Department of Mathematics

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Author: search.filters.author.Sharma, Bhupendra KumarHas files: No

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    Modeling and analysis of heat and mass transport in chemically reactive darcy−forchheimer flow of micropolar fluid with activation energy
    (Begell House, 2025-07) Sharma, Bhupendra Kumar
    The current study focuses on the temperature and mass distributions in the flow of exponentially heated, chemically reactive micropolar fluid over a horizontal porous stretching sheet within Darcy−Forchheimer porous medium with activation energy and viscous dissipation. The governing partial differential equations are transformed using similarity transformations and converted into a set of ordinary differential equations. Reduced ordinary differential equations are solved by similarity analysis through the bvp4c tool in MATLAB. This study examines how the transition in temperature, mass, and velocity of fluid are affected by different physical parameters. To enhance the overall transfer process, the concentration, temperature, and velocity distributions are discussed for the physical parameters. The study investigates the behavior of magnetohydrodynamic (MHD) micropolar fluids under varying physical parameters. Results indicate that an increase in the magnetic parameter enhances the temperature profile, while a rise in the porosity parameter decreases the velocity distribution. An enhancement in temperature distributions is observed with the rise of exponential heat source and thermal-dependent heat source. Furthermore, mass transfer improves with higher activation energy. These findings underscore the potential of MHD micropolar fluids in engineering applications such as oil exploration, geothermal energy extraction, and nuclear reactor cooling systems.
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    Artificial neural network analysis of Jeffrey hybrid nanofluid with gyrotactic microorganisms for optimizing solar thermal collector efficiency
    (Springer Nature, 2025-02) Sharma, Bhupendra Kumar
    This article investigates solar energy storage due to the Jeffrey hybrid nanofluid flow containing gyrotactic microorganisms through a porous medium for parabolic trough solar collectors. The mechanism of thermophoresis and Brownian motion for the graphene and silver nanoparticles are also encountered in the suspension of water-based heat transfer fluid. The gyrotactic microorganisms have the ability to move in an upward direction in the nanofluid mixture, which enhances the nanoparticle stability and fluid mixing in the suspension. Mathematical modeling of the governing equations uses the conservation principles of mass, momentum, energy, concentration, and microorganism concentration. The non-similar variables are introduced to the dimensional governing equations to get the non-dimensional ordinary differential equations. The Cash and Carp method is implemented to solve the non-dimensional equations. The artificial neural network is also developed for the non-dimensional governing equations using the Levenberg Marquardt algorithm. Numerical findings corresponding to the diverse parameters influencing the nanofluid flow and heat transfer are presented in the graphs. The thermal profiles are observed to be enhanced with the escalation in the Darcy and Forchheimer parameters. And the Nusselt number enhances with the escalation in the Deborah number and retardation time parameter. Entropy generation reduces with an enhancement in Deborah number and retardation time parameter. Solar energy is the best renewable energy source. It can fulfill the energy requirements for the growth of industries and engineering applications.
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    Entropy generation optimization for casson hybrid nanofluid flow along a curved surface with bioconvection mechanism and exothermic/endothermic catalytic reaction
    (Wiley, 2025-03) Sharma, Bhupendra Kumar
    This article deals with the heat and mass transfer analysis of Casson hybrid nanofluid flow over a curved Riga surface with slip conditions in the presence of gyrotactic microorganisms. The mechanism of Soret and Dufour effects, exothermic/endothermic catalytic reaction, and an exponential heat source are also investigated. The mixture of aluminum oxide and multi-walled carbon nanotubes with Therminol-VPI fluid is assumed as the hybrid nanofluid. Boundary layer assumptions are taken in the mathematical modeling of governing equations. Transformation variables are introduced to get the dimensionless governing equations. Numerical simulation of the transformed equations is done with the help of the Matlab computational tool using the Cash and Carp numerical method. Numerical results corresponding to the influential factors are plotted in graphs for velocity profile, temperature profile, concentration profile, drag coefficient, Nusselt number, Sherwood number, and entropy generation. It is observed that the fluid velocity diminishes with an enhancement in the curvature parameter, and fluid velocity enhances with an improvement in the suction parameter. Thermal profile improves for enhancing modified magnetic field parameter and drops with an increase in exponential index parameter. The microorganisms respond to temperature and concentration gradients, affecting the overall heat and mass transfer dynamics. This research aims to reveal the coupled effects of heat transfer, diffusion, and microorganism behavior in computational simulations, which have various applications in different sectors like electronics, chemical engineering, and material science.
