Natural convection heat transfer and fluid flow around a thick hollow vertical cylinder suspended in air: A numerical approach

Dash, Manoj Kumar; Dash, Sukanta Kumar

doi:10.1016/j.ijthermalsci.2020.106312

Cited by 30 publications

(14 citation statements)

References 31 publications

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“…2012; Alipour, Hosseini & Rezania 2013). Natural convection around a thick vertical cylinder was also simulated recently by Dash & Dash (2020). These studies mainly focused on the effect of the Rayleigh number and radius ratio on the Nusselt number and flow rate.…”

Section: Introductionmentioning

confidence: 93%

Effect of curvature on transient natural convection in a vertical circular pipe

Nie

2022

J. Fluid Mech.

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Natural convection adjacent to a curved vertical wall is widely present. Unfortunately, the effect of curvature on the transient thermal boundary layer (TBL) adjacent to the concave vertical wall has been neglected. In this study, dynamical evolution and thermal process of transient natural convection in a vertical circular pipe are discussed using scaling analysis, a boundary flow regime for the thin TBL without merging and a duct flow regime for the TBL with merging at the axis of the pipe are distinguished. The scaling laws quantifying the dependence of thickness, velocity and flow rate of the TBL of the fluid with the fixed Prandtl number in the vertical pipe on the Rayleigh number (RaT and Raq) and the ratio of height to radius of the pipe (A) are first reported for the isothermal and isoflux conditions. The curvature effect becomes stronger with the increase of the thickness of the TBL. Under the duct flow regime, the non-dimensional flow rate is scaled with $Ra_T^{1/2}{A^{ - 1}}$ for the isothermal condition and with $Ra_q^{1/2}{A^{ - 3/2}}$ for the isoflux condition. The scaling laws of the thickness, velocity and the flow rate of the TBL in the vertical pipe are validated based on the numerical results from direct numerical simulation (DNS) with good precision. The scaling coefficient is also presented under different regimes and conditions, which can serve as a design guide to determine natural convection in the vertical circular pipe.

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Section: Introductionmentioning

confidence: 93%

Effect of curvature on transient natural convection in a vertical circular pipe

Nie

2022

J. Fluid Mech.

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“…To use the data in ANN, the data must be normalized to the range of [0,1] and the normalization method is given by Equation (12). The logarithmic sigmoid (logsig) transfer function was defined Equation ( 12) [25].…”

Section: Ann Modelling Of Heat Transfermentioning

confidence: 99%

“…The results were compared with a circle cylinder. Dash and Dash [12] examined the flow and heat transfer around the hollow cylinder. By changing the length ratio of the cylinder diameter, the change in the Nusselt number was examined.…”

Section: Introductionmentioning

confidence: 99%

“…Many algorithms may be preferred when solving problems with ANN, but the most commonly used and best results algorithms are Levenberg-Marguardt (LM), Pola-Ribiere Conjugate Gradient (CGP), Scaled Conjugate Gradient (SCG) and Gradient descent with momentum and adaptive learning rate back propagation (GDX). It is seen in the literature that the LM algorithm gives the fastest and best results[9,10,24].To use the data in ANN, the data must be normalized to the range of [0,1] and the normalization method is given by Equation(12). The logarithmic sigmoid (logsig) transfer function was defined Equation (12)[25].…”

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confidence: 99%

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Modeling of Heat Transfer Around a Square and a Circle: A Novel ANN-Based Approch

