Jigar Parekh

• Development of surrogate based capabilities of DLR’s Surrogate Modeling for AeRo-data Toolbox in python (SMARTy). • Pursuing SE2A Cluster of Excellence project - Effective design methods and exploration for robust optimization of laminar wings. • Working towards AIAA 2023 UQ Challenge and algorithm development for multi-disciplinary uncertainty quantification problems. • Support SMARTy users in their data-driven engineering projects.

• Developed novel methods for uncertainty quantification in CFD applied to wind turbine wake prediction using physics-based and data-driven approaches. • Engineered a 3D U-Net CNN model, trained over a single wind turbine data, effectively employed to predict the flow field in a windfarm. • Developed a stochastic solver in OpenFOAM for propagating uncertainty in RANS turbulence models, while outperforming traditional UQ methods by 5–10 times. • Two open source codes.

• Analyzed CT-scan data to intricately reconstruct 3D microstructures of AP/HTPB energetic composites. • Developed bash and python automation scripts to perform simulations using ANSYS Fluent and streamlined post-processing. • Led an interactive tutorial session delving into the intricacies of running embarrassingly parallel jobs on supercomputer.