Machine learning-assisted design of fluid Mixer 1. Development of a deep-learning model that can predict a fluid flow over obstacles.2. Model is trained over the selected flow parameters for whole flow evolution.3. Flow prediction for unknown parameters has good accuracy. 4. Currently, the model is parameterized for the motion of the cylinder with the goal of finding the optimal motion of cylinder that will have the largest mixing at the outflow.

Developing new ML models based on Physics Informed neural network for modeling sound wave propagation. The model is trained on data from novel low-cost infrasound microphones developed by our team. Microphones are installed in Virgo, Italy under the Horizon 2020 Project.I have developed and evaluated a convolution neural network with multi-head self-attention for infrasound signal detection and categorization.Creation of scripts for running code and visualization on multiple GPU and CPU and Using Scikit-Learn, Pytorch, Pandas, Numpy, Scipy, Seaborn, and Matplotlib modules and Jupyter-notebook, SSH, and VS-Code for running code in interactive mode on the cloud.

I was taking the Computer science -1 laboratory of the 1-year undergraduate students. I was teaching the basics of the programming using C language. Currently, I am teaching numerical methods laboratory classes.

I have implemented a Discontinuous Galerkin based solver using Python programming language for compressible flows. It is based on Triangular meshes with h,p adaptation. The code is validated by running test cases of the compressible and incompressible flow with the results of OpenFOAM. Simultaneously Nektar++ (An Open-Source C++ library of h to p finite element framework which uses continuous and discontinuous basis functions) was examined as a viable alternative. Hydrodynamic stability problems were addressed using Nektar++ with the 2D Direct and 2D Adjoint stability analysis of the channel flow, backwards step, cylinder in channel and other 2D geometries. Eventually, research has been focused on viscous incomprehensible flow in a channel with transversely corrugated walls for efficient mixing at low Reynolds numbers. The goal of the research was to broaden the understanding of flow stability at a range of geometric and flow parameters. The stability of an incomprehensible flow in a channel with transversely corrugated walls has been investigated. Such flows are interesting due to their possible application for efficient laminar mixing.

1. Design of computational geometries and grids, simulation of base flows for an unbounded, periodic case.2. Analysis of the base flows focusing on the hydraulic losses introduced by the geometrical wall modulation.3. Investigation of the linear stability properties of the base flows using the Direct Numerical Simulation (DNS) technique.4. Investigation of the nonlinear saturation states.5. Design of computational geometries and grids, simulation of base flows for a case with side walls, periodic in the streamwise direction.6. Design of a computational method allowing for identification of the optimal disturbance using the adjoined operator. (Transient Growth).7. Investigation of the behavior of the optimal disturbances in the non-linear regime using the DNS approach.

Making comparison sheets of prices of different suppliers. Finalizing the specifications of materials and establishing quantity limits for effective inventory control and reducing wastages. Streamlining the system and procedures for effective inventory control for ensuring ready availability of materials to meet the production targets. Material procurement and scheduling. Follow up with customer for material procurement. Preparing material procurement tracking sheets & Receiving reports Accountable for meeting customer orders by implementing and monitoring effective procurementschedules & Planning.