动机控制结合。
深度强化学习旨在探索智能体如何在复杂和不确定的环境中学习到最优策略。
通过与发动机不断交互,智能控制器能够学习并优化控制策略,适应发动机工作状态的变化,从而表现出优越的环境适应性。
航空发动机控制的发展航空发动机的构型及控制系统自20世纪以来经历了多次变革,如图1所示。
诞生于20世纪中期的燃气涡轮航空发动机构型较为简单,采用液压-机械式控制系统,这要求飞行员根据飞行高度和速度手动操作油门以调整燃油流量,将发动机转速控制在一定范围内保持不变。
随后为解放飞行员的精力并提高发动机控制精度,1950—1980年,以比例-积分-微分(PID)为代表的单变量反馈控制器被应用且代替了早期的油门杆前馈控制,形成了结合电子调节器的液压机械式控制系统。
但随着飞机性能需求的提升,发动机的结构逐渐复杂化,可调部件从早期的1个增加至10个以上,系统的耦合性也逐渐变强,这也导致面向线性系统的经典控制方法已难以适应发动机高性能的控制要求,从而促进了现代控制理论技术的应用,线性二次型调节器(LQR)、线性二次型高斯/回路传输恢复(LQG/LTR)及多变量鲁棒控制等的各种控制方法在1980—2010年间被广泛研究和应用。
美国针对航空发动机多变量控制开展过许多研究,其中包括利用多变量控制解决F-35战斗机短距起飞垂直降落的特殊飞行任务、为确保控制精度和发动机的最佳性能对F135发动机进行多变量控制等。
航天远景自动化建模流程English Answer:Vision: Automated Modeling for Aerospace Engineering.Introduction:The aerospace industry is experiencing a paradigm shift driven by the convergence of advanced technologies such as artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC). This convergence enables the automation of complex and time-consuming engineering tasks, empowering aerospace engineers to innovate faster and more efficiently. In this context, the vision of automated modeling for aerospace engineering emerges as a critical enabler for unlocking new frontiers in aircraft design, analysis, and optimization.Challenges in Aerospace Modeling:Aerospace modeling involves a wide range of complex tasks, including:Geometry Modeling: Creating accurate and detailed representations of aircraft shapes and components.Simulation and Analysis: Conducting aerodynamic, structural, and thermal simulations to evaluate aircraft performance and safety.Optimization: Iteratively refining designs to improve efficiency, performance, and cost.Traditional approaches to these tasks are often manual, labor-intensive, and prone to errors. Automation can address these challenges by streamlining processes, reducing human intervention, and improving the accuracy and consistency of results.Benefits of Automated Modeling:Automated modeling offers numerous benefits foraerospace engineering:Increased Efficiency: Automation eliminates repetitive and time-consuming manual tasks, allowing engineers to focus on higher-value activities.Improved Accuracy: AI-powered algorithms can analyze vast amounts of data and identify subtle patterns, leading to more precise and reliable models.Reduced Costs: Automation can reduce the cost of modeling by eliminating human errors and minimizing rework.Faster Innovation: Automated modeling enables rapid design exploration and optimization, accelerating the development cycle and fostering innovation.Automation Technologies:A combination of AI, ML, and HPC technologies can be leveraged to automate aerospace modeling tasks:AI: AI algorithms can analyze data, identify patterns, and make predictions, enabling intelligent decision-making and model generation.ML: ML algorithms can learn from historical data and automatically generate models, reducing the need for manual parameter tuning.HPC: HPC platforms provide the computational power necessary to run complex simulations and optimize models efficiently.Implementation Roadmap:To realize the vision of automated modeling, a structured implementation roadmap is essential:Phase 1: Data Collection and Curation: Establish a comprehensive data repository and develop tools for data cleaning and transformation.Phase 2: Algorithm Development: Design and implementAI and ML algorithms tailored to specific aerospacemodeling tasks.Phase 3: Model Validation and Certification:Thoroughly validate and certify automated models to ensure accuracy and reliability.Phase 4: Integration and Deployment: Integrate automated modeling tools into existing engineeringworkflows and make them accessible to a wide range of users.Conclusion:Automated modeling is poised to revolutionize aerospace engineering by enabling faster, more efficient, and more accurate design and analysis. Through the strategic implementation of AI, ML, and HPC technologies, the aerospace industry can unlock new possibilities, drive innovation, and achieve unprecedented levels of performance and safety.Chinese Answer:远景,航天自动化建模流程。
航空航天工程师的航空器设计软件航空航天工程师在设计和开发飞行器过程中需借助先进的航空器设计软件。
这些软件能够提供必要的工具和功能,以支持工程师们进行全面的设计、分析和验证。
本文将介绍几种主流的航空器设计软件,包括CATIA、SolidWorks和ANSYS,并重点探讨它们在航空航天工程领域的应用。
CATIACATIA是由法国达索系统公司开发的三维设计和产品生命周期管理软件。
它广泛用于航空航天领域,能够支持从概念设计到详细设计和制造的全过程。
CATIA具有强大的建模和装配功能,可以实现复杂曲面和结构的设计。
此外,CATIA还提供了空气动力学分析、强度分析和流固耦合仿真等功能,帮助工程师们验证设计的性能和可靠性。
SolidWorksSolidWorks是一款基于三维计算机辅助设计(CAD)的软件,由美国达索系统公司开发。
它具有直观的用户界面和丰富的功能,适用于多个行业,包括航空航天。
SolidWorks提供了全面的建模和装配工具,支持工程师们快速构建复杂的飞行器模型。
其强大的仿真分析功能可用于评估结构强度、振动特性和热力学特性等。
此外,SolidWorks还配备了可视化渲染和动画制作工具,以便于工程师们向他人展示设计概念和方案。
ANSYSANSYS是一款广泛应用于工程仿真的软件,包括结构力学、热分析、流体力学等各个领域。
在航空航天工程中,ANSYS被广泛用于飞行器的结构和气动特性分析。
它提供了高度准确的数值求解器和多种模型库,能够模拟复杂的物理现象和工程场景。
ANSYS的结构分析模块可以用于评估飞行器在各种载荷下的强度和刚度。
其气动特性分析模块则可用于评估飞行器的空气动力学性能,并优化设计以提高飞行效率和稳定性。
综合应用航空航天工程师通常会将这些航空器设计软件综合应用,以满足设计和仿真的全面需求。
首先,工程师们可以使用CATIA进行飞行器的整体设计和装配。
随后,借助SolidWorks进行细节设计和零部件的建模。
国产三维电磁仿真软件RDSim2022R1版发布RDSim2022R1 简介三维电磁仿真软件RDSim2022R1是一款由霍莱沃自主研发的全波电磁仿真平台。
本软件运用改进的矩量法、快速算法及高频算法实现高精度、高效率的电磁仿真,覆盖天线、大尺寸RCS、微波器件及天线布局等应用方向,为高频电磁问题分析提供全方位的支持。
最新版本的RDSim中开启了“云平台”线上仿真、CMA特征模分析及阵列综合优化模块,并对算法、网格剖分、材料设置等功能进行了全方位的升级。
新功能列表1. 从单机版到云平台l 用户将不再受硬件和软件条件的制约,只需接入网络,便可以通过账号登录云平台,随时随地线上仿真l 在网页中,用户可以直接进行数据分析,更好地满足数据共享与调用的需求l 云平台分为用户界面与管理界面:用户界面面向于工程师,管理界面面向于系统管理员,形成系统的分级化管理l 仿真团队可以线上建立协作工程,指派分工并共享结果数据,实现团队间高效的在线协同设计2. 高精度全波仿真算法升级l 新增体等效(VEP)积分方程求解,适用于不均匀介质、薄介质及各向异性材料l 支持分层介质结构快速求解l 提升了双稳定多层快速多极子算法收敛性,大幅度提高迭代求解效率l 快速算法可实现亿级网格求解3. 网格剖分功能升级l 新增高精度体网格剖分,适用于各类复杂体结构的精确计算l 新增网格导入与网格模型材料设置4. 阻抗边界条件设置l 用以模拟已知阻抗值的电阻性表面,高效仿真多层媒质结构l 阻抗边界支持仿真频变与非频变材料,适用于模拟多种元器件、复合电路以及复杂超材料结构5. 添加元器件功能l 元器件功能支持集成电路耦合计算,适用于仿真天线负载l 支持复合电路微波器件仿真6. 阵列综合优化l 包含多种阵列与栅格格式,一键生成自定义天线阵,大幅度节约建模时间l 专用的阵列快速仿真算法可实现大型阵列天线快速仿真需求,提高大型阵列仿真效率l 配置阵列优化与稀疏功能,以最少的单元数量和最优的天线排布达到特定阵列性能指标,全方位辅助天线阵设计与计算7. CMA特征模天线设计与优化l 具备业界领先的复杂电磁结构特征模精确高效求解技术l 可对任意结构的天线固有模式进行分析并用于天线的设计和优化l 通过分析多种天线模式得到最佳天线结构与馈电位置,实现快速天线设计l 适用于复杂环境中的天线性能分析,并在天线与环境产生耦合的情况下将天线优化至最佳工作状态。
一种基于三维烟雾的实时模拟研究
邓定胜
【期刊名称】《电脑知识与技术》
【年(卷),期】2018(014)013
【摘要】随着当前电影行业及动画行业不断发展,对于烟雾模拟技术也有着越来越大的需求,因而研究烟雾实时模拟也就十分必要.在当前烟雾模拟中,在利用GPU的基础上可实现三维烟雾模拟,从而可使三维烟雾模拟真实感得以增强,达到更加理想的效果,使人们产生更好的视觉体验.本文主要就在GPU基础上实现三维烟雾模拟进行分析,以保证三维烟雾实时模拟能够得到更加理想的效果,增强其真实性.
