A quadrilateral optimization method for non-linear thermal properties determination in materials at high temperatures

Magalhães, Elisan dos Santos

doi:10.1016/j.ijheatmasstransfer.2021.121857

Cited by 9 publications

(8 citation statements)

References 23 publications

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“…For instance, LBW is currently by far the most simulated welding technique present in recent scientific publications [18]. The previous simulation of the process allows for various advantages such as optimization of the technique through new modeling and parameters tuning [19], enhanced materials selection [20], the prediction of the final weld bead mechanical characteristics [21,22], and the estimation of involved parameters through inverse analysis [23][24][25]. Hence, the present computational performance analysis was performed by simulating an LBW process conducted by an automated LASER head focused on an SAE 1020 steel specimen.…”

Section: The Laser Beam Welding (Lbw) Simulationmentioning

confidence: 99%

An Implementation of LASER Beam Welding Simulation on Graphics Processing Unit Using CUDA

Nascimento,

Magalhães,

Azevedo

et al. 2024

Computation

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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.

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Section: The Laser Beam Welding (Lbw) Simulationmentioning

confidence: 99%

An Implementation of LASER Beam Welding Simulation on Graphics Processing Unit Using CUDA

Nascimento,

Magalhães,

Azevedo

et al. 2024

Computation

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“…The QOM developed by Magalhães [26] is an optimization method that all the assessment of more than one variable concurrently by minimizing an o The forward model applied in this work underwent a rigorous verification process [31] to assess the quality of the resulting temperature fields. The verification steps involved investigations regarding the accuracy, computational performance, energy consumption, cost efficiency, and code memory optimization of the in-house CUDA-C code.…”

Section: The Inverse Problemmentioning

confidence: 99%

“…The QOM developed by Magalhães [26] is an optimization method that allows for the assessment of more than one variable concurrently by minimizing an objective function (F) given by the sum of squares of the difference between the simulated (T') and reference (T) temperatures. Moreover, F is minimized in a future time step rather than the present one to increase F sensitivity through the Future Time Regularization (FTR) [32].…”

Section: The Inverse Problemmentioning

confidence: 99%

“…The theory of linear propagation of uncertainty was applied in this work to extend the investigation and address the capabilities of the QOM when the input data contains added uncertainties and noise. Applying the theory of linear propagation of uncertainty results in [26] U…”

Section: Input Data With Added Noisementioning

confidence: 99%

“…Therefore, the present study showcases the application of a methodology developed to estimate a temperature-dependent thermal property by carefully balancing the solution between estimation reliability and computational cost, thereby addressing the gap in the literature. The applied multivariable optimization technique is the Quadrilateral Optimization Method (QOM) previously proposed by Magalhães [26], which enables the estimation of temperature-dependent thermal properties described by math functions. This inverse method was specifically developed to determine material thermal properties at elevated temperatures.…”

Section: Introductionmentioning

confidence: 99%

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Numerical Estimation of Nonlinear Thermal Conductivity of SAE 1020 Steel

de Oliveira,

Magalhães,

Zilnyk

et al. 2024

Computation

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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.

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A literature review in heat source thermal modeling applied to welding and similar processes

Nascimento

Magalhães

Paes

2023

Int J Adv Manuf Technol

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A quadrilateral optimization method for non-linear thermal properties determination in materials at high temperatures

Cited by 9 publications

References 23 publications

An Implementation of LASER Beam Welding Simulation on Graphics Processing Unit Using CUDA

An Implementation of LASER Beam Welding Simulation on Graphics Processing Unit Using CUDA

Numerical Estimation of Nonlinear Thermal Conductivity of SAE 1020 Steel

A literature review in heat source thermal modeling applied to welding and similar processes

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