Abstract

Combustion modeling is now playing an important role in the design and optimization of advanced combustion devices. For high-fidelity combustion modeling, it is essential, though challenging, to resolve the highly nonlinear turbulence-chemistry interaction (TCI) and to predict the near-limit combustion phenomena. This talk will first give a review on the grand challenges for turbulent flame simulations. The implication of stiff chemical kinetics and TCI on numerical methods will be discussed. Then the talk will discuss the potential use of machine learning in some aspects of physical modeling and computational acceleration for turbulent flame simulations. Specific examples include efficient evaluation of the nonlinear reaction mapping, the use of neural ODE for mechanism optimization, and exploring the intrinsic active subspace in uncertainty quantification.

任祝寅

清华大学航空发动机研究院教授

主要从事湍流燃烧和航空宇航推进领域的基础研究工作，包括湍流燃烧相互作用机制和污染物形成机理、和燃烧室燃烧过程优化研究。重点关注推进和动力系统瓶颈问题的机理研究，如高效、低排放燃烧，弹性燃料系统开发，和高速流中化学反应、湍流和流动压缩相互作用。发展和完善了湍流燃烧概率密度函数模拟方法，揭示了反应标量小尺度混合机制，发展了新的标量小尺度混合模型、污染物燃烧模型、及高效化学反应动力学计算理论，相关研究成果已被广泛应用在商业软件以及燃烧器与能源系统优化等方面。已在Combustion and Flame和AIAA等期刊上发表SCI论文70余篇。获吴仲华优秀青年学者奖。2020入选国家杰出青年基金项目。