【总页数】2页(P211-212)
【作者】邓定胜
【作者单位】四川民族学院理工学院,四川康定626001
【正文语种】中文
【中图分类】TP311
【相关文献】
1.一种基于模糊特征的火灾烟雾实时识别算法 [J], 曹希锋;梅真硕;张曦;于春雨
2.一种基于可编程GPU的实时烟雾模拟算法研究 [J], 邓定胜
3.一种基于GPU的实时烟雾模拟体绘制算法研究 [J], 邓定胜
4.一种基于改进SSD的烟雾实时检测模型 [J], 刘丽娟;陈松楠
5.基于GPU编程的三维烟雾实时渲染 [J], 陈阁;何伟;李云飞
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国内外自动驾驶仿真软件总结来源:希骥智能网联汽车前言自动驾驶汽车在真正商业化应用前,需要经历大量的道路测试才能达到商用要求。
采用路测来优化自动驾驶算法耗费的时间和成本太高,且开放道路测试仍受到法规限制,极端交通条件和场景复现困难,测试安全存在隐患。
目前,自动驾驶仿真测试已经被行业广泛接受,自动驾驶算法测试大约 90% 通过仿真平台完成,9% 在测试场完成,1% 通过实际路测完成。
自动驾驶仿真测试平台必须要具备几种核心能力:真实还原测试场景、高效利用路采数据生成仿真场景、云端大规模并行加速等,使得仿真测试满足自动驾驶感知、决策规划和控制全栈算法的闭环。
目前包括科技公司、车企、自动驾驶方案解决商、仿真软件企业、高校及科研机构等主体都在积极投身虚拟仿真平台的建设。
本文详细介绍了现有的自动驾驶仿真软件,供读者了解参考,软件排名不分先后。
0、CarSimCarSim,还有相关的TruckSim和BikeSim是Mechanical Simulation 公司开发的强大的动力学仿真软件,被世界各国的主机厂和供应商所广泛使用。
CarSim针对四轮汽车,轻型卡车,TruckSim 针对多轴和双轮胎的卡车,BikeSim针对两轮摩托车。
CarSim是一款整车动力学仿真软件,主要从整车角度进行仿真,它内建了相当数量的车辆数学模型,并且这些模型都有丰富的经验参数,用户可以快速使用,免去了繁杂的建模和调参的过程。
CarSim模型在计算机上运行的速度可以比实时快10倍,可以仿真车辆对驾驶员控制, 3D路面及空气动力学输入的响应,模拟结果高度逼近真实车辆,主要用来预测和仿真汽车整车的操纵稳定性、制动性、平顺性、动力性和经济性。
CarSim自带标准的Matlab/Simulink 接口,可以方便的与Matlab/Simulink进行联合仿真,用于控制算法的开发,同时在仿真时可以产生大量数据结果用于后续使用Matlab或者Excel进行分析或可视化。
基于计算流体动力学的风洞试验数据仿真研究随着计算机技术和数值模拟方法的不断发展,基于计算流体动力学(CFD)的风洞试验数据仿真研究在航空航天领域中得到了广泛应用。
这项研究的目的是通过模拟风洞试验来分析飞行器在不同飞行条件下的气动特性,对设计优化和性能提升起到重要的作用。
在进行基于CFD的风洞试验数据仿真研究之前,首先需要建立一个合适的模型。
模型是仿真的基础,对于风洞试验数据的准确再现至关重要。
模型的建立主要包括几何建模和网格划分两个方面。
几何建模是根据实际的飞行器形状和尺寸进行建模,通常使用计算机辅助设计(CAD)软件进行。
而网格划分是将模型划分为无数个小网格,以便进行离散数值计算。
在进行网格划分时,需要考虑到风洞试验数据的精度和仿真计算的效率之间的平衡。
完成模型建立后,接下来就是进行CFD仿真计算。
CFD是将Navier-Stokes方程组及其辅助方程通过数值方法离散求解的过程。
对于风洞试验数据的仿真计算,需要确定边界条件、流体模型和数值方法等。
边界条件包括流速、压力和温度等参数的设定,这些参数需要参考实际风洞试验数据。
流体模型是描述流体运动的数学模型,常用的模型包括雷诺平均Navier-Stokes方程模型和湍流模型等。
数值方法是解决离散方程的方法,常用的方法包括有限体积法、有限差分法和有限元法等。
选择合适的边界条件、流体模型和数值方法,能够有效地提高仿真计算的准确性和计算效率。
进行CFD仿真计算后,需要对仿真结果进行后处理和分析。
后处理主要包括对仿真结果的可视化和数据提取。
通过可视化手段,如流线图、压力分布图和速度矢量图等,可以直观地观察风洞试验数据的分布情况,识别可能存在的问题。
数据提取则是将仿真结果中感兴趣的参数提取出来进行进一步的分析。
常用的数据提取手段包括平均值、谱分析和涡旋识别等。
通过后处理和分析,可以得到准确的风洞试验数据仿真结果,并对飞行器的气动特性进行全面的评估。
基于CFD的风洞试验数据仿真研究的优势主要体现在以下几个方面。
dymola基础Dymola,全称为Dynamic Modeling Lab(动态建模实验室),是一个集成建模和仿真环境,它基于公开的Modelica系统建模语言。
这种环境被设计用于对复杂的多专业系统进行建模和分析,应用领域包括汽车、航空航天、机器人、加工以及其他领域。
Dymola的主要功能包括模型创建、测试、仿真和后处理,具有以下显著特点:多工程功能:Dymola可以包含来自许多工程域的零部件,这使得模型能够由能够更好地展示真实世界的完整系统组成。
它提供了涵盖机械、电气、控制、热、气动、液压、传动系统、热力、车辆动力、空调等领域的库。
Modelica语言:Dymola使用以对象为导向并得到正式定义的强大建模语言Modelica。
这种语言使得建模过程更加直观和高效。
开放和灵活:Dymola环境完全开放,用户可以轻松引入与自己独特需求相符的零部件。
这种开放性和灵活性使得Dymola成为模拟新的或替代设计及技术的卓越工具。
符号处理:Dymola具有符号处理能力,可以在进行数值计算之前自动减少方程组,从而提高计算效率。
总的来说,Dymola是一个功能强大的建模和仿真工具,适用于对复杂系统进行建模、测试和分析。
它的多工程功能、Modelica语言支持、开放性和灵活性以及符号处理能力都使其在工程领域中具有广泛的应用价值。
Dymola是一个多学科系统建模仿真工具,由法国Dassault Systemes公司开发,广泛应用于汽车、航空、航天、能源等行业系统的功能验证和硬件在环仿真。
以下是关于Dymola基础的一些主要特点:建模语言:Dymola基于Modelica建模语言,这是一种面向对象的、声明式的、多领域的物理系统建模语言。
Modelica语言支持多种工程领域的建模,包括机械、流体、电子电气、电磁、控制、传热等。
模型库:Dymola提供了丰富的模型库,包括Modelica基础库和商业库。
Modelica基础库与Modelica协会发布的最新版本保持同步,提供了在多个工程领域的最新研究成果。
分子动力学模拟软件概述分子动力学模拟是一种重要的计算物理方法,用于研究原子和分子在宏观尺度下的运动行为。
为了实现这种模拟,研究者们开发了许多分子动力学模拟软件。
本文将介绍几种常用的分子动力学模拟软件,包括LAMMPS、GROMACS和NAMD。
LAMMPSLAMMPS,全称为Large-scale Atomic/Molecular Massively Parallel Simulator,是一个开源的粒子模拟软件包。
它是一种经典的分子动力学模拟软件,可以模拟包括原子、分子和一些粒子模型在内的多种体系。
LAMMPS支持多种计算模式,包括分子动力学、蒙特卡洛模拟以及分子构象搜索等。
它具有高性能和可扩展性,可以在单机上运行,也可以部署在超级计算机集群上。
LAMMPS提供了丰富的功能和灵活的参数设置,支持从不同的输入文件读取模拟系统的初始信息。
它还内置了许多常用的力场和模拟算法,如势场计算、周期性边界条件等。
除此之外,LAMMPS还提供了丰富的输出选项和分析工具,可以对模拟结果进行后处理和可视化分析。
GROMACSGROMACS是一种用于生物分子动力学模拟的软件套件。
它具有高性能和可扩展性,特别适用于模拟大规模的生物系统,如蛋白质、核酸等。
GROMACS采用高效的并行计算算法,可以利用多核处理器和GPU进行加速计算。
GROMACS提供了丰富的模拟功能和工具,包括能量最小化、均衡化、动态模拟等。
它内置了多种力场和模拟算法,支持多种模拟选项,如周期性边界条件、隐式溶剂模型等。
此外,GROMACS还提供了灵活的参数设置和输出选项,方便用户进行模拟控制和结果分析。
NAMDNAMD是一种用于生物分子动力学模拟的软件。
与GROMACS类似,NAMD也专注于生物分子的模拟,特别适合模拟大规模的生物系统。
NAMD采用并行计算算法,可以利用多核处理器和GPU加速模拟。
NAMD具有高效的模拟引擎和丰富的模拟功能,支持多种力场和模拟算法。
基于Unity 3D的气相色谱仪虚拟仿真实验系统的构建周思洁;杨泽亮;董子和;周明达;曾冬铭【摘要】Virtual simulation technology has been widely proposed as a significant technological advance that can offer a novel form for education. Especially in the case of chemistry,virtual reality technology facilitates learning process surpassing major restrictions characterizing tradi-tional educational methods. In this system,some popular softwares including 3Ds Max and Unity 3D are used to develop a fully immersive,interactive and three-dimensional simulation system of gas chromatography( GC). Three modules are included in this system. First module is the introduction of the instrument. Second module is a three-dimensional display of the struc-tures,which are modeled by 3Ds Max and interacted by Unity 3D. The last module focuses on the simulation experiments,and this module is made by Unity 3D. All models created in this system are three-dimensional and the scenes are lifelike,so that all aspects of the instrument are presented to users clearly. Using this system to learn about the principles and structures of the instrument,users would feel that they were in a real laboratory and could master all related skills more easily. This system is not only a powerful tool to satisfy the need of instrument training and experimental teaching of chemistry,but also an excellent example of virtual simu-lation applied in chemistry.%虚拟仿真技术被广泛认为是一项重大技术进步,可以提供一种新颖的教学方式,能促进化学学习,打破传统教育方法的限制。
收稿日期:201X-xx-xx ; 修回日期:201X-xx-xx 基金项目:国家自然科学基金(90816027);航空科学基金(20135853037);航天技术支撑基金(2013-HT-XGD-15)基于飞行力学的惯导轨迹发生器及其在半实物仿真中的应用陈凯,卫凤,张前程,于云峰,闫杰 (西北工业大学 航天学院,西安710072)摘 要:讨论了在高超声速飞行器半实物仿真中,使用飞行器六自由度模型生成捷联惯导轨迹发生器的方案,使半实物仿真中的捷联惯导系统与飞行力学模型和飞行控制系统有机地融合到一起。
介绍了六自由度模型的坐标系定义,描述了发射坐标系下由32个方程组成的高精度六自由度模型。
指出了六自由度模型中惯性器件测量的比力和角速率理论值,比力和角速率是由飞行器飞控系统作用后所产生各种力和力矩的综合结果,而不同于传统轨迹发生器中由事先设定的速度和姿态变换获得。
将发射坐标系下的导航信息推导到高超声速飞行器需求的当地水平导航坐标系下。
高超声速飞行器数字仿真表明,提出的轨迹发生器满足半实物仿真算法精度要求;半实物仿真表明,导航系统与六自由度模型、飞行控制与制导系统能够有机结合,导航结果精度满足指标要求,支撑了高超声速飞行器飞控系统性能指标评估。
