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.

KSII Transactions on Internet and Information Systems TIIS Paper Details

The main purpose of this study is to investigate the thermal behavior of power law fluid within a plus-shaped cavity under the influence of natural convection, also taking into account the Darcy number and magnetohydrodynamics (MHD). The problem is formulated as a system of partial differential equations considering the power law fluid’s rheological behavior. The left-side walls are maintained at a specific low temperature while the lower and the right-side walls have uniform maximum temperatures. The boundary condition is designed to enhance heat transfer efficiency within the cavity, utilizing advanced thermal insulation methodologies. Finite element method (FEM) simulations are conducted, and a grid independence test is performed to validate the results. The impact of relevant parameters on the variation in momentum and thermal distributions is investigated using streamline and isothermal contour plots. The results indicate that as the Rayleigh number increases, the kinetic energy also increases, whereas the viscosity and circulation zones expand with an increase in the power law index. The Nusselt number exhibits a higher value in the shear-thinning case (n = 0.7) compared to the Newtonian (n = 1) and shear-thickening (n = 1.2) cases. This empirical observation underscores the vital role that fluid rheology plays in molding the overall heat transfer performance within the cavity. The study concludes that there is a distinct correlation between the heat transfer rate and the Rayleigh number (Ra). As Ra increases, there is a significant improvement in the heat transfer rate within the flow domain. Furthermore, the fluid behavior and heat transfer performance within the cavity are significantly influenced by the presence of magnetohydrodynamics (MHD) and the Darcy effect.

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.

mHealth apps are one of the most beneficial conveniences provided by smartphones for human beings. Free mHealth apps are extremely popular in facilitating healthcare for low-income populations. A vast variety of personal and identifiable data is handled by contemporary mHealth apps; hence their adherence to modern privacy standards and demands is vital. General Data Protection Regulation (GDPR) is a comprehensive, current privacy standard. This research analyzes 50 popular free Medical and Health and Fitness Android apps for privacy adherence to GDPR. Although the apps in the data set had 1 million or more downloads and a user rating of more than 4.00, their adherence to vital aspects of GDPR was discouraging. Declaration of types of personally identifiable information collected by the data collectors, identification details of the data controller, purposes of data processing and sharing, details of data protection officer, the legal basis for data processing, data storage period, data subjects’ rights, details of a supervisory authority and implementation of informed consent are the points on which privacy policies of apps in this research were analyzed. Fulfillment of informed consent, declaration of purposes of data processing, declaring details of data recipients, and provision of data subjects’ rights were among some of the weakest privacy aspects of the apps analyzed in this research. It is recommended that mHealth app developers focus on the implementation of privacy recommendations to enhance the effectiveness of their apps.