KASHMINDER SINGH MEHTA

I am a highly skilled Simulation Engineer with expertise in a wide array of simulation techniques, including Finite Volume Method (FVM), Finite Element Analysis (FEA), Immersed Boundary Method (IBM), Computational Fluid Dynamics (CFD), and Discrete Element Method (DEM). I specialize in solving complex engineering challenges across multiple industries, combining advanced simulations with Machine Learning and Neutronics for enhanced accuracy and predictive modeling.

🔹 Core Competencies:

Machine Learning: Applying data-driven techniques to optimize simulations, improve model accuracy, and accelerate the analysis process.

CFD (Computational Fluid Dynamics): Proficient in OpenFOAM and COMSOL for fluid flow simulations, including turbulence modeling, heat transfer, and fluid-structure interactions.

DEM (Discrete Element Method): Expertise in LAMMPS and LIGGGHTS for simulating particle dynamics, material handling, and granular flow in industries like manufacturing, mining, and pharmaceuticals.

Coupled CFD-DEM Simulations: Proficient in CFD-DEM coupling to model fluid-particle interactions for complex systems such as material processing, slurry transport, and combustion processes.

Neutronics: Specialized in OpenMC for neutron transport simulations, critical for nuclear reactor analysis and related applications.

🔹My key projects include:

Multiphysics Modeling: Developed a comprehensive computational model for high-temperature gas-cooled pebble bed reactors by integrating OpenFOAM (CFD), LIGGGHTS (DEM), and OpenMC (neutronics).

CFD-DEM Simulations: Created an in-house CFD-DEM framework for multiphase flows and applied it to study particle-fluid interactions in various systems.

Machine Learning Applications: Leveraged ML techniques to analyze and optimize reactor designs and fluid-particle dynamics.

Evaporative Dynamics: Simulated the transport and assembly of colloidal particles in evaporating droplets using dissipative particle dynamics.

Residue Control: Investigated residue detachment through magnetic repulsion forces, employing advanced modeling techniques like Surface Evolver.

Passionate about integrating computational techniques with machine learning to drive innovation in energy systems and beyond.

• A Study on Heat Transfer and Pressure Drop in a Turbulent Flow Regime of Thermally Insulated and Conducting Nanofluids, Journal Of Nanofluids
• Controlling Detachment Residue via Magnetic Repulsion force, Applied Physics Letters
• Neutronics Analysis on High-Temperature Gas-Cooled Pebble Bed Reactors by Coupling Monte Carlo Method and Discrete Element Method, Energies
• Transport and assembly of Colloidal Particles in an Evaporating sessible Droplet: A dissipative Particle Dynamics Investigation, Under Review

• CFD (OpenFOAM, Comsol)
• DEM (M.D. Simulations)
• Machine Learning
• CFD-DEM (Integration)
• Neutronics (OpenMC)
• FEA/FVM/IBM
• High Performance Computing (HPC)
• Matlab/C++/Python

• GATE, 2014, Awarded full scholarship for master's degree
• GRE, 2017

Qualified Gate 2014 (Awarded full scholarship for master’s degree) (to 5 % in country

Qualified Gre 2017