Arthur Mendonça de Azevedo

A generalized analytical framework based on the Distributed Transfer Function Method is developed for one-dimensional transient heat conduction in multilayer walls with imperfect thermal interfaces. The formulation accounts for layer-dependent thermophysical properties, prescribed thermal contact conductance, interfacial temperature jumps, and time-dependent convective and solar boundary conditions. To accommodate measured environmental excitations that are not globally differentiable, an adaptive temporal-segmentation strategy is introduced. Outdoor-air temperature and incident solar heat flux measured in Gaziantep, Turkey, are represented by piecewise Gaussian and sinusoidal regressions over analytically tractable intervals, with the terminal temperature field of each segment transferred as the initial condition of the next. Six three- and five-layer wall configurations, W1-W6, are analyzed with room air maintained at 25 °C. The analytical predictions are verified against an independent conservative finite-difference formulation using layer-conforming grids, duplicated one-sided interface nodes, direct contact coupling, and fully implicit backward Euler integration. Grid- and time-step-independence studies are performed for all configurations. Close agreement is obtained for temperature distributions, interfacial temperature jumps, and room-side heat fluxes, with a maximum temperature difference of 1.06 °C at the evaluated locations. Room-side heat fluxes are separated into heating and cooling contributions and integrated over 24 h to estimate air-conditioning electricity consumption. W5, containing extruded polystyrene insulation, exhibits the lowest total consumption (0.15546 kWh), followed by W6 (0.161744 kWh). Among the three-layer walls, W2 gives the lowest total consumption (0.273031 kWh). The results show that energy performance depends on insulation, wall thickness, thermal storage, layer arrangement, heating-cooling balance, and interfacial thermal resistance.

This research develops an innovative framework for accelerating Conjugate Heat Transfer (CHT) simulations within squared heated cavities through the application of Graphics Processing Units (GPUs). Although leveraging GPUs for computational speed improvements is well recognized, this study distinguishes itself by formulating a tailored optimization strategy utilizing the CUDA-C programming language. This approach is specifically designed to tackle the inherent challenges of modeling squared cavity configurations in thermal simulations. Comparative performance evaluations reveal that our GPU-accelerated framework reduces computation times by up to 99.7% relative to traditional mono-core CPU processing. More importantly, it demonstrates an increase in accuracy in heat transfer predictions compared to existing CPU-based models. These results highlight not only the technical feasibility but also the substantial enhancements in simulation efficiency and accuracy, which are crucial for critical engineering applications such as aerospace component design, electronic device cooling, and energy system optimization. By advancing GPU computational techniques, this work contributes significantly to the field of thermal management, offering a potential for broader application and paving the way for more efficient, sustainable engineering solutions.

The maximum number of parallel threads in traditional CFD solutions is limited by the Central Processing Unit (CPU) capacity, which is lower than the capabilities of a modern Graphics Processing Unit (GPU). In this context, the GPU allows for simultaneous processing of several parallel threads with double-precision floating-point formatting. The present study was focused on evaluating the advantages and drawbacks of implementing LASER Beam Welding (LBW) simulations using the CUDA platform. The performance of the developed code was compared to that of three top-rated commercial codes executed on the CPU. The unsteady three-dimensional heat conduction Partial Differential Equation (PDE) was discretized in space and time using the Finite Volume Method (FVM). The Volumetric Thermal Capacitor (VTC) approach was employed to model the melting-solidification. The GPU solutions were computed using a CUDA-C language in-house code, running on a Gigabyte Nvidia GeForce RTX^™ 3090 video card and an MSI 4090 video card (both made in Hsinchu, Taiwan), each with 24 GB of memory. The commercial solutions were executed on an Intel^® Core^™ i9-12900KF CPU (made in Hillsboro, Oregon, United States of America) with a 3.6 GHz base clock and 16 cores. The results demonstrated that GPU and CPU processing achieve similar precision, but the GPU solution exhibited significantly faster speeds and greater power efficiency, resulting in speed-ups ranging from 75.6 to 1351.2 times compared to the CPU solutions. The in-house code also demonstrated optimized memory usage, with an average of 3.86 times less RAM utilization. Therefore, adopting parallelized algorithms run on GPU can lead to reduced CFD computational costs compared to traditional codes while maintaining high accuracy.