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Applications of machine learning methods in modeling various types of heat pipes: a review

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Abstract

Accurate modeling of heat pipes, as a two-phase thermal medium, is the main goal of several research works. Since the effective thermal conductivity of the heat pipes is dependent on different factors, the modeling procedure is complicated. The main techniques presented for modeling the heat pipes are numerical methods, i.e., computational fluid dynamics, and machine learning approaches. Due to the simplicity of the use of machine learning methods, these types of models can be more applicable and interesting for scientists compared with numerical models. In this regard, different types of intelligence methods, including Support Vector Machine and Artificial Neural Network, have been employed for determining and estimating the thermal behavior of various kinds of heat pipes. The precision and applicability of these intelligence models depend on different items, such as the input variable, used algorithm, and structure of the model. In the present work, recent studies performed on the applications of intelligence models in the modeling of heat pipes are reviewed, and their primary outcomes are represented. Based on the findings of the studies, intelligence models can estimate the effective heat transfer coefficients of heat pipes reliably. Also, it is concluded that applying the optimization approach in the structure of models, for minimizing the deviation of the predicted values from the actual ones, results in the accuracy enhancement of the models. Finally, some suggestions are provided for future researches concerning the modeling of heat pipes by employing data-driven approaches.

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References

  1. Payambarpour SA, Alhuyi Nazari M, Ahmadi MH, Chamkha AJ. Effect of partially wet-surface condition on the performance of fin-tube heat exchanger. Int J Numer Methods Heat Fluid Flow. 2019;1:1. https://doi.org/10.1108/HFF-07-2018-0362.

    Article  Google Scholar 

  2. Chougule SS, Sahu SK, Pise AT. Thermal performance of two phase thermosyphon flat-plate solar collectors using nanofluid. J Sol Energy Eng. 2013;136:014503. https://doi.org/10.1115/1.4025591.

    Article  CAS  Google Scholar 

  3. Aghayari R, Maddah H, Zarei M, Dehghani M, Kaskari Mahalle SG. Heat transfer of nanofluid in a double pipe heat exchanger. Int Sch Res Not. 2014;2014:1–7. https://doi.org/10.1155/2014/736424.

    Article  Google Scholar 

  4. Webb RL, Kim N-H. Principles of enhanced heat transfer. New York: Taylor and Francis; 2005.

    Google Scholar 

  5. Ganvir RB, Walke PV, Kriplani VM. Heat transfer characteristics in nanofluid—a review. Renew Sustain Energy Rev. 2017;75:451–60. https://doi.org/10.1016/J.RSER.2016.11.010.

    Article  Google Scholar 

  6. Mohammadi M, Mohammadi M, Ghahremani AR, Shafii MB, Mohammadi N. Experimental investigation of thermal resistance of a ferrofluidic closed-loop pulsating heat pipe. Heat Transf Eng. 2014;35:25–33. https://doi.org/10.1080/01457632.2013.810086.

    Article  CAS  Google Scholar 

  7. Nazari MA, Ghasempour R, Ahmadi MH, Heydarian G, Shafii MB. Experimental investigation of graphene oxide nanofluid on heat transfer enhancement of pulsating heat pipe. Int Commun Heat Mass Transf. 2018;91:90–4. https://doi.org/10.1016/j.icheatmasstransfer.2017.12.006.

    Article  CAS  Google Scholar 

  8. Alhuyi Nazari M, Ghasempour R, Ahmadi MH. A review on using nanofluids in heat pipes. J Therm Anal Calorim 2019:1–9. doi:https://doi.org/10.1007/s10973-019-08094-y.

  9. Ma HB, Wilson C, Borgmeyer B, Park K, Yu Q, Choi SUS, et al. Effect of nanofluid on the heat transport capability in an oscillating heat pipe. Appl Phys Lett. 2006;88:143116. https://doi.org/10.1063/1.2192971.

