What do we hear from a drum? A data-consistent approach to quantifying irreducible uncertainty on model inputs by extracting information from correlated model output data
What do we hear from a drum? A data-consistent approach to quantifying irreducible uncertainty on model inputs by extracting information from correlated model output data
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DOI：
10.1016/j.cma.2020.113228
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发表时间：
2020-10
影响因子：
7.2
通讯作者：
T. Butler;H. Hakula
中科院分区：
文献类型：
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作者：
T. Butler;H. Hakula
在制造过程中，控制具有不确定系统输入的系统响应，例如，由于关键系统子组件的材料参数变化，因此，对系统设计进行可靠的质量控制和验证与确认（V&V）是一项至关重要的任务。作为一个模型的制造过程中，我们考虑生产的鼓，也就是说，薄的弹性膜，其属性通过Dirichlet拉普拉斯特征值问题与不确定的扩散系数建模。质量控制和V&V问题都是在一个数据一致的框架内制定的，采用了前推和后推措施。在这两个问题中，不确定的扩散系数参数化的每一个实例和相应的特征信息定义相关的数据流。随后，通过使用特征提取技术对这些数据流进行后验分析来确定解决数据一致性逆问题所需的感兴趣的量。虽然这里提出的方法是相当普遍的，所提出的方法的具体功效进行了全面探讨，在数值结果的质量控制和V&V问题与制造的鼓。
In manufacturing processes, controlling system responses with uncertain system inputs, e.g., due to variations in material parameters of critical system sub-components, is a crucial task for performing reliable quality control and verification & validation (V&V) of system design. As a model for a manufacturing process, we consider the production of drums, that is, thin elastic membranes, whose properties are modeled via Dirichlet Laplacian eigenproblems with uncertain diffusion coefficients. Both a quality control and V&V problem are formulated within a data-consistent framework utilizing push-forward and pullback measures. In both problems, the uncertain diffusion coefficients are parameterized for every instance and the corresponding eigen-information defines correlated data streams. Subsequently, the quantities of interest required in the solution to the data-consistent inverse problems are determined by an a posteriori analysis of these data streams using feature extraction techniques. While the methodology proposed here is quite general, the specific efficacy of the proposed methodology is comprehensively explored in the numerical results for both the quality control and V&V problems associated with the manufacturing of drums.