Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar

doi:10.1007/s00231-017-2261-7

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…Ghasemi et al 86 reported an experimental study on TiO 2 -Al 2 O 3 /water nanocomposite flows in inserting twisted tape. They evaluated the effect of nanoparticle concentration, twisted ratio, and Reynolds number on the exergetic efficiency.…”

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confidence: 99%

Advances of nanofluids in heat exchangers—A review

Menni

Chamkha

Ameur³

2020

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Recently, many researchers have focused on their studies on the analysis of nanofluid flows due to their participation in the enhancement of heat transfer rates in industrial processes. The ordinary fluids, such as water, mineral oils, and so on, are known for their low thermal conductivity in heat transfer processes. A significant enhancement in the thermal properties of ordinary fluid may be obtained by adding nanoparticles having a diameter of less than 100 nm or suspension of fibers. Better spreading, wetting, dispersion, and stability and with acceptable viscosity are the main advantageous properties of nanofluids on a solid surface. The nanofluids are encountered in various thermal engineering systems such as in heat exchangers, refrigeration, thermal management of fuel cells, cooling of nuclear reactors, microelectromechanical systems, and others. In particular, the thermal conversion is known as a great application of nanotechnology, and many studies have been achieved with such fluids in heat exchangers. Therefore, this paper aims to present a global insight into the different applications of nanofluids in various heat exchangers, that is, heat pipe and plate-fin heat exchangers. All research works have been summarized into three main parts: laminar, transition, and turbulent nanofluid flow regimes.

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confidence: 99%

Advances of nanofluids in heat exchangers—A review

Menni

Chamkha

Ameur³

2020

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“…These include: Gradient Descent: This fundamental optimization approach modifies the neural network's weights in the direction of the loss function's negative gradient. We will utilize Stochastic Gradient Descent (SGD), one of several gradient descent variations [31], [32]:…”

Section: Optimizationsmentioning

confidence: 99%

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

Ali,

Alakkari,

Abotaleb

et al. 2024

Mesopotamian Journal of Big Data

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In the realm of stock market prediction and classification, the use of machine learning algorithms has gained significant attention. In this study, we explore the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) algorithms in predicting and classifying stock prices, specifically amidst economic policy uncertainty. Stock market fluctuations are greatly influenced by economic policies implemented by governments and central banks. These policies can create uncertainty and volatility, which in turn makes accurate predictions and classifications of stock prices more challenging. By leveraging MLP and RBF algorithms, we aim to develop models that can effectively navigate these uncertainties and provide valuable insights to investors and financial analysts. The MLP algorithm, based on artificial neural networks, is able to learn complex patterns and relationships within financial data. The RBF algorithm, on the other hand, utilizes radial basis functions to capture non-linear relationships and identify hidden patterns within the data. By combining these algorithms, we aim to enhance the accuracy of stock price prediction and classification models. The results showed that both MLB and RBF predicted stock prices well for a group of countries using an index reflecting the impact of news on economic policy and expectations, where the MLB algorithm proved its ability to predict chain data. Countries were also classified according to stock price data and uncertainty in economic policy, allowing us to determine the best country to invest in according to the data. The uncertainty surrounding economic policy is what makes stock price forecasting so crucial. Investors must consider the degree of economic policy uncertainty and how it affects asset prices when deciding how to allocate their assets.

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“…The results demonstrated that although increased concentration resulted in enhancement of the thermal conduction coefficient and viscosity, the increase in temperature followed the expected results of increasing thermal conductivity and decreasing viscosity. Ghasemi et al [22] predicted and optimized exergetic efficiency of TiO 2 -Al 2 O 3 /water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using artificial neural networks and experimental data. The findings indicated successful prediction by the network.…”

Section: Introductionmentioning

confidence: 99%

Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)

et al. 2021

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This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of 150–350, temperature in the range of 70–90 K, and nanoparticle concentration in the range of 2–4% were selected as network input variables, while the corresponding Nusselt number and energy consumption were considered as the network target. The network has 3 inputs, 1 hidden layer with 22 neurons and an output layer. The SOM neural network was also used to determine the number of winner neurons. The advanced optimal artificial neural network model shows a reasonable agreement in predicting experimental data with mean square errors of 0.0023357 and 0.00011465 and correlation coefficients of 0.9994 and 0.9993 for the Nusselt number and energy consumption data set. The obtained values of eMAX for the Nusselt number and energy consumption are 0.1114, and 0.02, respectively. Desirable results obtained for the two factors of correlation coefficient and mean square error indicate the successful prediction by artificial neural network with a topology of 3-22-2.

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Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

Cited by 13 publications

References 18 publications

Advances of nanofluids in heat exchangers—A review

Advances of nanofluids in heat exchangers—A review

MLP and RBF Algorithms in Finance: Predicting and Classifying Stock Prices amidst Economic Policy Uncertainty

Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)

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