Sod Shock Tube: investigations of data-driven modeling methods for discontinuous physics (Read More)
Contact Surface: investigations of data-driven modeling methods for convection-dominated problems with sharp gradients while eliminating the complexity of chemical reaction (Read More)
Laminar Flame: investigations of data-driven modeling methods for convection-dominated problems with sharp gradients and highly nonlinear/stiff chemical reaction (Read More)
A 2D planar representation of a generic laboratory-scale combustor is simulated to assess the capabilities of reduced models for representing realistic combustion flowfields. The generated simulation dataset is established and made public to provide a testbed to build reduced models for relevant challenging reacting flow problems using different methods.
Relevant Publications:
Huang, C., Duraisamy, K., and Merkle, C.L., Investigations and Improvement of Robustness of Reduced-Order Models of Reacting Flow, AIAA Journal, 2019.
Swischuk, R., Kramer, B., Huang, C., and Willcox, K., Learning Physics-Based Reduced-Order Models for a Single-Injector Combustion Process, AIAA Journal, 2020.
McQuarrie, S. A., Huang, C., and Willcox, K., Data-driven reduced-order models via regularized operator inference for a single-injector combustion process, arXiv:2008.02862, 2020. (code available: https://github.com/Willcox-Research-Group/ROM-OpInf-Combustion-2D)
Huang, C., Wentland, C.R., Duraisamy, K., Merkle, C. Model Reduction for Multi-Scale Transport Problems using Structure-Preserving Least-Squares Projections with Variable Transformation, arXiv preprint arXiv:2011.02072.