Minimizing Conductor Consumption in High-Field HTS Solenoid Design Using Adaptive ANN-Based Optimization Algorithm

IF 1.8 3区 物理与天体物理 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Applied Superconductivity Pub Date : 2025-01-30 DOI:10.1109/TASC.2025.3536439
Di Wu;Dmitry Sotnikov;Mohsen Haajari;Tiina Salmi
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Abstract

Minimizing conductor cost is one of the goals in high-field HTS magnet design. Optimization of magnet design to minimize conductor length is one way to approach the problem. HTS tapes have anisotropic material properties and non-uniform current density within the tape width during operation, which must be accounted in modeling HTS tapes. Finite element method (FEM) based simulations can be used to obtain accurate magnetic field distribution and critical current in an HTS magnet, but these simulations are typically time consuming. For example, a detailed simulation for a 2 T HTS solenoid may take several hours. As a result, it is not efficient to implement the traditional optimization algorithms, which directly use the simulation results to obtain the response of the cost function. To overcome this challenge, a novel optimization algorithm was developed by the authors, namely L-ANN-GWO to reduce the time cost of optimization (L stands for LASSO, ANN stands for artificial neural network, and GWO stands for grey wolf optimizer). In this approach, ANN is trained by FEA set of HTS solenoid designs to approximate the time-consuming simulations of magnetic field distribution based on solenoid geometry. Instead of using the approximation model in a static way, ANN is first trained with a small number of samples and updated adaptively in L-ANN-GWO along with the optimization process. In this contribution, we present application of the L-ANN-GWO optimization algorithm to ReBCO solenoid coils to optimize magnet design to minimize conductor use. The design constraints come from field homogeneity and critical surface. We demonstrate that as a multi-objective optimization, L-ANN-GWO can output the minimized superconductor consumption in different peak magnetic fields from a single optimization run. Future developments foresee adding quench protection requirements into the design optimization as this is another aspect potentially impacting the conductor use.
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基于自适应神经网络优化算法的高场高温超导螺线管设计中导体损耗最小化
最小化导体成本是高场高温超导磁体设计的目标之一。优化磁体设计以最小化导体长度是解决这一问题的一种方法。高温超导带具有各向异性的材料特性,在运行过程中电流密度在带宽范围内不均匀,这是建模高温超导带时必须考虑的问题。基于有限元法的仿真可以获得高温超导磁体的精确磁场分布和临界电流,但这些仿真通常耗时较长。例如,对2thts螺线管的详细模拟可能需要几个小时。传统的优化算法直接利用仿真结果来获得代价函数的响应,其实现效率不高。为了克服这一挑战,作者开发了一种新的优化算法,即L-ANN-GWO,以减少优化的时间成本(L代表LASSO, ANN代表人工神经网络,GWO代表灰狼优化器)。在这种方法中,人工神经网络是通过一组HTS螺线管设计的FEA来训练的,以近似耗时的基于螺线管几何的磁场分布模拟。与静态地使用近似模型不同,人工神经网络首先使用少量样本进行训练,并在L-ANN-GWO中随优化过程自适应更新。在这篇贡献中,我们提出了L-ANN-GWO优化算法在ReBCO电磁线圈中的应用,以优化磁体设计以最小化导体的使用。设计约束来自于场均质性和临界面。我们证明了作为一个多目标优化,L-ANN-GWO可以在不同峰值磁场下的单次优化运行中输出最小的超导体消耗。未来的发展预计将在设计优化中加入淬火保护要求,因为这是另一个可能影响导体使用的方面。
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来源期刊
IEEE Transactions on Applied Superconductivity
IEEE Transactions on Applied Superconductivity 工程技术-工程:电子与电气
CiteScore
3.50
自引率
33.30%
发文量
650
审稿时长
2.3 months
期刊介绍: IEEE Transactions on Applied Superconductivity (TAS) contains articles on the applications of superconductivity and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Large scale applications include magnets for power applications such as motors and generators, for magnetic resonance, for accelerators, and cable applications such as power transmission.
期刊最新文献
Low-AC-Loss Nb3Sn Validation Model Coil in Solid Nitrogen for a Fast-Switching-Field MRI Magnet Prototype. Cooldown and Ramp Test of a Low-Cryogen, Lightweight, Head-Only 7T MRI Magnet. Front Cover Table of Contents IEEE Transactions on Applied Superconductivity Publication Information
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