Purpose—This study aims to perform an in-depth analysis of double-diffusive natural convection (DDNC) in an irregularly shaped porous cavity. We investigate the convective heat transfer process induced by the lower wall treated as a heat source while the side walls of the enclosure are maintained at a lower temperature and concentration, and the remaining wall is adiabatic. Various factors, such as the Rayleigh number, Darcy effects, Hartmann number, Lewis number and effects of magnetic inclination are evaluated for their influence on flow dynamics and heat distribution. Design/methodology/approach—After validating the results, the FEM (finite element method) is used to simulate the flow pattern, temperature variations, and concentration by solving the nonlinear partial differential equations with the modified Rayleigh number (10⁴ ≤ Ra ≤ 10⁷), Darcy number (10⁻⁴ ≤ Da ≤ 10⁻¹), Lewis number $(0.1\le Le\le 10)$, and Hartmann number $\left(0\le Ha\le 40\right)$ as the dimensionless operating parameters. Findings—The finding shows that the patterns of convection and the shape of the isotherms within porous enclosures are notably affected by the angle of the applied magnetic field. This study enhances our understanding of how double-diffusive natural convection (DDNC) operates in these enclosures, which helps improve heating and cooling technologies in various engineering fields. Research limitations/implications—Numerical and experimental extensions of the present study make it possible to investigate differences in thermal performance as a result of various curvatures, orientations, boundary conditions, and the use of three-dimensional analysis and other working fluids. Practical implications—The geometry configurations used in this study have wide-ranging applications in engineering fields, such as in heat exchangers, crystallization, microelectronics, energy storage, mixing, food processing, and biomedical systems. Originality/value—This study shows how an inclined magnetic field affects double-diffusive natural convection (DDNC) within a porous system featuring an irregularly shaped cavity, considering various multiphysical conditions.

There is lot of research work at enhancing the performance of energy conversion and energy storage devices such as solar cells, supercapacitors, and batteries. In this regard, the low bandgap and a high absorption coefficient of CdSe thin films in the visible region, as well as, the low electrical resistivity make them ideal for the next generation of chalcogenide-based photovoltaic and electrochemical energy storage devices. Here, we present the properties of CdSe thin films synthesized at temperatures (below 100°C using readily available precursors) that are reproducible, efficient and economical. The samples were characterized using XRD, FTIR, RBS, UV-vis spectroscopy. Annealed samples showed crystalline cubic structure along (111) preferential direction with the grain size of the nanostructures increasing from 2.23 to 4.13 nm with increasing annealing temperatures. The optical properties of the samples indicate a small shift in the bandgap energy, from 2.20 to 2.12 eV with a decreasing deposition temperature. The band gap is suitably located in the visible solar energy region, which make these CdSe thin films ideal for solar energy harvesting. It also has potential to be used in electrochemical energy storage applications.

In this paper we review some of the relationships existing between the optical properties, electrical conductivity, and thermal conductivity in graphene based transparent and conducting thin films. We provide a set of tools that are useful to identify the optimal trade‐off between transmittance and electrical conductivity in these films. In addition we present a set of thermal conductivity measurements of graphene thin films and relate them to the electrical properties in order to build a comprehensive picture of the electrical, thermal, and optical properties of graphene based thin films

Purpose: The main purpose of this study is to analyze the heat transfer phenomena in a dynamically bulging enclosure filled with Cu-water nanofluid. This study examines the convective heat transfer process induced by a bulging area considered a heat source, with the enclosure's side walls having a low temperature and top and bottom walls being treated as adiabatic. Various factors, such as the Rayleigh number (Ra), nanoparticle volume fraction, Darcy effects, Hartmann number (Ha) and effects of magnetic inclination, are analyzed for their impact on the flow behavior and temperature distribution.Design/methodology/approach: The finite element method (FEM) is employed for simulating variations in flow and temperature after validating the results. Solving the non-linear partial differential equations while incorporating the modified Darcy number (10⁻³ ≤ Da ≤ 10⁻¹), Ra (10³ ≤ Ra ≤ 10⁵) and Ha (0 ≤ Ha ≤ 100) as the dimensionless operational parameters.Findings: This study demonstrates that in enclosures with dynamically positioned bulges filled with Cu-water nanofluid, heat transfer is significantly influenced by the bulge location and nanoparticle volume fraction, which alter flow and heat patterns. The varying impact of magnetic fields on heat transfer depends on the Rayleigh and Has.Practical implications: The geometry configurations employed in this research have broad applications in various engineering disciplines, including heat exchangers, energy storage, biomedical systems and food processing.Originality/value: This research provides insights into how different shapes of the heated bulging area impact the hydromagnetic convection of Cu-water nanofluid flow in a dynamically bulging-shaped porous system, encompassing curved surfaces and various multi-physical conditions.

Recent advances in physics-informed neural networks (PINN) have highlighted the need for systematic criteria for selecting appropriate algorithms to solve differential equations. This paper presents a numerical comparison between standard PINNs and gradient-enhanced PINNs (gPINNs) used to solve a high-order partial differential equations (PDE). To verify the accuracy and convergence behavior of all the methods, we solve a fourth-order PDE whose analytical solution is known. gPINN is recommended for problems requiring high accuracy in gradient fields or operating with sparse data, whereas standard PINN is advised for strongly nonlinear or computationally constrained scenarios. We synthesize our findings into a practical selection guide; gPINN is recommended for problems requiring high accuracy in gradient fields or operating with sparse data, whereas standard PINN is advised for strongly nonlinear or computationally constrained scenarios. This framework provides a clear, evidence-based policy for algorithm choice in SciML. Beyond numerical comparison, we provide an analytical interpretation linking solver performance to the spectral and stiffness properties of each PDE class, offering a principled basis for algorithm selection.