    Article  CAS  Google Scholar 

  10. Kearney D, Griffin J. An Open Loop Pulsating Heat Pipe for Integrated Electronic Cooling Applications. J Heat Transfer. 2014;136:081401. https://doi.org/10.1115/1.4027131.

    Article  Google Scholar 

  11. Pise GA, Salve SS, Pise AT, Pise AA. Investigation of Solar Heat Pipe Collector Using Nanofluid and Surfactant. Energy Procedia. 2016;90:481–91. https://doi.org/10.1016/J.EGYPRO.2016.11.215.

    Article  CAS  Google Scholar 

  12. Ramezanizadeh M, Alhuyi Nazari M, Hossein Ahmadi M, Chen L. A review on the approaches applied for cooling fuel cells. Int J Heat Mass Transf. 2019;139:517–25. https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2019.05.032.

    Article  CAS  Google Scholar 

  13. Faegh M, Shafii MB. Experimental investigation of a solar still equipped with an external heat storage system using phase change materials and heat pipes. Desalination. 2017;409:128–35. https://doi.org/10.1016/J.DESAL.2017.01.023.

    Article  CAS  Google Scholar 

  14. Arab M, Soltanieh M, Shafii MB. Experimental investigation of extra-long pulsating heat pipe application in solar water heaters. Exp Therm Fluid Sci. 2012;42:6–15. https://doi.org/10.1016/J.EXPTHERMFLUSCI.2012.03.006.

    Article  Google Scholar 

  15. Alhuyi Nazari M, Ahmadi MH, Ghasempour R, Shafii MB, Mahian O, Kalogirou S, et al. A review on pulsating heat pipes: From solar to cryogenic applications. Appl Energy. 2018;222:475–84. https://doi.org/10.1016/j.apenergy.2018.04.020.

    Article  Google Scholar 

  16. Ramezanizadeh M, Alhuyi Nazari M, Ahmadi MH, Açıkkalp E. Application of nanofluids in thermosyphons: A review. J Mol Liq. 2018;272:395–402. https://doi.org/10.1016/J.MOLLIQ.2018.09.101.

    Article  CAS  Google Scholar 

  17. Schreiber M, Wits WW, te Riele GJ. Numerical and experimental investigation of a counter-current two-phase thermosyphon with cascading pools. Appl Therm Eng. 2016;99:133–46. https://doi.org/10.1016/J.APPLTHERMALENG.2015.12.095.

    Article  Google Scholar 

  18. Daimaru T, Yoshida S, Nagai H. Study on thermal cycle in oscillating heat pipes by numerical analysis. Appl Therm Eng. 2017;113:1219–27. https://doi.org/10.1016/J.APPLTHERMALENG.2016.11.114.

    Article  CAS  Google Scholar 

  19. Lee J, Ko J, Kim Y, Jeong S, Sung T, Han Y, et al. Experimental study on the double-evaporator thermosiphon for cooling HTS (high temperature superconductor) system. Cryogenics (Guildf). 2009;49:390–7. https://doi.org/10.1016/J.CRYOGENICS.2009.04.004.

    Article  CAS  Google Scholar 

  20. Liang Q, Li Y, Wang Q. Experimental investigation on the performance of a neon cryogenic oscillating heat pipe. Cryogenics (Guildf). 2017;84:7–12. https://doi.org/10.1016/J.CRYOGENICS.2017.03.004.

    Article  CAS  Google Scholar 

  21. Bai L, Lin G, Zhang H, Miao J, Wen D. Operating characteristics of a miniature cryogenic loop heat pipe. Int J Heat Mass Transf. 2012;55:8093–9. https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2012.08.044.

    Article  CAS  Google Scholar 

  22. Zamani R, Kalan K, Shafii MB. Experimental investigation on thermal performance of closed loop pulsating heat pipes with soluble and insoluble binary working fluids and a proposed correlation. Heat Mass Transf 2018:1–10. doi:https://doi.org/10.1007/s00231-018-2418-z.

