Towards Data Driven Aerodynamic Models: Data Fusion of Experiment and Simulation

在数字时代，无处不在的基于物理的计算工程工具，如计算流体动力学（CFD）、机器学习算法和不断增强的计算能力，有助于加速在民用运输部门以及国防和安全领域部署的新技术的发展，以应对最苛刻的经济、环境和社会挑战。设想首先探索未来的算法，包括人工智能代理模型，用于近实时联合实验/数值数据分析，这是不确定性意识，稳健和可量化的，以通知和优化风洞活动，包括在飞行中。其次，考虑到高保真CFD运行和全仪器风洞测试可以产生的大量数据，特别是对于非定常流动模拟，第一个目标需要利用未来的计算系统进行高并行化，例如在CDT中探索的那些。

The digital age with ubiquitous physics-based computational engineering tools, such as computational fluid dynamics (CFD), machine learning algorithms and ever-increasing computing power, helped accelerate the development of novel technologies deployed in the civil transport sector, as well as in defence and security, to meet the most demanding economic, environmental and societal challenges. It is envisaged to first explore future algorithms, including AI surrogate models, for near real-time joint experimental/numerical data analysis, that is uncertainty-aware, robust and quantifiable, to inform and optimise a wind-tunnel campaign, including on-the-fly. Second, considering the vast amount of data that a high-fidelity CFD run and a fully instrumented wind-tunnel test can produce, particularly for unsteady flow simulations, the first objective calls for high parallelisation utilising future computing systems, such as those explored within this CDT.