Three-dimensional temperature field inversion calculation based on an artificial intelligence algorithm
•The combination of finite element algorithms and machine learning algorithms.•A temperature field reconstruction model based on discrete boundary conditions.•A finite element program with modules applicable to the large datasets generating.•The optimal settings of hyperparameters under the general...
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| Veröffentlicht in: | Applied thermal engineering Jg. 225; S. 120237 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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05.05.2023
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| ISSN: | 1359-4311 |
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| Abstract | •The combination of finite element algorithms and machine learning algorithms.•A temperature field reconstruction model based on discrete boundary conditions.•A finite element program with modules applicable to the large datasets generating.•The optimal settings of hyperparameters under the general model is summarised.•Data-driven temperature field calculations decoupled from the prior knowledges.
An increasing number of practical problems in heat transfer area are attributed to inverse heat transfer problems (IHTPs). One of the typical applications of the inverse problem is the prediction of the temperature field of an object from discrete temperature measurements of the surface. The study combines machine learning algorithms with numerical heat transfer methods to inversely predict the heat transfer boundary conditions of a hexahedral geometry from a finite number of discrete temperature measurements on its surface and then calculate the overall temperature field distribution. For the implementation of the numerical heat transfer forward problem, we first complete the coding of the finite element program to generate a training dataset for the inverse calculation by batch inputting the predefined boundary conditions. The inverse problem is modelled by constructing a neural network (NN) approach. The model is trained by calling data from the above dataset. The discrete temperature data are brought into the trained neural network for temperature field prediction. The results are tested for accuracy and generalisability. Finally, by comparing different hyperparameters and different training methods, a method of improving the efficiency and accuracy of the reconstruction results is proposed. The inversion calculation error is finally controlled to less than 0.1. In addition, the model implements the validation of commercial software simulation data and application to aero-engine turbine blades and vapour chambers. The approach can be extended as a generalised 3D temperature field reconstruction method. |
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| AbstractList | •The combination of finite element algorithms and machine learning algorithms.•A temperature field reconstruction model based on discrete boundary conditions.•A finite element program with modules applicable to the large datasets generating.•The optimal settings of hyperparameters under the general model is summarised.•Data-driven temperature field calculations decoupled from the prior knowledges.
