5 citations · 9 across the 11 of their papers we have counts for
11 papers
Machine learning assisted modeling of thermohydraulic correlations for heat exchangers with twisted tape inserts
J. P. Panda, B. Kumar, A. K. Patil +1
This article presents the application of machine learning (ML) algorithms in modeling of the heat transfer correlations (e.g. Nusselt number and friction factor) for a heat exchang…
Data-driven prediction of complex flow field over an axisymmetric body of revolution using Machine Learning
J P Panda, H V Warrior
Computationally efficient and accurate simulations of the flow over axisymmetric bodies of revolution (ABR) has been an important desideratum for engineering design. In this articl…
Reynolds Stress Modeling Using Data Driven Machine Learning Algorithms
J P Panda
Fluid turbulence is an important problem for physics and engineering. Turbulence modeling deals with the development of simplified models that can act as surrogates for representin…
Machine Learning for Naval Architecture, Ocean and Marine Engineering
J P Panda
Machine Learning (ML) based algorithms have found significant impact in many fields of engineering and sciences, where datasets are available from experiments and high fidelity num…
Evaluation of machine learning algorithms for predictive Reynolds stress transport modeling
J. P. Panda, H. V. Warrior
The application machine learning (ML) algorithms to turbulence modeling has shown promise over the last few years, but their application has been restricted to eddy viscosity based…
Modelling the Pressure Strain Correlation in turbulent flows using Deep Neural Networks
J P Panda, H V Warrior
The pressure strain correlation plays a critical role in the Reynolds stress transport modelling. Accurate modelling of the pressure strain correlation leads to proper prediction o…