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, Se-Jin Yook
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, Nehad Ali Shah, Maddina Dinesh Kumar, Zeeshan
Materials Today Sustainability, 2026
Transformers encounter increasing thermal stress due to rising power densities and the integration of renewable energy. This makes it harder for traditional cooling fluids to work. This review's goal is to critically compare empirical studies of dielectric nanofluids as thermal media for power transformers. It integrates reported thermal performance of dielectric nanofluids in different nanoparticle and base fluid systems. Enhancements include thermal conductivity of 0.9-210%, flash point of 44%, fire point of 9%, and pour point of -45%, and reduced gassing tendency. These enhancements come with trade-offs such as specific heat reductions and viscosity increase. The mechanisms that control heat transport are analysed, with focus on nanoscale interfacial phenomena, percolation networks, and phonon transport. The review identifies and reconciles critical knowledge gaps, focussing on long-term stability and interfacial compatibility under thermal cycling. The novel contribution consists of the integration of interfacial physics, performance trade-offs, sustainability considerations, and intelligent monitoring into a cohesive framework. Sustainability is addressed through the exploration of nanoparticle green synthesis pathways. This article extends earlier reviews by adding nanofluids to smart cooling systems driven by Internet of Things (IoT) and artificial intelligence (AI). The main findings suggest that nanoparticle surface chemistry is necessary for thermal enhancement, AI-driven systems boost performance, and green synthesis supports sustainability in next-generation applications. Finally, it points out key areas of research, such as accelerated ageing protocols, standardised evaluation, and renewable-grid integration, to turn laboratory results into practical, sustainable transformer cooling solutions. • Links dielectric nanofluids with smart cooling (IoT/AI) for transformers. • Review literature and identifies key gaps related to stability, modelling, and sustainability. • Thermal performance evaluation of dielectric nanofluids and quantifies performance gains. • Promotes sustainable green synthesis and eco-friendly nanofluids. • Outlines actionable roadmap for industry adoption and standardization.
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, K.V. Nagaraja, Nehad Ali Shah, Ioannis E. Sarris, Elif Hasret Kumcu
Chemical Engineering Journal Advances, 2026
The rapid growth of industrialization and the depletion of global water resources have made the treatment and reuse of hazardous industrial effluents crucial for sustainable water management, directly supporting the Clean Water and Sanitation Sustainable Goals. As conventional treatment technologies have continued to be both expensive and energy consuming, biologically assisted technologies present environmentally friendly and scalable alternatives. This research has developed a new bio-rheological modeling framework to simulate the flow of effluents and the mechanisms used to transport pollutants based on microrotation induced by microbial motility combined with the non-Newtonian Casson fluid behavior of effluents. The bio-rheological model incorporates the complex physical, thermal, and biologically based interactions of microbial motility, heat, and hazardous contaminant transport through molecular-based nonlinear radiation, Arrhenius rate law reaction kinetics, and thermo-diffusive effects. Using a Central Composite Design based deep neural network (DNN) model, we can predict key engineering quantities such as heat and mass transfer rates, and rates of microbial motility with greater accuracy and efficiency than achieved with conventional analytical and empirical methods. In fact, the DNN model produced R² values of 0.9989 for the heat transfer rate, 0.9996 for the mass transfer rate and 0.9993 for the density of microbial motility, which demonstrates that the DNN provided an accurate representation of the real-world physical, thermal, and microbiological interactions between effluents and their associated pollutants. This integrated biofluid-AI approach provides practitioners with a data-driven and sustainable framework to optimally design and implement biological treatment systems for industrial effluent with minimal cost, energy consumption, and environmental impact.
