Even with exascale computing capabilities, high-fidelity, full-scale simulations of turbulent combustion in realistic applications remain computationally expensive and inaccessible for many query applications such as engineering design, optimization, and control. Projection-based model order reduction methods have shown promise in greatly improving computational efficiency. However, classical model order reduction methods that seek reduced solutions in low-dimensional subspaces fail for realistic turbulent combustion modeling in aerospace propulsion applications because reacting flows feature extreme stiffness, sharp gradients, and multi-scale transport, posing great challenges in deriving low-order representations.
In this talk, I will discuss recent advances in projection-based methods for reduced-order model (ROM) development of turbulent combustion problems. Specifically, I will introduce an adaptive ROM formulation that updates the low-dimensional space based on the evaluated dynamics during online calculations to greatly enhance predictive capabilities, thus circumventing representation barriers faced by static reduced-dimensional spaces.
The adaptive ROM formulation is evaluated using a test suite of benchmark turbulent combustion problems, including a 1D laminar flame, a 1D detonation tube, a 2D single rocket injector, and a 2D detonation wave. In addition, I will present the applications of the adaptive ROM method toward modeling systems for which full-order models are unaffordable (e.g., rocket engines). I will present the idea of a component-based modeling framework, which only requires high-fidelity simulations of small components in the full system. These component ROMs are then coupled together to enable the full-system simulations. The framework is demonstrated using a set of model rocket combustor configurations with different number of injectors exhibiting distinct combustion dynamics.
Cheng Huang is currently an assistant professor in the Department of Aerospace Engineering at the University of Kansas. Before that, he worked as an assistant research scientist in Aerospace Engineering at the University of Michigan – Ann Arbor and worked as a postdoctoral research assistant in the School of Aeronautics and Astronautics at Purdue University.
Cheng Huang received his Ph.D. in mechanical engineering from Purdue University in 2015, his master’s degree in mechanical engineering from Purdue University in 2012, and his bachelor’s degree in mechanical engineering from Shanghai Jiaotong University in 2011. He is the recipient of the 2022 Young Investigator Research Program (YIP) award by the Air Force Office of Scientific Research. He specializes in computational modeling of turbulent reacting flows in complex combustion systems such as rocket and gas turbine engines. His work primarily focuses on high-fidelity large eddy simulation (LES) and data-driven and reduced-order modeling (ROM) of combustion dynamics in aerospace propulsion systems.