Use of computational fluid dynamics as a tool for establishing process design space for mixing in a bioreactor

2011 ◽  
Vol 28 (2) ◽  
pp. 382-391 ◽  
Author(s):  
A. S. Rathore ◽  
C. Sharma ◽  
A. Persad
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Process Design ◽  
Design Space

scholarly journals Combining catalysis and computational fluid dynamics towards improved process design for ethanol dehydration

2018 ◽  
Vol 8 (23) ◽  
pp. 6163-6172 ◽  
Author(s):  
Matthew E. Potter ◽  
Lindsay-Marie Armstrong ◽  
Robert Raja
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Process Design ◽  
Solid Acid ◽  
Reactor Design ◽  
Ethanol Dehydration

Combining computational fluid dynamics with catalysis gives significant insights into reactor design for sustainable solid acid catalysed processes.

Design Space Exploration Using Uncertainty-Based Bounding Methods in Computational Fluid Dynamics

2018 ◽  
Author(s):  
Justin D. Valenti ◽  
Simon W. Miller ◽  
Michael A. Yukish ◽  
Michael P. Kinzel
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration

Optimization and Uncertainty Analysis of a Diesel Engine Operating Point Using Computational Fluid Dynamics

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
Daniel M. Probst ◽  
Peter K. Senecal ◽  
Peter Z. Chien ◽  
Max X. Xu ◽  
Brian P. Leyde
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Diesel Engine ◽  
Optimal Design ◽  
Fuel Consumption ◽  
Analytical Model ◽  
Design Space ◽  
Uncertainty Propagation ◽  
Injection Duration ◽  
Engine Operating Point

This study describes the use of an analytical model, constructed using sequential design of experiments (DOEs), to optimize and quantify the uncertainty of a diesel engine operating point. A genetic algorithm (GA) was also used to optimize the design. Three engine parameters were varied around a baseline design to minimize indicated specific fuel consumption without exceeding emissions (NOx and soot) or peak cylinder pressure (PCP) constraints. An objective merit function was constructed to quantify the strength of designs. The engine parameters were start of injection (SOI), injection duration, and injector included angle. The engine simulation was completed with a sector mesh in the commercial computational fluid dynamics (CFD) software CONVERGE, which predicted the combustion and emissions using a detailed chemistry solver with a reduced mechanism for n-heptane. The analytical model was constructed using the SmartUQ software using DOE responses to construct kernel emulators of the system. Each emulator was used to direct the placement of the next set of DOE points such that they improve the accuracy of the subsequently generated emulator. This refinement was either across the entire design space or a reduced design space that was likely to contain the optimal design point. After sufficient emulator accuracy was achieved, the optimal design point was predicted. A total of five sequential DOEs were completed, for a total of 232 simulations. A reduced design region was predicted after the second DOE that reduced the volume of the design space by 96.8%. The final predicted optimum was found to exist in this reduced design region. The sequential DOE optimization was compared to an optimization performed using a GA. The GA was completed using a population of nine and was run for 71 generations. This study highlighted the strengths of both methods for optimization. The GA (known to be an efficient and effective method) found a better optimum, while the DOE method found a good optimum with fewer total simulations. The DOE method also ran more simulations concurrently, which is an advantage when sufficient computing resources are available. In the second part of the study, the analytical model developed in the first part was used to assess the sensitivity and robustness of the design. A sensitivity analysis of the design space around the predicted optimum showed that injection duration had the strongest effect on predicted results, while the included angle had the weakest. The uncertainty propagation was studied over the reduced design region found with the sequential DoE in the first part. The uncertainty propagation results demonstrated that for the relatively large variations in the input parameters, the expected variation in the indicated specific fuel consumption and NOx results were significant. Finally, the predictions from the analytical model were validated against CFD results for sweeps of the input parameters. The predictions of the analytical model were found to agree well with the results from the CFD simulation.

FPSO Computational Fluid Dynamics (CFD) Analysis in Heavy Sea Storm Conditions for the Validation of Process Design

2013 ◽  
Author(s):  
Claudio Verga ◽  
Antonio Monesi ◽  
Michele Pinelli ◽  
Stefano Dell'Oglio
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Process Design ◽  
Cfd Analysis ◽  
Computational Fluid Dynamics Cfd ◽  
Sea Storm

Generalized Riemann Problems in Computational Fluid Dynamics

2003 ◽  
Author(s):  
Matania Ben-Artzi ◽  
Joseph Falcovitz
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Riemann Problems ◽  
Generalized Riemann Problems

Computational Fluid Dynamics

2002 ◽  
Author(s):  
T. J. Chung
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics

Pulsatile, non-pulsatile, ante- or retrograde aortic blood flow: all the same? A computational fluid dynamics study of different ECC techniques

2010 ◽  
Vol 58 (S 01) ◽  
Author(s):  
A Assmann ◽  
AC Benim ◽  
A Nahavandi ◽  
D Schubert ◽  
A Lichtenberg ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Blood Flow ◽  
Aortic Blood Flow ◽  
Aortic Blood
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