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, Muhammad Faisal
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, Kuppala Raja Sekhar
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, Nehad Ali Shah
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, Jae Dong Chung
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, Nehad Ali Shah, Jae Dong Chung
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, Se-Jin Yook
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, Walid Abdelfattah, Hakim AL Garalleh
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, Nehad Ali Shah
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, Ali J. Chamkha
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, Maddina Dinesh Kumar, C.S.K. Raju
Case Studies in Thermal Engineering, 2025
Optimising thermal performance of water-based hybrid nanofluids with magnetic and radiative effects over a spinning disc
Maddina Dinesh Kumar, Dharmaiah Gurram, Se-Jin Yook, C.S.K. Raju, Nehad Ali Shah
Chemometrics and Intelligent Laboratory Systems, 2025
Forecasting heat and mass transfer enhancement in magnetized non-Newtonian nanofluids using Levenberg-Marquardt algorithm: influence of activation energy and bioconvection
Maddina Dinesh Kumar, Muhammad Jawad, Mani Ramanuja, Refka Ghodhbani, Se-Jin Yook, Suhad Ali Osman Abdallah
Mechanics of Time Dependent Materials, 2025
Response surface optimisation on Non-Uniform shapes ternary hybrid nanofluid flow in stenosis artery with motile gyrotactic microorganisms
Maddina Dinesh kumar, D.Serafin Grace, P. Durgaprasad, José Luis Díaz Palencia
Chemical Physics, 2025
Deep neural network-based prediction and computational fluid dynamics analysis of convective heat transfer in dusty fluid flow over heated surface
Maddina Dinesh Kumar, C. S. K. Raju, Kiran Sajjan, Gurram Dharmaiah, Nehad Ali Shah, Se-Jin Yook
Physics of Fluids, 2025
ANFIS-PSO analysis on axisymmetric tetra hybrid nanofluid flow of Cu-CNT-Graphene-Tio2 with WEG-Blood under linear thermal radiation and inclined magnetic field: A bio-medicine application
Maddina Dinesh kumar, José Luis Díaz Palencia, G. Dharmaiah, A. Wakif, S. Noeiaghdam, U. Fernandez-Gamiz, S. Dinarvand
Heliyon, 2025
Entropy optimization of hybrid nanofluid flow over a radiated and magnetized disk with internal heat generation: a comparison study of numerical and analytical solutions
Maddina Dinesh Kumar
Radiation Effects and Defects in Solids, 2025
Unveiling the thermally dissipative peristaltic pumping characteristics of hydromagnetic nanofluid in an oblique micro-wavy conduit via artificial neural network-based optimization
Ajithkumar M, Dinesh Kumar Maddina, Nehad Ali Shah
International Journal of Numerical Methods for Heat and Fluid Flow, 2025
Artificial Neural Network Prediction and Numerical Analysis of Hall Current and Darcy-Forchheimer Effects on Dissipative Casson Fluid Flow Over a Thinner Surface
Maddina Dinesh kumar, Haider Ali, Y. C. A. Padmanabha Reddy, Ahmed M. Galal
Multiscale and Multidisciplinary Modeling Experiments and Design, 2025
Exploration of uniform and sudden lifting on hybrid nanofluid flow induced by the ramped motion of magnetized porous plate suspended by dust particles
Kiran Sajjan, M. Dinesh Kumar, C. S. K. Raju, S. Mamatha Upadhya, Sandeep N.
