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Analysis of microchannel resistance factor based on automated simulation framework and BP neural network

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Abstract

In this paper, self-design automated simulation and artificial neural network (ANN) model were developed to analyze and estimate the resistance factor in rectangle cross-section microchannels. The main purpose is to obtain a universal solution method through numerical simulation which can solve the resistance factor problem for invariant cross-section microchannels. Through Python language, the automatic coalescent of preprocessing Gambit, computing software CFD and post-processing Tecplot make the simulation framework realize the automatic acquisition of microchannel resistance factor samples. Then, 100 simulation samples with different aspect ratios for Reynolds numbers ranging from 50 to 500 were obtained. After validation, the width and height of microchannels were applied as input data set of the ANN model, and the resistance factor was determined as the target data. In order to improve BP algorithm for training ANN, a new swarm evolution algorithm was realized by combining the strong point of gradient descent method, genetic algorithm and particle swarm optimization, which is called particle swarm evolution algorithm. Finally, the result of resistance factor model was established and verified by several existing measurement value of pressure drop from remarkable experimental.

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References

  1. Bahrami M, Yovanovich MM, Culham JR (2006) Pressure drop of fully-developed, laminar flow in microchannels of arbitrary cross-section. J Fluids Eng 128(5):1036–1044

  2. Beigzadeh R, Rahimi M, Parvizi M, Eiamsa-ard S (2014) Application of ANN and GA for the prediction and optimization of thermal and flow characteristics in a rectangular channel fitted with twisted tape vortex generators. Numer Heat Transf Part A Appl 65(2):186–199

  3. Bui NT, Hasegawa H (2015) Training artificial neural network using modification of differential evolution algorithm. Int J Mach Learn Comput 5(1):1

  4. Chen Y, Wu J, Shi M, Peterson GP (2008) Numerical simulation for steady annular condensation flow in triangular microchannels. Int Commun Heat Mass Transfer 35(7):805–809

  5. Chu JC, Teng JT, Greif R (2010) Experimental and numerical study on the flow characteristics in curved rectangular microchannels. Appl Therm Eng 30(13):1558–1566

  6. Chu JC, Teng JT, Xu TT, Huang S, Jin S, Yu XF, Dang T, Zhang CP, Greif R (2012) Characterization of frictional pressure drop of liquid flow through curved rectangular microchannels. Exp Thermal Fluid Sci 38:171–183

  7. Ding S, Su C, Yu J (2011) An optimizing BP neural network algorithm based on genetic algorithm[J]. Artif Intell Rev 36(2):153–162

  8. Fan X, Ma X, Yang L, Lan Z, Hao T, Jiang R, Bai T (2016) Experimental study on two-phase flow pressure drop during steam condensation in trapezoidal microchannels. Exp Thermal Fluid Sci 76:45–56

  9. Hrnjak P, Tu X (2007) Single phase pressure drop in microchannels. Int J Heat Fluid Flow 28(1):2–14

  10. Hsieh SS, Lin CY, Huang CF, Tsai HH (2004) Liquid flow in a micro-channel. J Micromech Microeng 14(4):436

  11. Huang L, Nie W, Wang X, Shen T (2017) Feature coefficient prediction of micro-channel based on artificial neural network. Microsyst Technol 23(6):2297–2305

  12. Jung JY, Kwak HY (2008) Fluid flow and heat transfer in microchannels with rectangular cross section. Heat Mass Transf 44(9):1041–1049

  13. Kim B (2016) An experimental study on fully developed laminar flow and heat transfer in rectangular microchannels. Int J Heat Fluid Flow 62:224–232

  14. Kuang Y, Wang W, Miao J, Yu XG, Zhuan R (2017) Theoretical analysis and modeling of flow instability in a mini-channel evaporator. Int J Heat Mass Transf 104:149–162

  15. Lauga E, Stroock AD, Stone HA (2004) Three-dimensional flows in slowly varying planar geometries. Phys Fluids 16(8):3051–3062

  16. Mirmanto DBR, Lewis JS, Karayiannis TG (2012) Pressure drop and heat transfer characteristics for single-phase developing flow of water in rectangular microchannels. J Phys: Conf Ser 395:012085

