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Sahin F, Genc O, Gökcek M, Çolak AB. An experimental and new study on thermal conductivity and zeta potential of Fe3O4/water nanofluid: Machine learning modeling and proposing a new correlation. POWDER TECHNOL 2023. [DOI: 10.1016/j.powtec.2023.118388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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Gönül A, Çolak AB, Kayaci N, Okbaz A, Dalkilic AS. Prediction of heat transfer characteristics in a microchannel with vortex generators by machine learning. KERNTECHNIK 2023. [DOI: 10.1515/kern-2022-0075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Abstract
Because of the prompt improvements in Micro-Electro-Mechanical Systems, thermal management necessities have altered paying attention to the compactness and high energy consumption of actual electronic devices in industry. In this study, 625 data sets obtained numerically according to the change of five different geometric parameters and Reynolds numbers for delta winglet type vortex generator pairs placed in a microchannel were utilized. Four dissimilar artificial neural network models were established to predict the heat transfer characteristics in a microchannel with innovatively oriented vortex generators in the literature. Friction factor, Nusselt number, and performance evaluation criteria were considered to explore the heat transfer characteristics. Different neuron numbers were determined in the hidden layer of each of the models in which the Levethenberg–Marquardt training algorithm was benefited as the training algorithm. The predicted values were checked against the target data and empirical correlations. The coefficient of determination values calculated for each machine learning model were found to be above 0.99. According to obtained results, the designed artificial neural networks can provide high prediction performance for each data set and have higher prediction accuracy compared to empirical correlations. All data predicted by machine learning models were collected within the range of ±3% deviation bands, whereas the majority of the estimated data by empirical correlations dispersed within ±20% ones. For that reason, a full evaluation of the estimation performance of artificial neural networks versus empirical correlations data is enabled to fill a gap in the literature as one of the uncommon works.
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Affiliation(s)
- Alişan Gönül
- Department of Mechanical Engineering , Siirt University , 56100 Siirt , Turkey
| | - Andaç Batur Çolak
- Information Technologies Application and Research Center , Istanbul Commerce University , Istanbul , Turkey
| | - Nurullah Kayaci
- Department of Mechanical Engineering , Tekirdağ Namık Kemal University , 59860 Tekirdağ , Turkey
| | - Abdulkerim Okbaz
- Department of Mechanical Engineering , Dogus University , 34722 Istanbul , Turkey
| | - Ahmet Selim Dalkilic
- Department of Mechanical Engineering , Yildiz Technical University , 34349 Istanbul , Turkey
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Çolak AB, Mercan H, Açıkgöz Ö, Dalkılıç AS, Wongwises S. Prediction of nanofluid flows’ optimum velocity in finned tube-in-tube heat exchangers using artificial neural network. KERNTECHNIK 2022. [DOI: 10.1515/kern-2022-0097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Abstract
The average flow velocity in heat exchangers is considered less often and thus needs further and detailed investigation because of its crucial influence on the overall thermal performance of the application. The use of nanofluids has similar influences to finned tube designs. Considering the rise in heat transfer and pressure drop, uncertainties in cost analyses with the uses of fins and nanoparticles, evaluation of optimum operating velocity of the fluids is necessary. On the contrary, there aren’t enough experimental, parametric, or numerical investigations present on this subject. The use of machine learning techniques to heat transfer applications to make optimization becomes popular recently. In this work, important factors of the process as tube number, cleanliness factor, and overall cost as output factors have been estimated by an artificial intelligence method using 339 data points. The influence of input factors of Reynolds number, thermal conductivity, specific heat, viscosity, and total fin surface efficiency on the outputs have been studied. Total tube number, cleanliness factor, and total cost analysis have been determined with deviations of −0.66%, 0.001%, and 0.12% as a result of the solution with 6 inputs, correspondingly.
