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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
20 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Independent component analysis

Statistical analysis

Turbulence

Wavelets

Computer simulations

Wavelet transforms

Signal processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years