等离子体弧模拟的辐射传热模型降阶
Order Reduction of the Radiative Heat Transfer Model for the Simulation of Plasma Arcs
AI总结：
本文提出一种降阶方法，用于高效模拟电弧行为，通过非线性系统识别技术获取低维模型参数，保留原模型结构，适用于计算流体动力学模拟。
摘要：
本文提出了一种方法，旨在推导低复杂度模型以描述热辐射，从而模拟开关设备中电弧的行为。该方法通过非线性系统识别技术识别低维模型参数，以近似高维的全阶方程。低阶模型保留了全阶模型的主要结构特征，其参数可直接用于基于计算流体动力学的电弧模拟工具。特别是，模型参数可与常见的辐射求解方法结合使用，包括离散 ordinates 方法、P-N 方法和光电流体动力学方法。所提出的降阶方法能够系统地计算电磁谱在频率带中的划分以及相关的吸收系数，以最佳匹配所考虑气体介质的精细吸收光谱。还展示了如何利用问题结构来提高求解所得到非线性优化问题的计算效率。除了降阶方法及其相关的计算方面，还通过拉普拉斯变换进行了分析，为在降阶过程中使用非常低阶模型提供了理论依据。最后，展示了全阶模型与降阶模型之间的比较。
英文摘要：
An approach to derive low-complexity models describing thermal radiation for the sake of simulating the behavior of electric arcs in switchgear systems is presented. The idea is to approximate the (high dimensional) full-order equations, modeling the propagation of the radiated intensity in space, with a model of much lower dimension, whose parameters are identified by means of nonlinear system identification techniques. The low-order model preserves the main structural aspects of the full-order one, and its parameters can be straightforwardly used in arc simulation tools based on computational fluid dynamics. In particular, the model parameters can be used together with the common approaches to resolve radiation in magnetohydrodynamic simulations, including the discrete-ordinate method, the P-N methods and photohydrodynamics. The proposed order reduction approach is able to systematically compute the partitioning of the electromagnetic spectrum in frequency bands, and the related absorption coefficients, that yield the best matching with respect to the finely resolved absorption spectrum of the considered gaseous medium. It is shown how the problem's structure can be exploited to improve the computational efficiency when solving the resulting nonlinear optimization problem. In addition to the order reduction approach and the related computational aspects, an analysis by means of Laplace transform is presented, providing a justification to the use of very low orders in the reduction procedure as compared with the full-order model. Finally, comparisons between the full-order model and the reduced-order ones are presented.
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