  23. Heydarian R, Shafii MB, Rezaee Shirin-Abadi A, Ghasempour R, Alhuyi NM. Experimental investigation of paraffin nano-encapsulated phase change material on heat transfer enhancement of pulsating heat pipe. J Therm Anal Calorim. 2019;137:1603–13. https://doi.org/10.1007/s10973-019-08062-6.

    Article  CAS  Google Scholar 

  24. Gandomkar A, Kalan K, Vandadi M, Shafii MB, Saidi MH. Investigation and visualization of surfactant effect on flow pattern and performance of pulsating heat pipe. J Therm Anal Calorim. 2020;139:2099–107. https://doi.org/10.1007/s10973-019-08649-z.

    Article  CAS  Google Scholar 

  25. Nazari MA, Ghasempour R, Shafii MB, Ahmadi MH. Experimental Investigation of Triton X-100 Solution on Pulsating Heat Pipe Thermal Performance. J Thermophys Heat Transf. 2018;2018:1–7. https://doi.org/10.2514/1.T5295.

    Article  Google Scholar 

  26. Gandomkar A, Saidi MH, Shafii MB, Vandadi M, Kalan K. Visualization and comparative investigations of pulsating ferro-fluid heat pipe. Appl Therm Eng. 2017;116:56–65. https://doi.org/10.1016/j.applthermaleng.2017.01.068.

    Article  CAS  Google Scholar 

  27. Ramezanizadeh M, Alhuyi Nazari M, Ahmadi MH, Chau K. Experimental and numerical analysis of a nanofluidic thermosyphon heat exchanger. Eng Appl Comput Fluid Mech. 2019;13:40–7. https://doi.org/10.1080/19942060.2018.1518272.

    Article  Google Scholar 

  28. Ebrahimi M, Shafii MB, Bijarchi MA. Experimental investigation of the thermal management of flat-plate closed-loop pulsating heat pipes with interconnecting channels. Appl Therm Eng. 2015;90:838–47. https://doi.org/10.1016/J.APPLTHERMALENG.2015.07.040.

    Article  CAS  Google Scholar 

  29. Ramezanizadeh M, Alhuyi Nazari M, Ahmadi MH, Lorenzini G, Pop I. A review on the applications of intelligence methods in predicting thermal conductivity of nanofluids. J Therm Anal Calorim. 2019;2019:1–17. https://doi.org/10.1007/s10973-019-08154-3.

    Article  CAS  Google Scholar 

  30. Ramezanizadeh M, Ahmadi MA, Ahmadi MH, Alhuyi NM. Rigorous smart model for predicting dynamic viscosity of Al2O3/water nanofluid. J Therm Anal Calorim. 2019;137:307–16. https://doi.org/10.1007/s10973-018-7916-1.

    Article  CAS  Google Scholar 

  31. Ahmadi MH, Alhuyi Nazari M, Ghasempour R, Madah H, Shafii MB, Ahmadi MA. Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods. Colloids Surfaces A Physicochem Eng Asp. 2018;541:154–64. https://doi.org/10.1016/J.COLSURFA.2018.01.030.

    Article  CAS  Google Scholar 

  32. Rezaei MH, Sadeghzadeh M, Alhuyi Nazari M, Ahmadi MH, Astaraei FR. Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries. Int J Low-Carbon Technol. 2018;13:266–71. https://doi.org/10.1093/ijlct/cty026.

    Article  CAS  Google Scholar 

  33. Chen RH, Su GH, Qiu SZ, Fukuda K. Prediction of CHF in concentric-tube open thermosiphon using artificial neural network and genetic algorithm. Heat Mass Transf. 2010;46:345–53. https://doi.org/10.1007/s00231-010-0575-9.

