137 citations · 297 across the 9 of their papers we have counts for
9 papers
Investigations on Projection-Based Reduced Order Model Development for Rotating Detonation Engine
Ryan Camacho, Cheng Huang
The current study aims to evaluate and investigate the development of projection-based reduced-order models (ROMs) for efficient and accurate RDE simulations. Specifically, we focu…
Predictive Reduced Order Modeling of Chaotic Multi-scale Problems Using Adaptively Sampled Projections
Cheng Huang, Karthik Duraisamy
An adaptive projection-based reduced-order model (ROM) formulation is presented for model-order reduction of problems featuring chaotic and convection-dominant physics. An efficien…
Reduced models with nonlinear approximations of latent dynamics for model premixed flame problems
Wayne Isaac Tan Uy, Christopher R. Wentland, Cheng Huang +1
Efficiently reducing models of chemically reacting flows is often challenging because their characteristic features such as sharp gradients in the flow fields and couplings over va…
Non-intrusive Balancing Transformation of Highly Stiff Systems with Lightly-damped Impulse Response
Elnaz Rezaian, Cheng Huang, Karthik Duraisamy
Balanced truncation (BT) is a model reduction method that utilizes a coordinate transformation to retain eigen-directions that are highly observable and reachable. To address reali…
Model Reduction for Multi-Scale Transport Problems using Model-form Preserving Least-Squares Projections with Variable Transformation
Cheng Huang, Christopher R. Wentland, Karthik Duraisamy +1
A projection-based formulation is presented for non-linear model reduction of problems with extreme scale disparity. The approach allows for the selection of an arbitrary, but comp…
Data-driven reduced-order models via regularized operator inference for a single-injector combustion process
Shane A. McQuarrie, Cheng Huang, Karen E. Willcox
This paper derives predictive reduced-order models for rocket engine combustion dynamics via Operator Inference, a scientific machine learning approach that blends data-driven lear…
Learning physics-based reduced-order models for a single-injector combustion process
Renee Swischuk, Boris Kramer, Cheng Huang +1
This paper presents a physics-based data-driven method to learn predictive reduced-order models (ROMs) from high-fidelity simulations, and illustrates it in the challenging context…
Investigations and Improvement of Robustness of Reduced-Order Models of Reacting Flow
Cheng Huang, Karthik Duraisamy, Charles L. Merkle
The impact of chemical reactions on the robustness and accuracy of projection-based Reduced-Order Models (ROMs) of fluid flows is investigated. Both Galerkin and Least-Squares Petr…
Reduced Order Modeling Framework for Combustor Instabilities Using Truncated Domain Training
Jiayang Xu, Cheng Huang, Karthik Duraisamy
A multi-fidelity framework is established and demonstrated for prediction of combustion instabilities in rocket engines. The major idea is to adapt appropriate fidelity modeling ap…