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physics.flu-dyn2026
Realizability-Constrained Machine Learning for Turbulence Closures in Wake Flows
Talib Ansari, Priyank H. Mehta, Harshal D. Akolekar
Computational fluid dynamics (CFD)-driven machine learning frameworks based on symbolic regression offer a promising pathway for turbulence model discovery, but are often hindered…
physics.flu-dyn2024
Enhancing Accuracy of Transition Models for Gas Turbine Applications Through Data-Driven Approaches
Harshal D. Akolekar
Separated flow transition is a very popular phenomenon in gas turbines, especially low-pressure turbines (LPT). Low-fidelity simulations are often used for gas turbine design. Howe…
physics.flu-dyn2024
Surface roughness effects in a transonic axial flow compressor operating at near-stall conditions
Prashant B. Godse, Harshal D. Akolekar, A. M. Pradeep
Surface roughness is a major contributor to performance degradation in gas turbine engines. The fan and the compressor, as the first components in the engine's air path, are especi…