A review on selection of turbulence model for CFD analysis of air flow within a cold storage

Mishra, Pankaj; Aharwal, K. R.

doi:10.1088/1757-899x/402/1/012145

Cited by 20 publications

(9 citation statements)

References 25 publications

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“…The k-Ɛ model can manage a wide range of fluid flow conditions. It has a straightforward structure, delivers outstanding results, and is generally accepted [15].…”

Section: Mathematical Modelmentioning

confidence: 99%

Numerical Analysis of Aerodynamic Drag for the Hyperloop System

Prabhakar

Shekhar

Jain

et al. 2022

Advances in Transdisciplinary Engineering

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The Hyperloop vehicle is a means of transport in which the pod advances at a fast velocity inside a low-pressure tube to reduce the coefficient of drag. Aerodynamic drag is one of the most significant elements to consider while studying such systems. The design was created on ANSYS SpaceClaim and it consists of a Two-Dimensional model with tapered ends to diminish the drag. Since both ends are tapered, the drag is reduced significantly as compared to the other designs. In this paper, the pod design is analyzed at different speeds, namely 60 ms-1, 120 ms-1, 180 ms-1, 260 ms-1, and 300 ms-1 on ANSYS FLUENT and numerical simulations were performed keeping the blockage ratio fixed. Appropriate conditions, equations and k-epsilon turbulence model were applied to produce the desired result. All the drag coefficient values were analyzed, and hence, the graph was plotted. It was observed that the drag coefficient of the pod decreases with an increase in velocity.

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“…The k-Ɛ model can manage a wide range of fluid flow conditions. It has a straightforward structure, delivers outstanding results, and is generally accepted [15].…”

Section: Mathematical Modelmentioning

confidence: 99%

Numerical Analysis of Aerodynamic Drag for the Hyperloop System

Prabhakar

Shekhar

Jain

et al. 2022

Advances in Transdisciplinary Engineering

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“…:: IJEEIT :: The turbulence model based on the RANS equation is more extensively utilized in industry since the LES model requires more processing power and more intricate implementation. because turbulence equations-based RANS models have improved performance and are more widely applicable (Mishra & Aharwal, 2018).…”

Section: Computational Fluid Dynamics (Cfd) Model To Predict Sediment...mentioning

confidence: 99%

The Application of A Combined Computational Fluid Dynamics (CFD) Artificial Neural Network (ANN) to Increase The Prediction Accuracy of Sediment Grading in Subsea Pipes: A Literature Review

Dhanistha

Syarifudin

Damastuti

et al. 2022

ijeeit

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In recent years, the implementation of subsea pipelines for oil and gas transportation has increased. One of the important aspects of the design process of the subsea pipeline is scour prediction. Scouring causes the subsea pipeline to lose its support and is susceptible to failure due to deflection. This paper presents the result of a literature review of scouring-related research to obtain a method to increase scouring prediction accuracy. Based on the literature research, it is known that the errors found in Computational Fluid Dynamics (CFD) are mainly affected by the flow models. Existing flow models cannot fully represent the complexity of turbulent flow that occurs during the scouring process. Artificial Neural Network (ANN) can reduce the error value. But, the CFD-ANN hybrid methods can potentially decrease the error value by about 20% more than CFD. Therefore, the CFD-ANN hybrid method is expected to be a new method that could be used to predict subsea pipeline scouring in the oil and gas industry.

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“…Este modelo predice una buena concordancia con el medio poroso grande. Con el modelo RNG se produjeron mejores resultados que con el modelo k-ε estándar, debido a que considera condiciones anisotrópicas complejas [12].…”

Section: Modelo Rng K-εunclassified

Estudio numérico y comparativo del efecto de turbulencia en codos y dobleces para distribución de agua sanitaria

et al. 2019

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En este artículo se presenta el estudio numérico y comparativo del efecto de turbulencia en codos y dobleces para diámetros de una pulgada, mediante CFD y bajo las mismas condiciones de trabajo (velocidad, presión y temperatura), para determinar la fluctuación en la turbulencia de energía cinética entre estos dos accesorios variando los modelos de turbulencia. Se emplearon dos metodologías para esta investigación, kappa-épsilon (k- ε) y kappa-omega (k-ω). El método (k- ε) se divide en tres modelos: estándar, RNG y realizable, en los cuales se genera turbulencia de energía cinética y de disipación. El método (k-ω) también posee tres variantes: estándar, SST, BSL. El trabajo presenta una mayor turbulencia para el método de (k- ε) en energía cinética y de disipación bajo el modelo estándar tanto para codo como doblez, mientras que en el método (k-ω) se produce una mayor turbulencia de energía cinética en el modelo BSL para ambos accesorios, al igual que en el método (k- ε), el modelo estándar de (k-ω) representa una mayor turbulencia de frecuencia.

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A review on selection of turbulence model for CFD analysis of air flow within a cold storage

Cited by 20 publications

References 25 publications

Numerical Analysis of Aerodynamic Drag for the Hyperloop System

Numerical Analysis of Aerodynamic Drag for the Hyperloop System

The Application of A Combined Computational Fluid Dynamics (CFD) Artificial Neural Network (ANN) to Increase The Prediction Accuracy of Sediment Grading in Subsea Pipes: A Literature Review

Estudio numérico y comparativo del efecto de turbulencia en codos y dobleces para distribución de agua sanitaria

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