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- W4313141838 abstract "Abstract A combustion jet based on Sandia National Laboratories’ D Flame experiment will be studied computationally and validated by current available physical experimental results. In which the chemical reaction of methane-based fuel will be modeled. In which 36 species will be considered in the effects of entrainment, mixing, ignition and combustion. Based on the difficulty of finding the appropriate mesh, the most appropriate way to generate the mesh will be analyzed. A mesh refinement code incorporated into reactive foam will be used to supplement the OpenFOAM code used in this study. The P1 radiation model is used to exchange heat transfer, the k-e RANS model as our turbulence model, and the gases will be taken as compressible where the density changes. In addition to the mesh refinement code, Markov decision processes will be used to automate the mesh generation process based on reinforcement learning. In which reward and punishment will be used to train the network. Establishing a general and automated way to generate meshes in problems for national security in the matter of combustion based on jet flows." @default.
- W4313141838 created "2023-01-06" @default.
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- W4313141838 date "2022-08-03" @default.
- W4313141838 modified "2023-10-18" @default.
- W4313141838 title "Mesh Adaptability Technique for Canonical Turbulent Jet Flows via Reinforcement Learning" @default.
- W4313141838 doi "https://doi.org/10.1115/fedsm2022-87413" @default.
- W4313141838 hasPublicationYear "2022" @default.
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