Some Aspects of Uncertainty in Computational Fluid Dynamics Results

1991 ◽  
Vol 113 (4) ◽  
pp. 538-543 ◽  
Author(s):  
U. B. Mehta
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Sensitivity Analysis ◽  
Uncertainty Analysis ◽  
Dynamic Systems ◽  
Fluid Dynamic ◽  
Practical Method ◽  
Computational Fluid Dynamics Cfd

Uncertainties are inherent in computational fluid dynamics (CFD). These uncertainties need to be systematically addressed and managed. Sources of these uncertainties are identified and some aspects of uncertainty analysis are discussed. Some recommendations are made for quantification of CFD uncertainties. A practical method of uncertainty analysis is based on sensitivity analysis. When CFD is used to design fluid dynamic systems, sensitivity-uncertainty analysis is essential.

scholarly journals Analisis Distribusi Temperatur dan Aliran Fluida pada Proses Pengeringan Butiran Teh Bentuk Silinder Di Dalam Fluidized Bed Dryer Menggunakan Computational Fluid Dynamic (CFD)

ROTASI ◽  
2019 ◽  
Vol 20 (4) ◽  
pp. 237
Author(s):  
MSK Tony Suryo Utomo ◽  
Eflita Yohana ◽  
Mauli Astuti Khoiriyah
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Fluidized Bed ◽  
Computational Fluid Dynamic ◽  
Fluid Dynamic ◽  
Fluidized Bed Dryer ◽  
Computational Fluid Dynamics Cfd

Pengeringan merupakan proses perpindahan panas dan uap air secara simultan yang memerlukan energi panas untuk menguapkan kandungan air dari bahan yang akan dikeringkan. Penelitian ini dilakukan dengan cara simulasi. Produk yang dipilih untuk simulasi ini yaitu teh. Simulasi numerik perpindahan massa pada teh dilakukan dengan menempatkan material teh pada domain komputasi sebuah aliran eksternal. Penelitian ini bertujuan untuk menganalisis distribusi temperatur pada partikel teh dengan menggunakan Computational Fluid Dynamics (CFD) dan menganalisis pengaruh variasi kecepatan inlet dan temperatur inlet terhadap waktu pengeringan sehingga diperoleh metode pengeringan yang paling optimum pada pengeringan teh. Penurunan massa pada teh dihitung secara analitik dengan menggunakan persamaan laju penurunan massa. Teh dimodelkan dengan bentuk menyerupai silinder setelah dilakukan pelayuan untuk kemudian dikeringkan. Kecepatan masuk aliran udara dan temperatur masuk divariasikan sesuai dengan batas kecepatan minimum dan maksimum fluidisasi dan temperatur pengeringan teh untuk fluidized bed dryer. Waktu yang digunakan untuk menurunkan kadar air hingga 3% berdasarkan temperatur pada kecepatan 1,6 m/s secara berurutan adalah 354 s (880C), 300 s (930C), dan 256 s (980C). Sementara pada kecepatan 2,6 m/s waktu yag dibutuhkan adalah 277 s (880C), 234 s (930C), dan 200 s (980C) serta untuk kecepatan 3,6 m/s berturut-turut 235 s (880C), 199 s (930C), dan 169 s (980C). Untuk pengeringan teh lebih optimal dilakukan dengan menaikkan kececepatan masuk aliran fluida dibandingkan dengan menaikkan temperatur.

A Computational Fluid Dynamics-Based Sensitivity Analysis of the Chemical Vapor Analysis Process to Synthesize Carbon Nanotubes

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
C. Teixeira ◽  
A. F. Silva ◽  
L. A. Rocha
Keyword(s):  
Fluid Dynamics ◽  
Carbon Nanotubes ◽  
Computational Fluid Dynamics ◽  
Sensitivity Analysis ◽  
Chemical Vapor ◽  
Synthesis Process ◽  
Synthesis Conditions ◽  
Analysis Process ◽  
Computational Fluid Dynamics Cfd ◽  
Vapor Analysis

