Prediction of pool boiling heat transfer coefficient for various nano-refrigerants utilizing artificial neural networks

Zarei, Mohammad; Ansari, Hossein; Keshavarz, Peyman; Zerafat, Mohammad Mahdi

doi:10.1007/s10973-019-08746-z

Cited by 34 publications

(12 citation statements)

References 55 publications

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“…For the generation of a dependent variable, this paper utilized Garson's algorithm [61] to determine the relative importance (RI) of the various inputs as a predictor of predicted outputs. The Garson method was also used in different studies as underpinned by [62][63][64][65][66][67]. The algorithm, as shown in Eq.…”

Section: Investigation Of Accuracymentioning

confidence: 99%

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Najm

Paniti

2021

Int J Adv Manuf Technol

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Incremental Sheet Forming (ISF) has attracted attention due to its flexibility as far as its forming process and complexity in the deformation mode are concerned. Single Point Incremental Forming (SPIF) is one of the major types of ISF, which also constitutes the simplest type of ISF. If sufficient quality and accuracy without defects are desired, for the production of an ISF component, optimal parameters of the ISF process should be selected. In order to do that, an initial prediction of formability and geometric accuracy helps researchers select proper parameters when forming components using SPIF. In this process, selected parameters are tool materials and shapes. As evidenced by earlier studies, multiple forming tests with different process parameters have been conducted to experimentally explore such parameters when using SPIF. With regard to the range of these parameters, in the scope of this study, the influence of tool material, tool shape, tool-end corner radius, and tool surface roughness (Ra/Rz) were investigated experimentally on SPIF components: the studied factors include the formability and geometric accuracy of formed parts. In order to produce a well-established study, an appropriate modeling tool was needed. To this end, with the help of adopting the data collected from 108 components formed with the help of SPIF, Artificial Neural Network (ANN) was used to explore and determine proper materials and the geometry of forming tools: thus, ANN was applied to predict the formability and geometric accuracy as output. Process parameters were used as input data for the created ANN relying on actual values obtained from experimental components. In addition, an analytical equation was generated for each output based on the extracted weight and bias of the best network prediction. Compared to the experimental approach, analytical equations enable the researcher to estimate parameter values within a relatively short time and in a practicable way. Also, an estimate of Relative Importance (RI) of SPIF parameters (generated with the help of the partitioning weight method) concerning the expected output is also presented in the study. One of the key findings is that tool characteristics play an essential role in all predictions and fundamentally impact the final products.

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Section: Investigation Of Accuracymentioning

confidence: 99%

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Najm

Paniti

2021

Int J Adv Manuf Technol

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“…As a mathematical tool for making predictions in machine learning for the purpose of training multilayer networks, a backpropagation learning algorithm was used: this algorithm is called “multilayer perceptron” (MLP), the concept of which was established by Werbos in 1974 and Rumelhart, McClelland, and Hinton in 1986 [ 61 ]. The Garson method has also been used in many studies, as presented in [ 62 , 63 , 64 , 65 , 66 , 67 ]. Goh [ 68 ] applied the Garson algorithm and claimed that RI estimation requires the partitioning of the hidden output weights into elements connected to each neuron in the input layers.…”

Section: Resultsmentioning

confidence: 99%

Parametric Effects of Single Point Incremental Forming on Hardness of AA1100 Aluminium Alloy Sheets

et al. 2021

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When using a unique tool with different controlled path strategies in the absence of a punch and die, the local plastic deformation of a sheet is called Single Point Incremental Forming (SPIF). The lack of available knowledge regarding SPIF parameters and their effects on components has made the industry reluctant to embrace this technology. To make SPIF a significant industrial application and to convince the industry to use this technology, it is important to study mechanical properties and effective parameters prior to and after the forming process. Moreover, in order to produce a SPIF component with sufficient quality without defects, optimal process parameters should be selected. In this context, this paper offers insight into the effects of the forming tool diameter, coolant type, tool speed, and feed rates on the hardness of AA1100 aluminium alloy sheet material. Based on the research parameters, different regression equations were generated to calculate hardness. As opposed to the experimental approach, regression equations enable researchers to estimate hardness values relatively quickly and in a practicable way. The Relative Importance (RI) of SPIF parameters for expected hardness, determined with the partitioning weight method of an Artificial Neural Network (ANN), is also presented in the study. The analysis of the test results showed that hardness noticeably increased when tool speed increased. An increase in feed rate also led to an increase in hardness. In addition, the effects of various greases and coolant oil were studied using the same feed rates; when coolant oil was used, hardness increased, and when grease was applied, hardness decreased.

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“…In this study, the authors collected test results from several sources in the literature [14,16,[18][19][20][21][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. These works report the pool boiling performance of different working fluids such as water, dielectric liquids, and liquid nitrogen subject to different types of nanoporous coated surfaces.…”

Section: Data Collectionmentioning

confidence: 99%

“…It is therefore crucial to evaluate the boiling performance of various nanoporous surfaces produced by different fabrication methods. However, some efforts have been made recently for the prediction of the critical heat flux or heat transfer coefficient by artificial intelligence techniques for nanofluids [21], sintered coated microporous surfaces [22], nanorefrigerants [23], and so on.…”

Section: Introductionmentioning

confidence: 99%

Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings

et al. 2021

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The present study develops a deep learning method for predicting the boiling heat transfer coefficient (HTC) of nanoporous coated surfaces. Nanoporous coated surfaces have been used extensively over the years to improve the performance of the boiling process. Despite the large amount of experimental data on pool boiling of coated nanoporous surfaces, precise mathematical-empirical approaches have not been developed to estimate the HTC. The proposed method is able to cope with the complex nature of the boiling of nanoporous surfaces with different working fluids with completely different thermophysical properties. The proposed deep learning method is applicable to a wide variety of substrates and coating materials manufactured by various manufacturing processes. The analysis of the correlation matrix confirms that the pore diameter, the thermal conductivity of the substrate, the heat flow, and the thermophysical properties of the working fluids are the most important independent variable parameters estimation under consideration. Several deep neural networks are designed and evaluated to find the optimized model with respect to its prediction accuracy using experimental data (1042 points). The best model could assess the HTC with an R2 = 0.998 and (mean absolute error) MAE% = 1.94.

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Prediction of pool boiling heat transfer coefficient for various nano-refrigerants utilizing artificial neural networks

Cited by 34 publications

References 55 publications

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Artificial neural network for modeling and investigating the effects of forming tool characteristics on the accuracy and formability of thin aluminum alloy blanks when using SPIF

Parametric Effects of Single Point Incremental Forming on Hardness of AA1100 Aluminium Alloy Sheets

Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings

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