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

迈向数据驱动的空气动力学模型：实验与仿真的数据融合

• 批准号: 2447391
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2447391
• 关键词: Towards; Data; Driven; Aerodynamic; Models

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.

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长的计算能力，有助于加速民用运输部门以及国防和安全领域新技术的开发，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，包括人工智能替代模型，用于近实时的联合实验/数值数据分析，该分析具有不确定性，鲁棒性和可量化性，以通知和优化风洞活动，包括飞行。第二，考虑到高保真CFD运行和全仪表化风洞试验可以产生的大量数据，特别是对于非定常流模拟，第一个目标要求利用未来的计算系统进行高度并行化，例如在本CDT中探索的系统。