    Article  Google Scholar 

  34. Hemmat Esfe M, Ahangar MRH, Toghraie D, Hajmohammad MH, Rostamian H, Tourang H. Designing artificial neural network on thermal conductivity of Al2O3–water–EG (60–40 %) nanofluid using experimental data. J Therm Anal Calorim. 2016;126:837–43. https://doi.org/10.1007/s10973-016-5469-8.

    Article  CAS  Google Scholar 

  35. Ahmadi MH, Ahmadi MA, Mehrpooya M, Rosen MA. Using GMDH neural networks to model the power and torque of a stirling engine. Sustain. 2015;7:2243–55. https://doi.org/10.3390/su7022243.

    Article  Google Scholar 

  36. Abdollahpour A, Ahmadi MH, Mohammadi AH. Thermodynamic model to study a solar collector for its application to Stirling engines. Energy Convers Manag. 2014;79:666–73. https://doi.org/10.1016/J.ENCONMAN.2013.12.039.

    Article  Google Scholar 

  37. Ahmadi MH, Ahmadi MA, Nazari MA, Mahian O, Ghasempour R. A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach. J Therm Anal Calorim. 2019;135:271–81. https://doi.org/10.1007/s10973-018-7035-z.

    Article  CAS  Google Scholar 

  38. Ruhani B, Toghraie D, Hekmatifar M, Hadian M. Statistical investigation for developing a new model for rheological behavior of ZnO–Ag (50%–50%)/Water hybrid Newtonian nanofluid using experimental data. Phys A Stat Mech Its Appl. 2019;525:741–51. https://doi.org/10.1016/j.physa.2019.03.118.

    Article  CAS  Google Scholar 

  39. Baghban A, Kahani M, Nazari MA, Ahmadi MH, Yan W-M. Sensitivity analysis and application of machine learning methods to predict the heat transfer performance of CNT/water nanofluid flows through coils. Int J Heat Mass Transf. 2019;128:825–35. https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2018.09.041.

    Article  CAS  Google Scholar 

  40. Ahmadi MH, Hajizadeh F, Rahimzadeh M, Shafii MB, Chamkha AJ. Application GMDH artificial neural network for modeling of Al 2 O 3 / water and Al 2 O 3 / Ethylene glycol thermal conductivity. Int J Heat Technol. 2018;36:773–82.

    Article  Google Scholar 

  41. Latha A, Reddy KVK, Rao JCS, Raju AVSR. Performance Analysis on Modeling of Loop Heat Pipes Using Artificial Neural Networks. Indian J Sci Technol. 2010;3:463–7. https://doi.org/10.17485/IJST/2010/V3I4/29737.

    Article  Google Scholar 

  42. Patel VM, Mehta HB. Thermal performance prediction models for a pulsating heat pipe using Artificial Neural Network (ANN) and Regression/Correlation Analysis (RCA). Sādhanā. 2018;43:184. https://doi.org/10.1007/s12046-018-0954-3.

    Article  CAS  Google Scholar 

  43. Komeilibirjandi A, Raffiee AH, Maleki A, Alhuyi Nazari M, Safdari Shadloo M. Thermal conductivity prediction of nanofluids containing CuO nanoparticles by using correlation and artificial neural network. J Therm Anal Calorim. 2019;2019:1–11. https://doi.org/10.1007/s10973-019-08838-w.

    Article  CAS  Google Scholar 

  44. Wang N, Maleki A, Nazari MA, Tlili I, Shadloo MS. Thermal conductivity modeling of nanofluids contain MgO particles by employing different approaches. Symmetry (Basel). 2020;12:206.

    Article  CAS  Google Scholar 

  45. Ahmadi MH, Sadeghzadeh M, Raffiee AH, Chau K. Applying GMDH neural network to estimate the thermal resistance and thermal conductivity of pulsating heat pipes. Eng Appl Comput Fluid Mech. 2019;13:327–36. https://doi.org/10.1080/19942060.2019.1582109.

