Dr. Dinesh Kumar M

Scopus Publications

Deep learning-based prediction of heat transfer in Newtonian fluid flow over convective cone and wedge surfaces
Maddina Dinesh Kumar, S.P. Shivakumar, S. Mamatha Upadhya, K.V. Nagaraja, Nehad Ali Shah, et al.
International Communications in Heat and Mass Transfer, 2026
Dielectric nanofluids for transformer cooling: Performance, challenges, and towards smart and sustainable thermal management
Rizwan A. Farade, Noor Izzri Abdul Wahab, Jeyraj Selvaraj, Husam S. Samkari, Mohammed F. Allehyani, et al.
Materials Today Sustainability, 2026
Multi-Linear Regression Modeling of Non-Newtonian Boger Fluid Flow over a 3D Stretching Interface With Thermal Radiation Effects
Maddina Dinesh Kumar, Khalid Masood, S. U Mamatha, Usman Afzal, Nehad Ali Shah
ZAMM Zeitschrift Fur Angewandte Mathematik Und Mechanik, 2026
Heat transmission is enhanced when nanoparticles are mixed with a base liquid. Nanotechnology influences several fields, such as physics, industry, and medicine. Because of their exceptional thermal conductivity, hybrid nanofluids are frequently utilized in heat exchangers and other thermal applications. In this work, non‐Fourier flux and linear heat radiation are incorporated into the analysis of the three‐dimensional ternary hybrid nanofluid (Fe 3 O 4 + Diamond + TiO 2 /H 2 O) in a rectangular closed domain. Water containing titanium dioxide (TiO 2 ) nanoparticles, iron oxide (Fe 3 O 4 ), and diamond was examined over a stretched surface. Nonlinear ODEs are framed from the governing PDEs and further solved using MATLAB's BVP4C solver. The impact of changing factors on temperature and flow is investigated. It is noticed that decreasing dimensionless velocities and temperature distributions for higher nanoparticle volume fractions. A higher volume fraction (ϕ) affects friction and heat transmission, and essential fluid temperatures at 10°C and 50°C are used in the simulations.
Numerical Investigation of Heat and Mass Transfer in Diathermic Oil-Based AA7072-AA7075-Al2O3 Ternary Hybrid Nanofluid Flow Over Cone and Wedge Geometries
Maddina Dinesh kumar, Khalid Masood, D. Serafin Grace, P. Durgaprasad, Nehad Ali Shah
ZAMM Zeitschrift Fur Angewandte Mathematik Und Mechanik, 2026
Ternary hybrid nanofluids made of AA7072, AA7075, and Al 2 O 3 dispersed in diathermic oil have a high thermal carrying capacity and would be useful in the more sophisticated industrial setting of heat transfer. The purpose of this work is to investigate the mass and heat transfer rates of ternary hybrid nanofluids across a cone and wedge geometry under various physical parameters, including radiation, heat source, suction, and porous. The model takes into account steady, laminar, incompressible flow with no‐slip boundary conditions and takes into account effects like magnetic field, suction, thermal radiation, chemical reaction, and porous medium, along with Tiwari–Das model relations, which are used to evaluate the ternary nanofluid's thermophysical characteristics. The governing equations of nonlinear boundary value problems (BVPs) are numerically solved, and the results of heat and mass transfer are obtained using the bvp5c solver in MATLAB software. The findings show that the cone geometry has better heat and mass transfer than the wedge geometry because the thermal and concentration gradients are stronger. Additionally, suction improves the boundary layer's stability, whereas magnetic and radiative interactions have a considerable impact on temperature profiles; it was found that raising these parameter values causes an increase in temperature profiles.