An increasing number of practical problems in heat transfer area are attributed to inverse heat transfer problems (IHTPs). One of the typical applications of the inverse problem is the prediction of the temperature field of an object from discrete temperature measurements of the surface. The study combines machine learning algorithms with numerical heat transfer methods to inversely predict the heat transfer boundary conditions of a hexahedral geometry from a finite number of discrete temperature measurements on its surface and then calculate the overall temperature field distribution. For the implementation of the numerical heat transfer forward problem, we first complete the coding of the finite element program to generate a training dataset for the inverse calculation by batch inputting the predefined boundary conditions. The inverse problem is modelled by constructing a neural network (NN) approach. The model is trained by calling data from the above dataset. The discrete temperature data are brought into the trained neural network for temperature field prediction. The results are tested for accuracy and generalisability. Finally, by comparing different hyperparameters and different training methods, a method of improving the efficiency and accuracy of the reconstruction results is proposed. The inversion calculation error is finally controlled to less than 0.1. In addition, the model implements the validation of commercial software simulation data and application to aero-engine turbine blades and vapour chambers. The approach can be extended as a generalised 3D temperature field reconstruction method. |
| ArticleNumber | 120237 |
| Author | Lu, Depu Wang, Chengen |
| Author_xml | – sequence: 1 givenname: Depu surname: Lu fullname: Lu, Depu email: ludepu@sjtu.edu.cn – sequence: 2 givenname: Chengen surname: Wang fullname: Wang, Chengen email: c.wang@sjtu.edu.cn |
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| Cites_doi | 10.1016/j.jppr.2013.04.004 10.1016/j.ijheatmasstransfer.2015.08.010 10.1017/jfm.2019.545 10.1016/j.icheatmasstransfer.2017.05.009 10.1016/j.ijheatmasstransfer.2018.02.039 10.1016/j.applthermaleng.2005.12.009 10.1016/j.jqsrt.2006.09.001 10.1016/j.ijheatmasstransfer.2019.01.002 10.1016/j.applthermaleng.2016.05.123 10.1016/j.applthermaleng.2013.02.040 10.1108/HFF-11-2020-0684 10.1016/j.enconman.2014.06.096 10.1016/j.jpowsour.2004.11.019 10.1016/j.icheatmasstransfer.2022.106270 10.2514/6.2020-1530 10.1016/j.applthermaleng.2013.10.066 10.1016/j.applthermaleng.2021.117392 10.1016/j.ijthermalsci.2009.06.005 10.1016/j.applthermaleng.2021.117174 10.1016/j.icheatmasstransfer.2015.06.012 10.1080/10407788208913448 10.1016/j.applthermaleng.2022.118762 10.1080/10407782.2012.644166 10.1201/9781003155157 10.1016/j.applthermaleng.2021.117819 10.1016/j.icheatmasstransfer.2006.08.013 10.1007/978-94-015-8480-7 10.1016/j.applthermaleng.2022.119406 10.1016/j.ijheatmasstransfer.2011.01.032 10.1016/j.ijthermalsci.2021.107149 |
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| References | Wang, Lin, Yang (b0040) 2015; 67 Tikhonov, Andrei Nikolaevich, et al, Numerical methods for the solution of ill-posed problems, Springer Science & Business Media, 3(1995). W. Tao, Numer. Heat Transf (Second Edition), Xi'an Jiaotong University Press, 2001. Chang, Cheng (b0055) 2005; 142 Zalesak, Charvat, Klimes (b0135) 2021; 197 Cortés (b0155) 2007 D. Kingma, J.B. Adam, A Method for Stochastic Optimization, Computer Science, 2014. doi: 10.48550/arXiv.1412.6980. Tian, Qi (b0070) 2022; 137 Zhou, Zhang, Chen (b0060) 2012; 61 Sun, Ji (b0080) 2022 A.V.S. Oliveira, J. Teixeira, et al., Using a linear inverse heat conduction model to estimate the boundary heat flux with a material undergoing phase transformation, Appl. Therm. Eng., 219 (2023), 119406. 10.1016/j.applthermaleng.2022.119406. Huang, Than, Ngo (b0035) 2016; 105 Shi, Chen (b0140) 2015 Huntul, Lesnic (b0010) 2017; 85 Wang, Zhu, Chen (b0110) 2011; 54 Cao, Luo, Tang (b0130) 2022; 241 K.R. Holst, R.S. Glasby, J.T. Erwin, et al, Current status of the COFFE solver within HPCMP CREATETM-AV kestrel, AIAA Scitech 2020 Forum, American Institute of Aeronautics and Astronautics, 2020. 