Journal of Verification, Validation and Uncertainty Quantification最新文献

英文中文

Uncertainty Quantification in a Patient-Specific One-Dimensional Arterial Network Model: EnKF-Based Inflow Estimator. 患者特定的一维动脉网络模型中的不确定性量化:基于enkf的流入估计器。

IF 0.6 Q4 ENGINEERING, MECHANICAL

Journal of Verification, Validation and Uncertainty Quantification

Pub Date : 2017-03-01 Epub Date: 2017-02-22 DOI: 10.1115/1.4035918

Andrea Arnold, Christina Battista, Daniel Bia, Yanina Zócalo German, Ricardo L Armentano, Hien Tran, Mette S Olufsen

Successful clinical use of patient-specific models for cardiovascular dynamics depends on the reliability of the model output in the presence of input uncertainties. For 1D fluid dynamics models of arterial networks, input uncertainties associated with the model output are related to the specification of vessel and network geometry, parameters within the fluid and wall equations, and parameters used to specify inlet and outlet boundary conditions. This study investigates how uncertainty in the flow profile applied at the inlet boundary of a 1D model affects area and pressure predictions at the center of a single vessel. More specifically, this study develops an iterative scheme based on the ensemble Kalman filter (EnKF) to estimate the temporal inflow profile from a prior distribution of curves. The EnKF-based inflow estimator provides a measure of uncertainty in the size and shape of the estimated inflow, which is propagated through the model to determine the corresponding uncertainty in model predictions of area and pressure. Model predictions are compared to ex vivo area and blood pressure measurements in the ascending aorta, the carotid artery, and the femoral artery of a healthy male Merino sheep. Results discuss dynamics obtained using a linear and a nonlinear viscoelastic wall model.

心血管动力学的患者特异性模型的成功临床应用取决于在存在输入不确定性的情况下模型输出的可靠性。对于动脉网络的1D流体动力学模型，与模型输出相关的输入不确定性与血管和网络几何形状的规范、流体和壁方程内的参数以及用于指定入口和出口边界条件的参数有关。本研究调查了应用于1D模型入口边界的流动剖面的不确定性如何影响单个容器中心的面积和压力预测。更具体地说，本研究开发了一种基于集成卡尔曼滤波器（EnKF）的迭代方案，以根据曲线的先验分布来估计时间流入剖面。基于EnKF的流入估计器提供了对估计流入的大小和形状的不确定性的测量，该不确定性通过模型传播，以确定面积和压力的模型预测中的相应不确定性。将模型预测与健康雄性美利奴羊升主动脉、颈动脉和股动脉的离体面积和血压测量值进行比较。结果讨论了使用线性和非线性粘弹性墙模型获得的动力学。

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引用次数: 14

Validation Metrics for Deterministic and Probabilistic Data 确定性和概率数据的验证度量

IF 0.6 Q4 ENGINEERING, MECHANICAL

Journal of Verification, Validation and Uncertainty Quantification

Pub Date : 2017-02-01 DOI: 10.1115/1.4042443

K. Maupin, L. Swiler, N. Porter

Computational modeling and simulation are paramount to modern science. Computational models often replace physical experiments that are prohibitively expensive, dangerous, or occur at extreme scales. Thus, it is critical that these models accurately represent and can be used as replacements for reality. This paper provides an analysis of metrics that may be used to determine the validity of a computational model. While some metrics have a direct physical meaning and a long history of use, others, especially those that compare probabilistic data, are more difficult to interpret. Furthermore, the process of model validation is often application-specific, making the procedure itself challenging and the results difficult to defend. We therefore provide guidance and recommendations as to which validation metric to use, as well as how to use and decipher the results. An example is included that compares interpretations of various metrics and demonstrates the impact of model and experimental uncertainty on validation processes.

计算建模和模拟对现代科学至关重要。计算模型经常取代昂贵、危险或发生在极端规模下的物理实验。因此，至关重要的是，这些模型要准确地表示并可用作现实的替代品。本文对可用于确定计算模型有效性的度量进行了分析。虽然一些指标具有直接的物理意义和悠久的使用历史，但其他指标，尤其是那些比较概率数据的指标，更难解释。此外，模型验证过程往往是特定于应用程序的，这使得程序本身具有挑战性，结果难以辩护。因此，我们就使用哪种验证指标以及如何使用和解读结果提供了指导和建议。其中包括一个例子，比较了各种指标的解释，并证明了模型和实验不确定性对验证过程的影响。

引用次数: 21

Effective Convergence Checks for Verifying Finite Element Stresses at Three-Dimensional Stress Concentrations 三维应力集中下有限元应力验证的有效收敛校核

IF 0.6 Q4 ENGINEERING, MECHANICAL

Journal of Verification, Validation and Uncertainty Quantification

Pub Date : 2016-12-01 DOI: 10.1115/1.4042515

Jeffrey R. Beisheim, G. Sinclair, P. Roache

Current computational capabilities facilitate the application of finite element analysis (FEA) to three-dimensional geometries to determine peak stresses. The three-dimensional stress concentrations so quantified are useful in practice provided the discretization error attending their determination with finite elements has been sufficiently controlled. Here, we provide some convergence checks and companion a posteriori error estimates that can be used to verify such three-dimensional FEA, and thus enable engineers to control discretization errors. These checks are designed to promote conservative error estimation. They are applied to twelve three-dimensional test problems that have exact solutions for their peak stresses. Error levels in the FEA of these peak stresses are classified in accordance with: 1–5%, satisfactory; 1/5–1%, good; and <1/5%, excellent. The present convergence checks result in 111 error assessments for the test problems. For these 111, errors are assessed as being at the same level as true exact errors on 99 occasions, one level worse for the other 12. Hence, stress error estimation that is largely reasonably accurate (89%), and otherwise modestly conservative (11%).

当前的计算能力促进了有限元分析(FEA)对三维几何形状的应用，以确定峰值应力。如果有限元法确定三维应力集中的离散误差得到充分控制，那么这样量化的三维应力集中在实际应用中是有用的。在这里，我们提供了一些收敛性检查和伴随的后验误差估计，可用于验证这种三维有限元，从而使工程师能够控制离散化误差。这些检查旨在促进保守误差估计。它们被应用于12个三维测试问题，它们的峰值应力有精确的解。这些峰值应力在有限元分析中的误差水平按以下顺序分类:1-5%，满意;1/5 - 1%,好;<1/5%，很好。目前的收敛性检查对测试问题进行了111次错误评估。对于这111种情况，有99种情况的错误被评估为与真实精确错误处于同一级别，另外12种情况的一个级别更差。因此，应力误差估计在很大程度上是合理准确的(89%)，而在其他方面则适度保守(11%)。

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引用次数: 10

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