ارائه روشی جدید جهت حل مسائل انتقال گرمای وارون غیرخطی به کمک روش رگرسیون خوشه‌ای

[1]  Norouzifard V. and Hamedi M., A three-dimensional heat conduction inverse procedure to investigate tool–chip thermal interaction in machining process, Int. J. Adv. Manuf. Technol., Vol. 74, No. 9–12, pp. 1637–1648, 2014.

[2]  طهرانی م. ش.، شاهمردان م. م.، کیهانی م. ح. و محمدیون م., تخمین توان منبع گرمایی مورد نیاز جهت گرما درمانی تومور سینه با استفاده از روش معکوس. مجله مهندسی مکانیک دانشگاه تبریز، د. 50، ش. 3، ص 125-134، 1399.

[3]  Raudenský M., Woodbury K. A., Kral J., and Brezina T., GENETIC ALGORITHM IN SOLUTION OF INVERSE HEAT CONDUCTION PROBLEMS, Numer. Heat Transf. , Part B  Fundam.  An Int. J. Comput. Methodol., Vol. 28, No. 3, pp. 293–306, 1995.

[4]  Imani A., Ranjbar A. A., and Esmkhani M., Simultaneous estimation of temperature-dependent thermal conductivity and heat capacity based on modified genetic algorithm, Inverse Probl. Sci. Eng., Vol. 14, No. 7, pp. 767–783, 2006.

[5]  Mazidi Sharfabadi M., Alizadeh M., and Nourpour L., The Modification of the Conventional Genetic Algorithm for Solving Inverse Heat Transfer Problems, Modares Mech. Eng., Vol. 17, No. 11, pp. 408-418 (in Persian), 2018.

[6]  Deng S. and Hwang Y., Applying neural networks to the solution of forward and inverse heat conduction problems, Int. J. Heat Mass Transf., Vol. 49, No. 25–26, pp. 4732–4750, 2006.

[7]  Famouri M., Jannatabadi M., and Ardakani H. T. F., Simultaneous estimations of temperature-dependent thermal conductivity and heat capacity using a time efficient novel strategy based on MEGA-NN, Appl. Soft Comput. J., Vol. 13, No. 1, pp. 201–210, 2013.

[8]  Ghadimi B., Kowsary F., and Khorami M., Heat flux on-line estimation in a locomotive brake disc using artificial neural networks, Int. J. Therm. Sci., Vol. 90, pp. 203–213, 2015.

[9]  Tamaddon-Jahromi H. R., Chakshu N. K., Sazonov I., Evans L. M., Thomas H., and Nithiarasu P., Data-driven inverse modelling through neural network (deep learning) and computational heat transfer, Comput. Methods Appl. Mech. Eng., Vol. 369, Sep. 2020.

[10]         Sen A. K. and Srivastava M. S., Regression Analysis: Theory, Methods, and Applications.Springer, New York, 2012.

[11]         Bai Y., Wang P., Li C., Xie J., and Wang Y., A multi-scale relevance vector regression approach for daily urban water demand forecasting, J. Hydrol., Vol. 517, pp. 236–245, Sep. 2014.

[12]         Venkatesh K., Ravi V., Prinzie A., and Van Den Poel D., Cash demand forecasting in ATMs by clustering and neural networks, Eur. J. Oper. Res., Vol. 232, No. 2, pp. 383–392, 2014.

[13]         Cortez P., Cerdeira A., Almeida F., Matos T., and Reis J., Modeling wine preferences by data mining from physicochemical properties, Decis. Support Syst., Vol. 47, No. 4, pp. 547–553, Nov. 2009.

[14]         Davis E. and Ierapetritou M., A kriging-based approach to MINLP containing black-box models and noise, Ind. Eng. Chem. Res., Vol. 47, No. 16, pp. 6101–6125, Aug. 2008.

[15]         Caballero J. A. and Grossmann I. E., An algorithm for the use of surrogate models in modular flowsheet optimization, AIChE J., Vol. 54, No. 10, pp. 2633–2650, Oct. 2008.

[16]         Beck J., Friedrich D., Brandani S., Guillas S., and Fraga E. S., Surrogate based Optimisation for Design of Pressure Swing Adsorption Systems, Computer Aided Chemical Engineering, Vol. 30, pp. 1217–1221, 2012.

[17]         Viana F. A. C., Simpson T. W., Balabanov V., and Toropov V., Metamodeling in multidisciplinary design optimization: How far have we really come?, AIAA Journal, Vol. 52, No. 4, pp. 670–690, 2014.

[18]         Henao C. A. and Maravelias C. T., Surrogate-based superstructure optimization framework, AIChE J., Vol. 57, No. 5, pp. 1216–1232, May 2011.

[19]         Zhang Y. and Sahinidis N. V., Uncertainty quantification in CO2 sequestration using surrogate models from polynomial chaos expansion, Ind. Eng. Chem. Res., Vol. 52, No. 9, pp. 3121–3132, Mar. 2013.

[20]         Nuchitprasittichai A. and Cremaschi S., An algorithm to determine sample sizes for optimization with artificial neural networks, AIChE J., Vol. 59, No. 3, pp. 805–812, Mar. 2013.

[21]         Chen Q. L., Wu K. J., and He C. H., Thermal conductivity of ionic liquids at atmospheric pressure: Database, analysis, and prediction using a topological index method, Ind. Eng. Chem. Res., Vol. 53, No. 17, pp. 7224–7232, Apr. 2014.

[22]         Wu K. J., Chen Q. L., and He C. H., Speed of sound of ionic liquids: Database, estimation, and its application for thermal conductivity prediction, AIChE J., Vol. 60, No. 3, pp. 1120–1131, Mar. 2014.

[23]         Carrizosa E., Molero-Río C., and Romero Morales D., Mathematical optimization in classification and regression trees, TOP, Vol. 29, No. 1, pp. 5–33, Apr. 2021.

[24]         Prado E. B., Moral R. A., and Parnell A. C., Bayesian additive regression trees with model trees, Stat. Comput., Vol. 31, No. 3, Apr. 2021.

[25]         Osman A. M. and Beck J. V., Investigation of transient heat transfer coefficients in quenching experiments, J. Heat Transfer, Vol. 112, No. 4, pp. 843–848, 1990.

[26]         Incropera A. S., DeWitt F. P., Bergman D. P., and Lavine T. L., Fundamentals of heat and mass transfer, 6th ed. Wiley, New York, 1996.

[27]         Buczek A. and Telejko T.,  Investigation of heat transfer coefficient during quenching in various cooling agents, Int. J. Heat Fluid Flow, Vol. 44, pp. 358–364, 2013.