The K&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 speciesÂ’ 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 systemÂ’s transition path. Note that K&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 K&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 K&C. Anticipated
Benefits: Potential for NASA space missions in both Human Exploration and Operations Mission Directorate (HEOMD) and Science Mission Directorate (SMD) with an EDL segment. Missions depend on aerothermal CFD to define critical flight environments and would see significant, sustained reductions in cost and time-to-solution if an effective ML-based model is deployed. The scope has strong crosscutting benefits for tools used by ARMD to simulate airbreathing hypersonic vehicles, which have stringent accuracy requirements like those in aerothermodynamics. The CHEM-ML non-NASA market is extensive and covers Government, private sector, and academia from various scientific fields. The market size for the Phase II product includes all scientists and researchers working on reactive flows. The market includes a variety of applications such as: 1) weapon effects, 2) agent defeat, 3) modeling of hypersonic plumes, 4) propulsion, 5) combustion engines, etc.
