HDTRA224C0001

Small Technology Transfer Research Program (STTR) Phase II

Deep Operator Networks (DeepONet) for Reactive Flow

Accurate computational modeling and predictions of reactive flows with stiff chemical kinetics requires computational fluid dynamic (CFD) codes either: 1) with very small timesteps to accurately capture the non-linear kinetics, or 2) with simplified skeletal kinetics that can improve runtime but reduce accuracy. This is a severe limitation that makes accurate modeling of the chemical kinetics very challenging. To accurately model these types of reaction mechanisms in a CFD simulation without a loss in fidelity or accuracy, a data-driven machine learning approach is needed that can accurately predict the chemical kinetics with a large CFD timestep without compromising accuracy. Deep Operator Networks (DeepONet) is an exciting data-driven machine learning technique that can learn nonlinear operators from training data. In the proposed effort, a DeepONet software library will be developed that can be integrated into any CFD code using a flexible software interface. A pre-trained DeepONet database for various reaction mechanisms can then be used to enable CFD codes to accurately simulate reactive flows with stiff kinetics with speeds greater than x1000.

The goal of phase II is to continue the R&D efforts initiated in Phase I. Funding is based on the results achieved in Phase I and the scientific and technical merit and commercial potential of the project proposed in Phase II. STTRs are completed in conjunction with a research institution.

Brown University

DTRA21B-002

T2-0477

21.B

Brian P Leppert