关键词:轨迹发生器;捷联惯导;六自由度模型;高超声速飞行器;半实物仿真 中图分类号:V249.3文献标识码:A 文章编号:Trajectory Generator of Strapdown Inertial Navigation System on Flight Dy-namics with Application in Hardware-in-the-Loop SimulationCHEN Kai, WEI Feng, ZHANG Qian-cheng, YU Yun-feng, Y AN Jie(School of Astronautics, Northwestern Polytechnical University, Xi ’an 710072, China )Abstract : How to generate trajectory profile of strapdown inertial navigation system (SINS) based on flight dy-namics is discussed in the hypersonic vehicle hardware-in-the-loop (HWIL) simulation, which makes SINS work together in harmony with hypersonic vehicle six-degree-of-freedom (6DoF) model and flight control and guidance system. Firstly, the definition of coordinate system in 6DoF model is introduced. Then the high precision 6DoF model consists of 32 equations is described in launch centered earth-fixed (LCEF) coordinate system. The theoreti-cal value of the specific force and the angular velocity measured by inertial measurement unit (IMU) in 6DoF mod-el is pointed out. The specific force and the angular velocity is a combined result of a variety of forces and mo-ments by flight control system during flight, which is different with a traditional trajectory generator whose specific force and angular velocity is obtained from velocity and attitude change sets in advance. The navigation informa-tion in LCEF frame is converted to local ENU frame to meet hypersonic vehicle demand. The hypersonic vehicle digital simulation result reveals that the 6DoF model, the flight control and guidance system, and SINS can work together. The HWIL simulation indicates that the accuracy of SINS satisfies the requirement of hypersonic vehicle and can support the evaluation of the hypersonic vehicle flight control system performance. Keywords :Trajectory generator; Strapdown inertial navigation system (SINS); Six-degree-of-freedom model; Hypersonic vehicle; Hardware-in-the-loop (HWIL) simulation0 引 言捷联惯导系统具有导航信息全、自主性高、连续性好、更新率高等优点,是飞行器飞行控制系统的关键部件之一,各种飞行器都在广泛使用。
云计算仿真工具CloudSim介绍和使用CloudSim介绍和使用2009年4月8日,澳大利亚墨尔本大学的网格实验室和Gridbus项目宣布推出云计算仿真软件,称为CloudSim。
它是在离散事件模拟包SimJava上开发的函数库,可在Windows和Linux系统上跨平台运行,CloudSim继承了GridSim的编程模型,支持云计算的研究和开发,并提供了以下新的特点: (1)支持大型云计算的基础设施的建模与仿真;(2)一个自足的支持数据中心、服务代理人、调度和分配策略的平台。
其中CloudSim 独特功能有:一是提供虚拟化引擎,旨在数据中心节点上帮助建立和管理多重的、独立的、协同的的虚拟化服务;二是在对虚拟化服务分配处理核心时能够在时间共享和空间共享之间灵活切换。
CloudSim平台有助于加快云计算的算法、方法和规范的发展。
CloudSim的组件工具均为开源的。
CloudSim的软件结构框架和体系结构组件包括SimJava、GridSim、CloudSim、UserCode四个层次。
CloudSim是在GridSim模型基础上发展而来,提供了云计算的特性,支持云计算的资源管理和调度模拟。
云计算与网格计算的一个显著区别是云计算采用了成熟的虚拟化技术,将数据中心的资源虚拟化为资源池,打包对外向用户提供服务,CloudSim体现了此特点,扩展部分实现了一系列接口,提供基于数据中心的虚拟化技术、虚拟化云的建模和仿真功能。
通常,数据中心的一台主机的资源可以根据用户的需求映射到多台虚拟机上,因此,虚拟机之间存在对主机资源的竞争关系。
CloudSim提供了资源的监测、主机到虚拟机的映射功能。
CloudSim的CIS(Cloud Information Service)和DataCenterBroker实现资源发现和信息交互,是模拟调度的核心。
用户自行开发的调度算法可在DataCenterBroker的方法中实现,从而实现调度算法的模拟。
航空航天工程师的设计软件和工具使用航空航天工程师在设计和开发飞行器和航天器的过程中需要使用各种软件和工具来辅助设计、模拟和优化。
这些软件和工具能够帮助工程师完成复杂的计算、仿真和测试,提高设计效率,确保工程的可靠性和安全性。
本文将介绍航空航天工程师常用的设计软件和工具,并探讨它们的应用领域和作用。
一、CAD软件计算机辅助设计软件(CAD)是航空航天工程师最常用的工具之一。
它能够帮助工程师创建和编辑三维模型,进行零部件的装配和动画模拟。
CAD软件广泛应用于飞行器和航天器的结构设计、机械设计和系统集成等方面。
常见的CAD软件包括AutoCAD和CATIA等,在航空航天工程中有着广泛的应用。
二、CAE软件计算机辅助工程软件(CAE)是航空航天工程师进行仿真和分析的关键工具。
它能够帮助工程师进行结构强度、气动特性、热传导和振动等方面的仿真计算。
通过CAE软件,工程师可以模拟不同的负载情况,优化设计方案,提高飞行器和航天器的性能。
常用的CAE软件有ANSYS和Nastran等。
三、CFD软件计算流体力学软件(CFD)广泛应用于航空航天工程中的气动特性研究。
它能够帮助工程师模拟和分析飞行器或航天器在空气中的流动情况,包括气动阻力、升力和推进力等。
通过CFD软件,工程师可以优化飞行器的外形设计、燃烧室的燃烧效率等,提高飞行器的性能和燃油利用率。
常见的CFD软件有FLUENT和STAR-CCM+等。
四、控制与导航软件在航空航天工程中,控制与导航是一个至关重要的领域。
控制与导航软件能够帮助工程师设计飞行器和航天器的控制系统,包括姿态控制、轨道控制和姿态稳定等。
这些软件能够进行控制算法的仿真和优化,确保飞行器和航天器在不同飞行阶段的安全性和可控性。
常用的控制与导航软件有MATLAB和Simulink等。
五、飞行器和航天器模拟器飞行器和航天器模拟器是航空航天工程师进行飞行器性能分析和试验的关键工具。
模拟器能够模拟真实的飞行环境,包括飞行器的控制和导航系统、传感器和执行机构等。
航空航天工程师的航空器设计软件航空航天工程师在设计和开发航空器时,依赖于一系列专业软件来辅助完成各项工作。
这些软件不仅提供了设计、分析和模拟等功能,还能够帮助工程师有效地优化设计方案,并确保飞行器在安全、可靠和高性能的基础上运行。
本文将介绍一些航空航天工程师常用的航空器设计软件。
一、CAD软件(计算机辅助设计软件)CAD软件是航空航天工程师设计航空器的基础工具。
它们提供了3D建模和绘图功能,可用于创建、编辑和查看航空器的外形和内部结构。
工程师可以通过CAD软件进行构件的装配和碰撞检测,并获取设计模型的准确测量数据。
著名的CAD软件包括AutoCAD、CATIA和SolidWorks等。
二、CFD软件(计算流体力学软件)在航空器设计中,流体力学是一个关键领域,而CFD软件则可模拟和分析气动和流体流动现象。
它们通过数值计算方法解决流体力学方程,从而预测气流、涡流和阻力等参数。
CFD软件可以帮助工程师优化机翼和机身的气动外形,提高飞行器的气动性能和燃油效率。
常见的CFD软件包括ANSYS Fluent、OpenFOAM和STAR-CCM+等。
三、结构分析软件结构分析软件用于评估航空器的强度和刚度等结构性能。
它们能够模拟和计算各种载荷条件下的应力、变形和振动情况,以确保航空器在飞行期间不会发生破坏或失效。
工程师可以使用结构分析软件设计和优化每个构件的几何形状和材料特性,确保其满足航空器设计要求。
一些常用的结构分析软件有ANSYS、Nastran和ABAQUS等。
四、系统仿真软件系统仿真软件可模拟整个航空器的各个子系统之间的相互作用。
它们允许工程师将不同子系统(如动力、导航、控制和电气等)进行集成和测试,以评估整个航空器的性能和可靠性。
系统仿真软件还可用于验证设计方案、优化控制策略、评估飞行特性和开展事故模拟分析等。
常用的系统仿真软件包括MATLAB、Simulink和LabVIEW等。
五、飞行动力学软件飞行动力学软件用于模拟和分析航空器在不同飞行阶段的动力学行为。
航空航天科学技术科技创新导报 Science and Technology Innovation Herald15DOI:10.16660/ki.1674-098X.2018.10.015基于Unity3D的航空发动机虚拟维修仿真系统的开发①吴予忠 张渝舜 赵新宇 宋时雨 侯品帆(中国民航大学航空工程学院 天津 300300)摘 要:虚拟维护仿真系统以民用航空发动机工程底蕴为基础,本系统采用Autodesk Maya作为建模工具,构建虚拟场景和发动机三维模型,以Unity3D虚拟仿真软件作为虚拟现实开发引擎,Microsoft Visual Studio 2010为程序开发整合平台,开发了一套基于Unity3D的航空发动机虚拟维修仿真系统。
虚拟维修仿真系统可为高校教育节省成本,克服学校师资、资金、课时、场地等资源缺乏的困境。
关键词:民航发动机 Unity 3D 虚拟维修中图分类号:TP39 文献标识码:A 文章编号:1674-098X(2018)04(a)-0015-02Abstract: the System of Virtual Maintenance Simulation that Based on civil Aero Engine Engineering ,using Autodesk Maya as a modeling tool to build virtual scene, using 3D model of engine and Unity3D virtual simulation software as the development engine of virtual reality,using Microsoft Visual Studio 2010 as a Development and integration platform, developed a virtual maintenance simulation system for Aero Engine Based on Unity3D,which can save cost for college education, overcome the lack of resources in school teachers, funds, class hours and sites.Key Words: Civil aviation engine; Unity3D; Virtual maintenance①基金项目:大学生创新创业训练计划项目(项目编号:201710059037)资助。
超级计算技术在航空设计与性能模拟中的应用案例解析随着科技的不断进步,计算技术在航空领域的应用变得越来越重要。
特别是超级计算技术的出现,为航空设计与性能模拟带来了前所未有的机会与挑战。
本文将通过分析几个实际案例,解析超级计算技术在航空设计与性能模拟中的应用。
首先,超级计算技术在航空设计中的应用案例之一是飞机结构优化。
传统的飞机结构设计是通过试错方法,逐步改进设计方案,然后进行真实测试。
这种方法成本高昂且时间消耗巨大。
而借助超级计算技术,工程师可以利用高性能计算能力,快速建立复杂的数值模型,并通过参数化设计、优化算法和大规模计算实现飞机结构的自动优化。
这样,不仅能够减少设计周期,降低生产成本,还可以提高飞机的结构强度和性能。
第二个应用案例是超级计算技术在航空性能模拟中的应用。
航空性能模拟是指对飞机在不同飞行状态下的性能进行模拟与评估。
传统的方法是通过飞行试验和风洞试验来获取性能数据,但这些方法耗时且成本高。
而利用超级计算技术,可以在虚拟环境中对不同因素进行模拟,快速获得准确的性能参数,并进行飞机的性能评估。
通过这种方式,可以帮助航空公司和制造商在设计阶段预测飞机的性能,解决设计问题,并降低试飞风险。
另一个值得关注的案例是超级计算技术在航空气动优化中的应用。
航空气动优化是指通过改变飞机的外形、机翼横截面等参数,来改善飞机的气动性能。