10.2514/6.2020-153. Das (b0015) 2014; 87 Cebula, Taler (b0025) 2014; 63 Kowsary, Mohammadzaheri, Irano (b0120) 2006; 33 Chen, Yang (b0100) 2010; 49 Gostimirovic, Sekulic (b0020) 2021; 195 Rukolaine (b0045) 2007; 104 S. Liu, Active cooling mechanism and cooling capacity evaluation of thermal protection systems for hypersonic vehicle, Harbin Institute of Technology, Heilongjiang. https://kns.cnki.net/kcms2/article/abstract?v=C1uazonQNNhMXDnNywSHHBOx9cEiw2OSVhvxoYC4tkvzfIF7W4kfjG-OCxy6ReuoIfoy6-tQrceUTiCWuE2r0_PC8vQYgJHy13NXwoKRccP46BQJ39YpSg==&uniplatform=NZKPT&language=CHS. R. Lohner, et al. Deep learning or interpolation for inverse modelling of heat and fluid flow problems, Int J Numer Methods Heat Fluid Flow. (in press). doi:10.1108/HFF-11-2020-0684. Huang, Lo (b0105) 2006; 26 . N.S. Keskar, R. Socher, Improving Generalization Performance by Switching from Adam to SGD, 2017. doi:10.48550/arXiv.1712.07628. Zeng, Wang, Zhang, Cai, Li (b0125) 2019; 134 Beck, Litkouhi, Clair (b0095) 1982; 5 Gossard, Lartigue (b0115) 2013; 54 G. Guennebaud, B. Jacob, Eigen v3. Yu, Xu, Yao (b0030) 2018; 122 https://keras.io, Retrieved August 13, 2021. Reyhani, Alizadeh (b0085) 2013; 2 Wen, Zhu, Lu (b0160) 2021; 170 M.Ö. Necati, R. Helcio et al., Inverse heat transfer: fundamentals and applications, CRC Press, 2021. doi:10.1201/9781003155157. Tian, Qi (b0065) 2022; 201 Najafi, Woodbury (b0150) 2015; 91 Huang, Liu, Cai (b0075) 2019; 875 Beck (10.1016/j.applthermaleng.2023.120237_b0095) 1982; 5 10.1016/j.applthermaleng.2023.120237_b0005 Zalesak (10.1016/j.applthermaleng.2023.120237_b0135) 2021; 197 Huang (10.1016/j.applthermaleng.2023.120237_b0105) 2006; 26 Zhou (10.1016/j.applthermaleng.2023.120237_b0060) 2012; 61 Cebula (10.1016/j.applthermaleng.2023.120237_b0025) 2014; 63 Tian (10.1016/j.applthermaleng.2023.120237_b0065) 2022; 201 Sun (10.1016/j.applthermaleng.2023.120237_b0080) 2022 Zeng (10.1016/j.applthermaleng.2023.120237_b0125) 2019; 134 10.1016/j.applthermaleng.2023.120237_b0190 Tian (10.1016/j.applthermaleng.2023.120237_b0070) 2022; 137 10.1016/j.applthermaleng.2023.120237_b0090 Huang (10.1016/j.applthermaleng.2023.120237_b0075) 2019; 875 Najafi (10.1016/j.applthermaleng.2023.120237_b0150) 2015; 91 10.1016/j.applthermaleng.2023.120237_b0170 Chen (10.1016/j.applthermaleng.2023.120237_b0100) 2010; 49 Wang (10.1016/j.applthermaleng.2023.120237_b0110) 2011; 54 10.1016/j.applthermaleng.2023.120237_b0050 10.1016/j.applthermaleng.2023.120237_b0195 Kowsary (10.1016/j.applthermaleng.2023.120237_b0120) 2006; 33 10.1016/j.applthermaleng.2023.120237_b0175 Wen (10.1016/j.applthermaleng.2023.120237_b0160) 2021; 170 Huang (10.1016/j.applthermaleng.2023.120237_b0035) 2016; 105 Rukolaine (10.1016/j.applthermaleng.2023.120237_b0045) 2007; 104 Wang (10.1016/j.applthermaleng.2023.120237_b0040) 2015; 67 Das (10.1016/j.applthermaleng.2023.120237_b0015) 2014; 87 Reyhani (10.1016/j.applthermaleng.2023.120237_b0085) 2013; 2 Chang (10.1016/j.applthermaleng.2023.120237_b0055) 2005; 142 Huntul (10.1016/j.applthermaleng.2023.120237_b0010) 2017; 85 Gostimirovic (10.1016/j.applthermaleng.2023.120237_b0020) 2021; 195 Gossard (10.1016/j.applthermaleng.2023.120237_b0115) 2013; 54 10.1016/j.applthermaleng.2023.120237_b0180 Shi (10.1016/j.applthermaleng.2023.120237_b0140) 2015 10.1016/j.applthermaleng.2023.120237_b0185 Cao (10.1016/j.applthermaleng.2023.120237_b0130) 2022; 241 10.1016/j.applthermaleng.2023.120237_b0165 Yu (10.1016/j.applthermaleng.2023.120237_b0030) 2018; 122 10.1016/j.applthermaleng.2023.120237_b0145 Cortés (10.1016/j.applthermaleng.2023.120237_b0155) 2007 |
