Artificial Neural Network to Predict Pressure Drops in Heat Sinks

Mengesha, Betelhiem N.; Shaeri, Mohammad Reza; Sarabi, Soroush

doi:10.11159/ffhmt22.202

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…An artificial neural network (ANN), a subset of machine learning, is a data processing system that mimics the characteristics of the human brain to predict the performances of engineering systems [1,2]. The feed-forward multilayer perceptron ANN model consists of interconnected nodes, called neurons, which form an input layer, hidden layer(s), and an output layer [3,4]. Inputs are received in the input layer, and outputs are generated in the output layer.…”

Section: Introductionmentioning

confidence: 99%

Prediction Accuracy of Artificial Neural Networks in Thermal Management Applications Subject to Neural Network Architectures

Randriambololona¹,

Shaeri²,

Sarabi³

2022

Proceedings of the 8th World Congress on Mechanical, Chemical, and Material Engineering

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The present study investigates the dependency of prediction accuracy of an artificial neural network (ANN) on the network architecture using 65 different neural networks from seven architecture patterns. The accuracy of the ANNs is compared based on their capability to predict heat transfer coefficients of air-cooled heat sinks operating in laminar flow. Scattered input data is used for training the networks to make the modelling more realistic and closer to practical applications. The input variables for the neural network are heat sink width, channel height, channel length, number of channels, fin thickness, and Reynolds number. The output is heat transfer coefficient. The training process for all ANNs is performed using ReLU as the activation function. The accuracy of the neural networks is evaluated by the root mean square error. It is found that the prediction accuracy of an ANN is strongly dictated by the optimization of the network architecture, which corresponds to the proper number of hidden layers and the number of neurons at each layer. The most accurate architecture in the present study predicts heat transfer coefficients of 60% and 86% of heat sinks within ±10% and ±20% of the true values, respectively. However, an ANN with an unoptimized architecture results in a substantially reduced accuracy such that it predicts heat transfer coefficients of only 19% and 30% of heat sinks within ±10% and ±20% of the true values, respectively.

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Section: Introductionmentioning

confidence: 99%

Prediction Accuracy of Artificial Neural Networks in Thermal Management Applications Subject to Neural Network Architectures

Randriambololona¹,

Shaeri²,

Sarabi³

2022

Proceedings of the 8th World Congress on Mechanical, Chemical, and Material Engineering

Self Cite

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Comparative study of long short-term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction

Alizadegan,

Rashidi Malki,

Radmehr

et al. 2024

Energy Exploration & Exploitation

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Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the autoregressive integrated moving average (ARIMA) model and seasonal autoregressive integrated moving average (SARIMA) model. Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

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Xgboost-Based Model for Prediction of Heat Transfer Coefficients in Liquid Cold Plates

Shaeri

Ellis

Randriambololona

2023

Proceeding of 8th Thermal and Fluids Engineering Conference (TFEC)

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Artificial Neural Network to Predict Pressure Drops in Heat Sinks

Cited by 3 publications

References 7 publications

Prediction Accuracy of Artificial Neural Networks in Thermal Management Applications Subject to Neural Network Architectures

Prediction Accuracy of Artificial Neural Networks in Thermal Management Applications Subject to Neural Network Architectures

Comparative study of long short-term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction

Xgboost-Based Model for Prediction of Heat Transfer Coefficients in Liquid Cold Plates

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