超级计算技术可以帮助工程师进行大规模的数值模拟和高精度的计算,以确定最佳的气动设计方案。
通过优化飞机的气动性能,可以减小飞机的阻力,提高燃油效率,降低噪声和排放,进而提高飞机的性能和可持续性。
此外,超级计算技术还可以用于飞行模拟和飞行控制系统的设计。
通过将实际的飞行动力学和控制系统模型输入到超级计算机中,可以快速进行大规模的飞行模拟并优化控制算法。
这样可以更好地了解飞行性能和飞行操纵特性,并提供准确的控制指导。
在飞行员培训和飞行器设计过程中,这些模拟数据和算法对于提高飞行安全性和效率至关重要。
航空能源技术使用的一些常用工具与软件推荐航空能源技术是航空工程领域的一个重要分支,涉及到航空发动机、燃料系统、动力管理等方面。
为了有效地进行航空能源技术的研究和应用,研究人员需要使用一些工具和软件来辅助他们的工作。
本文将介绍一些常用的航空能源技术工具和软件,以帮助研究人员在其工作中取得更好的效果。
1. MatLabMatLab是一种广泛使用的科学计算软件,可在航空能源技术研究中发挥重要作用。
MatLab提供了丰富的工具箱,包括数值计算、信号处理、图像处理、优化等方面的功能。
航空能源技术研究可以利用MatLab进行数值模拟、数据分析、算法开发等工作。
例如,研究人员可以使用MatLab来建立发动机模型、分析燃料效率、进行燃烧过程模拟等。
MatLab的强大功能和灵活性使其成为航空能源技术研究的首选工具之一。
2. ANSYSANSYS是一种流行的工程仿真软件,广泛应用于航空能源技术领域。
它提供了强大的有限元分析和计算流体力学功能,可以用于模拟和分析航空发动机的结构强度、燃烧过程、气动特性等。
通过使用ANSYS,研究人员可以定量评估设计参数对发动机性能的影响,优化设计并提高效率。
此外,ANSYS还可以进行燃烧过程模拟、热力学分析等,有助于航空能源技术的研究和开发。
3. GT-SUITEGT-SUITE是一种多物理场仿真软件,用于模拟和分析发动机、车辆和系统的性能。
在航空能源技术领域,GT-SUITE被广泛应用于发动机研发、燃料系统优化和动力管理等方面。
它提供了建模、仿真和优化工具,可以对航空能源系统进行全面的仿真和优化。
研究人员可以使用GT-SUITE来研究发动机的燃烧过程、气体流动特性、燃料喷射和燃烧室设计等,从而改进发动机的性能和效率。
4. CFD软件计算流体力学(CFD)软件是航空能源技术研究中不可或缺的工具之一。
CFD软件可以模拟航空发动机的内部流动,通过求解流体动力学方程来预测流体的速度、温度和压力分布。
a r X i v :a s t r o -p h /0206242v 1 14 J u n 2002Astronomy &Astrophysics manuscript no.MS2563February 5,2008(DOI:will be inserted by hand later)Jet/cloud collision,3D gasdynamic simulations of HH 110A.C.Raga 1,E.M.de Gouveia Dal Pino 2,A.Noriega-Crespo 3,P.D.Mininni 4,P.F.Vel´a zquez 11Instituto de Ciencias Nucleares,UNAM,Ap.70-,04510D.F.,M´e xico e-mail:raga@astroscu.unam.mx,pablo@nuclecu.unam.mx2Instituto de Astronomia,Geofisica e Ciencias Atmosfericas,Universidade de S˜a o Paulo,R.do Mat˜a o 1226,055-08-090S˜a o Paulo,SP,Brasil e-mail:dalpino@p.br3SIRTF Science Center,California Institute of Technology,IPAC 100-22,Pasadena,CA 91125,USA e-mail:alberto@ 4Departamento de F ´ısica,Facultad de Ciencias Exactas y Naturales,Universidad de Buenos Aires,Ciudad Universitaria,1428Buenos Aires,Argentina e-mail:mininni@df.uba.arAbstract.We present 3D,gasdynamic simulations of jet/cloud collisions,with the purpose of modelling the HH 270/110system.From the models,we obtain predictions of H αand H 21-0s(1)emission line maps,which qualitatively reproduce some of the main features of the corresponding observations of HH 110.We find that the model that better reproduces the observed structures corresponds to a jet that was deflected at the surface of the cloud ∼1000yr ago,but is now boring a tunnel directly into the cloud.This model removes the apparent contradiction between the jet/cloud collision model and the lack of detection of molecular emission in the crossing region of the HH 270and HH 110axes.Key words.ISM:Herbig-Haro objects —ISM:jets and outflows —ISM:kinematics and dynamics —ISM:individual (HH 110)—shock waves1.IntroductionThe Herbig-Haro (HH)jet HH 110(discovered by Reipurth &Bally 1986)is the best observed example of a possible HH jet/dense cloud collision.Reipurth et al.(1996)have interpreted the rather unique,collimated but quite chaotic structure of HH 110as the result of a de-flection of the faint HH 270jet through a collision with a dense cloud.The evidence presented by Reipurth et al.(1996)for this interpretation can be summarized as follows:–no stellar source has been detected aligned with the HH 110jet,–the HH 270jet (ejected from a detected IR and radio source,see Rodr´ıguez et al.1998)points towards the beginning of the HH 110jet,–the proper motions of HH 270and HH 110have an approximately 2to 1ratio,which is completely con-sistent with the ≈60◦deflection angle defined by the locci of the two jets (the flow approximately lying on the plane of the sky).2Raga et al.:Jet/cloud collisionsimulationsFig.1.Time sequence of the density stratifications obtained from model A.The density stratifications on the y=0 plane(which includes the outflow axis and the centre of the spherical cloud)are shown for different integration times (as indicated at the bottom left of each plot).The densities are depicted with a logarithmic greyscale,with the values given(in g cm−3)by the bar on the top left of thefigure.The x(horizontal)and z(vertical)axes are labeled in cm. very high cloud-to-jet density ratio.Raga&Cant´o(1995)suggested that this problem might be overcome if the in-cident jet did not have a completely straight jet beam,sothat the impact point would roam over the surface of thedense cloud.The regime in which the jet has punched a holethrough a cloud was described by Cant´o&Raga(1996)and Raga&Cant´o(1996).If the cloud is stratified,thepath of the jet through the cloud is curved,though thecurvature is important only if the radius of the cloud iscomparable to the jet radius.3D gasdynamic simulationsof the penetration of a jet into and through a dense,strati-fied cloud were carried out by de Gouveia Dal Pino(1999).Finally,Hurka et al.(1999)have studied the bendingof the beam of a3D MHD,non-radiative jet by a mag-neticfield with a strong gradient(as would be found atthe surface of a dense cloud).These authors show thatthis effect would help to increase the timescale over whichRaga et al.:Jet/cloud collision simulations3Fig.2.Temperature stratification (top)and adaptive grid structure (bottom)on the y =0plane obtained from model A for a t =2600yr integration time.The tempera-ture stratification is depicted with a logarithmic greyscale with the values given (in K)by the bar on the top of the figure.In the bottom plot,two thick lines separating the jet,cloud and environmental material are shown (see the text).These lines show two values of the passive scalar :ψ=0(outer contour)and ψ=1.5(inner contour).the jet/cloud surface interaction takes place,before the deflected jet is pinched off.In the present paper,we discuss 3D gasdynamic simu-lations of the interaction of a radiative jet with the surface of a dense cloud.We show the results from two simulations with different assumptions for the incident jet:Fig.3.Constant density surface (corresponding to a n =20cm −3number density of atomic nuclei)from model A for a t =2600yr integration time.The two graphs show the surface as seen from two different directions.