    Article  Google Scholar 

  46. Ahmadi MH, Tatar A, Alhuyi Nazari M, Ghasempour R, Chamkha AJ, Yan W-M. Applicability of connectionist methods to predict thermal resistance of pulsating heat pipes with ethanol by using neural networks. Int J Heat Mass Transf 2018;126. doi:https://doi.org/10.1016/j.ijheatmasstransfer.2018.06.085.

  47. Parvin S, Chamkha AJ. An analysis on free convection flow, heat transfer and entropy generation in an odd-shaped cavity filled with nanofluid. Int Commun Heat Mass Transf. 2014;54:8–17. https://doi.org/10.1016/J.ICHEATMASSTRANSFER.2014.02.031.

    Article  CAS  Google Scholar 

  48. Heydari M, Toghraie D, Akbari OA. The effect of semi-attached and offset mid-truncated ribs and Water/TiO2 nanofluid on flow and heat transfer properties in a triangular microchannel. Therm Sci Eng Prog. 2017;2:140–50. https://doi.org/10.1016/j.tsep.2017.05.010.

    Article  Google Scholar 

  49. Irandoost Shahrestani M, Maleki A, Safdari Shadloo M, Tlili I. Numerical Investigation of Forced Convective Heat Transfer and Performance Evaluation Criterion of Al2O3/Water Nanofluid Flow inside an Axisymmetric Microchannel. Symmetry (Basel). 2020;12:120. https://doi.org/10.3390/sym12010120.

    Article  CAS  Google Scholar 

  50. Barnoon P, Toghraie D, Dehkordi RB, Abed H. MHD mixed convection and entropy generation in a lid-driven cavity with rotating cylinders filled by a nanofluid using two phase mixture model. J Magn Magn Mater. 2019;483:224–48. https://doi.org/10.1016/j.jmmm.2019.03.108.

    Article  CAS  Google Scholar 

  51. Kahani M, Vatankhah G. Thermal performance prediction of wickless heat pipe with Al 2 O 3 /water nanofluid using artificial neural network. Chem Eng Commun. 2019;206:509–23. https://doi.org/10.1080/00986445.2018.1505614.

    Article  CAS  Google Scholar 

  52. Taslimifar M, Mohammadi M, Afshin H, Saidi MH, Shafii MB. Overall thermal performance of ferrofluidic open loop pulsating heat pipes: An experimental approach. Int J Therm Sci. 2013;65:234–41. https://doi.org/10.1016/j.ijthermalsci.2012.10.016.

    Article  CAS  Google Scholar 

  53. Salehi H, Zeinali Heris S, Koolivand Salooki M, Noei SH. Designing a neural network for closed thermosyphon with nanofluid using a genetic algorithm. Brazilian J Chem Eng. 2011;28:157–68. https://doi.org/10.1590/S0104-66322011000100017.

    Article  CAS  Google Scholar 

  54. Alhuyi Nazari M, Ahmadi MH, Ghasempour R, Shafii MB. How to improve the thermal performance of pulsating heat pipes: A review on working fluid. Renew Sustain Energy Rev 2018;91. doi:https://doi.org/10.1016/j.rser.2018.04.042.

  55. Ahmadi MH, Ahmadi M-A, Açıkkalp E, Alhuyi Nazari M, Arab Pour Yazdi M, Kumar R. New thermodynamic analysis and optimization of performance of an irreversible diesel cycle. Environ Prog Sustain Energy 2018;37. doi:https://doi.org/10.1002/ep.12810.

  56. Ahmadi MH, Sayyaadi H, Dehghani S, Hosseinzade H. Designing a solar powered Stirling heat engine based on multiple criteria: Maximized thermal efficiency and power. Energy Convers Manag. 2013;75:282–91. https://doi.org/10.1016/J.ENCONMAN.2013.06.025.

    Article  Google Scholar 

  57. Ahmadi MH, Ahmadi M-A, Mehrpooya M, Feidt M, Rosen MA. Optimal design of an Otto cycle based on thermal criteria. Mech Ind. 2016;17:111. https://doi.org/10.1051/meca/2015049.

