采样(信号处理)
不确定度量化
频数推理
公制(单位)
计算
不确定性传播
计算机科学
不确定度分析
测量不确定度
敏感性分析
领域(数学)
数学
数据挖掘
计量经济学
算法
统计
贝叶斯概率
贝叶斯推理
模拟
机器学习
人工智能
工程类
滤波器(信号处理)
计算机视觉
运营管理
纯数学
作者
Pranay Seshadri,Anthony Duncan,Duncan Simpson,George Thorne,Geoffrey T. Parks
出处
期刊:Journal of turbomachinery
[ASME International]
日期:2020-01-24
卷期号:142 (2)
被引量:7
摘要
Abstract In this second part of our two-part paper, we provide a detailed, frequentist framework for propagating uncertainties within our multivariate linear least squares model. This permits us to quantify the impact of uncertainties in thermodynamic measurements—arising from calibrations and the data acquisition system—and the correlations therein, along with uncertainties in probe positions. We show how the former has a much larger effect (relatively) than uncertainties in probe placement. We use this non-deterministic framework to demonstrate why the well-worn metric for assessing spatial sampling uncertainty falls short of providing an accurate characterization of the effect of a few spatial measurements. In other words, it does not accurately describe the uncertainty associated with sampling a non-uniform pattern with a few circumferentially scattered rakes. To this end, we argue that our data-centric framework can offer a more rigorous characterization of this uncertainty. Our paper proposes two new uncertainty metrics: one for characterizing spatial sampling uncertainty and another for capturing the impact of measurement imprecision in individual probes. These metrics are rigorously derived in our paper and their ease in computation permits them to be widely adopted by the turbomachinery community for carrying out uncertainty assessments.
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