Advanced Turbulent Combustion Model for Scramjet Unstart Predictions

Accurate and efficient simulation of unsteady reacting flow phenomena within liquid fuel burning hypersonic engines is an extremely challenging simulation task requiring a detailed understanding of supersonic mixing, self-ignition, liquid film atomization, detailed chemical reaction pathways, turbulent combustion interactions, shock-shock and shock-boundary layer interactions, and fluid-solid energy transfers. Though such high-fidelity models exist within commercial CFD software their computational cost is intractable for typical industrial use cases, limiting their application to academic use. To mitigate this limitation, reduced order models are typically adopted which employ simplifying assumptions, often decoupling interactions between physical mechanisms to a point where the desired phenomena can no longer be accurately predicted. DeepFlow LLC proposed and developed a novel solution that allows for the deployment of high-fidelity combustion modelling capabilities, at a computational cost comparable to non-reacting CFD simulations. Enabling fast and accurate simulation of hypersonic engine combustor stability regimes, including stable, unstable, unstart and blowout modes. To achieve this the Conditional Moment Closure (CMC) turbulent combustion model has been reformulated to allow for tabulated solutions of reactive scalars, computed offline, which are looked-up during the CFD simulation.  Though the combustion table reduces the number of numerically solved transport equations, memory demands quickly become intractable with increasing table dimensionality and resolution. To address this a machine-learning inspired solution was developed to reduce the memory footprint of high-dimensional tables. An unsupervised non-linear manifold learning model is employed to reduce the dimensionality of the CMC combustion table. The reduced table is then used for supervised training of various regression surrogate models. By replacing the n-dimensional table with a surrogate model, the memory demands associated with table dimensionality and resolution are relieved, and instead are solely dependent on the complexity of the surrogate model. The DeepFlow team plans to develop this technology for production use by developing a software plugin for the three leading commercial CFD software, namely US3D, CharLES, and StarCCM. During the Phase II program the existing plugin software is to be further developed, validated, and ported to run efficiently on heterogeneous compute resources, leveraging both CPU and GPU hardware to accelerate the required plugin compute loads. In addition, the use of the CMC model will be integrated and tested within the reduced-order systems analysis software NPSS. This technology is expected to eliminate a technological barrier in hypersonic propulsion engine design, allowing for a significant competitive advantage in the design of the next-generation propulsion defense systems.