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CHEM-ML MODEL FOR NON-EQUILIBRIUM CHEMISTRY IN HYPERSONIC FLOWS

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC23PB461
Agency Tracking Number: 232144
Amount: $149,960.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: S17
Solicitation Number: SBIR_23_P1
Timeline
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-07-17
Award End Date (Contract End Date): 2024-02-02
Small Business Information
700 N Brand Blvd, Suite 700
Glendale, CA 91203-3215
United States
DUNS: 055775803
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Joseph Abraham
 (818) 240-1919
 abraham@kcse.com
Business Contact
 Liang Wei
Phone: (818) 844-1995
Email: wei@kcse.com
Research Institution
N/A
Abstract

The Kamp;C team plans to propose efficient artificial intelligence (AI) and machine learning (ML) based surrogate models (CHEM-ML) for non-equilibrium chemistry in hypersonic flows which is critical in designing hypersonic vehicles for space exploration. The CHEM-ML model can be coupled with reactive Navier-Stokes equations or high fidelity CFD models such as FUN3D and DPLR. In addition, CHEM-ML will be able to support both simple and complex chemical mechanisms. A deep operator network (DeepONet) will be employed to model the chemical kinetics in hypersonic flows such as gas-species reactions and gas surface reactions depending on the velocity, altitude and the materials of the hypersonic vehicle. DeepONet is based on the universal approximation of nonlinear operators which is suggestive of the potential application of neural networks in learning nonlinear operators from data. DeepONet can learn the stiff temporal evolution of chemical speciesrsquo; mass fractions over a given duration during offline training, so that during a prospective simulation inference from the learned algorithm can evolve the thermo-chemical state at a rate comparable to the hydrodynamic time scale, but without sacrificing the fidelity of the chemical systemrsquo;s transition path. Note that Kamp;C team has recent experience with DeepONet models for stiff chemical kinetics problems which were successfully used in reactive flow CFD simulations to speed up the calculation by over x1000 times. The Kamp;C team is poised to develop a model for a variety of chemical reaction mechanisms despite the short period of performance for Phase I due to the extensive expertise and existing DeepONet tools already used by Kamp;C.

* Information listed above is at the time of submission. *

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