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

早期的飞机设计必须科普由于设计不成熟、缺乏关于气动载荷和约束的知识以及由于对基本物理仿真模型缺乏信心而导致的模型形式不确定性而存在的不确定性。在早期飞机设计优化中正确考虑这些不确定性的能力，成为工业界正确权衡最佳性能与实现性能和满足约束（包括上市时间的关键约束）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），用于多学科框架中的飞机（A/C）结构的稳健设计和优化。RDO-Air将适用于早期阶段的A/C设计，这些设计约束是由考虑重量惩罚、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的风险最小化而施加的。需要强调的是，在不确定性条件下提出的设计优化最终将导致多目标优化，该优化明确考虑了平衡空调性能与无法满足设计负荷风险的成本损失。这项工作的具体应用领域与“夹具扭曲”优化有关。在制造工具和影响，这方面取得的飞行形状，气动性能，并对整个设计载荷包线。在此特定用例中，设计载荷已经“固定”，包括载荷包络线上的“设计裕度”，以涵盖不确定性，挑战是在存在设计不确定性的情况下定义“最佳”夹具扭转（如机翼结构刚度），以实现性能优势的信心与超过当前设计载荷包线的风险进行交易。该项目将涉及以下内容：- 使用空中客车公司的多学科飞机仿真平台在定义早期A/C机翼构型的高维参数空间中模拟空气动力载荷和性能指标-使用随机替代物进行降阶建模，以实现灵敏度分析所需的快速随机采样，用于贝叶斯推理的马尔可夫链蒙特卡罗算法。 - 采用基于贝叶斯推理的鲁棒优化框架对参数化飞机机翼进行多目标优化设计，在满足目标设计载荷的置信度下，给出最优机翼参数的概率估计沿着。- 在稳健的优化框架内，吸收平衡空气动力学性能的实际成本函数与重新设计的风险，以满足负载规格，这将为简化的A/C设计工作流程纳入明智的战略决策。

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.