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physics.flu-dyn2023★ 1 cited
Parametric reduced order models with machine learning for spatial emulation of mixing and combustion problems
Chenxu Ni, Siyu Ding, Xingjian Wang
High-fidelity simulations of mixing and combustion processes are generally computationally demanding and time-consuming, hindering their wide application in industrial design and o…
physics.flu-dyn2023
Flow Dynamics of a Dodecane Jet in Oxygen Crossflow at Supercritical Pressures
Siyu Ding, Jiabin Li, Longfei Wang +2
In advanced aero-propulsion engines, kerosene is often injected into the combustor at supercritical pressures, where flow dynamics is distinct from the subcritical counterpart. Lar…
physics.flu-dyn2021★ 1 cited
Common kernel-smoothed proper orthogonal decomposition (CKSPOD): An efficient reduced-order model for emulation of spatiotemporally evolving flow dynamics
Yu-Hung Chang, Xingjian Wang, Liwei Zhang +4
In the present study, we propose a new surrogate model, called common kernel-smoothed proper orthogonal decomposition (CKSPOD), to efficiently emulate the spatiotemporal evolution…