Yetik¹

2021

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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ÖzetArtificial neural network (ANN) is a modelling of the human brain. The aim of this study is to estimate the heat distribution with ANN around different cylinders. Within the scope of the study, square and circular cylinders were discussed. In this study, the temperature analysis of the cylinders was carried out with a program written in Fortran, not using a package program whose exact background is unknown. The results obtained from a writing code are more reliable. The study is discussed in two dimensions. The 3 most commonly used algorithms (Levenberg -Marguardt (LM), Pola-Ribiere Conjugate Gradient (CGP) and Scaled Conjugate Gradient (SCG)) were used to train with ANN. Cylinder type, x-coordinate and y-coordinate were the input variables; and temperature was the output variable. The most suitable algorithm was found to be LM-18 algorithm. The proximity of artificial neural networks and real values was evaluated statistically. These were R 2 , CoV and RMSE. R 2 , CoV and RMSE values in the training phase of Levenberg -Marguardt -18 neuron were determined to be 0.9939, 0.0044, 0.0107 while their values in the test phase were 0.9850, 0.0043 and 0.0190 respectively. The fact that the R 2 value is so close to 1 is an indication that ANN is working very well. Yapay sinir ağları (ANN) insan beyninin bir modellemesidir. Bu çalışmanın amacı da ANN ile farklı silindirler etrafındaki ısı dağılımını tahmin etmektir. Çalışma kapsamında kare ve daire silindirler ele alınmıştır. Bu çalışmada, silindirlerin sıcaklık analizi, tam arka planda ne yaptığı bilinmeyen bir paket program kullanılmadan, fortran dili kullanılarak gerçekleştirilmiştir. Kod yazarak elde edilen sonuçlar daha güvenilir sonuçlardır. Çalışma iki boyutlu olarak ele alınmıştır. ANN ile verilerin eğitilmesinde en yaygın kullanılan 3 farklı algoritma (Levenberg-Marguardt (LM), Pola-Ribiere Conjugate Gradient (CGP) ve Scaled Conjugate Gradient (SCG)) kullanılmıştır. Silindir tipi, x-koordinatı ve y koordinatı girdi verileri, sıcaklık ise çıktı verisidir. En uygun algoritma LM-18 algoritması olarak bulunmuştur. Yapay sinir ağları ile gerçek değerlerin birbirine yakınlığı istatistiksel olarak değerlendirilmiştir. Bunlar R 2 , CoV ve RMSE'dir. LM-18 algritmasının eğitim aşamasındaki R 2 , CoV ve RMSE değerleri sırasıyla 0.9939, 0.0044, 0.0107; test aşamasındaki değerleri sırasıyla 0.9850, 0.0043, 0.0190'dir. R 2 değerinin 1'e bu kadar yakın olması ANN'nin çok iyi çalıştığının da göstergesidir.

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“…They mentioned that the curvature effects were observed as zero for the different values of vertical locations on the cylinder (x). Dash and Dash [14] performed a numerical investigation on free convection heat transfer across a hollow vertical cylindrical tube subjected to air. They found that the average value of Nu was increased with L/D o however attain a maximum value then decreased to a minimum then further increased with L/D o .…”

Section: Introductionmentioning

confidence: 99%

Computational analysis of convective heat transfer across a vertical tube

Singh¹,

Singh²

2021

FME Transactions

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This paper deals with the numerical investigation of the convective mode of heat transfer across a vertical tube. Experiments were carried out using air as a fluid in a closed room by achieving a steady-state condition. Implicit scheme of finite difference method was adopted to numerically simulate the free convection phenomenon across vertical tube using LINUX based UBUNTU package. Numerical data were collected in the form of velocity, temperature profiles, boundary layer thickness, Nusselt number (Nu), Rayleigh's number (Ra), and heat transfer coefficient. The results of the Nusselt number showed a good agreement with the previous studies. Results data of heat transfer coefficient indicate that there were some minor heat losses due to radiation of brass tube and curvature of the tube.

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Natural convection heat transfer and fluid flow around a thick hollow vertical cylinder suspended in air: A numerical approach

Cited by 30 publications

References 31 publications

Effect of curvature on transient natural convection in a vertical circular pipe

Effect of curvature on transient natural convection in a vertical circular pipe

Modeling of Heat Transfer Around a Square and a Circle: A Novel ANN-Based Approch

Computational analysis of convective heat transfer across a vertical tube

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