Convergence Acceleration of Fluid Dynamics Solvers Using a Reduced-Order-Model

2017 ◽  
Author(s):  
Reza Djeddi ◽  
Andrew L. Kaminsky ◽  
Kivanc Ekici
Keyword(s):  
Fluid Dynamics ◽  
Convergence Acceleration ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order

scholarly journals Convergence Acceleration of Fluid Dynamics Solvers Using a Reduced-Order Model

AIAA Journal ◽  
2017 ◽  
Vol 55 (9) ◽  
pp. 3059-3071 ◽  
Author(s):  
Reza Djeddi ◽  
Andrew Kaminsky ◽  
Kivanc Ekici
Keyword(s):  
Fluid Dynamics ◽  
Convergence Acceleration ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order

scholarly journals Greedy Nonintrusive Reduced Order Model for Fluid Dynamics

AIAA Journal ◽  
2018 ◽  
Vol 56 (12) ◽  
pp. 4927-4943 ◽  
Author(s):  
Wang Chen ◽  
Jan S. Hesthaven ◽  
Bai Junqiang ◽  
Yasong Qiu ◽  
Zhang Yang ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order

Reduced Order Modeling of Entrained Flow Solid Fuel Gasification

2009 ◽  
Author(s):  
Rory F. D. Monaghan ◽  
Mayank Kumar ◽  
Simcha L. Singer ◽  
Cheng Zhang ◽  
Ahmed F. Ghoniem
Keyword(s):  
Fluid Dynamics ◽  
Reduced Order Modeling ◽  
Combined Cycle ◽  
Reduced Order Model ◽  
Temperature Profiles ◽  
Order Model ◽  
External Temperature ◽  
Reduced Order ◽  
Entrained Flow ◽  
Entrained Flow Gasifiers

Reduced order models that accurately predict the operation of entrained flow gasifiers as components within integrated gasification combined cycle (IGCC) or polygeneration plants are essential for greater commercialization of gasification-based energy systems. A reduced order model, implemented in Aspen Custom Modeler, for entrained flow gasifiers that incorporates mixing and recirculation, rigorously calculated char properties, drying and devolatilization, chemical kinetics, simplified fluid dynamics, heat transfer, slag behavior and syngas cooling is presented. The model structure and submodels are described. Results are presented for the steady-state simulation of a two-metric-tonne-per-day (2 tpd) laboratory-scale Mitsubishi Heavy Industries (MHI) gasifier, fed by two different types of coal. Improvements over the state-of-the-art for reduced order modeling include the ability to incorporate realistic flow conditions and hence predict the gasifier internal and external temperature profiles, the ability to easily interface the model with plant-wide flowsheet models, and the flexibility to apply the same model to a variety of entrained flow gasifier designs. Model validation shows satisfactory agreement with measured values and computational fluid dynamics (CFD) results for syngas temperature profiles, syngas composition, carbon conversion, char flow rate, syngas heating value and cold gas efficiency. Analysis of the results shows the accuracy of the reduced order model to be similar to that of more detailed models that incorporate CFD. Next steps include the activation of pollutant chemistry and slag submodels, application of the reduced order model to other gasifier designs, parameter studies and uncertainty analysis of unknown and/or assumed physical and modeling parameters, and activation of dynamic simulation capability.

scholarly journals Updating of aerodynamic reduced order models generated using computational fluid dynamics

2017 ◽  
Vol 232 (9) ◽  
pp. 1739-1763
Author(s):  
LM Griffiths ◽  
AL Gaitonde ◽  
DP Jones ◽  
MI Friswell
Keyword(s):  
Fluid Dynamics ◽  
Experimental Data ◽  
Computational Fluid Dynamics ◽  
Model Updating ◽  
Limited Range ◽  
Reduced Order Model ◽  
Reduced Order Models ◽  
Viscous Effects ◽  
Order Model ◽  
Reduced Order

Reduced order models of computational fluid dynamics codes have been developed to decrease computational costs; however, each reduced order model has a limited range of validity based on the data used in its construction. Further, like the computational fluid dynamics from which it is derived, such models exhibit differences from experimental data due to uncertainty in boundary conditions and numerical accuracy. Model updating provides the opportunity to use small amounts of additional data to modify the behaviour of a reduced order model, which means that the range of validity of the reduced order model can be extended. Whilst here computational fluid dynamics data have been used for updating, the approach offers the possibility that experimental data can be used in future. In this work, the baseline reduced order models are constructed using the Eigensystem realisation algorithm and the steps used to update these models are given in detail. The methods developed are then applied to remove the effects of wind tunnel walls and to include viscous effects.

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