| References_xml | – reference: K.R. Holst, R.S. Glasby, J.T. Erwin, et al, Current status of the COFFE solver within HPCMP CREATETM-AV kestrel, AIAA Scitech 2020 Forum, American Institute of Aeronautics and Astronautics, 2020. 10.2514/6.2020-153. – volume: 91 start-page: 808 year: 2015 end-page: 817 ident: b0150 article-title: Online heat flux estimation using artificial neural network as a digital filter approach publication-title: Int. J. Heat Mass Transf. – reference: G. Guennebaud, B. Jacob, Eigen v3. – volume: 197 start-page: 117392 year: 2021 ident: b0135 article-title: Identification of the effective heat capacity–temperature relationship and the phase change hysteresis in PCMs by means of an inverse heat transfer problem solved with metaheuristic methods publication-title: Appl. Therm. Eng. – volume: 142 start-page: 200 year: 2005 end-page: 210 ident: b0055 article-title: Non-destructive inverse method for determination of irregular internal temperature distribution in PEMFCs publication-title: J. Power Sources – reference: W. Tao, Numer. Heat Transf (Second Edition), Xi'an Jiaotong University Press, 2001. – volume: 5 start-page: 275 year: 1982 end-page: 286 ident: b0095 article-title: Efficient sequential solution of the nonlinear inverse heat conduction problem publication-title: Numer. Heat Transf. – reference: R. Lohner, et al. Deep learning or interpolation for inverse modelling of heat and fluid flow problems, Int J Numer Methods Heat Fluid Flow. (in press). doi:10.1108/HFF-11-2020-0684. – year: 2007 ident: b0155 publication-title: Artificial neural networks for inverse heat transfer problems, Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007) – volume: 201 start-page: 117819 year: 2022 ident: b0065 article-title: A novel parametric level set method coupled with Tikhonov regularization for tomographic laser absorption reconstruction publication-title: Appl. Therm. Eng. – volume: 26 start-page: 1515 year: 2006 end-page: 1529 ident: b0105 article-title: A three-dimensional inverse problem in estimating the internal heat flux of housing for high speed motors publication-title: Appl. Therm. Eng. – volume: 2 start-page: 148 year: 2013 end-page: 161 ident: b0085 article-title: Turbine blade temperature calculation and life estimation – a sensitivity analysis publication-title: Propul. Power Res. – year: 2015 ident: b0140 article-title: Convolutional LSTM network: A machine learning approach for precipitation nowcasting publication-title: Adv. Neural Inf. Proces. Syst. – reference: M.Ö. Necati, R. Helcio et al., Inverse heat transfer: fundamentals and applications, CRC Press, 2021. doi:10.1201/9781003155157. – volume: 105 start-page: 65 year: 2016 end-page: 76 ident: b0035 article-title: An inverse method for estimating heat sources in a high speed spindle publication-title: Appl. Therm. Eng – volume: 241 start-page: 118762 year: 2022 ident: b0130 article-title: A Bayesian model to solve a two-dimensional inverse heat transfer problem of gas turbine discs publication-title: Appl. Therm. Eng. – volume: 54 start-page: 2782 year: 2011 end-page: 2788 ident: b0110 article-title: A decentralized fuzzy inference method for solving the two-dimensional steady inverse heat conduction problem of estimating boundary condition publication-title: Int. J. Heat Mass Transf. – volume: 54 start-page: 549 year: 2013 end-page: 558 ident: b0115 article-title: Three-dimensional conjugate heat transfer in partitioned enclosures: Determination of geometrical and thermal properties by an inverse method publication-title: Appl. Therm. Eng. – volume: 195 start-page: 117174 year: 2021 ident: b0020 article-title: Stability analysis of the inverse heat transfer problem in the optimization of the machining process publication-title: Appl. Therm. Eng. – volume: 63 start-page: 158 year: 2014 end-page: 169 ident: b0025 article-title: Determination of transient temperature and heat flux on the surface of a reactor control rod based on temperature measurements at the interior points publication-title: Appl. Therm. Eng – reference: A.V.S. Oliveira, J. Teixeira, et al., Using a linear inverse heat conduction model to estimate the boundary heat flux with a material undergoing phase transformation, Appl. Therm. Eng., 219 (2023), 119406. 