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    Entropy generation and heat transfer in nonlinear Buoyancy–driven Darcy–Forchheimer hybrid nanofluids with activation energy
    (De Gruyter, 2025-04) Sharma, Bhupendra Kumar; Yadav, Sangita
    This study investigates the influence of a magnetic field, activation energy, and heat source on the heat and mass transfer within a cross fluid embedded with mono-, di-, and tri-nanoparticles, considering thermal radiation and Darcy–Forchheimer effects. Utilizing the Cattaneo–Christov theory, non-Fourier heat transfer is modeled for a vertical moving surface. A mathematical model is developed and subsequently converted into a dimensionless form through an appropriate similarity transformation, resulting in a system of first-order ordinary differential equations. The numerical approach to solve the system is BVP4C solver in MATLAB, a tool specifically designed for boundary value problems. Graphical representations have been analyzed for velocity profiles, temperature profiles, and concentration distributions for different values of physical parameters. It is observed that the velocity profiles exhibit an upward trend with an increase in the parameters associated with nonlinear thermal convection and nonlinear concentration convection. Additionally, the analysis of surface shear stress, heat transfer coefficients, and mass transfer coefficients revealed that an increase in the porosity parameter and Forchheimer number results in decreased shear stress. Entropy generation is also investigated to quantify irreversibilities in the system. The analysis showed that increasing the Brinkman number, diffusion parameter, and temperature and concentration difference parameters leads to higher entropy generation, indicating greater irreversibility in the system. A comparative analysis demonstrates that tri-nanoparticles substantially improve flow velocity, thermal conductivity, and solute diffusion compared to di- and mono-nanoparticles, with tri-nanofluids exhibiting the most optimal overall performance.
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    Advancing organic photovoltaic cells for a sustainable future: The role of artificial intelligence (AI) and deep learning (DL) in enhancing performance and innovation
    (Elsevier, 2025-05) Sharma, Bhupendra Kumar
    The convergence of Organic Photovoltaic (OPV) technology and artificial intelligence (AI) is examined in this review as a promising approach to advancing sustainable energy solutions. Recognized for their lightweight, flexible, and cost-effective properties, OPVs are highlighted as viable alternatives within renewable energy applications, particularly suited for integration in building infrastructure and portable energy sources. A discussion of OPV mechanisms and structures, such as single-layer, bilayer, and bulk heterojunction cells, is provided to outline the unique efficiencies and challenges each architecture presents. AI, especially through machine learning (ML) and deep learning (DL) models, is shown to transform OPV research, enhancing both material discovery and device optimization. Through AI-driven processes, rapid predictions of power conversion efficiency (PCE), material selection automation, and high-throughput screening are achieved, effectively minimizing experimental time and cost. Recent developments in AI applications, including convolutional neural networks (CNNs) and Bayesian optimization, are reviewed for their contributions to improving OPV performance, stability, and scalability. Case studies are included to demonstrate AI’s impact in areas such as predictive modeling, real-time monitoring, and optimization of device architecture, all of which contribute to efficiency gains and improved material durability. Challenges, however, are noted, with data quality issues, the need for interdisciplinary collaboration, and gaps in AI-aided material innovation identified as key areas for ongoing development. This review highlights how the intersection of AI and OPV technology not only accelerates the path toward efficient, scalable renewable energy but also underscores the importance of interdisciplinary research in advancing sustainable, high-performance photovoltaic solutions.
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    Numerical investigation of thermal and mass diffusion characteristics in chemically reactive and magnetite cu–al2o3 water-based hybrid nanofluid flow over riga plate
    (World Scientific, 2025-05) Sharma, Bhupendra Kumar
    The existing work intends to explore the thermal and mass diffusion properties in chemically reactive and magnetite hybrid nanofluids (consisting of nanoparticles dispersed in ) flow across a Riga plate, considering influential factors like radiation, exponential heat source, viscous dissipation, magnetic field, and activation energy. The governing equations are modified into ordinary differential equations by employing similarity transformation, The bvp4c solver in MATLAB is used to solve differential equations to obtain the numerical solution. The range of non-dimensional parameters 0.1 < Z < 1, 0 < Kp < 0.5, 1 < R < 5, 0.1 < Ec < 0.4, 0.1 < < 0.4, 0.1 < < 0.4, 0.1 < < 0.4, 0.1 < Bi < 0.4, 1 < < 5, and 0.1 < 𝛾 < 0.7. It is detected that temperature distribution enhances with the rise of Hartmann number and radiation, whereas velocity distribution increases with the rise of modified Hartmann number. As activation energy is raised, the concentration profile is also enhanced. Nusselt number increases with a rise of exponential heat source, for numerical values = 0.2 and = 0.3. The corresponding Nusselt numbers are 2.8232 and 5.5421 , respectively. The hybrid nanofluid flows over the Riga plate has numerous applications in mechanical engineering, including fuel systems, magnetohydrodynamic (MHD) pumps, energy systems, thermal reactors, etc.