Numerical Heat Transfer Part A Applications, 2025
Magnetohydrodynamics heat transfer rate under inclined buoyancy force for nano and dusty fluids: Response surface optimization for the thermal transport
Usman Afzal, Maddina Dinesh Kumar, Adnan Ashique, Nehad Ali Shah, Jae Dong Chung
Nanotechnology Reviews, 2025
Spectral quasi-linearization and response optimization on magnetohydrodynamic flow via stenosed artery with hybrid and ternary solid nanoparticles: Support vector machine learning
Maddina Dinesh Kumar, Mamatha Sadananda Upadhya, Se-Jin Yook, Nehad Ali Shah, Chakravarthula Siva Krishnam Raju
Nanotechnology Reviews, 2025
Adaptive neuro-fuzzy inference system prediction of thermal transport in Casson ternary hybrid nanofluid thin film flow for biomedical applications
Maddina Dinesh Kumar, , S. Mamatha Upadhya, Nehad Ali Shah, C. S. K. Raju, Se-Jin Yook, , , and
Aims Mathematics, 2025
A comprehensive review on artificial neural network techniques (Levenberg–Marquardt, Bayesian regularization, scaled conjugate gradient) for magnetohydrodynamic hybrid nanofluid flow with bioconvection and heat sources
Sohaib Abdal, Department of Mathematical Sciences, Saveetha School of Engineering, SIMATS, Chennai, Tamilnadu 602105, India, Adnan Ashique, Usman Afzal, Maddina Dinesh Kumar, Khalid Masood, Nehad Ali Shah, Department of Mechanical Engineering, Sejong University, Seoul 05006, South Korea, Department of Mathematics, Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi Arabia
Aims Mathematics, 2025
A Comprehensive Numerical and Data-Driven Investigations of Nanofluid Heat Transfer Enhancement Using the Finite Element Method and Artificial Neural Network
Adnan Ashique, Khalid Masood, Usman Afzal, Mati Ur Rahman, Maddina Dinesh Kumar, Sohaib Abdal, Nehad Ali Shah
CMES Computer Modeling in Engineering and Sciences, 2025
Comparison of SWCNT + MWCNT and SWCNT + MWCNT + Fe3O4 nanofluid across a spinning disk with suspended joule heating and non-linear thermal radiation: Multi-linear optimization
S. Mamatha Upadhya, Sathy Suresh, M. Dinesh Kumar, P. D. Selvi, S. Suresh Kumar Raju, C. S. K. Raju, Shanthi SR
Numerical Heat Transfer Part B Fundamentals, 2025
Dynamics of comparative analysis of Reynold's and Vogel's models (variable viscosity) in a wire coating process ¯lled with magnetized porous
Rekha Satish, B. T. Raju, P. Durga Prasad, S. V. Siva Rama Raju, C. S. K. Raju, M. Dinesh Kumar
International Journal of Modern Physics B, 2024
Film lifting and drainage of third-grade fluid on a vertical belt with surface tension
H. Ashraf, Nehad Ali Shah, Misbah Shahzadi, Hamood Ur Rehman, Amjad Ali, M. Dinesh Kumar, C. S. K. Raju, Abdelaziz Mennouni, Noor Muhammad, Abderrahim Wakif, A. Walait, Katta Ramesh, T. Oreyeni, B. C. Prasannakumara
Modern Physics Letters B, 2024
Analysis of Dynamical Assisting and Opposing Flow Characteristics of Darcy Surface-Filled Ternary Nanoparticles and Fourier Flux: Artificial Neural Network and Levenberg Method
Maddina Dinesh Kumar, C. S. K. Raju, H. Ashraf, Nehad Ali Shah, Amjad Ali, Abdelaziz Mennouni, Noor Muhammad, Abderrahim Wakif, Katta Ramesh, Hanumesh Vaidya, T. Oreyeni, B. C. Prasanna kumara
Journal of Circuits Systems and Computers, 2024
Dynamics of non-Newtonian Casson fluid and Cattaneo-Christov heat flux impacts on a rotating non-uniform surface due to Coriolis force: A comparison study of ANFIS-PSO and ANN
Dinesh Kumar Maddina, Suresh Kumar Raju S, Dharmaiah Gurram, Muneerah Al Nuwairan
Results in Engineering, 2024
Analysis of biomagnetic blood Carreau hybrid nanofluid flow in stenotic arteries with motile gyrotactic microorganisms: Response surface optimisation
Maddina Dinesh Kumar, P. Jayasri, José Luis Díaz Palencia, P. Durgaprasad, Ali J. Chamkha, C. S. K. Raju
European Physical Journal Plus, 2024
