48 citations · 66 across the 7 of their papers we have counts for
4 papers · 1 filter
DD-RNO: A Domain-Decomposed Routed Neural Operator for Airfoil Flow Prediction
T. A. Mehta, P. S. Bhati, H. D. Akolekar
Deep learning surrogates for RANS flow prediction around airfoils face two persistent bottlenecks. A single neural architecture cannot simultaneously resolve sharp near-wall bounda…
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…
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…
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…