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A method to calculate uncertainty of empirical compressor maps with the consideration of extrapolation effect and choice of training data

机译：一种计算实证压缩机地图的不确定性与考虑外推效应和训练数据的选择

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摘要

Although compressor maps are powerful tools to estimate compressor power consumption quickly, their ability to extrapolate outside their training data range is always questioned. In order to quantify the effect of extrapolation, a method to calculate the uncertainty of compressor map outputs is developed in the current article. The method considers four major components of uncertainties due to various sources such as measurement uncertainties of training data and equation of state. The change of the predicted map uncertainty with the degree of extrapolation and the map accuracy are shown for eight different compressor maps. The results indicate that the uncertainty from model random error increases significantly as the maps extrapolate though extrapolation does not necessarily imply inaccurate compressor map outputs. The results also show that the uncertainty due to training data is the most significant component of uncertainties when the maps are not extrapolated.
机译：虽然压缩机地图是强大的工具来估计压缩机功耗快速，但它们的推断能够外面的培训数据范围总是质疑。 为了量化外推的效果，在当前文章中开发了一种计算压缩机地图输出不确定性的方法。 该方法由于各种来源考虑了四个不确定性的主要组成部分，例如培训数据的测量不确定性和状态方程的测量不确定性。 对于八个不同的压缩机地图，示出了具有外推度和地图精度的预测地图不确定性的变化。 结果表明，由于外推的映射不一定意味着不准确的压缩机地图输出，所以模型随机误差的不确定性会增加显着增加。 结果还表明，由于训练数据引起的不确定性是当地图未推断时的不确定性最重要的成分。

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