517
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Neural network-based regression for heat transfer and fluid flow over in-line cylinder arrays with random pitch distances at low Reynolds number

, & ORCID Icon
Article: 2288235 | Received 07 Sep 2023, Accepted 19 Nov 2023, Published online: 30 Nov 2023

References

  • Acharya, M. S., Armaan, A., & Antony, A. S. (2019). A comparison of regression models for prediction of graduate admissions. In 2019 International conference on computational intelligence in data science (pp. 1–5). IEEE.
  • Adams, J. C., Swarztrauber, P. N., & Sweet, R.. (2016). FISHPACK90: Efficient fortran subprograms for the solution of separable elliptic partial differential equations. Astrophysics Source Code Library (ascl: 1609.004) [Source code]. https://github.com/NCAR/NCAR-Classic-Libraries-for-Geophysics/tree/main/FishPack_90
  • Araujo, A. D., Andrade, J. S., Jr., & Herrmann, H. J. (2006). Critical role of gravity in filters. Physical Review Letters, 97(13), 138001. https://doi.org/10.1103/PhysRevLett.97.138001
  • Baranyi, L., & Lewis, R. I. (2006). Comparison of a grid-based CFD method and vortex dynamics predictions of low Reynolds number cylinder flows. The Aeronautical Journal, 110(1103), 63–71. https://doi.org/10.1017/S0001924000004371
  • Barkley, D., & Henderson, R. D. (1996). Three-dimensional Floquet stability analysis of the wake of a circular cylinder. Journal of Fluid Mechanics, 322, 215–241. https://doi.org/10.1017/S0022112096002777
  • Bhuiyan, A. A., & Islam, A. S. (2016). Thermal and hydraulic performance of finned-tube heat exchangers under different flow ranges: A review on modeling and experiment. International Journal of Heat and Mass Transfer, 101, 38–59. https://doi.org/10.1016/j.ijheatmasstransfer.2016.05.022
  • Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020). Machine learning for fluid mechanics. Annual Review of Fluid Mechanics, 52(1), 477–508. https://doi.org/10.1146/annurev-fluid-010719-060214
  • Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249–259. https://doi.org/10.1016/j.neunet.2018.07.011
  • Che, M., & Elbel, S. (2021). Experimental quantification of air-side row-by-row heat transfer coefficients on fin-and-tube heat exchangers. International Journal of Refrigeration, 131, 657–665. https://doi.org/10.1016/j.ijrefrig.2021.06.012
  • Chen, X., & Papathanasiou, T. D. (2008). The transverse permeability of disordered fiber arrays: A statistical correlation in terms of the mean nearest interfiber spacing. Transport in Porous Media, 71(2), 233–251. https://doi.org/10.1007/s11242-007-9123-6
  • Chew, A. W. Z., & Law, A. W. K. (2019). Feature engineering using homogenization theory with multiscale perturbation analysis for supervised model-based learning of physical clogging condition in seepage filters. Journal of Computational Science, 32, 21–35. https://doi.org/10.1016/j.jocs.2019.02.003
  • Chollet, F.. (2018). Keras: The python deep learning library. Astrophysics Source Code Library (ascl: 1806.022) [Source code]. https://keras.io/
  • Chorin, A. J. (1968). Numerical solution of the Navier-stokes equations. Mathematics of Computation, 22(104), 745–762. https://doi.org/10.1090/S0025-5718-1968-0242392-2
  • Cindrella, L., Kannan, A. M., Lin, J., Saminathan, K., Ho, Y., Lin, C., & Wertz, J. (2009). Gas diffusion layer for proton exchange membrane fuel cells—A review. Journal of Power Sources, 194(1), 146–160. https://doi.org/10.1016/j.jpowsour.2009.04.005
  • Colburn, A. P. (1993). A method for correlating forced convection heat transfer data and a comparison with fluid friction. Transfer AIChE, 29, 174–210.
