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.
Three-dimensional non-linear grid showing bins of varying sizes in each dimension
Piechart representation of MATLAB's profiler results for a serial version of the 4-D granulation PBM code run on a single worker
Exponential increase in computation time with grids size. Grid count represents the total number of bins in each dimension
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.