Title :
Reveal: An Extensible Reduced-Order Model Builder for Simulation and Modeling
Author :
Agarwal, K. ; Sharma, Parmanand ; Jinliang Ma ; Chaomei Lo ; Gorton, Ian ; Yan Liu
Abstract :
Many science domains need to build computationally efficient and accurate representations of high fidelity, computationally expensive simulations known as reduced-order models (ROMs). This article presents the design and implementation of the Reveal toolset, a ROM builder that generates ROMs based on science- and engineering-domain-specific simulations executed on high-performance computing (HPC) platforms. The toolset encompasses a range of sampling and regression methods for ROM generation, automatically quantifies ROM accuracy, and supports an iterative approach to improve ROM accuracy. Reveal is designed to be extensible for any simulator that has published input and output formats. It also defines programmatic interfaces to include new sampling and regression techniques so users can mix and match mathematical techniques best suited to their model characteristics. The article describes the architecture of Reveal and demonstrates its use with a computational fluid dynamics model used in carbon capture.
Keywords :
carbon capture and storage; computational fluid dynamics; digital simulation; iterative methods; parallel processing; reduced order systems; regression analysis; sampling methods; HPC platform; ROM builder; ROM generation; Reveal toolset; carbon capture; computational fluid dynamics model; engineering-domain-specific simulation; extensible reduced-order model builder; high fidelity computationally expensive simulation; high-performance computing platform; iterative approach; mathematical technique; model characteristics; programmatic interface; regression method; regression technique; sampling method; sampling technique; science-domain-specific simulation; Analytical models; Computational modeling; Data models; Mathematical model; Read only memory; Reduced order systems; Simulation; modeling; reduced-order model; reusable software; scientific computing; simulation; surrogate model;
Journal_Title :
Computing in Science & Engineering
DOI :
10.1109/MCSE.2013.46