  17. Mohammadi M, Jovanovic GN, Sharp KV (2013) Numerical study of flow uniformity and pressure characteristics within a microchannel array with triangular manifolds. Comput Chem Eng 52:134–144

  18. Nawi NM, Rehman MZ, Khan A (2014) A new bat based back-propagation (BAT-BP) algorithm. In: Advances in systems science. Springer, Cham, pp 395–404

  19. Peng XF, Peterson GP, Wang BX (1994) Frictional flow characteristics of water flowing through rectangular microchannels. Exp Heat Transf Int J 7(4):249–264

  20. Pfahler J (1990) Liquid and gas transport in small channels. In: Proceedings of ASME winter annual meeting, micromechanical sensors, actuators and systems, Dallas, Nov. 25–30, 1990, pp 149–158

  21. Pfund D, Rector D, Shekarriz A, Popescu A, Welty J (2000) Pressure drop measurements in a microchannel. AIChE J 46(8):1496–1507

  22. Rahimi M, Hajialyani M, Beigzadeh R, Alsairafi AA (2015) Application of artificial neural network and genetic algorithm approaches for prediction of flow characteristic in serpentine microchannels. Chem Eng Res Des 98:147–156

  23. Rawool AS, Mitra SK, Kandlikar SG (2006) Numerical simulation of flow through microchannels with designed roughness. Microfluid Nanofluid 2(3):215–221

  24. Rezaei O, Akbari OA, Marzban A, Toghraie D, Pourfattah F, Mashayekhi R (2017) The numerical investigation of heat transfer and pressure drop of turbulent flow in a triangular microchannel. Physica E 93:179–189

  25. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

  26. Sisavath S, Jing X, Zimmerman RW (2001) Creeping flow through a pipe of varying radius. Phys Fluids 13(10):2762–2772

  27. Sui Y, Lee PS, Teo CJ (2011) An experimental study of flow friction and heat transfer in wavy microchannels with rectangular cross section. Int J Therm Sci 50(12):2473–2482

  28. Sui Y, Teo CJ, Lee PS (2012) Direct numerical simulation of fluid flow and heat transfer in periodic wavy channels with rectangular cross-sections. Int J Heat Mass Transf 55(1–3):73–88

  29. Tafarroj MM, Mahian O, Kasaeian A, Sakamatapan K, Dalkilic AS, Wongwises S (2017) Artificial neural network modeling of nanofluid flow in a microchannel heat sink using experimental data. Int Commun Heat Mass Transfer 86:25–31

  30. Wang H, Wang Y (2007) Flow in microchannels with rough walls: flow pattern and pressure drop. J Micromech Microeng 17(3):586

  31. Wang P, Du W, Liang M, et al (2016) Prediction model of total organic carbon content on hydrocarbon source rocks in coal measures established by BP neural network based on logging parameters. In: 7th international conference on environment and engineering geophysics and summit forum of Chinese Academy of engineering on engineering science and technology

  32. Weilin Q, Mala GM, Dongqing L (2000) Pressure-driven water flows in trapezoidal silicon microchannels. Int J Heat Mass Transf 43(3):353–364

  33. Wu HY, Cheng P (2003) Friction factors in smooth trapezoidal silicon microchannels with different aspect ratios. Int J Heat Mass Transf 46(14):2519–2525

  34. Yu D, Warrington R, Barron R, Ameel T (1995) An experimental investigation of fluid flow and heat transfer in microtubes. In: Proceedings of the ASME/JSME. Thermal engineering conference, vol 1, pp 523–530

  35. Yu S, Zhu K, Diao F (2008) A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction. Appl Math Comput 195(1):66–75

  36. Zhao B, Su Y (2010) Artificial neural network-based modeling of pressure drop coefficient for cyclone separators. Chem Eng Res Des 88(5–6):606–613

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Acknowledgements

This research was supported by the National Natural Science Foundation of China, No. 51475245.

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Correspondence to Teng Shen.

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Shen, T., Chang, J., Xie, J. et al. Analysis of microchannel resistance factor based on automated simulation framework and BP neural network. Soft Comput 24, 3379–3391 (2020). https://doi.org/10.1007/s00500-019-04101-4

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Keywords

  • Microchannel
  • Resistance factor
  • Automatic simulation
  • Improved BP network