A computationally efficient reduced order model to generate multi-parameter fluid-thermal databases

Abstract

a b s t r a c tA reduced order model (ROM) is proposed to generate multi-parameter databases of some fluid-thermal problems, using a combination of proper orthogonal decomposition, a gradient-like method, and a continuation method. The resulting ROM greatly reduces the CPU time required by slower methods based on genetic algorithm formulations. As a byproduct, the number of required snapshots is also reduced, which yields an additional improvement of the computational efficiency. The work presented in this article aims to facilitate the use of ROMs in industrial environments, in which time is a very important asset. The methodology is illustrated with the non-isothermal flow past a backward-facing step in the laminar regime, which is a representative problem, related to the engineering design of micro-heat sinks.

Developing an Integrated Computational Environment for the Detailed Design of a Mixing Impeller

“…42 For example, ROMs have been used to reduce the time needed to compute a flow field by one to two orders of magnitude over computational fluid dynamics. [43][44][45] There are various types of reduced order models; these include reduced basis method, 46 balanced truncation, 47 boundary element, 48 and goal-oriented. 49 While each of these has advantages and disadvantages, proper orthogonal decomposition has been found to be particularly effective at the reproduction of detailed flow fields.…”

Orthogonal decomposition as a design tool: With application to a mixing impeller

“…For example, ROMs have been used to reduce the time needed to compute a flow field by one to two orders of magnitude over computational fluid dynamics (Alonso, Velaquez and Vega 2009, Barone et al 2009, Bache et al 2012, Walton, Hassan, and Morgan 2013. There are various types of reduced order models; these include the reduced basis method (Knezevic and Patera 2011), balanced truncation method (Ma, Ahuja, and Rowley 2011), boundary element method (Noorian, Firouz-Abadi, and Haddadpour 2012) and goal-oriented method (Carlberg and Farhat 2011).…”

Orthogonal decomposition as a design tool: With application to a mixing impeller

“…Reduced order models are commonly used to reduce the compute and wall clock time needed to find a new result rather perform another set of detailed computations (Fang et al 2009). For example, ROMs have been used to reduce the time needed to compute a flow field by one to two orders of magnitude over computational fluid dynamics (Alonso, Velaquez and Vega 2009, Barone et al 2009, Bache et al 2012, Walton, Hassan, and Morgan 2013. There are various types of reduced order models; these include the reduced basis method (Knezevic and Patera 2011), balanced truncation method (Ma, Ahuja, and Rowley 2011), boundary element method (Noorian, Firouz-Abadi, and Haddadpour 2012) and goal-oriented method (Carlberg and Farhat 2011).…”

“…ROMs) have been used to reduce the time needed to compute a flow field by one to two orders of magnitude over computational fluid dynamics(Alonso, Velaquez and Vega 2009, Barone et al 2009, Bache et al 2012, Walton, Hassan, and Morgan 2013. There are various types of reduced order models, these include the reduced basis method(Knezevic, Ngoc-Cuong, and Patera 2011), balanced truncation(Singler and Batten 2009, Ma, Ahuja, andRowley 2011), and goal-oriented(Carlberg and Farhat 2011).…”