참고문헌
- Annaratone, D. (2010), Engineering Heat Transfer, Springer Science & Business Media.
- Baehr, H.D. and Stephan, K. (2006), Waerme-und Stoffuebertragung, Volume 5, Springer.
- Buhmann, M.D. (2000), "Radial basis functions", Acta Numerica, 9, 1-38. https://doi.org/10.1017/S0962492900000015.
- Giunta, A. and Watson, L. (1998), "A comparison of approximation modeling techniques-polynomial versus interpolating models", 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 4758.
- Goodfellow, I., Bengio, Y., Courville, A. and Bengio, Y. (2016), Deep Learning, Volume 1, MIT Press Cambridge.
- Hadzalic, E., Ibrahimbegovic, A. and Dolarevic, S. (2020), "3d thermo-hydro-mechanical coupled discrete beam lattice model of saturated poro-plastic medium", Couple. Syst. Mech., 9(2), 125-145. https://doi.org/10.12989/csm.2020.9.2.125.
- Islamoglu, Y. (2003), "A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger-Use of an artificial neural network model", Appl. Therm. Eng., 23(2), 243-249. https://doi.org/10.1016/S1359-4311(02)00155-2.
- Jambunathan, K., Hartle, S., Ashforth-Frost, S. and Fontama, V. (1996), "Evaluating convective heat transfer coefficients using neural networks", Int. J. Heat Mass Transf., 39(11), 2329-2332. https://doi.org/10.1016/0017-9310(95)00332-0.
- Jeong, S., Murayama, M. and Yamamoto, K. (2005), "Efficient optimization design method using kriging model", J. Aircraft, 42(2), 413-420. https://doi.org/10.2514/1.6386.
- Julien, C., Mauger, A., Vijh, A. and Zaghib, K. (2016), "Lithium batteries", Lithium Batteries: Science and Technology, Springer International Publishing, Cham.
- Kleijnen, J.P. (2009), "Kriging metamodeling in simulation: A review", Eur. J. Oper. Res., 192(3), 707-716. https://doi.org/10.1016/j.ejor.2007.10.013
- Kutz, J.N. (2017), "Deep learning in fluid dynamics", J. Fluid Mech., 814, 1-4. https://doi.org/10.1016/j.ejor.2007.10.013.
- Moreno-Navarro, P., Ibrahimbegovich, A. and Perez-Aparicio, J.L. (2018), "Linear elastic mechanical system interacting with coupled thermo-electro-magnetic fields", Couple. Syst. Mech., 7(1), 5-25. https://doi.org/10.12989/csm.2018.7.1.005.
- Noesis Solutions (2019), OPTIMUS REV 2019.2 - USERS MANUAL, Noesis Solutions, Gaston Geenslaan 11, 3001 Leuven, Belgium, 2019.2nd Edition.
- Noesis Solutions, Gaston Geenslaan 11, 3001 Leuven, Belgium (2020), Optimus 2020.1 Theoretical Background, 1st Edition.
- Park, K., Oh, P.K. and Lim, H.J. (2006), "The application of the cfd and kriging method to an optimization of heat sink", Int. J. Heat Mass Transf., 49(19-20), 3439-3447. https://doi.org/10.1016/j.ijheatmasstransfer.2006.03.009.
- Peng, J.Z., Liu, X., Aubry, N., Chen, Z. and Wu, W.T. (2020), "Data-driven modeling of geometry- adaptive steady heat transfer based on convolutional neural networks: Heat conduction", arXiv preprint arXiv:2010.03854.
- Pesaran, A.A. (2001), "Battery thermal management in ev and hevs: issues and solutions", Battery Man, 43(5), 34-49.
- Prasad, V. and Bequette, B.W. (2003), "Nonlinear system identification and model reduction using artificial neural networks", Comput. Chem. Eng., 27(12), 1741-1754. https://doi.org/10.1016/S0098- 1354(03)00137-6.
- Rao, Z. and Wang, S. (2011), "A review of power battery thermal energy management", Renew. Sustain. Energy Rev., 15, 4554-4571. https://doi.org/10.1016/j.rser.2011.07.096.
- Ryu, J.S., Kim, M.S., Cha, K.J., Lee, T.H. and Choi, D.H. (2002), "Kriging interpolation methods in geostatistics and dace model", KSME Int. J., 16(5), 619-632. https://doi.org/10.1007/BF03184811.
- Sancarlos, A., Cameron, M., Abel, A., Cueto, E., Duval, J.L. and Chinesta, F. (2020), "From rom of electro-chemistry to ai-based battery digital and hybrid twin", Arch. Comput. Meth. Eng., 28(3), 979-1015. https://doi.org/10.1007/s11831-020-09404-6.
- Santner, T.J., Williams, B.J., Notz, W.I. and Williams, B.J. (2003), The Design and Analysis of Computer Experiments, Volume 1, Springer.
- Schenck, C. and Fox, D. (2018), "Spnets: Differentiable fluid dynamics for deep neural networks", arXiv preprint arXiv:1806.06094.
- Simpson, T.W., Poplinski, J.D., Koch, P.N. and Allen, J.K. (2001), "Metamodels for computer-based engineering design: survey and recommendations", Eng. Comput., 17(2), 129-150. https://doi.org/10.1007/PL00007198.
- Skala, V. (2017), "Rbf interpolation with csrbf of large data sets", Procedia Comput. Sci., 108, 2433-2437. https://doi.org/10.1016/j.procs.2017.05.081.
- Szardenings, A. "Verfahren und vorrichtung zum ueberwachen eines elektrischen energiespeichers, computerprogramm produkt", German Patent DE 10 2020 203 004 A1, to be published in 2022.
- Szardenings, A., Petersen, N. and Fassbender, H. (2020), "Concept for thermal analysis of batteries using reduced order modeling", AIP Conference Proceedings, 2293, AIP Publishing LLC.
- Thibault, J. and Grandjean, B.P. (1991), "A neural network methodology for heat transfer data analysis", Int. J. Heat Mass Transf., 34(8), 2063-2070. https://doi.org/10.1016/0017-9310(91)90217-3.
- Volpi, S., Diez, M., Gaul, N.J., Song, H., Iemma, U., Choi, K., Campana, E.F. and Stern, F. (2015), "Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification", Struct. Multidisc. Optim., 51, 347-368. https://doi.org/10.1007/s00158-014-1128-5.
- Weigand, B., Koehler, J. and von Wolfersdorf, J. (2013), Thermodynamik Kompakt, Springer Berlin Heidel berg, 3rd Edition.
- White, C., Ushizima, D. and Farhat, C. (2019), "Neural networks predict fluid dynamics solutions from tiny datasets", arXiv preprint arXiv:1902.00091.
- Xie, G., Wang, Q., Zeng, M. and Luo, L. (2007), "Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural networks approach", Appl. Therm. Eng., 27(5-6), 1096-1104. https://doi.org/10.1016/j.applthermaleng.2006.07.036.
- Xie, X., Zhang, G. and Webster, C.G. (2019), "Non-intrusive inference reduced order model for fluids using deep multistep neural network", Math., 7(8), 757. https://doi.org/10.3390/math7080757.