A generative sampling method that runs a full-order stochastic differential equation in a Double Diffusion Maps latent space and lifts samples back to the data space via Geometric Harmonics.
Uncertainty Quantification and Flow Dynamics in Rotating Detonation Engines
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abstract
Rotating detonation engines (RDEs) are a critical technology for advancing combustion engines, particularly in applications requiring high efficiency and performance. Understanding the supersonic detonation structure and how various parameters influence these phenomena is essential for optimizing RDE design. In this study, we perform detailed simulations of detonations in an RDE and analyze how the flow patterns are affected by key parameters associated with the droplet arrangement within the engine. To further explore the system's sensitivity, we apply polynomial chaos expansion to investigate the propagation of uncertainties from input parameters to quantities of interest (QOIs). Additionally, we develop a framework to accurately characterize the joint distributions of QOIs with a limited number of simulations. Our findings indicate that the strategic release of droplets may be crucial for sustaining continuous detonation waves in the engine, and accurate representations of the solution (e..g via high-order chaos expansions) are essential to accurately capture the dependence between QOIs and input uncertainties. These insights provide a quantitative foundation for further optimization of RDE designs.
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Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps
A generative sampling method that runs a full-order stochastic differential equation in a Double Diffusion Maps latent space and lifts samples back to the data space via Geometric Harmonics.