Dynamically parameterising CAD models using sensitivities for optimisation

要设计复杂的产品，工程师需要考虑和优化许多不同的属性。在航空航天领域，优化主要考虑结构属性(例如位移、加速度)和流体属性(例如作用在物体上的压力)。影响性能的主要因素之一是产品形状，它影响到许多学科。当更改设计的形状时，可以选择更改表示设计的分析模型(即网格)或几何模型。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在一起(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或西门子NX)，则情况尤其如此。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。一个挑战是，定义设计形状并控制其如何改变的变量实际上可能不太适合驱动优化的学科。这意味着，无论优化器付出多少努力，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稳健的方法来确保参数化适合于优化，以自动将新特征插入到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度的稳健和高效的新方法。该项目得益于与英国主要工业合作伙伴(空中客车)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功能，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的改变。”已经设定了以下目标：1.实施改进CAD参数化的策略，通过基于敏感度自动向模型中插入特征来进行多学科优化。2.研究计算气动结构灵敏度的有效而稳健的方法。这将看到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划分策略。这是任何气动结构分析都需要的一项关键研究。在自动化优化框架中，针对一系列测试用例，将气动结构敏感性与CAD参数化策略相结合。这就是这项工作的好处将向业界展示的地方。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。这将在基于特征的CAD系统支撑设计过程的行业中特别相关，并且在这些行业中，问题的物理可能会识别在建立CAD模型时可能不明显的形状特征的需求。一个例子是，表面灵敏度表明需要小翼，但基本机翼的参数不包括允许形成这种特征的参数。好处包括：1.能够发现新的、最佳的配置。这是一条通往创新设计解决方案的道路，这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过程，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;