Prediction of heat transfer coefficients of shell and coiled tube heat exchangers using numerical method and experimental validation

Alimoradi, Ashkan; Veysi, Farzad

doi:10.1016/j.ijthermalsci.2016.04.010

Cited by 89 publications

(16 citation statements)

References 21 publications

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“…In 2016 Ashkan Alimoradi et al [19] have proposed that the heat transfer of shell and helically coiled tube heat exchangers. To investigate the effect of physical properties of fluid, operational, and geometrical parameters, numerical and experimental methods were used on Nusselt numbers of both sides.…”

Section: Literature Reviewmentioning

confidence: 99%

An effective krill herd based optimal NN for parameter evaluation in Shell-And-Tube heat exchangers

Roy¹,

Majumder²

2018

IJHT

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Helical baffles are employed increasingly in shell-and-tube heat exchangers for their significant advantages in reducing pressure drop, vibration, and fouling while maintaining a higher heat transfer performance. In order to make good use of helical baffles, serial improvements have been made by many researchers. In this paper create model of optimal NN for prediction analysis in parameters compared to existing works. This hidden layer and neuron optimization process fish asked KHO technique used. For more verification, KHO is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is compared with that of various algorithms representative of the state-of-the-art in the area. The results of different algorithms are breaking down and stood out from comparative systems, and the finest results rising out of them are discovered by standing out the results from least MSE values. From the results our proposed method achieves minimum MSE compared to existing works and maximum prediction accuracy in optimization model.

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Section: Literature Reviewmentioning

confidence: 99%

An effective krill herd based optimal NN for parameter evaluation in Shell-And-Tube heat exchangers

Roy¹,

Majumder²

2018

IJHT

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“…With the condenser pressure and fluid composition determined, the cycle can then be analysed using the real world climate data provided, according to well-established thermodynamic techniques [19,38]. The pinch point temperature in the evaporator is taken to be 5 • C as before, and a further 5 • C of superheat is added at the expander inlet to ensure a pure vapour with no droplets is fed into the expander, allowing the evaporator pressure to be calculated using the equations T 3 = T heat source − 5 and P evap = P sat at (T = T 3 − 5).…”

Section: Numerical Modellingmentioning

confidence: 99%

“…The Nusselt number is the ratio of total to conductive heat transfer in a fluid, and is generally calculated for each case using empirical correlations to experimental data. Alimoradi and Veysi [38] give a rough correlation for the Nusselt number in a shell-and tube heat exchanger of: (20) where Re is the Reynolds Number and Pr is the Prandtl Number of the fluid:…”

Section: Numerical Modellingmentioning

confidence: 99%

Numerical Analysis of an Organic Rankine Cycle with Adjustable Working Fluid Composition, a Volumetric Expander and a Recuperator

Collings

2017

Energies

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Conventional Organic Rankine Cycles (ORCs) using ambient air as their coolant cannot fully utilize the greater temperature differential available to them during the colder months. However, changing the working fluid composition so its boiling temperature matches the ambient temperature as it changes has been shown to have potential to increase year-round electricity generation. Previous research has assumed that the cycle pressure ratio is able to vary without a major loss in the isentropic efficiency of the turbine. This paper investigates if small scale ORC systems that normally use positive-displacement expanders with fixed expansion ratios could also benefit from this new concept. A numerical model was firstly established, based on which a comprehensive analysis was then conducted. The results showed that it can be applied to systems with positive-displacement expanders and improve their year-round electricity generation. However, such an improvement is less than that of the systems using turbine expanders with variable expansion ratios. Furthermore, such an improvement relies on heat recovery via the recuperator. This is because expanders with a fixed expansion ratio have a relatively constant pressure ratio between their inlet and outlet. The increase of pressure ratio between the evaporator and condenser by tuning the condensing temperature to match colder ambient condition in winter cannot be utilised by such expanders. However, with the recuperator in place, the higher discharging temperature of the expander could increase the heat recovery and consequently reduce the heat input at the evaporator, increasing the thermal efficiency and the specific power. The higher the amount of heat energy transferred in the recuperator, the higher the efficiency improvement.

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“…A few investigations on finless heat exchangers were carried out in order to find Nusselt number of shell side [39]. A few relationships were derived by Alimoradi et al [40], to estimate the Nusselt number of these sort of heat exchanger. According to the literature review, by using size and volume fraction of solid phase in addition to temperature accurate predictive models are achievable for a single nanofluid [41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Approaches for Thermal Conductivity Estimation of CNT/Water Nanofluid

Ahmadi¹,

Ghazvini²,

Baghban³

et al. 2019

RCMA

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One of the auspicious nanomaterials which has exceptionally enticed researchers is carbon nanotubes (CNTs) as the result of their excellent thermal properties. In this investigation, an experiment was carried out on three kinds of CNTs-nanofluids with various CNTs added to de-ionized water to compared and analyze their thermal conductivity properties. The main purpose of this study was to introduce a combination of experimental and modelling approaches to forecast the amount of thermal conductivity using four different neural networks. Between MLP-ANN, ANFIS, LSSVM, and RBF-ANN Methods, it was found that the LSSVM produced better results with the lowest deviation factor and reflected the most accurate responses between the proposed models. the regression diagram of experimental and estimated values shows an R2 coefficient of 0.9806 and 0.9579 for training and testing sections of the ANFIS method in part a, and in the b, c and d parts of the diagram, coefficients of determination were 0.9893& 0.9967 and 0.9974 & 0.9992 and 0.9996& 0.9989 for training and testing part of MLP-ANN, RBF-ANN and LSSVM models. Also, the effect of different parameters was investigated using a sensitivity analysis method which demonstrates that the temperature is the most affecting parameter on the thermal conductivity with a relevancy factor of 0.66866.

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Prediction of heat transfer coefficients of shell and coiled tube heat exchangers using numerical method and experimental validation

Cited by 89 publications

References 21 publications

An effective krill herd based optimal NN for parameter evaluation in Shell-And-Tube heat exchangers

An effective krill herd based optimal NN for parameter evaluation in Shell-And-Tube heat exchangers

Numerical Analysis of an Organic Rankine Cycle with Adjustable Working Fluid Composition, a Volumetric Expander and a Recuperator

Soft Computing Approaches for Thermal Conductivity Estimation of CNT/Water Nanofluid

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