REVIEW 1 cited by
Uncertainty Quantification and Flow Dynamics in Rotating Detonation Engines
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 1 Pith paper
-
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.
Discussion (0). Continue with ORCID to comment.