Development of an Analytical Model: Tank Pressure Control Experiment

2000 ◽  
Author(s):  
Jihad M. Albayyari ◽  
Karteek K. Subramanya

Abstract The “Reduced-Fill Tank Pressure Control Experiment (TPCE/RF)” is a space experiment developed to meet the need for a critical aspect of cryogenic fluid management technology: “control of storage tank pressures in the absence of gravity by forced-convection mixing”. The experiment used Freon-113, at near saturation conditions and a constant 40% fill level, to simulate the fluid dynamics and thermodynamics of cryogenic fluids in space applications. The objectives of TPCE/RF were: Characterize the fluid dynamics of an axial jet-induced mixing in low gravity. Evaluate the validity of empirical mixing models, and provide data for use in developing and validating computational fluid dynamics model of mixing processes. TPCE/RF accomplished all of its objectives in flight on the Space Shuttle flight in May 1996. The flow patterns observed generally agreed with a prior correlation derived from drop tower tests. Several existing mixing correlations were found to provide reasonable performance predictions. Low-energy mixing jets, dissipating on the order of 1% of the kinetic energy of previous mixer designs, were found to be effective and reliable at reducing thermal non-uniformities, promoting heat and mass active mixing, whether continuous or periodic, offers increased reliability and predictability in space cryogenic systems and can be accomplished with no significant boiloff penalty caused by kinetic energy dissipation.

1997 ◽  
Author(s):  
Michael Bentz ◽  
Jihad Albayyari ◽  
Richard Knoll ◽  
Mohammmad Hasan ◽  
Chin Lin ◽  
...  

2021 ◽  
Vol 11 (4) ◽  
pp. 520
Author(s):  
Emily R. Nordahl ◽  
Susheil Uthamaraj ◽  
Kendall D. Dennis ◽  
Alena Sejkorová ◽  
Aleš Hejčl ◽  
...  

Computational fluid dynamics (CFD) has grown as a tool to help understand the hemodynamic properties related to the rupture of cerebral aneurysms. Few of these studies deal specifically with aneurysm growth and most only use a single time instance within the aneurysm growth history. The present retrospective study investigated four patient-specific aneurysms, once at initial diagnosis and then at follow-up, to analyze hemodynamic and morphological changes. Aneurysm geometries were segmented via the medical image processing software Mimics. The geometries were meshed and a computational fluid dynamics (CFD) analysis was performed using ANSYS. Results showed that major geometry bulk growth occurred in areas of low wall shear stress (WSS). Wall shape remodeling near neck impingement regions occurred in areas with large gradients of WSS and oscillatory shear index. This study found that growth occurred in areas where low WSS was accompanied by high velocity gradients between the aneurysm wall and large swirling flow structures. A new finding was that all cases showed an increase in kinetic energy from the first time point to the second, and this change in kinetic energy seems correlated to the change in aneurysm volume.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  

AbstractComputational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.


2016 ◽  
Vol 819 ◽  
pp. 356-360
Author(s):  
Mazharul Islam ◽  
Jiří Fürst ◽  
David Wood ◽  
Farid Nasir Ani

In order to evaluate the performance of airfoils with computational fluid dynamics (CFD) tools, modelling of transitional region in the boundary layer is very critical. Currently, there are several classes of transition-based turbulence model which are based on different methods. Among these, the k-kL- ω, which is a three equation turbulence model, is one of the prominent ones which is based on the concept of laminar kinetic energy. This model is phenomenological and has several advantageous features. Over the years, different researchers have attempted to modify the original version which was proposed by Walter and Cokljat in 2008 to enrich the modelling capability. In this article, a modified form of k-kL-ω transitional turbulence model has been used with the help of OpenFOAM for an investigative CFD analysis of a NACA 4-digit airfoil at range of angles of attack.


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