|Computational Modelling of Human Eye
Narayana Nethralaya Research Award (open to CDS-CP students, who join in Aug 2017, Fellowship Rs. 32,000/- per month)
This project is a collaborative work with Imaging and Biomechanics Lab, Narayana Nethralaya Bangalore. The overall scope of the collaboration is to develop a customized finite element code and 3D mesh to perform patient specific inverse finite element simulations for quantifying the non-linear hyperelastic properties of the human cornea.
Customized meshing: The human cornea is an irregular geometry that cannot be mesh with mapped settings. At the same time, the corneal collagen distribution follows an ordered pattern, which can be described with continuous functions. Since we propose to implement fiber based hyperelastic material models, a customized 3-D mesh with brick elements generator will be developed using 3-D measurements of corneal geometry.
Finite element solver: The 3-D mesh will be populated with appropriate boundary conditions and material model. To solve this model, we propose to develop a parallel finite element code.
Inverse simulations: General finite element simulations are forward where material properties are known. However, we need to determine the patient material properties in this case. The experimental measurement will be obtained from air-puff applanation (Corvis-ST, OCULUS Optikgerate Gmbh, Germany). Using the measurement and tools developed under (1) and (2), an inverse routine will be set up to determine the material properties. A combination of global and iterative search methods may be used so as to optimize the time required to reach a unique solution fit to the material model parameters.
|Modeling and Simulation of Crystallization Processes
Simulations of population balance systems can be used to study the behavior of crystallization, polymerization, pharmaceutical productions, dispersed phase distribution in multiphase flows, growth of microbial and cell populations.
Population balance systems model the behavior of complex flow systems involving numbers of entities such as particles, drops, bubbles or cells. Typical examples are droplets in clouds or particles in chemical processes like precipitation. The model of population balance systems does not describe the behavior of individual particles but the behavior of a particle size distribution (PSD). This is the quantity of interest in applications.
|Modeling and Simulation of Energy Extraction Processes in Fuel Cells
The aim of the project is to develop an accurate and efficient numerical (finite element) scheme for simulation of electrochemical processes. This project also involves parallel implementation of the developed numerical scheme in our in-house finite element package ParMooN.
Fuel cell is one of the promising renewable energy technologies that generates electricity by a chemical reaction. Most importantly, fuel cells generate electricity with very little pollution, and emits a harmless byproduct, water. Unlike batteries, fuel cells produce electricity as long as fuel (hydrogen) is supplied. Nevertheless, fuel cell is often compared to batteries as it is used as primary and backup power source.
Computational models are cost effective way to understand and optimize the electrochemical energy extraction processes in fuel cells. The model consists of coupled nonlinear partial differential equations (PDEs).
|Computations of thermofluidic transport of liquid metal in reactors
Modelling of thermofluidic transport of liquid metal in reactors to study the dynamics of self-cooled liquid metal fusion blanket in fusion reactors such as International
Thermonuclear Experimental Reactor (ITER) and Liquid Metal Fast Breeder Reactors
(LMFBR) need to be performed.
|A Parallel (MPI+OpenMP) Finite Element Solver
The development of robust and efficient high order algorithms and their use in HPC systems is an active area of research, which needs more investigation. The drive towards high order schemes also needs scalable time integration schemes and interesting new ideas are emerging, time accurate local time stepping, the swept rule etc.
Apart from other challenges associated with the parallel implementations, parallel computations require not only efficient parallel algorithms, but also highly scalable numerical methods. For instance, the choice of finite elements in finite element discretization will influence the parallel efficiency. The sparsity structure of the matrices also depends on the type of numerical scheme and can have an influence on the parallel performance of the algorithm. The use of heterogeneous architectures like CPUs and GPUs also needs a rethink on the algorithm side to exploit the full power of the hardware.
The main topics are: