بررسی ضریب هدایت حرارتی نانوسیال هیبریدی سه‌گانه پایه آبی حاوی MWCNTs به روش شبکه عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشگاه جامع امام حسین(علیه السلام)، تهران، ایران

2 دانشجوی دکتری، دانشگاه جامع امام حسین(ع)، تهران، ایران

چکیده

هدایت حرارتی نانوسیال MWCNT(40%)-CuO(30%)-TiO2(30%) /Water در کسر حجمی‌ها و دماهای مختلف با روش شبکه عصبی مصنوعی، مدل‌سازی و تحلیل می‌شود. شبکه عصبی مصنوعی از نوع MLP هست. 48 سری داده تجربی مورداستفاده قرار گرفت که به ترتیب %70، %15 و %15 برای آموزش، اعتبارسنجی و آزمون به کار رفت. ساختار عصبی بهینه دارای دو لایه پنهان است که در لایه اول 4 نورون و در لایه دوم 5 نورون به ترتیب با تابع انتقال logsig و tansig قرار دارد. آموزش شبکه عصبی با الگوریتم لونبرگ-مارکوارت (ML) انجام می‌شود. مقادیر پارامترهای ضریب رگرسیون R و میانگین خطا MSE برای ساختار بهینه به ترتیب برابر با 9995753/0 و 2.8734E-06 به دست آمدند. رابطه همگرایی نیز برای پیش‌بینی هدایت حرارتی نانوسیال ارائه می‌شود. مقایسه بین مدل همگرایی و شبکه عصبی مصنوعی، نشان از برتری شبکه عصبی مصنوعی دارد. مقادیر MOD برای شبکه عصبی مصنوعی نیز در محدوده %3- تا %7+ قرار گرفت.

کلیدواژه‌ها

عنوان مقاله [English]

Investigating the Thermal Conductivity Coefficient of Water-based Ternary Hybrid Nanofluid Containing Mwcnts by Artificial Neural Network Method

نویسندگان [English]

  • Mohammad Hemmat Esfe 1
  • Sayyid majid Motallebi 2
1 Assistant Professor, Imam Hossein University, Tehran, Iran
2 PhD student, Imam Hossein University, Tehran, Iran
چکیده [English]

Thermal conductivity of MWCNT(40%)-CuO(30%)-TiO2(30%) /Water nanofluid in different volume fractions and temperatures is modeled and analyzed by artificial neural network method. The type of artificial neural network is MLP. 48 experimental data series are used, 70%, 15%, and 15% are used for training, validation, and testing, respectively. The optimal neural structure has two hidden layers, in which there are 4 neurons in the first layer and 5 neurons in the second layer, with the transfer functions of logsig and tansig, respectively. Neural network training is done with Levenberg-Marquardt (ML) algorithm. The values of regression coefficient, R, and mean squared error, MSE, for the optimal structure are obtained as 0.9995753 and 2.8734E-06, respectively. The correlation equation is also presented to predict the thermal conductivity of nanofluid. The comparison between the correlation equation and the artificial neural network shows the superiority of the artificial neural network. MOD values for artificial neural network are also in the range of -3% to +7%.

کلیدواژه‌ها [English]

  • Nanofluid Levenberg-Marquardt
  • Thermal Conductivity Artificial Neural Network

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https://creativecommons.org/licenses/by/4.0/

مراجع

  1. Choi SU, Eastman JA. Enhancing Thermal Conductivity of Fluids with Nanoparticles. Argonne National Lab. (ANL), Argonne, IL (United States); 1995.
  2. Kamsuwan C, Wang X, Piumsomboon P, Pratumwal Y, Otarawanna S, Chalermsinsuwan B. Artificial Neural Network Prediction Models for Nanofluid Properties and Their Applications with Heat Exchanger Design and Rating Simulation. International Journal of Thermal Sciences. 2023;184:107995.
  3. Aghaei-Meybodi Z, Ghambarian M, Khandan Barani K, Sheikholeslami-Farahani F. Green Synthesis and Study of Biological Activity of New Benzopyrimidoazepines: Reduction of Organic Pollutants Using Synthesized Fe3O4/TiO2/CuO@ MWCNTs MNCs. Polycyclic Aromatic Compounds. 2023;43(7):6138-59.
  4. Xuan Z, Zhai Y, Li Y, Li Z, Wang H. Guideline for Selecting Appropriate Mixing Ratio of Hybrid Nanofluids in Thermal Management Systems. Powder Technology. 2022;403:117425.
  5. Kumar H, Sokhal GS, Editors. Effect of Different Types of Nanoparticles on Thermophysical Properties of Water Based Hybrid Nanofluids. IOP Conference Series: Materials Science and Engineering; 2022: IOP Publishing.
  6. Hammet Esfe M, Motallebi SM. Experimental Study of the Effect of Effective Parameters on the Coefficient of Thermal Conductivity of Five-Component Hybrid Nanofluid. Aerospace Mechanics Quarterly. 2022;18:141-54.
  7. Mukhtar M, Oluwasanmi A, Yimen N, Qinxiu Z, Ukwuoma CC, Ezurike B, et al. Development and Comparison of two Novel Hybrid Neural Network Models for Hourly Solar Radiation prediction. Applied Sciences. 2022;12(3):1435.
  8. Tian J, Liu Y, Zheng W, Yin L. Smog prediction Based on the Deep Belief-BP Neural Network Model (DBN-BP). Urban Climate. 2022;41:101078.
  9. Ferreira FPV, Shamass R, Limbachiya V, Tsavdaridis KD, Martins CH. Lateral–Torsional Buckling Resistance Prediction Model for Steel Cellular Beams Generated by Artificial Neural Networks (ANN). Thin-Walled Structures. 2022;170:108592.
  10. Zhang Z, Tian J, Huang W, Yin L, Zheng W, Liu S. A haze Prediction Method Based on one-Dimensional Convolutional Neural Network. Atmosphere. 2021;12(10):1327.
  11. Veza I, Afzal A, Mujtaba M, Hoang AT, Balasubramanian D, Sekar M, et al. Review of Artificial Neural Networks for Gasoline, Diesel and Homogeneous Charge Compression Ignition Engine. Alexandria Engineering Journal. 2022;61(11):8363-91.
  12. Sepehrnia M, Mohammadzadeh K, Rozbahani MH, Ghiasi MJ, Amani M. Rheological Behavior of oil-silicon Dioxide-multi walled Carbon Nanotube Hybrid Nanofluid: Experimental Study and Neural Network Prediction. 2022.
  13. Wang Y, Wang H, Zhou B, Fu H. Multi-Dimensional Prediction Method Based on Bi-LSTMC for Ship roll. Ocean Engineering. 2021;242:110106.
  14. Meng F, Cheng W, Wang J. Semi-supervised Software Defect Prediction Model Based on tri-Training. KSII Transactions on Internet & Information Systems. 2021;15(11).
  15. Yin G, Alazzawi FJI, Bokov D, Marhoon HA, El-Shafay A, Rahman ML, et al. Multiple Machine learning Models for Prediction of CO2 Solubility in Potassium and Sodium Based Amino Acid salt Solutions. Arabian Journal of Chemistry. 2022;15(3):103608.
  16. Zhao TH, Khan MI, Chu YM. Artificial Neural Networking (ANN) analysis for heat and Entropy Generation in Flow of non‐Newtonian Fluid Between two Rotating Disks. Mathematical Methods in the Applied Sciences. 2023;46(3):3012-30.
  17. Zhong Q, Yang J, Shi K, Zhong S, Li Z, Sotelo MA. Event-Triggered H∞ load Frequency Control for Multi-Area Nonlinear Power systems Based on non-Fragile proportional Integral Control Strategy. IEEE Transactions on Intelligent Transportation Systems. 2021;23(8):12191-201.
