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Evaluating the predictive capability of numerical models considering robustness to non-probabilistic uncertianty in the input parameters.

机译：考虑输入参数对非概率不确定性的鲁棒性，评估数值模型的预测能力。

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摘要

The paradigm of model evaluation is challenged by compensations between various forms of errors and uncertainties that are inherent to the model development process due to, for instance, imprecise model input parameters, scarcity of experimental data and lack of knowledge regarding an accurate mathematical representation of the system. When calibrating model input parameters based on fidelity to experiments, such compensations lead to non-unique solutions. In turn, the existence of non-unique solutions makes the selection and use of one `best' numerical model risky. Therefore, it becomes necessary to evaluate model performance based not only on the fidelity of the predictions to experiments but also the model's ability to satisfy fidelity threshold requirements in the face of uncertainties. The level of inherent uncertainty need not be known a priori as the model's predictions can be evaluated for increasing levels of uncertainty, and a model form can be sought that yields the highest probability of satisfying a given fidelity threshold. By implementing these concepts, this manuscript presents a probabilistic formulation of a robust-satisfying approach, along with its associated metric. This new formulation evaluates the performance of a model form based on the probability that the model predictions match experimental data within a predefined fidelity threshold when subject to uncertainty in their input parameters. This approach can be used to evaluate the robustness and fidelity of a numerical model as part of a model validation campaign, or to compare multiple candidate model forms as part of a model selection campaign. In this thesis, the conceptual framework and mathematical formulation of this new probabilistic treatment of robust-satisfying approach is presented. The feasibility and application of this new approach is demonstrated on a structural steel frame with uncertain connection parameters, which has undergone static loading conditions.
机译：模型评估的范式受到模型开发过程中固有的各种形式的错误和不确定性之间的补偿的挑战，例如，由于模型输入参数不准确，实验数据稀缺以及缺乏关于模型的准确数学表示的知识系统。当基于对实验的保真度来校准模型输入参数时，这种补偿会导致非唯一解。反过来，非唯一解的存在也使得选择和使用“最佳”数值模型具有风险。因此，有必要不仅基于对实验的预测的逼真度来评估模型的性能，而且还必须基于面对不确定性时模型满足逼真度阈值要求的能力。固有的不确定性级别不需要事先知道，因为可以针对不确定性的增加来评估模型的预测，并且可以寻找能够满足给定保真度阈值的最高概率的模型形式。通过实施这些概念，该手稿提出了一种鲁棒满意方法的概率表述及其相关度量。当模型预测在输入参数中存在不确定性时，基于模型预测与预定义保真度阈值内的实验数据匹配的概率，此新公式将评估模型形式的性能。此方法可以用作模型验证活动的一部分，评估数值模型的鲁棒性和保真度，或者作为模型选择活动的一部分，比较多个候选模型形式。本文提出了一种新的概率满意的鲁棒满意方法的概念框架和数学公式。这种新方法的可行性和应用在具有不确定连接参数的钢结构钢框架上得到了证明，该钢框架承受了静态载荷条件。

著录项

  • 作者

    Shields, Parker L.;

  • 作者单位

    Clemson University.;

  • 授予单位 Clemson University.;
  • 学科 Engineering Civil.
  • 学位 M.S.
  • 年度 2013
  • 页码 77 p.
  • 总页数 77
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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