Mass Transfer Characteristics and Entropy Generation of Rotating Non-Uniform Surfaces With Coriolis Forces and Dissipative Eyring–Powell Flows
Gurram Dharmaiah, Maddina Dinesh Kumar, C. S. K. Raju, Nehad Ali Shah, Se‐Jin Yook
ZAMM Zeitschrift Fur Angewandte Mathematik Und Mechanik, 2026
This study delves into the heat and mass transfer Coriolis force of the Eyring–Powell fluid on a rotating upper horizontal surface of a paraboloid of revolution (UHSPR). The reigning equations are formulated to have terms of rotation, mass transfer and non‐linearity of fluids. Using similarity transformations, the partial differential equations system governing them are facilitated to a coupled ordinary differential equations system. It is addressed with the bvp4c solver numerically in MATLAB. An impact of the major dimensionless framework variable on temperature, velocity and concentration profiles is examined and visually represented, in which it is discovered, with a progressing Grashof number, the velocity distributions will be greatly improved since the buoyancy forces will be more potent. The findings provide valuable insights into mass and heat transfer behaviour in the process of non‐Newtonian rotating fluids with practical engineering applications.
DoE-driven deep learning analysis of bioactive effluent transport for sustainable sanitation
S.P. Shivakumar, Sujesh Areekara, Maddina Dinesh Kumar, T.V. Smitha, S. Devanathan, et al.
Chemical Engineering Journal Advances, 2026
Optimization of Heat Transfer Rate in Dusty Fluid Flow Over a Stretching Riga Sheet Using Response Surface Methodology: Application to Industrial Coating Processes
Usman Afzal, Maddina Dinesh Kumar, Nehad Ali Shah, Adnan Ashique, Jae Dong Chung
ZAMM Zeitschrift Fur Angewandte Mathematik Und Mechanik, 2026
This study investigates the transfer of heat performance of nanofluids and dusty fluids over a stretching Riga plate in a porous medium under the influence of the modified Hartmann number. Key parameters considered include the Prandtl number, nanoparticle volume fraction, and interaction parameters for temperature and velocity. Nano‐ and dusty‐fluids are essential in energy systems and thermal management. Unlike previous works, this study examines their combined behavior under magnetohydrodynamic (MHD) effects. The integration of response surface methodology (RSM) provides a statistical approach for optimization. Through similarity transformations, the governing equations are reduced to nonlinear ordinary differential equations and solved numerically using MATLAB's BVP4C solver. RSM is used to analyze and visualize parametric effects through 3D response surfaces. An increase in dust volume fraction improves heat transfer, while the Hartmann number, porosity, and interaction parameters influence velocity and temperature distributions. Graphical and tabular results show complex boundary layer behavior. Dusty nano‐fluids effectively enhance heat transfer in porous media, with potential applications in electronics cooling, heat exchangers, and industrial systems. The findings support further work on hybrid multiphase models under MHD conditions.
AI-assisted FEM investigation of heat transfer under diverse configurations of three rotating squares in a harmonic wall cavity with internal heat generation
Adnan Ashique, Khalid Masood, Maddina Dinesh Kumar, Nehad Ali Shah
International Communications in Heat and Mass Transfer, 2026
Hybrid Nanoparticles Dynamics in Blood Plasma: Thermal Diffusion and Radiation Effects for Biomedical and Thermal Energy Applications
Tosin Oreyeni, Maddina Dinesh Kumar, Nehad Ali Shah, Talha Anwar, M. Siva Sankari, et al.
International Journal of Applied and Computational Mathematics, 2026
Analysis of Von Karman transport considering Stefan blowing, isotropic slip and motile density: Adaptive neuro-fuzzy inference system and optimisation of particle swarms
Nehad Ali Shah, Khalid Masood, Maddina Dinesh Kumar, B.C. Prasannakumara, G. Dharmaiah
Engineering Applications of Artificial Intelligence, 2026
Gradient Descent-Based Prediction of Heat-Transmission Rate of Engine Oil-Based Hybrid Nanofluid over Trapezoidal and Rectangular Fins for Sustainable Energy Systems
Maddina Dinesh Kumar, S. U. Mamatha, Khalid Masood, Nehad Ali Shah, Se-Jin Yook
CMES Computer Modeling in Engineering and Sciences, 2026
Numerical investigation on MHD non-Newtonian pulsating Fe3O4-blood nanofluid flow through vertical channel with nonlinear thermal radiation, entropy generation, and Joule heating
Kannaiah Govindarajulu, Anala Subramanyam Reddy, Devendiran Rajkumar, Thiyagarajan Thamizharasan, Maddina Dinesh Kumar, et al.
