Abstract

The System Analysis Module (SAM), developed and maintained by Argonne National Laboratory, is designed to provide whole-plant transient safety analysis capabilities for a number of advanced non–light water reactors, including sodium-cooled fast reactor (SFR), lead-cooled fast reactor (LFR), and molten salt reactor (MSR)/fluoride-salt-cooled high-temperature reactor (FHR) designs. SAM is primarily constructed as a systems-level analysis tool, with the potential to incorporate reduced order models from three-dimensional computational fluid dynamics (CFD) simulations to improve characterization of complex, multidimensional physics. It is recognized that the computational expense associated with CFD can be intractable for various engineering analyses, such as uncertainty quantification, inference, and design optimization. This paper explores the reducibility of a SAM model using recent advances in randomized linear algebra techniques, which attempt to find recurring patterns in the various realizations generated by a model after randomly perturbing all its input parameters. The reduction is described in terms of fewer degrees of freedom (DOFs), referred to as the active DOFs, for the model variables such as input model parameters and model responses. The results indicate that there is significant room for additional reduction that may be leveraged for additional computational gains when employing SAM for engineering-intensive analyses that require repeated model executions. Different from physics-based reduction approaches, the proposed approach allows one to estimate upper bounds on the reduction errors, which are rigorously developed in this work. Finally, different methods for surrogate model construction, such as regression and neural network–based training, are employed to correlate the input and output active DOFs, which are related back to the original variables using matrix-based linear transformations.

摘要

由阿贡国家实验室开发和维护的系统分析模块（SAM），旨在为许多先进的非轻水反应堆提供全厂瞬态安全分析功能，包括钠冷快堆（SFR），铅冷却快堆（LFR）和熔融盐反应堆（MSR）/氟化物盐冷却高温反应堆（FHR）设计。SAM主要是作为系统级分析工具构建的，具有整合来自三维计算流体动力学（CFD）模拟的降阶模型的潜力，从而可以改善复杂的多维物理场的表征。公认的是，与CFD相关的计算费用对于各种工程分析（例如不确定性量化，推断和设计优化）可能是棘手的。本文利用随机线性代数技术的最新进展探索SAM模型的可约性，该方法试图在随机扰动模型的所有输入参数后在模型生成的各种实现中找到重复出现的模式。针对模型变量（例如输入模型参数和模型响应）的减少的自由度（DOF）（称为活动DOF）来描述降低。结果表明，当将SAM用于需要重复执行模型的工程密集型分析时，可以有更多的余地用于额外的计算增益。与基于物理学的归约方法不同，所提出的方法允许人们估算归约误差的上限，该误差是在这项工作中严格发展的。最后，