论文标题
用于评估剪切应力熵的贝叶斯蒙特卡洛不确定性模型
A Bayesian Monte-Carlo Uncertainty Model for Assessment of Shear Stress Entropy
论文作者
论文摘要
最近在许多研究中采用了熵模型,以评估剪切应力在圆形通道中的分布。但是,他们的预测和可靠性的不确定性仍然是一个悬而未决的问题。我们提出了一种新的方法,可以评估四个流行熵模型的不确定性,包括香农,香农 - 驱动低位(PL),tsallis和Renyi,并在圆形通道中进行剪切应力估计。考虑到95%的置信度(CB),简化了贝叶斯蒙特卡洛(BMC)不确定性方法。我们使用统计指标预测误差估计范围(免费)和CB(NIN)中观察到的数据的百分比,开发了一种称为基于FoopoPT的OCB(focB)的新统计指数,该指数整合了它们的合并效果。 Shannon和Shannon PL熵的焦点的近距值分别等于8.781和9.808,在计算圆形通道中剪切应力值之后的剪切应力值的最高确定性随后是传统的均匀流动应力和TSALLIS模型,其近距离值分别为14.491和14.491和14.895。然而,与其他模型相比,等于57.726的焦点值等于57.726的Renyi熵在剪切应力的估计中的确定性较小。利用本研究中提出的结果,确定了剪切应力的计算熵方法的置信度,以设计和实施不同类型的开放通道及其稳定性。
The entropy models have been recently adopted in many studies to evaluate the distribution of the shear stress in circular channels. However, the uncertainty in their predictions and their reliability remains an open question. We present a novel method to evaluate the uncertainty of four popular entropy models, including Shannon, Shannon-Power Low (PL), Tsallis, and Renyi, in shear stress estimation in circular channels. The Bayesian Monte-Carlo (BMC) uncertainty method is simplified considering a 95% Confidence Bound (CB). We developed a new statistic index called as FREEopt-based OCB (FOCB) using the statistical indices Forecasting Range of Error Estimation (FREE) and the percentage of observed data in the CB (Nin), which integrates their combined effect. The Shannon and Shannon PL entropies had close values of the FOCB equal to 8.781 and 9.808, respectively, had the highest certainty in the calculation of shear stress values in circular channels followed by traditional uniform flow shear stress and Tsallis models with close values of 14.491 and 14.895, respectively. However, Renyi entropy with much higher values of FOCB equal to 57.726 has less certainty in the estimation of shear stress than other models. Using the presented results in this study, the amount of confidence in entropy methods in the calculation of shear stress to design and implement different types of open channels and their stability is determined.