Dr. Mubbashar Nazeer | Editorial Board Member

Dr. Mubbashar Nazeer | Editorial Board Member

Government College University Faisalabad | Pakistan

Dr. Mubbashar Nazeer is a prominent researcher in applied mathematics, specializing in fluid mechanics, bio-fluids, nanofluid dynamics, heat transfer, cavity flows, and finite element analysis. With an h-index of 23, over 90+ documents, and more than 1,800 citations, his research has made significant contributions to nonlinear rheology, multiphase flow modeling, magnetohydrodynamics, and thermal transport in complex fluids. His academic journey includes advanced training in applied mathematics and computational fluid dynamics, followed by extensive experience in numerical modeling, perturbation methods, and simulation-based analysis of non-Newtonian fluid flows. Dr. Nazeer’s research consistently addresses real-world engineering and physiological flow problems, emphasizing novel rheological models such as Eyring–Powell, Casson, Rabinowitsch, Ellis, Jeffrey, and Maxwell fluids. He has collaborated widely across international research groups and published influential work in high-impact journals such as International Communications in Heat and Mass Transfer, Case Studies in Thermal Engineering, Surfaces and Interfaces, and Numerical Methods for Partial Differential Equations. His contributions have earned recognition within the fluid mechanics community, including acknowledgments for outstanding research productivity and high-impact publications. Overall, Dr. Nazeer remains committed to advancing computational modeling and thermal–fluid sciences through innovative problem-solving and interdisciplinary collaboration.

Profile : Google Scholar

Featured Publications

Nayak, M. K., Shaw, S., Khan, M. I., Pandey, V. S., & Nazeer, M. (2020). Flow and thermal analysis on Darcy–Forchheimer flow of copper–water nanofluid due to a rotating disk: A static and dynamic approach. Journal of Materials Research and Technology, 9(4), 7387–7408.

Chu, Y. M., Nazeer, M., Khan, M. I., Hussain, F., Rafi, H., Qayyum, S., & Abdelmalek, Z. (2021). Combined impacts of heat source/sink, radiative heat flux, temperature-dependent thermal conductivity on forced convective Rabinowitsch fluid. International Communications in Heat and Mass Transfer, 120, 105011.

Nazeer, M., Khan, M. I., Rafiq, M. U., & Khan, N. B. (2020). Numerical and scale analysis of Eyring–Powell nanofluid towards a magnetized stretched Riga surface with entropy generation and internal resistance. International Communications in Heat and Mass Transfer, 119, 104968.

Nazir, M. W., Javed, T., Ali, N., & Nazeer, M. (2021). Effects of radiative heat flux and heat generation on magnetohydrodynamics natural convection flow of nanofluid inside a porous triangular cavity. Numerical Methods for Partial Differential Equations.

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

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