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University of Connecticut

Combustion Simulations Using Graphic Processing Units

Abstract

dc:description.abstract

<p>Graphic processing units (GPUs) are powerful graphics engines featuring high levels of parallelism and extreme memory bandwidth, which constitute a powerful computing platform to solve complex problems involving chemically reacting flows. In the present study, computer programs for combustion simulations with detailed chemical kinetic mechanisms were compiled in the Compute Unified Device Architecture (CUDA) language for NVIDIA GPU architecture. Ignition processes were simulated under constant pressure and constant volume conditions using an explicit 4<sup>th</sup> order Runge-Kutta algorithm for time integration. Sufficiently small time steps were identified with time scale analysis to ensure the integration stability. The program was validated with the results from simulations with CPUs using detailed mechanisms of various fuels including H<sub>2</sub>, and CH<sub>4</sub>. It was found that the GPU-accelerated simulations can be approximately 10-20 times faster than those on CPUs for solving identical problems. Furthermore, the newly implemented GPU solver for detailed chemical kinetics was employed for quasi 2-D simulations.</p>

Degree

thesis:*
Name thesis:degree_name
Master of Science
Discipline thesis:degree_discipline
Mechanical Engineering
Year dc:date.available
2012

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Wang, Mingjie
Contributors dc:contributor
  • Chih-Jen (Jackie) Sung; Mandhapati P. Raju
  • Tianfeng Lu

Subjects

dc:subject × 3

Identifiers

dc:identifier.*
Repository record dc:identifier
https://digitalcommons.lib.uconn.edu/gs_theses/226
OAI identifier oai:identifier
oai:digitalcommons.lib.uconn.edu:gs_theses-1258

Chain of custody

source
Harvested from
University of Connecticut
Base URL
digitalcommons.lib.uconn.edu/do/oai/
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Wang, Mingjie. Combustion Simulations Using Graphic Processing Units. 2012. https://digitalcommons.lib.uconn.edu/gs_theses/226