Research Projects

Thermo-bioconvection refers to the emergence of organized flow patterns driven by the interaction of motile microorganisms, thermal gradients, and gravity in a fluid. These microorganisms, such as algae or bacteria, navigate via mechanisms like chemotaxis or thermotaxis. In porous cavities, the behavior of these systems is described by equations based on conservation laws, including mass, momentum, and concentration. Solving these equations numerically has traditionally relied on methods like finite difference, finite element, and similar techniques. However, these methods face challenges due to the non-linearity of the equations and boundary conditions. In recent times, AI-driven approaches, particularly Physics-Informed Neural Networks (PINNs), are being explored for thermo-fluidic problems. PINNs excel in handling complex, unstructured domains, non-linear boundary conditions, and extensive parameter spaces, making them suitable for problems that are otherwise computationally intensive using traditional methods. The proposed project aims to leverage PINNs to study thermo-bioconvection in porous cavities, an area with limited but emerging research. The results are expected to have wide-ranging applications in industrial, engineering, and domestic systems. Accurate Prediction of Coupled Dynamics: The project aims to use AI to model the interconnected behavior of temperature, fluid velocity, and microorganism concentration fields in thermo-bioconvection problems to ensure accurate and holistic simulation. Innovative Use of Physics-Informed Neural Networks (PINNs): By embedding governing equations directly into the learning framework, the proposed approach eliminates the need for traditional meshing, simplifying the computational process and reducing errors associated with numerical discretization. Efficiency in Complex and High-Dimensional Systems: The methodology handles parameter-sensitive and high-dimensional systems efficiently. It is particularly suitable for complex thermo-bioconvection scenarios in porous cavities.