Phase based statistics from direct numerically simulated imagery of sediment-laden oscillatory flow for Bayesian belief network analysis

Nicholas V. Scott; Tian-Jian Hsu

doi:10.1117/12.2303961

25 May 2018 Phase based statistics from direct numerically simulated imagery of sediment-laden oscillatory flow for Bayesian belief network analysis

Nicholas V. Scott, Tian-Jian Hsu

Author Affiliations +

Proceedings Volume 10631, Ocean Sensing and Monitoring X; 1063115 (2018) https://doi.org/10.1117/12.2303961
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Direct numerically simulated data can serve as a proxy for understanding many issues concerning multidimensional remotely sensed data. As a step towards performing operational Bayesian belief network modeling for rivers, which is of practical utility to naval intelligence, direct numerically simulated sediment-laden oscillatory flow is used to estimate statistical surface layer spatial eddy scales. This is done using spatial realizations of the sediment concentration, vertical velocity, and pressure fields along with feature extraction algorithms which utilize self-organizing mapping, independent component analysis, and two-dimensional omnidirectional Morlet wavelet analysis. Stress versus scale distributions exhibit distinct phase modulation over the three ambient forcing phases of maximum negative velocity, zero velocity, and maximum positive velocity. The stress versus sediment concentration scale distribution, which is of great pertinence to riverine remote sensing, exhibits a significant amount of large eddy scales suggesting coherent large-scale sediment structure formation possibly due to particle interstitial forces. The estimated statistical results can serve as feature parameters for naïve Bayesian belief network prediction of bottom boundary layer stress from surface eddy scale observations.

Citation Download Citation

Nicholas V. Scott and Tian-Jian Hsu "Phase based statistics from direct numerically simulated imagery of sediment-laden oscillatory flow for Bayesian belief network analysis", Proc. SPIE 10631, Ocean Sensing and Monitoring X, 1063115 (25 May 2018); https://doi.org/10.1117/12.2303961

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