    Article  Google Scholar 

  58. Ashouri M, Astaraei FR, Ghasempour R, Ahmadi MH, Feidt M. Optimum insulation thickness determination of a building wall using exergetic life cycle assessment. Appl Therm Eng. 2016;106:307–15. https://doi.org/10.1016/J.APPLTHERMALENG.2016.05.190.

    Article  Google Scholar 

  59. Omer S. Design optimization of thermoelectric devices for solar power generation. Sol Energy Mater Sol Cells. 1998;53:67–82. https://doi.org/10.1016/S0927-0248(98)00008-7.

    Article  CAS  Google Scholar 

  60. Jokar A, Godarzi AA, Saber M, Shafii MB. Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm. Heat Mass Transf. 2016;52:2437–45. https://doi.org/10.1007/s00231-016-1759-8.

    Article  CAS  Google Scholar 

  61. Shanbedi M, Jafari D, Amiri A, Heris SZ, Baniadam M. Prediction of temperature performance of a two-phase closed thermosyphon using Artificial Neural Network. Heat Mass Transf. 2013;49:65–73. https://doi.org/10.1007/s00231-012-1066-y.

    Article  Google Scholar 

  62. Shanbedi M, Amiri A, Rashidi S, Heris SZ, Baniadam M. Thermal Performance Prediction of Two-Phase Closed Thermosyphon Using Adaptive Neuro-Fuzzy Inference System. Heat Transf Eng. 2015;36:315–24. https://doi.org/10.1080/01457632.2014.916161.

    Article  CAS  Google Scholar 

  63. Patel V, Science HM-WA of, Engineering undefined, 2016 undefined. Artificial neural network modeling of a closed loop pulsating heat pipe. World Acad Sci Eng Technol J Mech Aerospace, Ind Mechatronics, Manuf Eng 2016;10:1754–7.

  64. Toghraie D, Sina N, Jolfaei NA, Hajian M, Afrand M. Designing an Artificial Neural Network (ANN) to predict the viscosity of Silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles. Phys A Stat Mech Its Appl. 2019;534:122142. https://doi.org/10.1016/j.physa.2019.122142.

    Article  CAS  Google Scholar 

  65. Akhgar A, Toghraie D, Sina N, Afrand M. Developing dissimilar artificial neural networks (ANNs) to prediction the thermal conductivity of MWCNT-TiO2/Water-ethylene glycol hybrid nanofluid. Powder Technol. 2019;355:602–10. https://doi.org/10.1016/j.powtec.2019.07.086.

    Article  CAS  Google Scholar 

  66. Sadeghinezhad E, Mehrali M, Rosen MA, Akhiani AR, Tahan Latibari S, Mehrali M, et al. Experimental investigation of the effect of graphene nanofluids on heat pipe thermal performance. Appl Therm Eng. 2016;100:775–87. https://doi.org/10.1016/J.APPLTHERMALENG.2016.02.071.

    Article  CAS  Google Scholar 

  67. Khalili M, Shafii MB. Experimental and numerical investigation of the thermal performance of a novel sintered-wick heat pipe. Appl Therm Eng. 2016;94:59–75. https://doi.org/10.1016/j.applthermaleng.2015.10.120.

    Article  Google Scholar 

  68. Mehrali M, Sadeghinezhad E, Azizian R, Akhiani AR, Tahan Latibari S, Mehrali M, et al. Effect of nitrogen-doped graphene nanofluid on the thermal performance of the grooved copper heat pipe. Energy Convers Manag. 2016;118:459–73. https://doi.org/10.1016/J.ENCONMAN.2016.04.028.

    Article  CAS  Google Scholar 

  69. Kwon GH, Kim SJ. Experimental investigation on the thermal performance of a micro pulsating heat pipe with a dual-diameter channel. Int J Heat Mass Transf. 2015;89:817–28. https://doi.org/10.1016/j.ijheatmasstransfer.2015.05.091.

