Himanshu Goyal Department of Chemical Engineering Areas of interest:Clean Energy, Process Intensification, Microwave Heating, Multiscale and Multiphysics Simulations, Computational Fluid Dynamics, Uncertainty Quantification

Dr Himanshu Goyal is an Assistant Professor in the Department of Chemical Engineering at IIT Madras. He received his BTech in Chemical Engineering from IIT Guwahati in 2011. Subsequently, he worked for two years in Reliance Industries Limited and Indian Institute of Science (IISc) Bangalore before starting his PhD program at the School of Chemical and Biomolecular Engineering at Cornell University. After obtaining his PhD in 2018, Dr Goyal worked for a year as a postdoctoral researcher at the University of Delaware, before joining IIT Madras.

What is the area of your research?

My research focuses on the development and application of multiscale and multiphysics simulation tools for emerging clean energy technologies and process intensification techniques. Examples include conversion of biomass into biofuels, carbon (CO₂) capture, and electrification of chemical industries. The relevant physics to our applications is reactive multiphase flows and microwave heating, for which we use an in-house computational fluid dynamics (CFD) code as well as commercial software like COMSOL. Due to their complexity, these processes heavily rely on empirical correlations that are not accurate and cannot be used for optimization. To reduce this reliance on empiricism, my group develops computational tools that provide a scientific basis for the development of design, optimization, and scale-up tools.

The high computational cost and the associated uncertainties make the use of multiscale and multiphysics simulations problematic in industries. To tackle these challenges, my group utilizes machine learning algorithms and uncertainty quantification (UQ). Machine learning allows the development of computationally inexpensive tools relevant for industry, whereas UQ provides a quantitative assessment of the uncertainty in the simulation predictions, thereby improving the reliability of simulations in engineering analysis.

How was your PhD and PostDoc experience with the computing facilities?

The multiscale and multiphysics nature of the simulations I perform, make them computationally expensive requiring High Performance Computing (HPC) facilities. During my PhD and postdoc, I heavily utilized in-house clusters as well as several supercomputing facilities, such as STAMPEDE and COMET. Many of my simulations required 100s of cores for weeks to complete. I was fortunate that I had direct access to several large-scale computing resources to run such simulations. I feel the definition of large-scale computations is rapidly changing with the ever-growing computing power across the world.

What do you suggest to PostDocs who wish to join academia in your field?

My research area requires close collaboration with experimental groups. Therefore, to the postdocs working in my area, I suggest that whenever possible, collaborate with experimentalists. Apart from making the work more impactful, it also provides a different perspective. There are numerous challenging problems of significant importance to the scientific community and society, which are primarily dominated by experimentalists. Applying the HPC/modelling/simulation skills in those areas can have a tremendous impact on the field and your contribution.