  18. Chen T-C, Alazzawi FJI, Salameh AA, Ayub Ahmed AA, Pustokhina I, Surendar A, et al. Application of Machine learning in Rapid Analysis of Solder Joint Geometry and Type on Thermomechanical useful lifetime of Electronic Components. Mechanics of Advanced Materials and Structures. 2023;30(2):373-81.
  19. Nemati M, Sefid M. Numerical Investigation Through Boltzmann Network Method to Analyze the Amount of Entropy Produced due to heat Transfer of non-Newtonian Conjugated Nanofluid under the Effect of Magnetic Field Aerospace Mechanics Quarterly. 2022;19:113-29.
  20. Dostdar MM, Yakani M. Numerical Study of Mixed Displacement of NNanofluid in a Square Chamber with Movable Roof and Hot Barriers. Aerospace Mechanics Quarterly. 2015;12.
  21. [21] Wang X, Yan X, Gao N, Chen G. Prediction of Thermal Conductivity of Various Nanofluids with Ethylene Glycol using Artificial Neural Network. Journal of Thermal Science. 2020;29:1504-12.
  22. Nazari M, Kihani MH, Sultanzadeh H. Experimental Study of Heat Transfer of Alumina/Water Nanofluid Inside a Horizontal Tube. Aerospace Mechanics Quarterly. 2012;9.
  23. Nanofluid Through Boltzmann Network Method. Aerospace Mechanics Quarterly. 2022:1-25.
  24. Javadpour R, Heris SZ, Mohammadfam Y, Mousavi SB. Optimizing the Heat Transfer Characteristics of MWCNTs and TiO2 water-Based Nanofluids Through a Novel Designed pilot-scale Setup. Scientific Reports. 2022;12(1):15154.
  25. Asadi A, Alarifi IM, Foong LK. An Experimental Study on Characterization, Stability and Dynamic viscosity of CuO-TiO2/water Hybrid Nanofluid. Journal of Molecular Liquids. 2020;307:112987.
  26. Abbas F, Ali HM, Shaban M, Janjua MM, Shah TR, Doranehgard MH, et al. Towards convective heat transfer optimization in aluminum tube automotive radiators: Potential assessment of novel Fe2O3-TiO2/water hybrid nanofluid. Journal of the Taiwan Institute of Chemical Engineers. 2021;124:424-36.
  27. Longo GA, Zilio C, Ceseracciu E, Reggiani M. Application of artificial neural network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids. Nano Energy. 2012;1(2):290-6.
  28. Esfe MH, Saedodin S, Sina N, Afrand M, Rostami S. Designing an Artificial Neural network to Predict Thermal Conductivity and dynamic viscosity of Ferromagnetic Nanofluid. International Communications in Heat and Mass Transfer. 2015;68:50-7.
  29. Esfe MH, Rostamian H, Afrand M, Karimipour A, Hassani M. Modeling and Estimation of Thermal Conductivity of MgO–water/EG (60: 40) by Artificial Neural Network and correlation. International Communications in Heat and Mass Transfer. 2015;68:98-103.
  30. Afrand M, Esfe MH, Abedini E, Teimouri H. Predicting the Effects of Magnesium oxide Nanoparticles and Temperature on the Thermal Conductivity of water using Artificial neural Network and Experimental data. Physica E: Low-Dimensional Systems and Nanostructures. 2017;87:242-7.
  31. Esfe MH, Esfandeh S, Afrand M, Rejvani M, Rostamian SH. Experimental Evaluation, New Correlation Proposing and ANN Modeling of Thermal Properties of EG Based Hybrid Nanofluid Containing ZnO-DWCNT Nanoparticles for Internal Combustion Engines Applications. Applied Thermal Engineering. 2018;133:452-63.
  32. Bahiraei M, Hosseinalipour SM, Zabihi K, Taheran E. Using Neural Network for Determination of viscosity in water-TiO 2 Nanofluid. Advances in Mechanical Engineering. 2012;4:742680.
  33. Safikhani H, Abbassi A, Khalkhali A, Kalteh M. Multi-objective Optimization of Nanofluid Flow in Flat Tubes using CFD, Artificial Neural Networks and Genetic Algorithms. Advanced Powder Technology. 2014;25(5):1608-17.

