Development & Implementation of Efficient Numerical & High-performance Computational Techniques 

Here, we seek to develop better numerical techniques and model reduction techniques to solve complex process models (e.g. PBM and PBM-DEM ) with reduced simulation times whilst maintaining high accuracy. We also work on the massive parallelization of population balance models using high performance and distributed computing techniques. 

List of Publications from this Research Area:

• D. Barrasso, A. Tamrakar, R. Ramachandran. A reduced order  PBM-ANN model of a multi-scale PBM-DEM description of a wet granulation process. Chemical Engineering Science,  119, 319-329, 2014.

• A. Chaudhury, I. Oseledets, R. Ramachandran, A computationally efficient technique for the solution of multi-dimensional PBMs of granulation via tensor decomposition, Computers & Chemical Engineering, 61, 234-244, 2013.

• Anuj V. Prakash, Anwesha Chaudhury, and Rohit Ramachandran, “Parallel Simulation of Population Balance Model-Based Particulate Processes Using Multicore CPUs and GPUs,” Modelling and Simulation in Engineering, vol. 2013, Article ID 475478, 16 pages, 2013.

• A.V. Prakash, A. Chaudhury, D. Barrasso and R. Ramachandran. Simulation of population balance model-based particulate processes via parallel and distributed computing. Chemical Engineering Research & Design, 91(7),1259–1271, 2013. 

• A. Chaudhury, A. Kapadia, D. Barrasso, A.V. Prakash and R. Ramachandran. An Extended Cell-average Technique for Multi-Dimensional Population Balance Models describing Aggregation and Breakage. Advanced Powder Technology, 24(6), 962-971, 2013. 

• D. Barrasso and R. Ramachandran. A comparison of model order reduction techniques for a four-dimensional population balance model describing multi-component wet granulation processes. Chemical Engineering Science, 80, 380-392, 2012.