–that the jet is ejected with a constant direction and velocity–that it is produced with a precessing outflow direction and a sinusoidally varying velocity.Through a comparison of these two simulations,we can evaluate the effect of a “roving”impact point on the pro-duction of the deflected jet.Our simulations include a treatment of the dissociation and ionization of the gas.Therefore,we can use the results to obtain predictions of atomic and molecular lines,which we directly compare with previously published images of HH 110.4Raga et al.:Jet/cloud collision simulationsIn particular,we compute predicted maps in the H2 1-0s(1)line.This is of interest because the morphology of HH110in this IR line is quite strikingly different from its morphology in atomic/ionic lines.Davis et al.(1994)and Noriega-Crespo et al.(1996)found that the H2emission is much better collimated,and lies along one of the edges of the HH110jet beam.This led Noriega-Crespo et al. (1996)to present a simple model of the molecular emission as coming from material from the dense cloud which is entrained by the jet as it brushes past the cloud surface. Our present simulations allow us to make a more definite assesment of whether or not such a mechanism actually succeeds in explaining the molecular emission of HH110.We should point out that Choi(2001)present HCO+ emission maps,in which they detect emission in the HH270/110region,but not around the“point of impact”in which the“incident”HH270jet is presumably redi-rected into the“deflected”HH110jet.This result leads them to suggest that HH110might actually not be the result of a jet/cloud collision,but that it could instead be a“straight”jet ejected from a low luminosity,undetected stellar source which is presumably more or less aligned with the direction of the HH110flow.In the conlcusions, we discuss the possible ways of reconciling the jet/cloud interaction model with the observations of Choi(2001) which are suggested by our3D gasdynamic simulations.2.The numerical simulations2.1.General featuresWe have carried out3D gasdynamic simulations of jet/cloud interactions with the yguaz´u-a adaptive grid code.This code integrates the3D(or2D)gasdynamic equations together with a set of continuity equations for atomic/ionic or chemical species.The details of the gas-dynamic and the adaptive grid algorithms have been pre-sented by Raga et al.(2000),and tests of the code are given by Sobral et al.(2000)and Raga et al.(2001).For the present simulations,we have used the follow-ing set of species:H2,H I and II,C II,III and IV,and O I,II and III(with abundances by number relative to hy-drogen:y C=6.6×10−4and y O=3.3×10−4).For the atomic/ionic reactions,we have included the collisional ionization(from Cox1970),radiative+dielectronic recom-bination(from Aldrovandi&P´e quignot1973,1976)and O/H charge exchange processes.The H2dissociation and cooling has been included in the same way as in Raga et al.(1995).The cooling associated with the atomic/ionic species has been included as described in appendix A.We have computed two jet/cloud interaction models, which share the following characteristics.In both mod-els,an initially atomic jet(except for C,which is singly ionized)of number density n j=50cm−3and temper-ature T j=1000K interacts with a spherical,homoge-neous molecular cloud(with all H in the form of H2) of number density n c=5000cm−3and T c=1K. The cloud is surrounded by a homogeneous,neutral en-vironment of density d env=10cm−3and temperature T env=1000K.Therefore,the jet to cloud(mass)density ratio isρj/ρc=1/100.In both simulations,the cloud has a r c=4×1017cm radius,and the jet has an initial,top-hat cross section of radius r j=1.5×1016cm.The jet is injected at (x,y,z)=(0,0,0)with the outflow axis in the z-direction. Free outflow conditions are applied on all of the outer boundaries of the computational domain,except for the z=0plane,on which a reflection condition is imposed outside of the injection jet cross section.The computations are carried out on a5-level,bi-nary adaptive grid(the two coarsest levels being defined over the full computational domain,see Raga et al.2000) with a maximum resolution(along the three axes)of 3.91×1015cm.The highest resolution grid is only allowed in the regions occupied either by jet or by cloud material (which are traced by advecting a passive scalar),so that the maximum resolution allowed in the environmental ma-terial is only of7.81×1015cm.We have then computed two models,one with a jet with time-independent injection conditions(model A), and one with a precessing,variable ejection velocity jet (model B).These two models are discussed in the follow-ing two subsections.2.2.Jet with time-independent injection(model A)In this simulation,the jet is injected parallel to the z-axis,with a constant v j=300km s−1injection velocity. The computational domain extends from−4.5×1017< x<0.5×1017cm,−2.5×1017<y<2.5×1017cm and 0<z<5×1017cm.The centre of the spherical cloud is placed at(x c,y c,z c)=(3.5,0,3)×1017cm,so that the jet has a glancing collision with the surface of the cloud.Figure1shows a time series(spanning an integration time of t=2400yr)of the density stratifications on the y=0plane(this plane includes the outflow axis and the centre of the dense cloud).In this time series one sees the incident jet beam(injected at the origin,and travelling along the z-axis)impinging on the surface of the dense cloud,and being deflected onto a direction towards the top left of the xz-cuts.At t=1200yr,the jet has already dug a hole into the cloud(this hole becoming deeper at later integration times),and the deflected jet beam basically becomes cut offat its base.At this time,the jet/cloud impact point lies within,rather than at the surface of the cloud.However,the material deflected by the cloud surface at earlier times continues to travel away from