The aim of present work is focused on determining accuracy the measurement using Particle Image Velocimetry based an analysis synthetic Uniform flow test and Couette test. We testing Standard Cross Correlation algorithm interdependence between several metrics ratio signal noise peak at each point. This describing influence new corrected metric that by a displacement measured in last iteration, ...

Machine-learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale, and complexity. Given the interpolative nature these models, reliability predictions depends on position in phase space, it is crucial obtain an estimate error that derives from finite number reference structur...

We present a novel approach to estimate the uncertainty in ensemble particle image velocimetry (PIV) measurements. Ensemble PIV is widely used when cross-correlation signal-to-noise ratio (SNR) insufficient perform reliable instantaneous velocity measurement. Despite utility of PIV, quantification for this type measurement has not been studied. The existing algorithms are developed and only do ...

Uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. The objective of this study is to develop and apply a Bayesian-mixing model that estimates probability distributions of source contributions to a mixture associated with multiple sources for assessing the uncertainty estimation in sediment fingerprinting in Zidasht catchment, Iran....