Computational Heat Transfer Analysis and Genetic Algorithm-Artificial Neural Network-Genetic Algorithm-Based Multiobjective Optimization of Rectangular Perforated Plate Fins

Chidambaram, Balachandar; Ravichandran, M.; Seshadri, Arunkumar; Venkatesan, M.

doi:10.1109/tcpmt.2016.2646718

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…The DE algorithm generates population individuals by using floating-point vectors for encoding (Fan, 2009). In the process of DE algorithm optimization, first, two individuals are selected from the parent individuals to generate a difference vector; second, another individual is selected to sum with the difference vector to generate the experimental individual; the parent individual and the corresponding experimental individual are cross-operated to generate new offspring individuals; finally, the selection is made between the parent individuals and the qualified individuals are saved for the next generation population (Chidambaram et al, 2017;Ramos and Susteras, 2006).…”

Section: The Optimization Characteristics Of Differential Evolution A...mentioning

confidence: 99%

A Wind Power Prediction Method Based on DE-BP Neural Network

Wang

et al. 2022

Front. Energy Res.

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With the continuous increase of installed capacity of wind power, the influence of large-scale wind power integration on the power grid is becoming increasingly apparent. Ultra-short-term wind power prediction is conducive to the dispatching management of the power grid and improves the operating efficiency and economy of the power system. In order to overcome the intermittency and uncertainty of wind power generation, this article proposes the differential evolution–back propagation (DE-BP) algorithm to predict wind power and addresses such shortcomings of the BP neural network as its falling into local optimality and slow training speed when predicting. In this article, the DE algorithm is used to find the optimal value of the initial weight and threshold of the BP neural network, and the DE-BP neural network prediction model is obtained. According to the data of a wind farm in Northwest China, the short-term wind power is predicted. Compared with the application of the BP model in wind power prediction, the results show that the accuracy of the DE-BP algorithm is improved by about 5%; compared with the genetic algorithm–BP model, the prediction time is shortened by 23.1%.

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Section: The Optimization Characteristics Of Differential Evolution A...mentioning

confidence: 99%

A Wind Power Prediction Method Based on DE-BP Neural Network

Wang

et al. 2022

Front. Energy Res.

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“…Also, they found that increasing the size and number of perforations reduced the pressure drop. Chidambaram et al [10] performed a numerical simulation to study plate fins with longitudinal rectangular perforations. They used a multi-objective optimization based on genetic algorithms and artificial neural networks to determine optimal parameters for minimizing the temperature of the base plate and maximizing the weight reduction of perforated fins.…”

Section: Introductionmentioning

confidence: 99%

Thermal and Hydraulic Performance of Longitudinal Perforated Rectangular Fins With Perforation Shape and Size Variations

Maleki

Safaei

León

et al. 2019

Proceeding of 4th Thermal and Fluids Engineering Conference

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Improving heat transfer rate and reducing manufacturing costs are two significant objectives in the optimal design of heat sinks, which are used in electrical devices. Due to economic reasons in cooling applications, different methods were used to increase the efficiency of thermal devices such as creating perforations in fins, which reduces the volume of fins and increases thermal dissipation. In this study, an attempt was made to improve the thermal and hydraulic performance of fluid flow over a three-dimensional array of rectangular perforated fins with different perforations shapes and sizes. Finite volume method with the SIMPLE algorithm was applied to solve the incompressible laminar Navier-Stokes and energy equations. Flow and heat transfer characteristics were presented for Reynolds number (based on fin thickness) from 100 to 350 and a Prandtl number of 0.71 (air). In this study, the effects of perforation size and shape on the average Nusselt number, total drag force, and perforated fin effectiveness were assessed. The results of this investigation confirmed that for a constant heat transfer surface area, the highest values of perforated fin effectiveness and average Nusselt number are obtained for fins with circular perforations.

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Evolution of Constants in Rohsenow Pool Boiling Correlation using Genetic Algorithm

Swarnkar¹,

Lakhera²

2023

Lecture Notes in Mechanical Engineering

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Computational Heat Transfer Analysis and Genetic Algorithm-Artificial Neural Network-Genetic Algorithm-Based Multiobjective Optimization of Rectangular Perforated Plate Fins

Cited by 7 publications

References 26 publications

A Wind Power Prediction Method Based on DE-BP Neural Network

A Wind Power Prediction Method Based on DE-BP Neural Network

Thermal and Hydraulic Performance of Longitudinal Perforated Rectangular Fins With Perforation Shape and Size Variations

Evolution of Constants in Rohsenow Pool Boiling Correlation using Genetic Algorithm

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