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Machine Learning Based Data Compression in High-Fidelity Numerical Simulation

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-SC0022806
Agency Tracking Number: 0000266574
Amount: $200,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: C54-36b
Solicitation Number: N/A
Timeline
Solicitation Year: 2022
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-06-27
Award End Date (Contract End Date): 2023-06-26
Small Business Information
5335 Far Hills Ave Suite 2010
Dayton, OH 45459-4248
United States
DUNS: 141943030
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Ben O'Neill
 (937) 433-2886
 BONeill@RNET-Tech.com
Business Contact
 V. Nagarajan
Phone: (937) 433-2886
Email: VNagarajan@RNET-Tech.com
Research Institution
 The Ohio State University
 
1960 Kenny Rd.
Columbus, OH 43210-1016
United States

 Nonprofit College or University
Abstract

NE has invested heavily in the development of a wide range of computational frame-works over the past few decades. This considerable investment in advanced high fidelity codes has allowed the Nuclear engineering community to greatly reduce the non-recurring costs associated with reactor develop- ment. However, as the fidelity of these simulations grows, so too do the difficulties associated with using the simulations in industrial settings. Issues associated with writing, storing and analyzing the extreme levels of high-fidelity simulation data are not unique to NE. In fact, NASA, the DOE and CERN have all expressed the need for software and tools that can marshal high-fidelity simulation data into a format that can be quickly and efficiently used to rapidly inform real-world design decisions. RNET and OSU are proposing the development of robust framework for in-situ, scalable and data-driven compression in high-fidelity numerical simulation. This framework will allow simulations to output data in efficient reduced representations that occupy less memory, speed up input/output, and enable faster knowledge extraction. At the heart of the framework will be the aforementioned DLS data compression algorithm, a novel algorithm that uses modern machine learning based feature extraction techniques and the generalized finite element method (GFEM) to compress high-fidelity data into accurate reduced representations that may be queried in real-time. The three main objectives of the Phase I project are: 1)implement a perform-ant hybrid (MPI ,OpenMP) C++ implementation of the DLS algoirthm utilizing spatial domain decomposition and local multi- threading; 2) demonstrate the exceptional value of DLS data compression by comparing it other state-of-the-art lossy data compression algorithms for spatiotemporal solution fields; and 3) produce system design documents for the database and rapid analysis portal to be implemented in Phase II. For the developers of numerical software, the library will pro- vide a fast, in-situ method for exporting scientific data from advanced numerical simulations. The customers falling into this group include the many government agencies using numerical simulation including the DOE, NASA, Air Force, Navy, Army, and Marines. The commercial sector is also a heavy user of mesh based ap- plications. Those applications include the aeronautical, the biomechanical, and the automotive industries. The underlying algorithms of the proposed library could be used for data compression and/or fusion of large-scale experimental data sets, or multi-physics datasets such as those common in fracture analysis and space vehicle design, among many others.

* Information listed above is at the time of submission. *

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