CFD Predictions of Damköhler Number Fields for Reduced Order Modeling of V-Gutter Flame Stability

2008 ◽  
Author(s):  
John Roach ◽  
Travis Fisher ◽  
Steven Frankel ◽  
Balu Sekar ◽  
Barry Kiel
Keyword(s):  
Number Fields ◽  
Reduced Order Modeling ◽  
Flame Stability ◽  
Damkohler Number ◽  
Reduced Order ◽  
Damköhler Number

Lean Blowout Predictions of a Non-Premixed V-Gutter Stabilized Flame Using a Damkohler Number Methodology

2011 ◽  
Author(s):  
Hui-ru Wang ◽  
Jie Jin
Keyword(s):  
Experimental Data ◽  
Number Fields ◽  
Inlet Temperature ◽  
Damkohler Number ◽  
Atmosphere Pressure ◽  
Inlet Velocity ◽  
Lean Blowout ◽  
Cell Basis ◽  
Damköhler Number ◽  
A Cell

The lean blowout stability of a non-premixed, V-gutter stabilized flame was investigated using a Damkohler number methodology. The flow and chemical timescales were extracted from the reacting RANS CFD results on a cell-by-cell basis. Assessment of three representative definitions of flow and chemical timescales for Damkohler number based on different blowout mechanisms was performed. By examining the Damkohler number fields, the structure of the flame or the possibility of blowout can be estimated. The results demonstrated that a distinct transition between stable and unstable flames was observed by decreasing the fuel-air ratio or increasing the inlet velocity at atmosphere pressure and an inlet temperature of 537K. All three definitions can predict the lean blowout limit in a reasonable consistent with the available experimental data through adjusting the critical Damkohler number of each definition in the current study. The performances and physical differences of three definitions were also discussed.

Damköhler Number Similarity for Static Flame Stability in Gaseous-Fueled Augmentor Flows

2011 ◽  
Vol 183 (7) ◽  
pp. 718-737 ◽  
Author(s):  
Hossam A. El-Asrag ◽  
Heinz Pitsch ◽  
Wookyung Kim ◽  
Hyungrok Do ◽  
M. Godfrey Mungal
Keyword(s):  
Flame Stability ◽  
Damkohler Number ◽  
Damköhler Number

Observation of the flame structures emerging at low Damköhler number fields

2007 ◽  
Vol 31 (1) ◽  
pp. 1353-1359 ◽  
Author(s):  
Manabu Fuchihata ◽  
Masashi Katsuki ◽  
Yukio Mizutani ◽  
Tamio Ida
Keyword(s):  
Number Fields ◽  
Damkohler Number ◽  
Damköhler Number ◽  
Flame Structures

Data-Driven Reduced Order Modeling of Flows Around Two-Dimensional Bluff Bodies of Various Shapes

2019 ◽  
Author(s):  
Kazuto Hasegawa ◽  
Kai Fukami ◽  
Takaaki Murata ◽  
Koji Fukagata
Keyword(s):  
Number Fields ◽  
Flow Fields ◽  
Reduced Order Modeling ◽  
Reduced Order Model ◽  
Data Driven ◽  
Bluff Bodies ◽  
Two Dimensional ◽  
Order Model ◽  
Reduced Order ◽  
Low Dimensional

Abstract We propose a reduced order model for predicting unsteady flows using a data-driven method. As preliminary tests, we use two-dimensional unsteady flow around bluff bodies with different shapes as the training datasets obtained by direct numerical simulation (DNS). Our machine-learned architecture consists of two parts: Convolutional Neural Network-based AutoEncoder (CNN-AE) and Long Short Term Memory (LSTM), respectively. First, CNN-AE is used to map into a low-dimensional space from the flow field data. Then, LSTM is employed to predict the temporal evolution of the low-dimensional data generated by CNN-AE. Proposed machine-learned reduced order model is applied to two-dimensional circular cylinder flows at various Reynolds numbers and flows around bluff bodies of various shapes. The flow fields reconstructed by the machine-learned architecture show reasonable agreement with the reference DNS data. Furthermore, it can be seen that our machine-learned reduced order model can successfully map the high-dimensional flow data into low-dimensional field and predict the flow fields against unknown Reynolds number fields and shapes of bluff body. As concluding remarks, we discuss the extension study of machine-learned reduced order modeling for various applications in experimental and computational fluid dynamics.

Lattice Boltzmann Flow Simulations With Applications of Reduced Order Modeling Techniques.

2014 ◽  
Author(s):  
Donald L. Brown ◽  
Jun Li ◽  
Victor M. Calo ◽  
Mehdi Ghommem ◽  
Yalchin Efendiev
Keyword(s):  
Lattice Boltzmann ◽  
Reduced Order Modeling ◽  
Reduced Order ◽  
Flow Simulations ◽  
Modeling Techniques
Sign in / Sign up

Export Citation Format

Share Document