Gawronska Elzbieta, Zych Maria, Dyja Robert, Domek Grzegorz
Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, Dabrowskiego 69, 42-201, Czestochowa, Poland.
Faculty of Mechatronics, Kazimierz Wielki University, Jana Karola Chodkiewicza 30, 85-064, Bydgoszcz, Poland.
Sci Rep. 2023 Sep 15;13(1):15343. doi: 10.1038/s41598-023-42536-w.
The article shows the usage of swarming algorithms for reconstructing the heat transfer coefficient regarding the continuity boundary condition. Numerical calculations were performed using the authors' own application software with classical forms of swarm algorithms implemented. A functional determining error of the approximate solution was used during the numerical calculations. It was minimized using the artificial bee colony algorithm (ABC) and ant colony optimization algorithm (ACO). The considered in paper geometry comprised a square (the cast) in a square (the casting mold) separated by a heat-conducting layer with the coefficient [Formula: see text]. Due to the symmetry of that geometry, for calculations, only a quarter of the cast-mold system was considered. A Robin's boundary condition was assumed outside the casting mold. Both regions' inside boundaries were insulated, but between the regions, a continuity boundary condition with nonideal contact was assumed. The coefficient of the thermally conductive layer was restored using the swarm algorithms in the interval [Formula: see text]] and compared with a reference value. Calculations were carried out using two finite element meshes, one with 111 nodes and the other with 576 nodes. Simulations were conducted using 15, 17, and 20 individuals in a population with 2 and 6 iterations, respectively. In addition, each scenario also considered disturbances at 0[Formula: see text], 1[Formula: see text], 2[Formula: see text], and 5[Formula: see text] of the reference values. The tables and figures present the reconstructed value of the [Formula: see text] coefficient for ABC and ACO algorithms, respectively. The results show high satisfaction and close agreement with the predicted values of the [Formula: see text] coefficient. The numerical experiment results indicate significant potential for using artificial intelligence algorithms in the context of optimization production processes, analyze data, and make data-driven decisions.
本文展示了群体算法在关于连续性边界条件下重建传热系数方面的应用。数值计算是使用作者自己开发的应用软件进行的，该软件实现了经典形式的群体算法。在数值计算过程中使用了一个确定近似解误差的函数。使用人工蜂群算法（ABC）和蚁群优化算法（ACO）将其最小化。本文所考虑的几何结构包括一个正方形（铸件）置于一个正方形（铸模）内，两者由一个导热系数为[公式：见原文]的导热层隔开。由于该几何结构的对称性，为了计算，仅考虑铸模系统的四分之一。在铸模外部假定为罗宾边界条件。两个区域的内部边界是绝热的，但在区域之间，假定为具有非理想接触的连续性边界条件。使用群体算法在区间[公式：见原文]]内恢复导热层的系数，并与参考值进行比较。使用两个有限元网格进行计算，一个有111个节点，另一个有576个节点。分别使用群体规模为15、17和20个个体进行模拟，迭代次数为2次和6次。此外，每种情况还考虑了参考值在0[公式：见原文]、1[公式：见原文]、2[公式：见原文]和5[公式：见原文]时的干扰。表格和图形分别给出了ABC算法和ACO算法下[公式：见原文]系数的重建值。结果表明，重建值与[公式：见原文]系数的预测值高度吻合。数值实验结果表明，在优化生产过程、分析数据和进行数据驱动决策的背景下，使用人工智能算法具有巨大潜力。
