Fuzzy Logic Modeling of Heat Transfer in an Air Cooler Equipped with Butterfly Insert

Karami, Amin; Rezaei, Ehsan; Shahhosseni, Mohsen; Jafari, Shahram

doi:10.7763/ijmo.2011.v1.45

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…ANFIS [23][24][25] is a fuzzy inference system [26,27] implemented in the framework of neural networks [28,29]. This combination merges the advantages of fuzzy systems and neural networks.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 98%

Neuro-Fuzzy Modeling of the Free Convection Heat Transfer From a Wavy Surface

Karami

Yousefi

Harsini

et al. 2014

Heat Transfer Engineering

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In this paper an adaptive neuro-fuzzy inference system (ANFIS) is applied to model and predict the experimental results of free convection heat transfer from a vertical array of attached cylinders, which can be considered as a wavy surface, in the presence of a vertical wall. The effects of the wall-wavy surface spacing and Rayleigh number variation on average heat transfer from the wavy surface are considered via this prediction. The training data for optimizing the ANFIS structure are based on available experimental data. A hybrid learning algorithm consisting of gradient descends method and least-squares method is used for ANFIS training. The proposed ANFIS model is developed using MATLAB functions. For the best ANFIS structure obtained in this study, the mean relative errors of the train and test data were found to be 0.02% and 1.2%, respectively. The predicted results showed that ANFIS can predict the experimental results precisely.

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Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 98%

Neuro-Fuzzy Modeling of the Free Convection Heat Transfer From a Wavy Surface

Karami

Yousefi

Harsini

et al. 2014

Heat Transfer Engineering

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“…ANFIS is regarded as a fuzzy inference system implemented in the mold of neural networks . In fact, ANFIS is a hybrid model which provides the capabilities of neural network topology along with fuzzy logic.…”

Section: The Modeling Approach; Principlesmentioning

confidence: 99%

A comparison between radial basis function (RBF) and adaptive neuro‐fuzzy inference system (ANFIS) to model the free convection in an open round cavity

Akbari

Karami

Ashjaee

2018

Heat Trans. Asian Res.

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This study demonstrates the capability of radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) to model and predict the free convection heat transfer in an open round cavity. In fact, the effects of the Rayleigh number (Ra) and ratio of the nonconductor barrier distance from the bottom of the cavity to the cavity diameter (H/D), on the free convection in the cavity, are modeled via the RBF and ANFIS models. To start modeling, sufficient data are gathered. Here, data are experimentally generated using a Mach-Zehnder interferometer. In the next step, the RBF and ANFIS models are trained. According to the results, there is an optimum ratio (H/D), in which the heat transfer is maximum. This maximum value increases 2 AKBARI ET AL. 870 agreement with similar ones obtained experimentally. The mean relative errors of the training, testing, and checking data for the RBF model were found as 0.1348%, 1.1972%, and 2.4967%, respectively. Moreover, for the ANFIS model, the error values were 0.0731%, 0.9110%, and 1.9144%, which shows that RBF and ANFIS can predict the results precisely. K E Y W O R D Sadaptive neuro-fuzzy inference system (ANFIS), free convection, Mach-Zender interferometer, Nusselt number (Nu), radial basis function (RBF), round cavity

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“…There are some papers dealing with these bio-inspired modeling methods. A Neural Network was used to calculate and estimate heat transfer in an air-cooled heat exchanger with butterfly inserts [56] and to predict the average heat transfer from the arrays of horizontal isothermal cylinders [57]. A direct adaptive fuzzy controller was used to study the central air conditioning evaporator in [58].…”

Section: Introductionmentioning

confidence: 99%

A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods

Krzywański

2019

Energies

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The paper introduces the artificial intelligence (AI) approach as a general method for the design and optimization study of heat exchangers. Genetic Algorithms (GA) and Artificial Neural Networks (ANN) are applied in the paper. An AGENN model, combining Genetic Algorithms with Artificial Neural Networks, was developed and validated against the desired data on a large falling film evaporator. A broad range of operating conditions and geometric configurations are considered in the study. Four kinds of tubes are deliberated, including plain and enhanced tubes. Different tube pass arrangements, i.e., top-to-bottom, bottom-to-top, and side-by-side, are discussed. Finally, the effects of liquid refrigerant mass flow rate, as well as the number of flooded tubes on the performance of the evaporator, are analyzed. The total heat transfer rate of the evaporator, predicted by the model, is in good agreement with the desired data; the maximum error is lower than ±3%. The highest heat transfer rate of the evaporator is 1140.01 kW and corresponds to Turbo EHP tubes, and bottom-to-top tubes pass arrangements, which guarantee the best thermal energy conversion. The presented approach can be referred to as a complementary technique in heat exchanger design procedures, besides the common rating and sizing tasks. It is an effective and alternative method for the existing approaches, considering the complexity of analytical and numerical techniques as well as the high costs of experiments.Construction features of the direct-transfer-type heat exchangers are the base of their classification, according to the geometry of construction criterion. Considerable flexibility in heat exchangers design can be observed for tubular ones where tube diameter, the number of tubes, the tube length, the pitch of the tubes, and the tube arrangement can be easily modified. Tubular heat exchangers can be classified into double-pipe, shell-and-tube and spiral-tube-type heat exchangers as heat transfer coefficients in a spiral tube are higher than those in a straight one. Plate heat exchangers are built of thin plates (plain, wavy, or corrugated) forming flow channels and can be categorized into: gasketed plate (consists of a series of thin plates with corrugation or wavy surfaces that separate the fluids), spiral plate (formed by rolling two long, parallel plates into a spiral), and lamella (Ramen) type of heat exchanger (a set of parallel, thin plate channels or lamellae (flat tubes or rectangular channels) placed longitudinally in a shell) [2].Extended surface heat exchangers contain fins or appendages on primary heat transfer surfaces to increase the heat transfer area (usually on the gas side).According to the heat transfer mechanisms criterion, there can be distinguished: 1. single-phase convection on both sides, 2. single-phase convection on one side and two-phase convection on other side, 3. two-phase convection on both sides.Finally, according to the flow arrangements criterion, the following fluid-flow path through the heat exchanger can exist:...

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Fuzzy Logic Modeling of Heat Transfer in an Air Cooler Equipped with Butterfly Insert

Cited by 6 publications

References 17 publications

Neuro-Fuzzy Modeling of the Free Convection Heat Transfer From a Wavy Surface

Neuro-Fuzzy Modeling of the Free Convection Heat Transfer From a Wavy Surface

A comparison between radial basis function (RBF) and adaptive neuro‐fuzzy inference system (ANFIS) to model the free convection in an open round cavity

A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods

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