  • da Silva, B. L., Luciano, R. D., Utzig, J., & Meier, H. F. (2018). Flow patterns and turbulence effects in large cylinder arrays. International Journal of Heat and Fluid Flow, 69, 136–149. https://doi.org/10.1016/j.ijheatfluidflow.2017.12.013
  • Dhar, B., Mahapatra, S., Maharana, S., Sarkar, A., & Sahoo, S. (2016). Numerical study on phase change of water flowing across two heated rotating circular cylinders in tandem arrangement. The Journal of Computational Multiphase Flows, 8(4), 201–212. https://doi.org/10.1177/1757482X16674218
  • Dhar, B., Mahapatra, S., Maharana, S., Sarkar, A., & Sahoo, S. (2017). Numerical study on phase change of water flowing across two heated circular cylinders in tandem arrangement. Heat Transfer—Asian Research, 46(7), 656–680. https://doi.org/10.1002/htj.21236
  • Duraisamy, K., Iaccarino, G., & Xiao, H. (2019). Turbulence modeling in the age of data. Annual Review of Fluid Mechanics, 51(1), 357–377. https://doi.org/10.1146/annurev-fluid-010518-040547
  • Estabrooks, A., Jo, T., & Japkowicz, N. (2004). A multiple resampling method for learning from imbalanced data sets. Computational Intelligence, 20(1), 18–36. https://doi.org/10.1111/j.0824-7935.2004.t01-1-00228.x
  • Faghri, A., & Zhang, Y. (2006). Transport phenomena in multiphase systems. Elsevier.
  • Ferziger, J. H., & Peric, M. (2002). Computational methods for fluid dynamics. Springer.
  • Ge, Y., Lin, Y., Tao, S., He, Q., Chen, B., & Huang, S.-M. (2021). Shape optimization for a tube bank based on the numerical simulation and multi-objective genetic algorithm. International Journal of Thermal Sciences, 161, 106787. https://doi.org/10.1016/j.ijthermalsci.2020.106787
  • Gorman, J. M., Sparrow, E. M., & Ahn, J. (2019). In-line tube-bank heat exchangers: Arrays with various numbers of thermally participating tubes. International Journal of Heat and Mass Transfer, 132, 837–847. https://doi.org/10.1016/j.ijheatmasstransfer.2018.11.167
  • Grimison, E. (1937). Correlation and utilization of new data on flow resistance and heat transfer for cross flow of gases over tube banks. Transactions of the American Society of Mechanical Engineers, 59(7), 583–594.
  • Gunzburger, M. D. (2012). Finite element methods for viscous incompressible flows: A guide to theory, practice, and algorithms. Elsevier.
  • Halkarni, S. S., Sridharan, A., & Prabhu, S. V. (2017). Measurement of local wall heat transfer coefficient in randomly packed beds of uniform sized spheres using infrared thermography (IR) and water as working medium. Applied Thermal Engineering, 126, 358–378. https://doi.org/10.1016/j.applthermaleng.2017.07.174
  • Han, R., Wang, Y., Qian, W., Wang, W., Zhang, M., & Chen, G. (2022). Deep neural network based reduced-order model for fluid–structure interaction system. Physics of Fluids, 34(7), 073610. https://doi.org/10.1063/5.0096432
  • Harlow, F. H., & Welch, J. E. (1965). Numerical calculation of time-dependent viscous incompressible flow of fluid with free surface. The Physics of Fluids, 8(12), 2182–2189. https://doi.org/10.1063/1.1761178
  • Hasan, M. I. (2014). Investigation of flow and heat transfer characteristics in micro pin fin heat sink with nanofluid. Applied Thermal Engineering, 63(2), 598–607. https://doi.org/10.1016/j.applthermaleng.2013.11.059
  • He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284. https://doi.org/10.1109/TKDE.2008.239
  • He, K., & Zhang, L.-Z. (2020). Cross flow and heat transfer of hollow-fiber tube banks with complex distribution patterns and various baffle designs. International Journal of Heat and Mass Transfer, 147, 118937. https://doi.org/10.1016/j.ijheatmasstransfer.2019.118937
  • Henderson, R. D. (1997). Nonlinear dynamics and pattern formation in turbulent wake transition. Journal of Fluid Mechanics, 352, 65–112. https://doi.org/10.1017/S0022112097007465
  • Herwig, H., & Mahulikar, S. P. (2006). Variable property effects in single-phase incompressible flows through microchannels. International Journal of Thermal Sciences, 45(10), 977–981. https://doi.org/10.1016/j.ijthermalsci.2006.01.002
  • Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366. https://doi.org/10.1016/0893-6080(89)90020-8
  • IEEE Standards Board. (1990). IEEE standard glossary of software engineering terminology (610.12-1990). IEEE.
  • Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (1996). Fundamentals of heat and mass transfer. Wiley.
