基于进化神经网络和稀疏多项式展开的液汞靶模型标定
Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions
摘要：
预测靶容器中的应变和应力的汞本构模型在改进散中子源（SNS）处的汞靶的寿命预测和未来靶设计中起着核心作用。我们利用多年来收集的实验应变数据，通过结合目标行为的大规模模拟和使用机器学习工具进行参数估计来改进汞本构模型。我们提出了两个跨学科的方法，使用进化神经网络和稀疏多项式展开的昂贵的模拟基于代理的模型校准。实验结果表明，两种方法对汞溅射靶的固体力学模拟具有很好的一致性。所提出的方法被用来校准的张力截止阈值，汞密度，和汞声速在强质子脉冲实验。使用应变实验数据从汞靶传感器，新校准的模拟实现了7\%的平均改善信号预测精度和8\%的平均绝对误差减少相比，以前报道的参考参数，与一些传感器经历了高达30\%的改善。建议的校准模拟可以显着帮助疲劳分析，以估计汞靶的寿命和完整性，这减少了突然的目标故障，节省了大量的成本。然而，从这项工作的一个重要结论指出，在目前的本构模型的基础上的状态方程在捕捉完整的物理spectrometry反应的不足之处。鉴于一些校准的参数，显示出良好的协议与实验数据可以是非物理汞的属性，我们需要一个更先进的两相流模型来捕捉气泡动力学和汞空化。
英文摘要：
The mercury constitutive model predicting the strain and stress in the target vessel plays a central role in improving the lifetime prediction and future target designs of the mercury targets at the Spallation Neutron Source (SNS). We leverage the experiment strain data collected over multiple years to improve the mercury constitutive model through a combination of large-scale simulations of the target behavior and the use of machine learning tools for parameter estimation. We present two interdisciplinary approaches for surrogate-based model calibration of expensive simulations using evolutionary neural networks and sparse polynomial expansions. The experiments and results of the two methods show a very good agreement for the solid mechanics simulation of the mercury spallation target. The proposed methods are used to calibrate the tensile cutoff threshold, mercury density, and mercury speed of sound during intense proton pulse experiments. Using strain experimental data from the mercury target sensors, the newly calibrated simulations achieve 7\% average improvement on the signal prediction accuracy and 8\% reduction in mean absolute error compared to previously reported reference parameters, with some sensors experiencing up to 30\% improvement. The proposed calibrated simulations can significantly aid in fatigue analysis to estimate the mercury target lifetime and integrity, which reduces abrupt target failure and saves a tremendous amount of costs. However, an important conclusion from this work points out to a deficiency in the current constitutive model based on the equation of state in capturing the full physics of the spallation reaction. Given that some of the calibrated parameters that show a good agreement with the experimental data can be nonphysical mercury properties, we need a more advanced two-phase flow model to capture bubble dynamics and mercury cavitation.
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