Biography

Dr. Kotteda is a computational flow modeling engineer with broad expertise in high-performance computing and multiphase flows. He was working as a senior research scientist in the Digital Rock Technology, Center of Innovation for Flow through Porous media. He was a postdoctoral research associate at the University of Wyoming and working with Dr. M Stoellinger. Prior to that, he was a postdoctoral researcher in multiphysics and multiscale laboratories of Dr. V Kumar at the University of Texas at El Paso. Dr. Kotteda was also a guest researcher at Sandia National Laboratories and worked with his co-mentor (Dr. W Spotz) to develop an interface to integrate NETL's MFiX with Sandia's Trilinos. He had also collaborated with several other researchers at Sandia National Laboratories to incorporate their tools, such as Trilinos, and Dakota with other open-source softwares. He was a research scholar in Professor S Mittal's computational fluid dynamics group at the Indian Institute of Technology Kanpur.

As a postdoc at UWyo, he designed and implemented advanced spectral models for thermal radiation in MFiX. The fossil fuel community widely uses MFiX suite for simulating flow in fluidized beds. During his postdoctoral training at UTEP, he developed an interface to integrate the advanced linear solvers in Trilinos with MFiX. Trilinos provides a framework for simulating large-scale, sophisticated multiphysics engineering and scientific problems. The interface was written in Fortran and C/C++, and he had verified and validated it on various fluid bed problems. He also tested the linear solvers' performance, which was based on the Kokkos programming model, on various computer architectures. The iterative solvers in the integrated multiphase flow solver are relatively fast compared to the built-in solvers in MFiX. Further, he designed and developed a framework to integrate MFiX with Dakota for uncertainty quantification, sensitivity analysis, and optimization of multiphysics problems. Besides, Dr. Kotteda also contributed to developing an in-house exascale capable pore-network simulator and combined it with Dakota. Dr. Kotteda has used machine learning algorithms in various python libraries to predict pressure difference/flow rate in Hagen-poiseuille flow, friction factor, metrics for characterizing laser propagation in atmospheric turbulence, and depth of penetration of molten salt into a pore network. He also has experience in using deep neural networks in TensorFlow.

He developed a stabilized finite-element method with higher-order interpolation functions to solve turbulent flows via Reynolds-Averaged Navier Stokes equations in three dimensions during his doctoral studies. MPI library implemented in the code for interprocessor communications allows simulating engineering flow problems. Dr. Kotteda compared the performance of 3-noded linear and 6-noded quadratic triangular elements. In 3D, the relative performance is evaluated for 6-noded linear and 18-noded quadratic wedge elements. Numerical results are compared for the solutions to Euler, laminar, and turbulent flows at subsonic, transonic, and supersonic speeds. He employed this in-house CFD code written in Fortran and C to simulate flow in various ramjet engine components.