Dynamically parameterising CAD models using sensitivities for optimisation

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。在航空航天领域，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。当更改设计的形状时，选项是更改分析模型（即网格）或表示设计的几何模型。首选的选择是优化几何模型，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，使用定义构成模型本身的特征的参数来修改对象形状。其中一个挑战是，定义设计形状并控制其变化的变量实际上可能并不适合驱动优化的学科。这意味着无论优化器投入多少努力，都不可能达到真正的最佳设计。这个为期三年的项目将通过研究鲁棒的方法来确保参数化适合于优化，以自动将新特征插入CAD模型，相关参数将成为新的优化变量。这将依赖于强大而有效的计算多学科灵敏度的新方法。该项目得益于与英国主要工业合作伙伴（空客）和关键分析软件开发商（DLR）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阶段性变化”。设立了下列目标：通过基于灵敏度自动插入特征到模型中，实现改进CAD参数化的多学科优化策略。2. 研究计算空气结构灵敏度的有效和稳健的方法。这将是一种计算灵敏度的新方法。制定固体和流体模型的耦合和连贯网格化策略。这是任何航空结构分析所需要的关键研究。结合空气结构的敏感性与CAD参数化策略，在一个自动化的优化框架，为一系列的测试用例。这是这项工作将向工业界展示的地方。量化由于这项研究而导致的上市时间的缩短和性能的提高。本研究的应用领域包括需要优化复杂形状的产品设计。它将特别适用于基于特征的CAD系统支撑设计过程的行业，以及在CAD模型建立时可能不明显的问题的物理特性可能确定形状特征的需求的行业。例如，表面敏感性表明需要小翼，但基本翼的参数化不包括允许形成这种特征的参数。福利包括:1。发现新的、最佳的配置的能力。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；改进的产品性能由于改进的优化变量（CAD参数）的基础上创建的物理问题的要求。对于航空旅行来说，这将带来更环保的飞机和更低的旅行价格；由于自动化和高效的优化流程，缩短了开发时间，导致新的，性能更好的产品更快上市；

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;