 

  1. Vakili M, Yahyaei M, Kalhor K. Thermal Conductivity Modeling of Graphene Nanoplatelets/Deionized Water Nanofluid by MLP Neural Network and Theoretical Modeling using Experimental Results. International Communications in Heat and Mass Transfer. 2016;74:11-7.
  2. Wei B, Zou C, Li X. Experimental Investigation on Stability and Thermal Conductivity of Diathermic oil Based TiO2 Nanofluids. International Journal of Heat and Mass Transfer. 2017;104:537-43.
  3. Pryazhnikov M, Minakov A, Rudyak VY, Guzei D. Thermal Conductivity Measurements of Nanofluids. International Journal of Heat and Mass Transfer. 2017;104:1275-82.
  4. Satti JR, Das DK, Ray D. Investigation of the Thermal Conductivity of Propylene Glycol Nanofluids and Comparison with Correlations. International Journal of Heat and Mass Transfer. 2017;107:871-81.
  5. Shahsavar A, Bahiraei M. Experimental Investigation and Modeling of Thermal Conductivity and viscosity for non-Newtonian Hybrid Nanofluid Containing Coated CNT/Fe3O4 Nanoparticles. Powder Technology. 2017;318:441-50.
  6. Vafaei M, Afrand M, Sina N, Kalbasi R, Sourani F, Teimouri H. Evaluation of Thermal Conductivity of MgO-MWCNTs/EG Hybrid Nanofluids Based on Experimental data by Selecting Optimal Artificial Neural Networks. Physica E: Low-Dimensional Systems and Nanostructures. 2017;85:90-6.
  7. Esfe MH, Behbahani PM, Arani AA, Sarlak MR. Thermal Conductivity Enhancement of SiO2–MWCNT (85: 15%) –EG Hybrid Nanofluids. J Therm Anal Calorim. 2017;128(1):249-58.
  8. Zadkhast M, Toghraie D, Karimipour A. Developing a New Correlation to Estimate the Thermal Conductivity of MWCNT-CuO/water Hybrid Nanofluid via an Experimental Investigation. Journal of Thermal Analysis and Calorimetry. 2017;129:859-67.
  9. Vakili M, Karami M, Delfani S, Khosrojerdi S, Kalhor K. Experimental Investigation and Modeling of Thermal Conductivity of CuO–Water/EG Nanofluid by FFBP-ANN and Multiple Regressions. Journal of Thermal Analysis and Calorimetry. 2017;129:629-37.
  10. Maddah H, Ghasemi N. Experimental Evaluation of Heat Transfer Efficiency of Nanofluid in a Double pipe Heat Exchanger and Prediction of Experimental Results using Artificial Neural Networks. Heat and Mass Transfer. 2017;53:3459-72.
  11. Karlik B, Olgac AV. Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural networks. International Journal of Artificial Intelligence and Expert Systems. 2011;1(4):111-22.
  12. Chatterjee S, Dey N, Sen S. Soil Moisture Quantity Prediction using Optimized Neural Supported Model for Sustainable Agricultural Applications. Sustainable Computing: Informatics and Systems. 2020;28:100279.
  13. Vaferi B, Samimi F, Pakgohar E, Mowla D. Artificial Neural Network approach for Prediction of Thermal Behavior of Nanofluids Flowing Through Circular Tubes. Powder Technology. 2014;267:1-10.
  14. Esfe MH, Alidoust S, Toghraie D. Correlation and Thermal Conductivity Sensitivity Analysis of Ternary Hybrid Nanofluids Containing CuO and TiO2 Nanoparticles and Multi-walled Carbon Nanotubes. Korean Journal of Chemical Engineering. 2023;40(9):2312-20.
  15. Taud H, Mas J. Multilayer Perceptron (MLP). Geomatic Approaches for Modeling land Change Scenarios. 2018:451-5.
  16. Esfe MH, Toghraie D, Esfandeh S, Alidoust S. Measurement of Thermal Conductivity of triple Hybrid Water Based Nanofluid Containing MWCNT (10%)-Al2O3 (60%)-ZnO (30%) Nanoparticles. Colloids and Surfaces A: Physicochemical and Engineering Aspects. 2022;647:129083.
  17. Hemmat Esfe M, Motallebi SM. Optimization, Modeling, and prediction of Relative viscosity and Relative Thermal Conductivity of AlN nano-Powders Suspended in EG. The European Physical Journal plus. 2021;136:1-19.
  18. Lu SY, Lin HC. Effective Conductivity of Composites Containing Aligned Spheroidal Inclusions of Finite Conductivity. Journal of Applied Physics. 1996;79(9):6761-9.

 

مکانیک سیالات و آیرودینامیک
دوره 12، شماره 2 - شماره پیاپی 32
پاییز و زمستان 1402
اسفند 1402
صفحه 155-168

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سابقه مقاله

  • تاریخ دریافت: 23 مهر 1402
  • تاریخ بازنگری: 01 دی 1402
  • تاریخ پذیرش: 25 دی 1402
  • تاریخ انتشار: 30 بهمن 1402

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