Assist. Prof. Dr. Shravan Kumar Rudrabhatla | Best Researcher Award

Published on by Physics and Quantum Physics

Assist. Prof. Dr. Shravan Kumar Rudrabhatla | Best Researcher Award

Anurag University | India

Dr. Shravan Kumar Rudrabhatla is an Assistant Professor at Anurag University, Hyderabad, specializing in fluid dynamics and artificial neural networks. He earned his Ph.D. in Applied Mathematics from the National Institute of Technology (NIT), Warangal in 2023 under the supervision of Prof. D. Srinivasacharya, focusing on the artificial neural network treatment of Casson fluid flow over a radially stretching sheet. His research integrates deep learning, computational fluid dynamics, and heat and mass transfer modeling, contributing to the understanding of complex non-Newtonian flows. Dr. Rudrabhatla has authored 6 research articles, accumulated 49 citations from 43 documents, and achieved an h-index of 4, as indexed by Scopus. His recent works include publications in European Journal of Mechanics B/Fluids, Physics of Fluids, Mathematical Models and Computer Simulations, and Journal of Thermal Analysis and Calorimetry. He has participated in numerous faculty development programs, workshops, and GIAN courses focused on machine learning and computational modeling. His academic journey is complemented by strong technical skills in Python, MATLAB, and C++, and a teaching background spanning over a decade. Dr. Rudrabhatla’s work continues to advance the intersection of mathematics, fluid mechanics, and artificial intelligence, contributing significantly to modern computational sciences.

Profiles : Orcid | Google Scholar | Scopus

Featured Publications

Srinivasacharya, D., & Kumar, R. S. (2022). Artificial neural network modeling of the Casson fluid flow over unsteady radially stretching sheet with Soret and Dufour effects. Journal of Thermal Analysis and Calorimetry, 147, 14891–14903. https://doi.org/10.1007/s10973-022-11694-w

Srinivasacharya, D., & Shravan Kumar, R. (2023). Neural network analysis for bioconvection flow of Casson fluid over a vertically extending sheet. International Journal of Applied and Computational Mathematics, 9(5), 80. https://doi.org/10.1007/s40819-023-01556-w

Srinivasacharya, D., & Kumar, R. S. (2023). An artificial neural network solution for the Casson fluid flow past a radially stretching sheet with magnetic and radiation effect. Mathematical Models and Computer Simulations, 15(5), 944–955. https://doi.org/10.1134/S2070048223050101

Nallapu, S., Sneha, G. S., & Kumar, S. R. (2018). Effect of slip on Jeffrey fluid flow through an inclination tube. Journal of Physics: Conference Series, 1000(1), 012041. https://doi.org/10.1088/1742-6596/1000/1/012041

Rudrabhatla, S. K., & Srinivasacharya, D. (2025). Deep learning framework for Casson fluid flow: A PINN approach to heat and mass transfer with chemical reaction and viscous dissipation. European Journal of Mechanics – B/Fluids, 204401. https://doi.org/10.1016/j.euromechflu.2025.204401

Dr. Awani Bhushan | Best Researcher Award

Published on by Physics and Quantum Physics

Dr. Awani Bhushan | Best Researcher Award

Velllore Institute of Technology, University (Chennai Campus) | India

Dr. Awani Bhushan is an Assistant Professor (Senior Grade) in the School of Mechanical Engineering at Vellore Institute of Technology, Chennai, with an h-index of 6, 33 documents, and 113 citations. He earned his Ph.D. in Machine Design from the Indian Institute of Technology (BHU), Varanasi, and holds prior degrees in machine and mechanical engineering. His research and teaching experience span nuclear engineering, solid mechanics, fracture mechanics, finite element analysis, and reliability assessment, combining analytical, experimental, and numerical approaches. He develops and validates computational models using ANSYS and COMSOL Multiphysics alongside custom FORTRAN and C++ implementations, and applies statistical Weibull-based methods for strength and size-effect characterization. His publications appear in Journal of Nuclear Materials, Journal of Testing and Evaluation, RSC Advances, and other peer-reviewed outlets. Key contributions include Weibull design criteria for nuclear graphite, fracture parameter correlation for unimodular and bimodular graphite, and studies on composites and functionally graded materials. He has secured research funding, filed patents, led collaborative proposals, and mentors students in interdisciplinary projects aimed at energy, defense, and structural-integrity applications. Overall, his work advances computational mechanics and material reliability with practical engineering impact and ongoing scholarly growth.

Profiles : Google Scholar | Scopus | Orcid

Featured Publications

Bhushan, A., & Panda, S. K. (2018). Experimental and computational correlation of fracture parameters KIc, JIc, and GIc for unimodular and bimodular graphite components. Journal of Nuclear Materials, 503, 205–225.

Kumar, H., Tengli, P. N., Mishra, V. K., Tripathi, P., Bhushan, A., & Mishra, P. K. (2017). The effect of reduced graphene oxide on the catalytic activity of Cu–Cr–O–TiO₂ to enhance the thermal decomposition rate of ammonium perchlorate: An efficient fuel oxidizer. RSC Advances, 7(58), 36594–36604.

Bhushan, A., Panda, S. K., Khan, D., Ojha, A., Chattopadhyay, K., & Kushwaha, H. S. (2016). Weibull effective volumes, surfaces, and strength scaling for cylindrical flexure specimens having bi-modularity. Journal of Testing and Evaluation, 44(5), 1978–1997.

Ram, S. C., Chattopadhyay, K., & Bhushan, A. (2023). A literature review on Al–Si alloy matrix based in situ Al–Mg₂Si FG-composites: Synthesis, microstructure features, and mechanical characteristics. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

Suman, S., Yadav, A. M., Tomar, N., & Bhushan, A. (2020). Combustion characteristics and behaviour of agricultural biomass: A short review. Renewable Energy – Technologies and Applications.

Suman, S., Yadav, A. M., Bhushan, A., Bhaskara Rao, L., & Rajak, D. K. (2022). Substitution of coking coal with biochar for thermal and metallurgical utilisation. International Journal of Sustainable Energy, 41(11), 1778–1794.*