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Title: Contextual Compression of Large-Scale Wind Turbine Array Simulations: Preprint

Abstract

Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interactive visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contexualized representation is a valid approach and encourages contextual data management.

Authors:
 [1];  [1];  [1];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. National Center for Atmospheric Research
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1411517
Report Number(s):
NREL/CP-2C00-70265
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2nd International Workshop on Data Reduction for Big Scientific Data (DRBSD-2) at SC17, 12-17 November 2017, Denver, Colorado
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; HPC applications; wind turbine; array simulations; modeling; wavelet coefficients

Citation Formats

Gruchalla, Kenny M, Brunhart-Lupo, Nicholas J, Potter, Kristin C, and Clyne, John. Contextual Compression of Large-Scale Wind Turbine Array Simulations: Preprint. United States: N. p., 2017. Web.
Gruchalla, Kenny M, Brunhart-Lupo, Nicholas J, Potter, Kristin C, & Clyne, John. Contextual Compression of Large-Scale Wind Turbine Array Simulations: Preprint. United States.
Gruchalla, Kenny M, Brunhart-Lupo, Nicholas J, Potter, Kristin C, and Clyne, John. Fri . "Contextual Compression of Large-Scale Wind Turbine Array Simulations: Preprint". United States. doi:. https://www.osti.gov/servlets/purl/1411517.
@article{osti_1411517,
title = {Contextual Compression of Large-Scale Wind Turbine Array Simulations: Preprint},
author = {Gruchalla, Kenny M and Brunhart-Lupo, Nicholas J and Potter, Kristin C and Clyne, John},
abstractNote = {Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interactive visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contexualized representation is a valid approach and encourages contextual data management.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Nov 03 00:00:00 EDT 2017},
month = {Fri Nov 03 00:00:00 EDT 2017}
}

Conference:
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