MULTIDISCIPLINARY ROBUST OPTIMIZATION OF EARLY-STAGE AIRCRAFT WING DESIGN UNDER UNCERTAINTY

早期的飞机设计必须应对由于设计不成熟、缺乏对气动载荷和约束的了解以及由于对底层物理仿真模型缺乏信心而导致的模型形式的不确定性。在飞机设计优化的早期阶段，正确考虑这些不确定性的能力成为行业正确权衡最佳性能与实现性能和满足限制（包括上市时间的关键限制）的风险的关键因素。该项目旨在建立一个数据驱动的计算框架（RDO-Air），用于多学科框架下飞机（a /C）结构的稳健设计和优化。RDO-Air将适用于早期的A/C设计，在一系列严格的设计约束下，考虑到重量惩罚、飞行条件的灵活性、飞行包线和使用基于贝叶斯推理的优化技术实现风险最小化。需要强调的是，在不确定性下提出的设计优化将最终导致多目标优化，该优化明确考虑了平衡a /C性能与无法满足设计负载风险的成本惩罚。这项工作的具体应用领域与制造工具中的“夹具扭转”的优化及其对实现的飞行形状，空气动力学性能和总体设计负载包络的影响有关。在这个特定的用例中，设计负载已经被“固定”，包括负载包上的“设计余量”，以覆盖不确定性，挑战是在设计不确定性（如机翼结构刚度）存在的情况下定义“最佳”夹具扭转，以实现性能优势的信心与超过当前设计负载包的风险进行交易。该项目将涉及以下内容：-使用空中客车多学科飞机仿真平台，在高维参数空间中模拟空气动力载荷和性能指标，定义早期a /C机翼配置-使用随机替代品进行降阶建模，以实现灵敏度分析所需的快速随机抽样，马尔可夫链蒙特卡罗算法用于贝叶斯推理。-使用基于贝叶斯推理的鲁棒优化框架对参数化的空调机翼进行多目标优化，该框架给出了最优机翼参数的概率估计以及满足目标设计载荷的置信度。-吸收平衡空气动力学性能的实际成本函数与在稳健优化框架内未能满足负载规格而重新设计的风险，这将为简化的a /C设计工作流程提供明智的战略决策。

Early aircraft design must cope with uncertainties that exist due to lack of design maturity, lack of knowledge about aerodynamic loads and constraints as well as model form uncertainties due to lack of confidence in the underlying physics simulation models. The ability to properly account for these uncertainties in early stage aircraft design optimisation becomes a key enabler for industry to properly trade optimum performance against the risks of achieving the performance and meeting the constraints (including key constraints on time to market). The project aims to build a data-driven computational framework (RDO-Air) for robust design and optimization of aircraft (A/C) structures in a multidisciplinary framework. RDO-Air would be applicable for early stage A/C design under a set of stringent design constraints imposed by considerations of weight penalty, flexibility in flight conditions, flight envelopes and risk minimization using a Bayesian inference-based optimization technique. It is important to highlight that the proposed design optimization under uncertainty would lead to ultimately lead to a multi-objective optimizing which explicitly considers the cost penalty for balancing the A/C performance with the risk of failing to meet the design loads.The specific application area for this work is related to the optimisation of the "jig twist" in the manufacture tooling and the impacts of this in terms of the achieved flight shape, aerodynamic performance, and on the overall design loads envelope. Within this specific use case the design loads have already been "fixed" including a "design margin" on the loads envelopes to cover uncertainties, and the challenge is to define the "optimal" jig twist in presence of design uncertainties (such as wing structural stiffness) to trade the confidence of achieving the performance benefits against the risk of exceeding the current design loads envelope.The project will involve the following: - Simulation of aerodynamic loads and performance indicators in a high dimensional parameter space defining early stage A/C wing configuration using Airbus' multidisciplinary aircraft simulation platform - Reduced order modelling using stochastic surrogates which to enable rapid stochastic sampling required for sensitivity analysis, Markov Chain Monte Carlo algorithms for Bayesian inference. - Multi-objective optimization of parametrized A/C wing using Bayesian inference-based robust optimization framework which gives the probabilistic estimates of optimal wing parameters along with the confidence of meeting the target design loads. - Assimilating the actual cost function of balancing the aerodynamic performance with the risk of re-design for failing to meet the load specs within the robust optimization framework, which would incorporate informed strategic decision making for a streamlined A/C design workflow.