Abstract Over the last years, there has been a high interest in carbon nanotubes' (CNTs) applications due to their unique properties, mainly at mechanical and electrical levels. However, current synthesis processes, such as chemical vapor deposition (CVD), are highly unpredictable and inconsistent, which leads to an exhaustive trial-and-error methodology when extrapolating results. A sensitivity analysis based on computational fluid dynamics (CFD) is performed here to two distinct setups of the CVD process as a way to understand the synthesis process. Setups were computationally designed and simulated for various synthesis scenarios, where only the hydrocarbon flow and the process temperature were changed. Measuring synthesis conditions, such as concentrations and velocity, inside the tube furnace, for these scenarios allows the identification of which compound affects most each condition. Results showed that, when envisioning the process extrapolation, the synthesis conditions can be tuned via the accessed parameters.

scholarly journals Computational fluid dynamic analysis reveals the underlying physical forces playing a role in 3D multiplex brain organoid cultures

2018 ◽  
Author(s):  
Livia Goto-Silva ◽  
Nadia M. E. Ayad ◽  
Iasmin L. Herzog ◽  
Nilton P. Silva ◽  
Bernard Lamien ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shear Stress ◽  
Suspension Culture ◽  
Fluid Dynamic ◽  
Spinner Flask ◽  
List Type ◽  
Computational Fluid Dynamic Analysis ◽  
Computational Fluid Dynamics Cfd ◽  
Brain Organoid

AbstractOrganoid cultivation in suspension culture requires agitation at low shear stress to allow for nutrient diffusion, which preserves tissue structure. Multiplex systems for organoid cultivation have been proposed, but whether they meet similar shear stress parameters as the regularly used spinner flask and its correlation with the successful generation of brain organoids, has not been determined. Herein, we used computational fluid dynamics (CFD) analysis to compare two multiplex culture conditions: steering plates on an orbital shaker and the use of a previously described bioreactor. The bioreactor had low speed and high shear stress regions that may affect cell aggregate growth, depending on volume, whereas the CFD parameters of the steering plates were closest to the parameters of the spinning flask. Our protocol improves the initial steps of the standard brain organoid formation, and organoids produced therefrom displayed regionalized brain structures, including retinal pigmented cells. Overall, we conclude that suspension culture on orbital steering plates is a cost-effective practical alternative to previously described platforms for the cultivation of brain organoids for research and multiplex testing.HighlightsImprovements to organoid preparation protocolMultiplex suspension culture protocol successfully generate brain organoidsComputational fluid dynamics (CFD) reveals emerging properties of suspension culturesCFD of steering plates is equivalent to that of spinner flask cultures

Efficiency prediction for storage chambers using computational fluid dynamics

1996 ◽  
Vol 33 (9) ◽  
pp. 163-170 ◽  
Author(s):  
Virginia R. Stovin ◽  
Adrian J. Saul
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shear Stress ◽  
Particle Tracking ◽  
Critical Value ◽  
Complex Geometries ◽  
Bed Shear Stress ◽  
Breadth Ratio ◽  
Computational Fluid Dynamics Cfd ◽  
Simulated Flow

Research was undertaken in order to identify possible methodologies for the prediction of sedimentation in storage chambers based on computational fluid dynamics (CFD). The Fluent CFD software was used to establish a numerical model of the flow field, on which further analysis was undertaken. Sedimentation was estimated from the simulated flow fields by two different methods. The first approach used the simulation to predict the bed shear stress distribution, with deposition being assumed for areas where the bed shear stress fell below a critical value (τcd). The value of τcd had previously been determined in the laboratory. Efficiency was then calculated as a function of the proportion of the chamber bed for which deposition had been predicted. The second method used the particle tracking facility in Fluent and efficiency was calculated from the proportion of particles that remained within the chamber. The results from the two techniques for efficiency are compared to data collected in a laboratory chamber. Three further simulations were then undertaken in order to investigate the influence of length to breadth ratio on chamber performance. The methodology presented here could be applied to complex geometries and full scale installations.

Improvement of real-scale raceway bioreactors for microalgae production using Computational Fluid Dynamics (CFD)

2021 ◽  
Vol 54 ◽  
pp. 102207
Author(s):  
Cristian Inostroza ◽  
Alessandro Solimeno ◽  
Joan García ◽  
José M. Fernández-Sevilla ◽  
F. Gabriel Acién
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Computational Fluid Dynamics Cfd ◽  
Real Scale
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