Numerical Heat Transfer Part A Applications, 2026
Gradient descent optimisation and contour analysis of Cu–CuO/blood hybrid nanofluid unveiling non-Newtonian flow dynamics for sports biomedical applications
Jian Cui, TongHao Yao, Khalid Masood, Maddina Dinesh Kumar, Adnan Ashique, et al.
Nanotechnology Reviews, 2026
Predicting thermal transport of blood-based penta-hybrid nanofluid in Fin geometries using deep neural networks and finite difference approach
Maddina Dinesh Kumar, Nehad Ali Shah, Dharmaiah Gurram, Se-Jin Yook
Engineering Applications of Artificial Intelligence, 2025
Impact of rotating cylinders configurations on Cu-water nanofluid heat transfer in a vented cavity: a COMSOL multiphysics base study
Usman Afzal, Khalid Masood, Nehad Ali Shah, Jinyoung Chang, Maddina Dinesh Kumar, et al.
Scientific Reports, 2025
Response surface optimization for Minsta-Gherasim hybrid nanofluid flow over a porous surface with varying water temperature levels and magnetic influence
Adnan Ashique, Maddina Dinesh Kumar, Sang-ro Lee, Seonhui Kang, Khalid Masood, et al.
Scientific Reports, 2025
Deep learning-driven heat transfer prediction in irregular ternary hybrid nanofluid flow over fin geometries via the Adam optimization algorithm
Maddina Dinesh Kumar, P. Durgaprasad, C.S.K. Raju, Nehad Ali Shah, Se-Jin Yook
Chemometrics and Intelligent Laboratory Systems, 2025
Optimization and classification of thermal transport on a convective surface with non-uniformly shaped ternary hybrid nanofluid flows
Maddina Dinesh Kumar, Nehad Ali Shah, Gurram Dharmaiah, Se-Jin Yook
Engineering Applications of Artificial Intelligence, 2025
Artificial neural networks for mass transfer and bioconvection analysis in radiative Eyring-Powell flow over a convective cylinder surface: Application to microbial fuel cells
Maddina Dinesh Kumar, C.S.K. Raju, Kiran Sajjan, Gurram Dharmaiah, Nehad Ali Shah, et al.
Engineering Applications of Artificial Intelligence, 2025
Thermal Assessment of Maxwell–Sutterby fluid with gyrotactic microorganisms and activation energy effects induced by Riga plate via Levenberg–Marquardt algorithm
Waqed H. Hassan, Maddina Dinesh Kumar, Kannaiah Govindarajulu, Narinderjit Singh Sawaran Singh, Muhammad Jawad, et al.
Results in Engineering, 2025
Deep learning-based Adam optimization for magnetohydrodynamics radiative thin film flow of ternary hybrid nanofluid with oscillatory boundary conditions
Jian Wang, Maddina Dinesh Kumar, S.U. Mamatha, Thandra Jithendra, Marouan Kouki, et al.
Chaos Solitons and Fractals, 2025
Gradient descent and response surface optimisation for nonlinear dynamics over an unstable heated wedge: lie group and sensitivity analysis
Maddina Dinesh Kumar, José Luis Díaz Palencia, Gurram Dharmaiah, Vanessa Fernández Chamorro, Abderrahim Wakif, et al.
European Physical Journal Plus, 2025
Support vector machine learning classification of heat transfer rate in tri-hybrid nanofluid over a 3D stretching surface with suction effects for water at 10°C and 50°C
Maddina Dinesh Kumar, C.S.K. Raju, Nehad Ali Shah, Se-Jin Yook, Dharmaiah Gurram
Alexandria Engineering Journal, 2025
Deep learning approach for predicting heat transfer in water-based hybrid nanofluid thin film flow and optimization via response surface methodology
Maddina Dinesh Kumar, Gurram Dharmaiah, Se-Jin Yook, C.S.K. Raju, Nehad Ali Shah
Case Studies in Thermal Engineering, 2025
Exploration of physical characteristics of gyrotactic microorganisms and Cattaneo-Christov heat flux past a cone and a wedge with thermal radiation
G. Dharmaiah, K.S. Balamurugan, H. Saxena, U. Fernandez-Gamiz, S. Noeiaghdam, et al.
Case Studies in Thermal Engineering, 2025

Dr. Dinesh Kumar M

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