the cloud, leaving a complex“wake”,joining it to the point at which the incident jet impacted the cloud surface.In order to illustrate the configurations adopted by the adaptive grid,Figure2shows the temperature stratifica-tion and the adaptive grid structure on the y=0plane obtained for t=2400yr.It is clear from thisfigure that the higher resolution is not allowed on the regions occu-Raga et al.:Jet/cloud collision simulations5Fig.4.Time sequence of the density stratifications obtained from model B.The density stratifications on the y =0plane (which includes the outflow axis and the centre of the spherical cloud)are shown for different integration times (as indicated at the bottom left of each plot).The densities are depicted with a logarithmic greyscale,with the values given (in g cm −3)by the bar on the top left of the figure.The x (horizontal)and z (vertical)axes are labeled in cm.pied by environmental gas,so that the leading bow shock is only resolved at the second highest resolution level.Figure 2also shows the following.We have integrated an advection equation for a passive scalar ψ.This scalar has been given a value of ψ=1for the jet,2for the dense cloud,and −1for the surrounding environment.In the plot showing the adaptive grid,we have also drawn two contours on the stratification of the passive scalar corresponding to values ψ=0(outer contour)and ψ=1.5(inner contour).The region in between the two contourscorresponds to the jet material (which has ψ=1).From Figure 2it is then clear that the jet material occupies the injection region and the hole in the cloud,as well as a “plug”of material (at (x,y )≈(−2.5,4.5)×1017cm)with wings which extend towards the jet/cloud impact point.The region in between the wings is filled in by a tongue of cloud material which has been swept into the deflected jet flow.We find that this entrained cloud material has interesting observational properties,which are described in section 3.6Raga et al.:Jet/cloud collision simulationsThe structure of this dense tongue is more clearly seen in Figure3,which shows a constant density3D sur-face(corresponding to a n=20cm−3number density of atomic nuclei)obtained for a t=2600yr integration time.Thisfigure shows the jet penetrating into the cloud, part of the bow shock,the denser region of the deflected jet beam and the entrained molecular gas material(which forms a structure which surrounds the incident jet,and has an elongation in the direction of the deflected jet beam).2.3.Precessing,variable ejection velocity jet(model B) We have computed a second simulation,in which the ejec-tion direction precessses around the jet axis.The pre-cession cone has anα=5◦half-opening angle,and aτp=400yr precession period.Also,the jet is in-jected with a constant density(n j=50cm−3,see§2.1), but with a sinusoidally varying ejection velocity v j(t)= (300+80sin2πt/τv)km s−1,with aτv=200yr period.For this simulation,we choose a computational domain with the same extent as the one of model A along the x and y axis(but with a diferent centering:−3.5×1017< x<1.5×1017cm and−2.5×1017<y<2.5×1017cm) but with a larger,0<z<1018cm extent along the axis. The center of the spherical cloud is placed at(x c,y c,z c)= (3.5,0,6)×1017cm.We have chosen to have a larger dis-tance from the point of injection to the jet/cloud colli-sion region in order to allow the internal working surfaces of the jet(which result from the ejection velocity time-variability)to form before colliding with the dense cloud.Figure4shows a time sequence of the density strati-fications obtained on the y=0plane(which includes the precession axis and the centre of the cloud).In thisfigure, one sees the internal working surfaces whichfirst form,and then impact the surface of the dense cloud.Because of the precession in the ejection direction,the successive working surfaces impact the cloud at different points.Through a comparison with Figure1,it is clear that this effect in-creases the time that the jet takes to dig a hole into the cloud(and therefore pinching offthe deflected jet beam). In fact,for the t=3000yr time-integration shown in Figure4,the depth of the hole is still smaller than the diameter of the impinging jet(for a similar time frame obtained from model A,the depth of the hole is of ap-proximately two jet diameters,see Figure1).Similar re-sults are deduced by analyzing different y=const.cuts through the3D density stratification.Figure5shows the temperature stratification,and the grid structure on the y=0plane obtained for a t= 2500yr time-integration(equivalent results for model A are shown in Figure2).On the graph with the adaptive grid,we again show the contours that separate the jet, cloud and environmental material(for model A,see Figure 2and the discussion at the end of§2.2).It is clear that model B has a more complex structure than model A, showing a number of condensations of jet material inthe Fig.5.Temperature stratification(left)and adaptive grid structure(right)on the y=0plane obtained from model A for a t=2500yr integration time.The temperature stratification is depicted with a logarithmic greyscale with the values given(in K)by the bar on the top of thefigure. In the right hand side plot,two thick lines separating the jet,cloud and environmental material are shown(see the text).