Ernandes José Gonçalves Do Nascimento

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.

Thermally characterizing high-thermal conductivity materials is challenging, especially considering high temperatures. However, the modeling of heat transfer processes requires specific material information. The present study addresses an inverse approach to estimate the thermal conductivity of SAE 1020 relative to temperature during an autogenous LASER Beam Welding (LBW) experiment. The temperature profile during LBW is computed with the aid of an in-house CUDA-C algorithm. Here, the governing three-dimensional heat diffusion equation is discretized through the Finite Volume Method (FVM) and solved using the Successive Over-Relaxation (SOR) parallelized iterative solver. With temperature information, one may employ a minimization procedure to assess thermal properties or process parameters. In this work, the Quadrilateral Optimization Method (QOM) is applied to perform estimations because it allows for the simultaneous optimization of variables with no quantity restriction and renders the assessment of parameters in unsteady states valid, thereby preventing the requirement for steady-state experiments. We extended QOM’s prior applicability to account for more parameters concurrently. In Case I, the optimization of the three parameters that compose the second-degree polynomial function model of thermal conductivity is performed. In Case II, the heat distribution model’s gross heat rate (Ω) is also estimated in addition to the previous parameters. Ω [W] quantifies the power the sample receives and is related to the process’s efficiency. The method’s suitability for estimating the parameters was confirmed by investigating the reduced sensitivity coefficients, while the method’s stability was corroborated by performing the estimates with noisy data. There is a good agreement between the reference and estimated values. Hence, this study introduces a proper methodology for estimating a temperature-dependent thermal property and an LBW parameter. As the performance of the present algorithm is increased using parallel computation, a pondered solution between estimation reliability and computational cost is achieved.

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.