  • Jiang, R., Yang, M., Chen, S., Huang, S.-M., & Yang, X. (2014). Fluid flow and heat transfer across an elliptical hollow fiber membrane tube bank with randomly distributed features. International Journal of Heat and Mass Transfer, 76, 559–567. https://doi.org/10.1016/j.ijheatmasstransfer.2014.05.004
  • Jin, X., Cai, S., Li, H., & Karniadakis, G. E. (2021). NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations. Journal of Computational Physics, 426, 109951. https://doi.org/10.1016/j.jcp.2020.109951
  • Jin, X., Cheng, P., Chen, W.-L., & Li, H. (2018). Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder. Physics of Fluids, 30(4), 047105. https://doi.org/10.1063/1.5024595
  • Khan, W. A., Culham, J. R., & Yovanovich, M. M. (2006). Convection heat transfer from tube banks in crossflow: Analytical approach. International Journal of Heat and Mass Transfer, 49(25–26), 4831–4838. https://doi.org/10.1016/j.ijheatmasstransfer.2006.05.042
  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
  • Kutz, J. N., Brunton, S. L., Brunton, B. W., & Proctor, J. L. (2016). Dynamic mode decomposition: Data-driven modeling of complex systems. SIAM.
  • Lee, D., Ahn, J., & Shin, S. (2013). Uneven longitudinal pitch effect on tube bank heat transfer in cross flow. Applied Thermal Engineering, 51(1–2), 937–947. https://doi.org/10.1016/j.applthermaleng.2012.10.031
  • Lee, S., & You, D. (2019). Data-driven prediction of unsteady flow over a circular cylinder using deep learning. Journal of Fluid Mechanics, 879, 217–254. https://doi.org/10.1017/jfm.2019.700
  • Li, W., Shen, S., & Li, H. (2016). Study and optimization of the filtration performance of multi–fiber filter. Advanced Powder Technology, 27(2), 638–645. https://doi.org/10.1016/j.apt.2016.02.018
  • Ling, J., Kurzawski, A., & Templeton, J. (2016). Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. Journal of Fluid Mechanics, 807, 155–166. https://doi.org/10.1017/jfm.2016.615
  • Maulud, D., & Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 1(4), 140–147. https://doi.org/10.38094/jastt1457
  • Meneghini, J. R., Saltara, F., Siqueira, C. L. R., & Ferrari, J., Jr. (2001). Numerical simulation of flow interference between two circular cylinders in tandem and side-by-side arrangements. Journal of Fluids and Structures, 15(2), 327–350. https://doi.org/10.1006/jfls.2000.0343
  • Munson, B. R., Okiishi, T. H., Huebsch, W. W., & Rothmayer, A. P. (2013). Fluid mechanics. Wiley Singapore.
  • Nayak, R. K., Ray, S., Sahoo, S. S., & Satapathy, P. K. (2019). Effect of angle of attack and wind direction on limiting input heat flux for solar assisted thermoelectric power generator with plate fin heat sink. Solar Energy, 186, 175–190. https://doi.org/10.1016/j.solener.2019.05.010
  • Nemec, D., & Levec, J. (2005). Flow through packed bed reactors: 1. Single-phase flow. Chemical Engineering Science, 60(24), 6947–6957. https://doi.org/10.1016/j.ces.2005.05.068
  • Noorman, S., van Sint Annaland, M., & Kuipers, H. (2007). Packed bed reactor technology for chemical-looping combustion. Industrial & Engineering Chemistry Research, 46(12), 4212–4220. https://doi.org/10.1021/ie061178i
  • Printsypar, G., Bruna, M., & Griffiths, I. M. (2018). The influence of porous-medium microstructure on filtration. Journal of Fluid Mechanics, 861, 484–516. https://doi.org/10.1017/jfm.2018.875
  • Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2017). Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations. arXiv preprint arXiv:1711.10561.
  • Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045
  • Raissi, M., Yazdani, A., & Karniadakis, G. E. (2020). Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science, 367(6481), 1026–1030. https://doi.org/10.1126/science.aaw4741
  • Rastan, M. R., Sohankar, A., & Alam, M. M. (2021). Flow and heat transfer across two inline rotating cylinders: Effects of blockage, gap spacing, Reynolds number, and rotation direction. International Journal of Heat and Mass Transfer, 174, 121324. https://doi.org/10.1016/j.ijheatmasstransfer.2021.121324
  • Schmid, P. J. (2010). Dynamic mode decomposition of numerical and experimental data. Journal of Fluid Mechanics, 656, 5–28. https://doi.org/10.1017/S0022112010001217
  • Sharman, B., Lien, F. S., Davidson, L., & Norberg, C. (2005). Numerical predictions of low Reynolds number flows over two tandem circular cylinders. International Journal for Numerical Methods in Fluids, 47(5), 423–447. https://doi.org/10.1002/fld.812
  • Shu, C.-W., & Osher, S. (1988). Efficient implementation of essentially non-oscillatory shock-capturing schemes. Journal of Computational Physics, 77(2), 439–471. https://doi.org/10.1016/0021-9991(88)90177-5
  • Sumner, D. (2010). Two circular cylinders in cross-flow: A review. Journal of Fluids and Structures, 26(6), 849–899. https://doi.org/10.1016/j.jfluidstructs.2010.07.001
  • Tamayol, A., & Bahrami, M. (2011). Water permeation through gas diffusion layers of proton exchange membrane fuel cells. Journal of Power Sources, 196(15), 6356–6361. https://doi.org/10.1016/j.jpowsour.2011.02.069
  • Tseng, Y.-H., & Ferziger, J. H. (2003). A ghost-cell immersed boundary method for flow in complex geometry. Journal of Computational Physics, 192(2), 593–623. https://doi.org/10.1016/j.jcp.2003.07.024
  • Wang, J.-X., Wu, J.-L., & Xiao, H. (2017). Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data. Physical Review Fluids, 2(3), 034603. https://doi.org/10.1103/PhysRevFluids.2.034603
  • Wang, X., & Sun, X. (2016). An improved weighted naive bayesian classification algorithm based on multivariable linear regression model. In 2016 9th International symposium on computational intelligence and design (pp. 219–222). IEEE.
  • Williams, M. V., Begg, E., Bonville, L., Kunz, H. R., & Fenton, J. M. (2004). Characterization of gas diffusion layers for PEMFC. Journal of the Electrochemical Society, 151(8), A1173. https://doi.org/10.1149/1.1764779
  • Wu, P., Sun, J., Chang, X., Zhang, W., Arcucci, R., Guo, Y., & Pain, C. C. (2020). Data-driven reduced order model with temporal convolutional neural network. Computer Methods in Applied Mechanics and Engineering, 360, 112766. https://doi.org/10.1016/j.cma.2019.112766
  • Xu, H., & Mannor, S. (2012). Robustness and generalization. Machine Learning, 86(3), 391–423. https://doi.org/10.1007/s10994-011-5268-1
  • Yu, C., Zhu, X., Li, Z., Ma, Y., Yang, M., & Zhang, H. (2023). Optimization of elliptical pin-fin microchannel heat sink based on artificial neural network. International Journal of Heat and Mass Transfer, 205, 123928. https://doi.org/10.1016/j.ijheatmasstransfer.2023.123928
  • Zargartalebi, M., & Azaiez, J. (2019). Flow dynamics and heat transfer in partially porous microchannel heat sinks. Journal of Fluid Mechanics, 875, 1035–1057. https://doi.org/10.1017/jfm.2019.491
  • Zargartalebi, M., Benneker, A. M., & Azaiez, J. (2020). The impact of heterogeneous pin based micro-structures on flow dynamics and heat transfer in micro-scale heat exchangers. Physics of Fluids, 32(5), 052007. https://doi.org/10.1063/5.0006577
  • Zhang, X., Ji, T., Xie, F., Zheng, C., & Zheng, Y. (2022). Data-driven nonlinear reduced-order modeling of unsteady fluid–structure interactions. Physics of Fluids, 34(5), 053608. https://doi.org/10.1063/5.0090394
  • Zhang, Z., Li, Y., Li, L., Li, Z., & Liu, S. (2019). Multiple linear regression for high efficiency video intra coding. In ICASSP 2019–2019 IEEE international conference on acoustics, speech and signal processing (pp. 1832–1836). IEEE.
  • Zhou, Z., He, G., Wang, S., & Jin, G. (2019). Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network. Computers & Fluids, 195, 104319. https://doi.org/10.1016/j.compfluid.2019.104319
  • Zukauskas, A., & Ulinskas, R. (1988). Heat transfer in tube banks in crossflow. Springer.