Digitalisation in Aeronautics

所在平台: Coursera

课程主页: https://www.coursera.org/learn/aeronautics

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课程简介

课程名称：航空数字化 概述：在线课程“航空数字化”由多位讲师共同授课，涵盖了航空研究和应用的各个领域，探讨数字化在该领域的影响，包括飞机部件相互作用的数字化仿真、整体飞机开发及相关决策过程，以及飞机内部使用的通信渠道。课程的各个模块详细探讨了丰富多样的应用和数字解决方案。 课程大纲： 第一部分：协同飞机设计 描述：此部分介绍了飞机设计过程，特别关注早期生产阶段的挑战。课程讲解了飞机设计建模决策的制定、TUM ADEBO设计工具箱的使用以及飞机开发中的协作流程。 讲师：米尔科·霍尔农教授（慕尼黑工业大学和包豪斯航空航天研究所）。 第二部分：数字航空电子网络 描述：航空电子系统的日益复杂及其大量点对点数据连接促成了数字网络在航空航天领域的引入。此网络旨在将飞机/航天器中安装的系统组件互联，并将子系统集成到更高层次的系统中，从而实现高效的机载通信。讲座内容涵盖一般需求、拓扑结构和信号传输类型，并解释当前使用的总线系统，如ARINC429、Mil-Std-1553和AFDX。 讲师：彼得·斯图兹博士（慕尼黑国防军大学）。 第三部分：使用Modelica对航空航天系统进行建模和仿真 描述：飞机系统涉及多个物理领域，包括液压、气动和电气系统，这些系统在极端环境条件下在有限空间内进行工作。航空器的全球优化需要协调的建模方法，以便在统一平台上模拟和评估所有系统。Modelica提供了多领域物理系统建模的开放标准，本讲座将解释这种建模语言的基本原理及其在飞机系统中的应用。 讲师：德克·齐默博士（德国宇航中心）。 第四部分：数字模型及其在结构-控制交互问题中的压缩 描述：航空系统行为的数字模型通常是高阶的，在与其他学科模型结合使用时需要进行降阶。讲座讨论了如何从高阶模型实现降阶模型（ROM），并介绍了在参数变化情况下优化系统设计的相关方法。 讲师：霍斯特·拜尔教授（慕尼黑工业大学）。

课程大纲

Part: 1

Title:Collaborative Aircraft Design

Description:The online course unit Collaborative Aircraft Design provides an introduction to the aircraft design process. It provides an overview of specifics in the design process, focusing particularly on challenges in early production phases. The course unit explains how aircraft design modelling decisions are made, illustrates usage of the TUM ADEBO design tool box and looks at worksharing processes in aircraft development. - Lecturer: Professor Mirko Hornung (Technical University Munich & Bauhaus Luftfahrt).

Part: 2

Title:Digital Avionics Networks

Description:The growing complexity of avionics systems and the associated high number of point-to-point data connections have led to the introduction of digital networks in aerospace. These serve to interconnect the system components installed in aircraft/spacecraft and integrate subsystems into a higher-level system, enabling efficient on-board communication. Because of their key role such networks are subject to special requirements. In addition to bandwidth failure resilience and interference are therefore of major importance. The lecture addresses general requirements, topologies and signal transmission types and explains bus systems currently in use such as ARINC429, Mil-Std-1553 and AFDX. - Lecturer: Dr.-Ing. Peter Stütz (Universität der Bundeswehr München).

Part: 3

Title:Modeling and Simulation of Aerospace Systems with Modelica

Description:Aircraft systems embrace a large set of physical domains. Hydraulic, pneumatic and electrical systems all act within a confined space under extreme environmental conditions. Global optimisation of an aircraft including all of its systems thus demands a harmonised modelling approach so that all systems can be simulated and evaluated on a common platform. Modelica offers an open and free standard for multi-domain modelling of physical systems. Individual components are modelled using differential-algebraic equations while large complex systems can be composed using object-oriented methods. The underlying principles of such modelling languages are explained in this lecture and how they can be applied for aircraft systems. The resulting multi-domain models afford digital representations of aircraft systems which can then be used to optimise aircraft design. Modern energy management methods can improve system performance, while model-based fault analysis methods help ensure safety and reliability. - Lecturer: Dr. Dirk Zimmer (DLR German Aerospace Center).

Part: 4

Title:Digital Models and their Compression in Structure-Control Interaction Problems

Description:Digital models for (flexible) Aerospace system behaviour are often high-order and have to be reduced when used in combination with models from complementary disciplines or where many parametric iteration steps are required, such as for design optimisation. Typical examples are control-structure interaction problems, such as vibration attenuation of satellite solar arrays and aero-elasticity and dynamic load alleviation of aircraft. In this module methods are discussed for realising a reduced order model (ROM) from a higher order model. To avoid multiple repetitions of often-costly reduction processes in cases where model parameters are varied, e.g. for system optimization, model parameters should be set for these ROMs to yield so-called P-ROMs. The effect of varied parameters can then be covered on the level of the previously established ROM simply by updating these via the new relevant parameters. The related methods are also called ‘hard computing’ methods because of the mathematically based approach, requiring a relatively well-structured set of the initial full-order systems of equations. These methods can be applied to a multitude of aerospace problems such as vibration control for satellites in orbit and dynamic aero-elastic load alleviation for aircraft. Possibilities for reducing computational effort when using P-ROMS, such as for design optimisation tasks for space structures, are also discussed. - Lecturer: Professor Horst Baier (Technical University Munich).

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