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Ranking Constituents of Coupled Models for Improved Performance

机译：耦合模型的排名成分，提高性能

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摘要

In partitioned analysis, constituent models representing different scales or physics are routinely coupled to simulate complex physical systems. Such constituent models are invariably imperfect and thus, yield a degree of disagreement with reality, known as model form error. This error propagates through coupling interfaces and degrades the accuracy of the coupled system. To efficiently improve the coupled system, resources must be allocated to systematically improve the constituent models. This study proposes a tool and an associated metric that exploits the availability of experimental data to prioritize constituent models. This metric is useful in tracing the error of coupled systems to their origins and to quantify the contribution of constituent error to the overall error of coupled systems. The proposed metric is used to rank constituents based on (i) the relative model form error of the constituents, (ii) the sensitivity of the model form error of the coupled system to the model form error in the constituents, and (iii) the cost to improve their performance. The applicability of the proposed metric is demonstrated through a proof-of-concept structural example, by coupling individual frame elements to model a portal frame. Coupling and uncertainty inference of the inexact constituent models are achieved using optimization, where both separate-effect and integral-effect experiments are employed to train the model form error of the constituents and coupled system.
机译：在分区分析中，表示不同尺度或物理学的组成模型是常规耦合的，以模拟复杂的物理系统。这种组成模型总是不完美的，因此产生了与现实的分歧程度，称为模型形式误差。此错误通过耦合接口传播并降低耦合系统的精度。为了有效地改善耦合系统，必须分配资源以系统地改善组成模型。本研究提出了一种工具和相关的指标，其利用实验数据的可用性来优先考虑组成模型。该度量标准可用于将耦合系统的错误跟踪到其来源，并量化组成错误对耦合系统总体误差的贡献。所提出的指标用于基于（i）组分的相对模型形式误差，（ii）耦合系统的模型形式误差对成分中的模型形式误差的敏感性，（iii）的敏感性提高他们的表现的成本。通过耦合单个框架元件来模拟门户框架，通过概念验证结构示例来证明所提出的度量的适用性。使用优化实现不精确组成模型的耦合和不确定化推断，其中采用单独效应和积分实验进行培训组成和耦合系统的模型形式误差。

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