A Kriging surrogate model of an electromagnetic acoustic transducer (EMAT) generating omnidirectional Lamb waves
A Kriging surrogate model of an electromagnetic acoustic transducer (EMAT) generating omnidirectional Lamb waves
复制标题
产生全向兰姆波的电磁声换能器 (EMAT) 的克里金代理模型
DOI：
10.3233/jae-170030
复制
发表时间：
2018-01-01
影响因子：
0.6
通讯作者：
Zhao, Wei
中科院分区：
文献类型：
--
作者：
Wang, Shen;Huang, Songling;Zhao, Wei
电磁声换能器 (EMAT) 是非接触式超声换能器，用于直接在被测金属样品中产生超声波，适用于测试移动或热金属固体等特殊情况。 EMAT依赖于电磁耦合，其传导效率相对较低，因此通过建立精确的模型和实施结构优化来提高其性能至关重要。现代进化优化算法涉及对优化问题的目标函数的多次评估，因此有必要探索代理模型来减少计算量大的目标函数评估过程中的时间消耗，特别是当函数对应于复杂的数值模型（如 EMAT 模型）时。在这项工作中，我们重点关注克里金代理模型（DACE）在 EMAT 建模中的应用。回顾了DACE模型的公式，然后介绍了全向EMAT模型。讨论并比较了三种计算位移分量幅值的方法，包括时域法、多频法（带有FFT/IFFT处理）和直接应用相量的单频法。然后建立 EMAT 的 DACE 模型来预测设计变量离散网格上的输出。最后，将获得的 DACE 模型用作模拟退火算法驱动的 EMAT 优化中的代理模型。通过DACE模型，EMAT优化的总耗时大大减少。
Electromagnetic acoustic transducers (EMATs) are non-contact ultrasonic transducers used to generate ultrasonic waves directly in the tested metal samples, suitable for special situations like testing moving or hot metal solids. EMATs rely on electromagnetic coupling, and the transduction efficiency of EMATs is relatively low, so it's crucial to improve their performance through building accurate models and implementing structural optimisations. Modern evolutionary optimization algorithms involve a number of evaluations of the objective function of the optimization problem, so it's necessary to explore surrogate models to reduce the time consumption during evaluation of the computationally expensive objective function, especially when the function corresponds to complex numerical models, like the EMAT model. We focused on the application of Kriging surrogate model (DACE) in EMAT modeling in this work. The formulations of the DACE model were reviewed, then the model of an omnidirectional EMAT was introduced. Three methods to calculate the amplitudes of the displacement components were discussed and compared, including the time-domain method, the multifrequency method (with FFT/IFFT processing), and the single frequency method applying directly the phasors. The DACE model of the EMAT was then built to predict outputs on a discrete grid of the design variables. Finally the obtained DACE model was used as the surrogate model in the optimization of the EMAT driven by the simulated annealing algorithm. With the DACE model, the total time consumption of optimization of the EMAT was reduced greatly.