“deflectedflow”region,which correspond to the different working surfaces that have been deflected on collision with the cloud surface.Interestingly,the center of the deflected flow region isfilled with material from the molecular cloud, as is also the case for model A(see Figure2).In the following section,we present predictions of emis-sion line intensity maps carried out from the results of models A and B.These predictions can then be compared directly with the available observations of the HH270/110 system.3.Predicted intensity maps3.1.General considerationsIn order to compare the jet/cloud interaction models de-scribed in§2with the published images of the HH270/110 system,we have obtained predicted emission line maps from the models.From the computed temperature,den-sity,electron density and H ionization and molecular frac-tions,we have computed the Hαand H21-0s(1)(2.12µm) emission coefficients.For the Hαemission line coefficient,we have included the recombination cascade and the n=1→3collisional excitation(using the corresponding excitation coefficient of Giovanardi&Palla1989).The H21-0s(1)coefficientRaga et al.:Jet/cloud collision simulations7 Fig.6.Hαmaps predicted from model A,correspondingto the integration times given on the bottom left of eachframe.The maps are depicted with the greyscale givenby the bar at the top of the graph(which gives the in-tensity values in erg cm−2s−1sterad−1).The two topframes have been computed including the dust extinctionof the dense cloud(see§3.2),and the bottom frame showsa map computed without considering this extinction.Theproper motions computed from the positions of three in-tensity maxima(as measured in the t=2000and2600yrmaps)are shown in the top frame.The x(horizontal)andz(vertical)axes are labeled incm.Fig.7.Hαand H21-0s(1)intensity maps computed frommodel A for a t=2600yr integration time(see§3.2).Theleft plot shows the Hαmap in factor of2contours and theH2map in the greyscale described by the bar at the topof the plot.The right plot shows the H2map in factor of2contours and the Hαmap in gresycale(corresponding tothe bar at the top of the plot).The greyscales(given inerg cm−2s−1sterad−1by the corresponding bars)and thecontours of the intensity maps of a given line correspondto the same range of intensities.The x(horizontal)and z(vertical)axes are labeled in cm.was computed by solving the level population equationsaccording to the prescription of Draine et al.(1983,cor-rected according to Flower et al.1986).This is by nomeans the more accurate calculation of H2level popu-lations available(see,e.g.,Flower&Pineau des Forˆe ts1999),but it is appropriate given the limited accuracy ofour rather low resolution numerical simulations.We have then computed intensity maps by integratingthe emission coefficients along lines of sight.We have as-sumed that the y=0plane(which includes the axis of theincidentflow and the centre of the cloud)coincides withthe plane of the sky.This is probably a reasonable approx-imation to the orientation of the HH270/110flow,sinceit is known that both HH270and HH110approximatelylie on the plane of the sky(Reipurth et al.1986).3.2.Intensity maps predicted from model AIn Figure6,we show the Hαemission line maps predictedfrom model A(see§2.2).From the bottom plot,it is com-pletely clear that the emission is dominated by the regionin which the incident jet beam is digging into the cloud.As the optical emission maps of HH110do not show thisemitting region,we have to conclude that it has to beabsorbed by the dust in the dense cloud(at least,if webelieve in the jet/cloud interaction model for this object).In order to illustrate the effect on the Hαmaps of suchan extinction,we have computed emission maps includingthis effect(top two maps offigure6).We have computed8Raga et al.:Jet/cloud collision simulationsthe maps assuming that the dust extinction coefficient is κd=10−20(n H/cm−3)cm−1,giving aτd=40optical depth through the radius of the cloud.We should note that if we use the standard,κd= 10−21(n H/cm−3)cm−1dust absorption coefficient,our cloud would have aτd=4central optical depth.The region on the edge of the cloud into which the jet is pen-etrating would then have a low optical depth,producing little extinction of the emission from the impact region. This,however,is not a major problem given the fact that the dense cloud present in the HH270/110region is by no means either spherical or homogeneous,and could easily produce a large extinction towards the current jet/cloud impact region(which would lie within the cloud).The Hαmaps obtained from model A(for integra-tion times t=2000and2600yr)computed with the dust extinction as described above are shown in the two top frames of Figure6.It is clear that these maps do present a qualitative similarity to HH110(see,e.g.,Reipurth et al.1996).In agreement with the observations,the emitting re-gion starts with a bright rim(in contact with the sur-face of the cloud,seefigure6)which points to a broader structure(with a complex structure of curved ridges)at larger distances from the impact region.Also,there is a faint emission halo extending parallel to the main emis-sion structure on the side directed away from the dense cloud.This is also in qualitative agreement with the Hαmaps of HH110(Reipurth et al.1996).From the two time frames shown in Figure6,we have computed proper motions for the three main intensity maxima seen in the maps.We have also computed proper motions for some of the local maxima in the region in which the emission is in contact with the cloud surface, but the resulting velocities lie between3and8km s−1, and have not been plotted in Figure6.The proper motions of the knots farther away from the cloud(shown infigure6)have values of15to45km s−1. These proper motions are substantially lower than the ones measured for the HH110knots,as Reipurth et al. (1996)have found values ranging from35to150km s−1.In Figure7,we show a comparison between the Hαand the H21-0s(1)intensity maps obtained for t=2600yr. For computing the H2map,we have considered an extinc-tion equal to1/10of the visual extinction.This results in only small optical depths towards the current jet/cloud impact region,so that the emission from this region is clearly visible.The H2emission in the base of the deflected jet re-gion is much more concentrated towards the surface of the cloud than the Hαemission.This result is in clear qualitative agreement with the morphology observed in HH110(Davis et al.1994;Noriega-Crespo et al.1996). At larger distances along the deflected jetflow,the H2 and Hαemission show spatially coincident condensations, again