    Article  CAS  Google Scholar 

  70. Wang XH, Zheng HC, Si MQ, Han XH, Chen GM. Experimental investigation of the influence of surfactant on the heat transfer performance of pulsating heat pipe. Int J Heat Mass Transf. 2015;83:586–90. https://doi.org/10.1016/j.ijheatmasstransfer.2014.12.010.

    Article  CAS  Google Scholar 

  71. Tanshen MR, Munkhbayar B, Nine MJ, Chung H, Jeong H. Effect of functionalized MWCNTs/water nanofluids on thermal resistance and pressure fluctuation characteristics in oscillating heat pipe. Int Commun Heat Mass Transf. 2013;48:93–8. https://doi.org/10.1016/J.ICHEATMASSTRANSFER.2013.08.011.

    Article  CAS  Google Scholar 

  72. Hussein AK, Li D, Kolsi L, Kata S, Sahoo B. A Review of Nano Fluid Role to Improve the Performance of the Heat Pipe Solar Collectors. Energy Procedia. 2017;109:417–24. https://doi.org/10.1016/J.EGYPRO.2017.03.044.

    Article  CAS  Google Scholar 

  73. Katpradit T, Wongratanaphisan T, Terdtoon P, Kamonpet P, Polchai A, Akbarzadeh A. Correlation to predict heat transfer characteristics of a closed end oscillating heat pipe at critical state. Appl Therm Eng. 2005;25:2138–51. https://doi.org/10.1016/J.APPLTHERMALENG.2005.01.009.

    Article  CAS  Google Scholar 

  74. Ahmadi MA, Mahmoudi B. Development of robust model to estimate gas–oil interfacial tension using least square support vector machine: Experimental and modeling study. J Supercrit Fluids. 2016;107:122–8. https://doi.org/10.1016/J.SUPFLU.2015.08.012.

    Article  CAS  Google Scholar 

  75. Tatar A, Barati A, Yarahmadi A, Najafi A, Lee M, Bahadori A. Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models. Int J Greenh Gas Control. 2016;47:122–36. https://doi.org/10.1016/J.IJGGC.2016.01.048.

    Article  CAS  Google Scholar 

  76. Ahmadi MA, Ebadi M, Yazdanpanah A. Robust intelligent tool for estimating dew point pressure in retrograded condensate gas reservoirs: Application of particle swarm optimization. J Pet Sci Eng. 2014;123:7–19. https://doi.org/10.1016/J.PETROL.2014.05.023.

    Article  CAS  Google Scholar 

  77. Ahmadi M-A, Ahmadi MH, Fahim Alavi M, Nazemzadegan MR, Ghasempour R, Shamshirband S. Determination of thermal conductivity ratio of CuO/ethylene glycol nanofluid by connectionist approach. J Taiwan Inst Chem Eng. 2018. https://doi.org/10.1016/J.JTICE.2018.06.003.

    Article  Google Scholar 

  78. Shahsavar A, Khanmohammadi S, Toghraie D, Salihepour H. Experimental investigation and develop ANNs by introducing the suitable architectures and training algorithms supported by sensitivity analysis: Measure thermal conductivity and viscosity for liquid paraffin based nanofluid containing Al2O3 nanoparticles. J Mol Liq. 2019;276:850–60. https://doi.org/10.1016/j.molliq.2018.12.055.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood, Iran

    Mohammad Hossein Ahmadi

  2. School of Mechanical Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India

    Ravinder Kumar

  3. SREE Department, University of Sharjah, Sharjah, United Arab Emirates

    Mamdouh El Haj Assad

  4. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Phuong Thao Thi Ngo

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Ahmadi, M.H., Kumar, R., Assad, M.E.H. et al. Applications of machine learning methods in modeling various types of heat pipes: a review. J Therm Anal Calorim 146, 2333–2341 (2021). https://doi.org/10.1007/s10973-021-10603-x

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