Analysis of Microchannel Heat Sink Performance Using Nanofluids in Turbulent and Laminar Flow Regimes and Its Simulation Using Artificial Neural Network

The feasibility of a transportable artificial neural network (ANN)–based technique for the classification of flow regimes in three phase gas/liquid/pulp fiber systems, using pressure signals as input, was demonstrated in this study. Both supervised and unsupervised neural network models were applied for implementing regime classification. Data obtained in a vertical column (1.8m high and 5.08cm in diameter) were used, and a supervised ANN was designed and successfully tested that used some characteristics of the power density spectrum of the recorded signals of a pressure sensor as input. The developed ANN showed encouraging transportability. An ANN-based method was also developed for adjusting the processed signals of one sensor before feeding them as input to an ANN that had been trained based on data from another similar sensor. The method further improved transportability. The objectivity of the experimentally-identified flow regimes and their transition conditions was verified by the application of a Kohonen self-organizing neural network.

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A new design of liquid-cooled heat sink by altering the heat sink heat pipe application: Experimental approach and prediction via artificial neural network

Energy Conversion and Management ◽

10.1016/j.enconman.2020.112485 ◽

2020 ◽

Vol 206 ◽

pp. 112485 ◽

Cited By ~ 4

Author(s):

Amin Taheri ◽

Mohammadamir Ghasemian Moghadam ◽

Majid Mohammadi ◽

Mohammad Passandideh-Fard ◽

Mohammad Sardarabadi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heat Pipe ◽

Heat Sink ◽

Experimental Approach ◽

Artificial Neural

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A Low-Cost System for Measuring Wind Speed and Direction Using Thermopile Array and Artificial Neural Network

Applied Sciences ◽

10.3390/app11094024 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4024

Author(s):

Shang-Chen Wu ◽

Jong-Chyuan Tzou ◽

Cheng-Yu Ding

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wind Speed ◽

Heat Sink ◽

Low Cost ◽

Temperature Data ◽

Coefficient Of Determination ◽

Support Vector ◽

Recent Developments ◽

Artificial Neural

Recent developments in wind speed sensors have mainly focused on reducing the size and moving parts to increase reliability and stability. In this study, the development of a low-cost wind speed and direction measurement system is presented. A heat sink mounted on a self-regulating heater is used as means to interact with the wind changes and a thermopile array mounted atop of the heat sink is used to collect temperature data. The temperature data collected from the thermopile array are used to estimate corresponding wind speed and direction data using an artificial neural network. The multilayer artificial neural network is trained using 96 h data and tested on 72 h data collected in an outdoor setting. The performance of the proposed model is compared with linear regression and support vector machine. The test results verify that the proposed system can estimate wind speed and direction measurements with a high accuracy at different sampling intervals, and the artificial neural network can provide significantly a higher coefficient of determination than two other methods.

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