Contour Analysis for Heat Transfer Rate in a Wedge Geometry with Non-Uniform Shapes Nanofluid: Gradient Descent Machine Learning Technique
S. Suresh Kumar Raju, P Durgaprasad, José Luis Díaz Palencia, Abderrahim Wakif, C.S.K. Raju, Maddina Dinesh kumar, Al Nuwairan Muneerah, J.Chamkha Ali
Results in Engineering, 2024
Comprehensive scrutinization of ternary hybrid Casson nanofluid flow in a conducting porous rotating disk with internal heating
Maddina Dinesh Kumar, Sangapatnam Suneetha, Gunisetty Ramasekhar, M. Ramesha, C. S. K Raju, S. V. Sivarama Raju
Modern Physics Letters B, 2024
Investigating the Thermal Efficiency of Al2O3-Cu-CuO-Cobalt with Engine Oil Tetra-Hybrid Nanofluid with Motile Gyrotactic Microorganisms Under Suction and Injection Scenarios: Response Surface Optimization
Maddina Dinesh Kumar, Gurram Dharmaiah, Vanessa Fernández Chamorro, José L. Díaz Palencia
Nano, 2024
Artificial neural network of thermal Buoyancy and Fourier flux impact on suction/injection-based Darcy medium surface filled with hybrid and ternary nanoparticles
Maddina Dinesh Kumar, Chakravarthula Siva Krishnam Raju, Essam R. El‐Zahar, Nehad Ali Shah, Se‐Jin Yook
ZAMM Zeitschrift Fur Angewandte Mathematik Und Mechanik, 2024
Heat transfer analysis for 3d ternary hybrid nanofluid flow with MHD and non-fourier flux impact over a linearly stretching surface: Response surface optimization
Shahad Gharbi Al Ruwaili, S. Suresh Kumar Raju, Maddina Dinesh Kumar, Fatemah H.H. Al Mukahal
Case Studies in Thermal Engineering, 2024
Enhanced heat transfer analysis on axisymmetric hydro-magnetic flow through a contracting or expanding Darcy porous channels
Maddina Dinesh Kumar, Gunisetty Ramasekhar, C. S. K. Raju, Bander Almutairi, Nehad Ali Shah
European Physical Journal Plus, 2023
Uniform structure of solid hybrid particles and fourier flux on MLR significance of dual dynamical jumps on MHD flow: Lie group similarities
M. Dinesh Kumar, C. S. K. Raju
International Journal of Modern Physics B, 2023
Dual dynamical jumps on Lie group analysis of hydro-magnetic flow in a suspension of different shapes of water-based hybrid solid particles with Fourier flux
M. Dinesh Kumar, C.S.K. Raju, Mansoor Alshehri, Shalan Alkarni, Nehad Ali Shah, Mohamed R. Ali, R. Sadat
Arabian Journal of Chemistry, 2023
Response surface methodology optimization of dynamical solutions of Lie group analysis for nonlinear radiated magnetized unsteady wedge: Machine learning approach (gradient descent)
M. Dinesh Kumar, N. Ameer Ahammad, C.S.K. Raju, Se-Jin Yook, Nehad Ali Shah, Sayed M. Tag
Alexandria Engineering Journal, 2023
Non-Linear Dynamic Movements of CNT/Graphene/Aluminum Oxide and Copper/Silver/Cobalt Ferrite Solid Particles in a Magnetized and Suction-Based Internally Heated Surface: Sensitivity and Response Surface Optimization
C. S. K. Raju, M. Dinesh Kumar, N. Ameer Ahammad, Ahmed A. El-Deeb, Barakah Almarri, Nehad Ali Shah
Mathematics, 2022
Nonlinear movements of axisymmetric ternary hybrid nanofluids in a thermally radiated expanding or contracting permeable Darcy Walls with different shapes and densities: Simple linear regression
C.S.K. Raju, N. Ameer Ahammad, Kiran Sajjan, Nehad Ali Shah, Se-Jin Yook, M. Dinesh Kumar
International Communications in Heat and Mass Transfer, 2022
Linear and quadratic convection on 3D flow with transpiration and hybrid nanoparticles
M. Dinesh Kumar, C.S.K. Raju, Kiran Sajjan, Essam R. El-Zahar, Nehad Ali Shah
International Communications in Heat and Mass Transfer, 2022
Nonlinear Boussinesq and Rosseland approximations on 3D flow in an interruption of Ternary nanoparticles with various shapes of densities and conductivity properties
Kiran Sajjan, Nehad Ali Shah, N. Ameer Ahammad, C.S.K. Raju, M. Dinesh Kumar, Wajaree Weera
Aims Mathematics, 2022

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