Dynamically parameterising CAD models using sensitivities for optimisation

使用灵敏度动态参数化 CAD 模型以进行优化

• 批准号: EP/P025692/1
• 负责人: Trevor Robinson
• 金额: $ 75.82万
• 依托单位: Queen's University Belfast
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP025692%2F1
• 关键词: Dynamically; parameterising; CAD; models; using

To design complex products, engineers need to consider and optimise many different attributes. In aerospace, optimisation mainly considers both structural (e.g. displacements, accelerations) and fluid (e.g. pressures acting on a body) attributes. One of the main factors which can impact performance is product shape, which affects a number of disciplines. When changing the shape of the design the options are to change the analysis model (i.e. a mesh) or the geometry model which represents the design. The preferred option is to optimise the geometry model as the result is integrated with the wider design enterprise (e.g. it can also be used for manufacturing considerations). This is particularly true if the geometry model is a feature based CAD model (e.g. Catia V5 or Siemens NX). In a feature based CAD system, the object shape is modified using the parameters which define the features that make up the model itself.One challenge is that the variables which define the shape of the design and control how it can change, may not actually be well suited for the disciplines driving the optimisation. This means that regardless of how much effort the optimiser puts in, it will not be possible to reach a truly optimum design. This three year project will ensure the parameterisation is suited to optimisation by investigating robust methodologies to automatically insert new features into the CAD model, for which the associated parameters will be new optimisation variables. This will rely on robust and efficient new methods for computing multi-disciplinary sensitivities. The project benefits from collaboration with a major UK industrial partner (Airbus) and developers of key analysis software (DLR). They will assist in researching a new capability with the overall aim of "delivering a step change in the configuration, time to market and performance of new designs." The following objectives have been set: 1. Implement strategies for improving CAD parameterisations for multi-disciplinary optimisation by automatically inserting features into the model based on sensitivity. 2. Investigate efficient and robust methodologies for computing aero-structural sensitivities. This will see a novel approach to the calculation of the sensitivities.3. Develop strategies for coupling and coherently meshing solid and fluid models. This is a key piece of research required in any aero-structural analysis.4. Combine aero-structural sensitivities with CAD parameterisation strategies, in an automated optimisation framework, for a range of test cases. This is where the benefits of the work will be demonstrated to industry.5. Quantify the decrease in time to market and increase in performance due to this research. Application areas for this research include the design of products which require the optimisation of complex shapes. It will be particularly relevant in industries where feature based CAD systems underpin the design process, and where the physics of the problem may identify the need for shape features which may not be apparent when the CAD models are being setup. An example may be where the surface sensitivities suggest the need for a winglet, but where the parameterisation of a basic wing does not include the parameters to allow such a feature to form. Benefits include:1. the ability to discover new, optimum, configurations. This is a route to innovative design solutions which will help to keep the UK as a world leader in the design and manufacture of complex products;2. improved product performance due to the improved optimisation variables (CAD parameters) created based on the requirements of the physics of the problem. For air travel this will result in more environmentally friendly aircraft and lower travel prices;3. reduced development times due to an automated and efficient optimisation processes, leading to new, better performing, products being available sooner;

为了设计复杂的产品，工程师需要考虑和优化许多不同的属性。在航空航天中，优化主要考虑结构（例如位移，加速度）和流体（例如作用在物体上的压力）属性。影响性能的主要因素之一是产品形状，它会影响许多学科。当改变设计的形状时，选项是改变表示设计的分析模型（即网格）或几何模型。首选方案是优化几何模型，因为结果与更广泛的设计企业集成（例如，它也可以用于制造考虑）。如果几何模型是基于特征的CAD模型（例如Catia V5或Siemens NX），则尤其如此。在基于特征的CAD系统中，对象形状是使用定义构成模型本身的特征的参数来修改的。一个挑战是，定义设计形状并控制其如何变化的变量实际上可能并不适合驱动优化的学科。这意味着无论优化者付出多少努力，都不可能达到真正的最优设计。这个为期三年的项目将通过研究强大的方法来确保参数化适合于优化，以自动将新功能插入CAD模型中，相关参数将成为新的优化变量。这将依赖于计算多学科敏感性的强大而有效的新方法。该项目得益于与英国主要工业合作伙伴（空中客车公司）和关键分析软件（DLR）开发商的合作。他们将协助研究一种新的能力，其总体目标是“在新设计的配置、上市时间和性能方面实现一个台阶式的变化。“确定了以下目标：1.通过基于灵敏度自动将特征插入模型，实施用于改进CAD参数化的多学科优化策略。2.研究计算气动结构灵敏度的有效和可靠的方法。这将为灵敏度的计算提供一种新的方法。为耦合和一致网格化的固体和流体模型制定策略。这是任何航空结构分析所需的关键研究。4.在自动优化框架中，将联合收割机的航空结构灵敏度与CAD参数化策略相结合，用于一系列测试用例。这就是工作的好处将向工业界展示的地方。量化由于这项研究而缩短的上市时间和提高的性能。这项研究的应用领域包括需要优化复杂形状的产品设计。在基于特征的CAD系统支撑设计过程的行业中，以及在问题的物理学可能识别对形状特征的需要的行业中，这将是特别相关的，当CAD模型被设置时，形状特征可能不明显。一个例子可能是表面敏感性表明需要小翼，但基本机翼的参数化不包括允许形成这种特征的参数。优点包括：1.发现新的最佳配置的能力。这是一条通往创新设计解决方案的道路，将有助于保持英国在复杂产品设计和制造方面的世界领先地位;2.由于根据问题的物理要求创建了改进的优化变量（CAD参数），从而提高了产品性能。对于航空旅行，这将导致更环保的飞机和更低的旅行价格;3.由于自动化和高效的优化流程，缩短了开发时间，从而更快地推出性能更好的新产品;

项目成果

Mesh and Geometry Manipulations for Optimization and Inverse Design

• DOI: 10.2514/6.2021-1901
• 发表时间: 2020-01
• 期刊: AIAA Scitech 2021 Forum
• 作者: Benoit Lecallard;T. Robinson;Simao Marques

Nonintrusive Aerodynamic Shape Optimisation with a POD-DEIM Based Trust Region Method

基于 POD-DEIM 信任域方法的非侵入式气动形状优化

• DOI: 10.3390/aerospace10050470
• 发表时间: 2023
• 期刊: Aerospace
• 影响因子: 2.6
• 作者: Marques S

Generalized Bezier components and successive component refinement using moving morphable components

• DOI: 10.1007/s00158-022-03289-8
• 发表时间: 2022-07-01
• 期刊: STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
• 影响因子: 3.9
• 作者: Shannon, T.;Robinson, T. T.;Armstrong, C. G.
• 通讯作者: Armstrong, C. G.

Aerodynamic Shape Optimisation Using Parametric CAD and Discrete Adjoint

使用参数化 CAD 和离散伴随进行空气动力学形状优化

• DOI: 10.3390/aerospace9120743
• 发表时间: 2022
• 期刊: Aerospace
• 影响因子: 2.6
• 作者: Agarwal D

Non-intrusive aerodynamic shape optimisation with a discrete empirical interpolation method

• DOI: 10.2514/6.2021-0172
• 发表时间: 2020-01
• 期刊: AIAA Scitech 2021 Forum
• 作者: S. Marques;Lucas L. Kob;T. Robinson;W. Yao;Liang Sun