A General-Purpose Software Tool for Multi-disciplinary Simulation Data Management and Learning

Period of Performance: 02/15/2014 - 02/14/2016

$750K

Phase 2 STTR

Recipient Firm

CFD Research Corp.
701 McMillian Way NW Suite D
Huntsville, AL 35806
Principal Investigator
Firm POC

Research Institution

University of Alabama, Huntsville
301 Sparkman Drive
Huntsville, AL 35899
Institution POC

Abstract

ABSTRACT: The overall goal of the proposed effort is to develop and demonstrate a general-purpose, fast, and reliable management and learning software tool for analyzing massive data sets generated by dynamic multi-disciplinary simulation. In Phase I, key technology elements were developed and proof-of-principle was successfully demonstrated. Data management software encapsulating salient feature specification algorithms, proper orthogonal decomposition (POD) engine, feature detection module, and data utilization module were all developed in an integrated architecture. By way of USAF relevant case studies, critical evidence was established that our tool enables accurate capture of coherent flow structures with unprecedented data reduction (10-40X) and focused visualization and learning (only 3-10% of original data). In Phase II, the developed software will be optimized in performance and functionality. The feature library will be expanded and improved. An incremental POD technique will be developed to tackle the massive data sets and enable co-processing capabilities. Advanced feature detection algorithms will be developed to improve accuracy and computational efficiency. Data utilization will be extended to compressible and turbulent flow. All the modules will be integrated onto a high-performance, parallel computing platform with facile user interface. The software will be extensively validated and demonstrated by selected case studies of USAF interest. BENEFIT: The need for next-generation data management and learning tools for deciphering massive data information generated by large-scale multi-disciplinary simulation is widely recognized. The end-product arising from this effort will be a novel, general-purpose management and learning software tool for fast, reliable, automated analysis of massive multi-disciplinary simulation data. The proposed technology will be of direct commercial value in military (e.g., DoD, MDA), NASA, and civilian sectors. The product will deliver DoD researchers a valuable tool to: (1) enable accurate feature detection and Region Of Interest (ROI) identification for selective visualization and storage; (2) gain an increased understanding and interpretation of the underlying physics; (3) provide guidance on diagnostics and reconfiguration of the multi-physics model and simulation for targeted applications; and (4) assist in concept evaluation, prediction, optimization, and design of high-performance systems. In the civilian sectors, the proposed data management tool will find widespread uses in various engineering areas due to its capability of handling data from various sources, including multi-physics modeling and simulation, experimental data reconstruction and system identification, sensor data fusion and mining, as well as low-dimensional model extraction for real-time control and diagnostics, graphical and image analysis, weather forecasting, bioinformatics, etc.