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    Simulation of magnetically targeted drug delivery for two-phase blood flow in stenotic arteries under hall and ion influence
    (World Scientific, 2025-05) Sharma, Bhupendra Kumar
    The present study investigates the efficacy of targeted drug delivery mechanisms in unsteady blood flow by incorporating the infusion of magnetic nanoparticles within a stenosed artery. The study employs a two-phase mathematical model, including a power law fluid model in the core region and a Newtonian model in plasma regions. The study systematically examines several critical parameters, including Hall and ion effects, radiation, and viscous dissipation, to determine their impact on the diseased arterial segment. The discretized governing equations are solved using Method of Lines (MOL) approach that transforms the spatial and time variables into adjoint ordinary differential equations (ODEs) in the time variable domain. The results obtained from the study reveal that an increase in the particle mass parameter () is associated with a reduction in the velocities of both nanoparticles and nanofluid. Additionally, a detailed time series analysis of flow rate profiles indicates a declining trend in the Weissenberg parameter (), particularly in the context of shear-thickening fluid. Overall, this research advances the understanding of magnetic drug targeting and contributes valuable knowledge to biomedical fluid dynamics, which has significant implications for developing targeted drug delivery systems and their potential applications in healthcare.
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    Solar energy storage optimization using fractional derivative simulations of maxwell hybrid nanofluid flow: entropy generation analysis
    (Wiley, 2025-04) Sharma, Bhupendra Kumar
    This attempt examines the heat transfer enhancement from unsteady bioconvective Maxwell nanofluid flow under the incidence of solar radiation influenced by viscous dissipation and chemical reaction through a porous medium. The nanofluid contains silver and titanium alloy hybrid nanoparticles with gyrotactic micro-organisms in ethylene glycol and water-based fluid. The fundamental governing equations are formulated and simulated with a novel fractional derivative approach. The time-fractional derivatives are approximated with the Atangana–Baleanu Caputo solution approach and discretized using the Crank–Nicolson type finite differences scheme. Graphical results present the outcomes of diverse physical parameters for the concentration, temperature, and velocity profile. The primary outcomes revealed that the bioconvection diffusion declines as fractional parameters escalate, and this Atangana–Baleanu Caputo definition gives an excellent approximation of the time derivative. The temperature and velocity profile are enhanced with increased radiation parameter, whereas concentration decreases with increased chemical reaction parameter. The resulting nanofluid provides a well-balanced blend of thermal efficiency, uniformity, and operational flexibility that would be impossible to achieve with a single base fluid through the complementary properties of ethylene glycol and water. This characteristic contributes to the improved efficiency of heat transfer in solar collectors. Optimizing the radiation absorption in solar collectors is essential for improving the performance and efficiency of the solar thermal collectors to reduce thermal energy losses.
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    Revolutionizing battery thermal management: hybrid nanofluids and PCM in cylindrical pack cooling
    (Springer, 2025-07) Sharma, Bhupendra Kumar
    The thermal management of cylindrical battery packs, widely used in electric vehicles and energy storage systems, is a critical aspect of ensuring their safety, performance, and longevity. As energy densities increase, effective cooling solutions become essential to address the challenges posed by excessive heat generation and uneven temperature distribution. This review has highlighted the promising potential of hybrid nanofluids and phase change materials (PCMs) in advancing thermal management systems for battery packs. Hybrid nanofluids, offering enhanced heat transfer properties, and PCMs, capable of storing and dissipating latent heat, represent a promising synergy for improving thermal management systems. This review provides a comprehensive analysis of the role of hybrid nanofluids and PCM in addressing the thermal challenges of cylindrical battery packs. The paper discusses heat generation mechanisms, the drawbacks of existing cooling methods, and the advantages of integrating these advanced materials into thermal management systems. By identifying research gaps and opportunities, this review offers a pathway for optimizing battery performance and highlights future research directions necessary for scalable and sustainable solutions. According to this review, future research should concentrate on creating hybrid cooling systems that effectively combine active, passive, and hybrid cooling techniques. Additional advancements in computer modeling, nanotechnology, and material science will be crucial to achieving the full potential of these innovative materials and overcoming existing limitations.
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    Entropy generation prediction for nanofluid flow using artificial neural networks: a comparative study of training algorithms
    (Springer, 2025-08) Sharma, Bhupendra Kumar
    The present study deals with the artificial neural network approach for entropy generation optimization of a micropolar tetra-hybrid nanofluid passing through a curved artery. In this work, the fluid is exposed to an inclined magnetic field, along with homogeneous and heterogeneous chemical reactions, viscous dissipation, and Joule heating combined with an external heat source. The governing partial differential equations are transformed into a set of ordinary differential equations by applying a group of self-similar transformations. These resulting ODEs are then solved using a shooting technique, ensuring an accurate resolution of the complex boundary conditions. The impacts of physical factors are examined for axial velocity, micro-rotational velocity, temperature, and concentration profiles with entropy generation optimization. A detailed comparison with previously published results confirms a strong agreement, validating the current approach. It is observed that the micro-rotational velocity increases by increasing the radius of curvature parameter, whereas a reverse trend is noted for the micropolar parameter. The Nusselt number is enhanced by an increase in the radiation parameter, while it is reduced by a higher radius of curvature parameter and Brinkman number. This study may help in selecting appropriate nanofluids for sterilization processes, hyperthermia applications, drug delivery systems, bioimaging techniques, maintenance of orthopedic implants, and micro-drug and hormone delivery devices. Furthermore, the integration of artificial intelligence methods enables real-time monitoring and prediction, thereby improving precision and effectiveness in medical and industrial applications involving nanofluids.