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Affiliation(s)
- Andaç Batur Çolak
- Information Technologies Application and Research Center , Istanbul Commerce University , Istanbul 34445 , Türkiye
| | - Hatice Mercan
- Department of Mechatronics Engineering, Mechanical Engineering Faculty , Yildiz Technical University (YTU) , Istanbul 34349 , Türkiye
| | - Özgen Açıkgöz
- Department of Mechanical Engineering , Mechanical Engineering Faculty , Istanbul 34349 , Türkiye
| | - Ahmet Selim Dalkılıç
- Department of Mechanical Engineering , Mechanical Engineering Faculty , Istanbul 34349 , Türkiye
| | - Somchai Wongwises
- Department of Mechanical Engineering, Faculty of Engineering , King Mongkut’s University of Technology Thonburi (KMUTT) , Bangmod , Bangkok 10140 , Thailand
- National Science and Technology Development Agency (NSTDA) , Khlong Luang , Pathum Thani 12120 , Thailand
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Shafiq A, Batur Çolak A, Naz Sindhu T, Ahmad Lone S, Alsubie A, Jarad F. Comparative study of artificial neural network versus parametric method in COVID-19 data analysis. Results Phys 2022; 38:105613. [PMID: 35600673 PMCID: PMC9110000 DOI: 10.1016/j.rinp.2022.105613] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/10/2022] [Accepted: 05/12/2022] [Indexed: 05/25/2023]
Abstract
Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was -0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.
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Affiliation(s)
- Anum Shafiq
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Andaç Batur Çolak
- Niğde Ömer Halisdemir University, Mechanical Engineering Department, Niğde, Turkey
| | - Tabassum Naz Sindhu
- Department of Statistics, Quaid-i-Azam University, 45320, Islamabad 44000, Pakistan
| | - Showkat Ahmad Lone
- Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, (Jeddah-M), Riyadh-11673, Saudi Arabia
| | - Abdelaziz Alsubie
- Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, (Jeddah-M), Riyadh-11673, Saudi Arabia
| | - Fahd Jarad
- Department of Mathematics, Faculty of Arts and Sciences, Cankaya University, 06530 Ankara, Turkey
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
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Çolak AB, Celen A, Dalkılıç AS. Numerical determination of condensation pressure drop of various refrigerants in smooth and micro-fin tubes via ANN method. KERNTECHNIK 2022. [DOI: 10.1515/kern-2022-0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
In the current work, the pressure drop of the refrigerant flow in smooth and micro-fin pipes has been modeled with artificial neural networks as one of the powerful machine learning algorithms. Experimental analyses have been evaluated in two groups for the numerical model such as operation parameters/physical properties and dimensionless numbers used in two-phase flows. Feed forward back propagation multi-layer perceptron networks have been developed evaluating the practically obtained dataset having 673 data points covering the flow of R22, R134a, R410a, R502, R507a, R32 and R125 in four different pipes. The outputs acquired from the artificial neural network have been evaluated with the target ones, and the performance factors have been estimated and the prediction accuracy of the network models has been resourced comprehensively. The results revealed that the neural networks could predict the pressure drop of the refrigerant flow in smooth and micro-fin pipes between 10% deviation bands.
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Affiliation(s)
- Andaç Batur Çolak
- Mechanical Engineering Department , Engineering Faculty, Niğde Ömer Halisdemir University , Niğde 51240 , Turkey
| | - Ali Celen
- Department of Mechanical Engineering , Engineering and Architecture Faculty, Erzincan Binali Yildirim University , Erzincan 24100 , Turkey
| | - Ahmet Selim Dalkılıç
- Heat and Thermodynamics Division, Department of Mechanical Engineering , Mechanical Engineering Faculty, Yildiz Technical University , Istanbul 34349 , Turkey
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Shafiq A, Çolak AB, Swarup C, Sindhu TN, Lone SA. Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network. Advcd Theory and Sims 2022. [DOI: 10.1002/adts.202200100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Anum Shafiq
- School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing 210044 China
| | - Andaç Batur Çolak
- Niğde Ömer Halisdemir University Mechanical Engineering Department Niğde 51240 Turkey
| | - Chetan Swarup
- Department of Basic Sciences College of Science and Theoretical Studies Saudi Electronic University Riyadh 11673 Kingdom of Saudi Arabia
| | - Tabassum Naz Sindhu
- Department of Statistics Quaid‐i‐Azam University 45320 Islamabad 44000 Pakistan
| | - Showkat Ahmad Lone
- Department of Basic Sciences College of Science and Theoretical Studies Saudi Electronic University Riyadh 11673 Kingdom of Saudi Arabia
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Çolak AB. A novel comparative analysis between the experimental and numeric methods on viscosity of zirconium oxide nanofluid: Developing optimal artificial neural network and new mathematical model. POWDER TECHNOL 2021. [DOI: 10.1016/j.powtec.2020.12.053] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Çolak AB, Akçaözoğlu K, Akçaözoğlu S, Beller G. Artificial Intelligence Approach in Predicting the Effect of Elevated Temperature on the Mechanical Properties of PET Aggregate Mortars: An Experimental Study. Arab J Sci Eng 2021. [DOI: 10.1007/s13369-020-05280-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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