in agreement with the observations of HH110.3.3.Intensity maps predicted from model BIn Figure8,we show the Hαintensity maps computed from model B for t=2200and2400yr integration times, and the H21-0s(1)map obtained for t=2400yr.Because of the fact that the jet/cloud impact region still lies on the surface of the cloud,the effect of the extinction due to the dust present in the cloud is not important,and has not been included.However,the extinction would be important for maps computed for different orientations of the jet/cloud structure with respect to the plane of the sky.As a result of the precession and ejection velocity time variability of the incident jet(see§2.3),the emission maps show more complex structures than the ones obtained from model A.In particular,one can clearly see the emis-sion from bow shocks around dense“bullets”,which re-sult from the successive internal working surfaces present in the incident jet.Even though the morphology observed in the emission line maps of HH110is very complex(see, e.g.,Reipurth et al.1996),it does not appear to have such bow shock structures.Actually,the intensity maps pre-dicted from model A do resemble the structure of HH110 in a more convincing way.An interesting feature of model B is that the proper motions of the different intensity maxima(obtained by comparing the t=2200and2400yr Hαintensity maps, seefigure8)have values of close to100km s−1.These velocities are in better agreement with the ones measured for HH110than the ones obtained from model A(see§3.2 and Reipurth et al.1996).4.ConclusionsWe have presented two jet/cloud collision3D gasdy-namic simulations:one with an incident jet with time-independent injection conditions(model A),and a second one with a variable velocity,precessing incident jet(model B).Aρc/ρj=100cloud to initial jet density ratio has been chosen for both models.Model A produces a deflection of the jet beam only for a∼500-1000yr timescale,after which the incident jet starts digging a straight tunnel through the dense cloud.At later times,the deflected jet material contin-ues to travel away from the impact region,leaving behind a complex“wake”.Model B produces a broader jet/cloud impact region as a result of the jet precession.This effect results in a longer timescale for the duration of the jet deflection on the cloud surface(in fact,the jet deflection is still occuring at the end of our t=3000yr numerical simulation).Model B is more successful at reproducing the proper motions of HH110,giving∼100km s−1velocities for the Hαintensity maxima along the deflected jet beam. Model A gives velocities of∼15-45km s−1,which are substantially lower than the proper motion velocities of HH110(see Reipurth et al.1996).Raga et al.:Jet/cloud collision simulations9Fig.8.Intensity maps obtained from model B (see §3.3).The left and central frames show the H αmaps obtained for two integration times (given on the bottom left of each frame).The right hand side frame shows the H 21-0s(1)map obtained for t =2400yr.The maps are depicted with the greyscales given (in erg cm −2s −1sterad −1)by the bars on the top of each frame.The left plot shows the proper motions of several intensity maxima,computed from the t =2200and 2400yr H αintensity maps.The x (horizontal)and z (vertical)axes are labeled in cm.This difference between model A and model B is due to two effects.The first effect is that in model A,the jet is deflected only for a ∼500-1000yr timescale,and that this deflected jet material then slows down as it interacts with surrounding,environmental gas.In model B,succes-sive deflected “bullets”(i.e.,internal working surfaces)travel into the low density region left behind by the pas-sage of the head of the deflected jet,and do not interact directly with the higher density environment.The second effect is that because of the precession of model B,some of the bullets have trajectories which are more tangential to the surface of the molecular cloud.These more tangential bullets are less deflected,and therefore preserve larger ve-locities than the ones that have a more normal incidence on the cloud surface (or than the deflected jet of model A).However,in most other counts,model A is more suc-cessful than model B at reproducing the observations of HH 110:–the general qualitative appearance of the H αmaps is in better agreement with the HH 110H αimages,–the features of the H 21-0s(1)emission and their spa-tial relation to the H αemission also are in good qual-itative agreement with HH 110,–the maps that better resemble HH 110correspond to times (e.g.,the t =2400yr frame of figures 1and 6)in which the impact region is already immersed within the cloud.This last feature offers an interesting way of reconciling the jet/cloud collision model with the HCO +observations of Choi (2001).In these observations,no HCO +emission was detected in the region in which the axis of the “incident”HH 270jet crosses the axis of the “deflected”HH 110.Choi (2001)noted that this appeared to be in disagreement with a jet/cloud collision model for this system,as the cloud shock produced in the impact region should indeed pro-duce HCO +emission.The situation found in model A,however,could indeed be in agreement with the observations of Choi (2001).In this model,the impact region does not lie in the point in which the incident and deflected jet axes cross,but is instead located further along the axis of the incident jet.Interestingly,Choi (2001)does find substantial HCO +emission West of HH 110,approximately aligned with HH 270.As we have discussed in §3.2,the fact that the impact region is not observed optically can in principle be a result of the dust extinction in the dense cloud.Interestingly,some H 21-0s(1)emission is apparently detected to the West of HH 110(more or less aligned with HH 270,see Noriega-Crespo et al.1996),which in principle might be associated with the impact region.To conclude,we note the two main features of our re-sults :。