10.1016/j.applthermaleng.2022.119406. – volume: 61 start-page: 85 year: 2012 end-page: 100 ident: b0060 article-title: Inverse Estimation of Surface Temperature Induced by a Moving Heat Source in a 3-D Object Based on Back Surface Temperature with Random Measurement Errors publication-title: Numer. Heat Transf. Part A Appl. – reference: https://keras.io, Retrieved August 13, 2021. – reference: S. Liu, Active cooling mechanism and cooling capacity evaluation of thermal protection systems for hypersonic vehicle, Harbin Institute of Technology, Heilongjiang. https://kns.cnki.net/kcms2/article/abstract?v=C1uazonQNNhMXDnNywSHHBOx9cEiw2OSVhvxoYC4tkvzfIF7W4kfjG-OCxy6ReuoIfoy6-tQrceUTiCWuE2r0_PC8vQYgJHy13NXwoKRccP46BQJ39YpSg==&uniplatform=NZKPT&language=CHS. – reference: D. Kingma, J.B. Adam, A Method for Stochastic Optimization, Computer Science, 2014. doi: 10.48550/arXiv.1412.6980. – volume: 67 start-page: 1 year: 2015 end-page: 7 ident: b0040 article-title: Geometry estimation for the inner surface in a furnace wall made of functionally graded materials publication-title: Int. Commun. Heat Mass – start-page: 1 year: 2022 end-page: 14 ident: b0080 article-title: Reconstruction of surface laser power and internal temperature of biological tissue during laser-induced thermal therapy publication-title: Numer. Heat Transf., Part A: Appl. – reference: . – volume: 170 start-page: 107149 year: 2021 ident: b0160 article-title: Experimental and artificial neural network based study on the heat transfer and flow performance of ZnO-EG/water nanofluid in a mini-channel with serrated fins publication-title: Int. J. Therm. Sci. – volume: 875 start-page: R2 year: 2019 ident: b0075 article-title: Online in Situ Prediction of 3-D Flame Evolution from Its History 2-D Projections via Deep Learning publication-title: J. Fluid Mech. – volume: 134 start-page: 185 year: 2019 end-page: 197 ident: b0125 article-title: A novel adaptive approximate Bayesian computation method for inverse heat conduction problem publication-title: Int. J. Heat Mass Transf. – reference: N.S. Keskar, R. Socher, Improving Generalization Performance by Switching from Adam to SGD, 2017. doi:10.48550/arXiv.1712.07628. – reference: Tikhonov, Andrei Nikolaevich, et al, Numerical methods for the solution of ill-posed problems, Springer Science & Business Media, 3(1995). – volume: 33 start-page: 1291 year: 2006 end-page: 1296 ident: b0120 article-title: Training based, moving digital filter method for real time heat flux function estimation publication-title: Int. Commun. Heat Mass Transf. – volume: 104 start-page: 171 year: 2007 end-page: 195 ident: b0045 article-title: Regularization of inverse boundary design radiative heat transfer problems publication-title: J. Quant. Spectrosc. Radiat. Transf. – volume: 87 start-page: 496 year: 2014 end-page: 1106 ident: b0015 article-title: Forward and inverse solutions of a conductive, convective and radiative cylindrical porous fin publication-title: Energ. Conver. Manage. – volume: 85 start-page: 147 year: 2017 end-page: 154 ident: b0010 article-title: An inverse problem of finding the time-dependent thermal conductivity from boundary data publication-title: Int. Commun. Heat Mass – volume: 49 start-page: 86 year: 2010 end-page: 98 ident: b0100 article-title: Inverse estimation for unknown fouling-layer profiles with arbitrary geometries on the inner wall of a forced-convection duct publication-title: Int. J. Therm. Sci. – volume: 122 start-page: 823 year: 2018 end-page: 845 ident: b0030 article-title: Estimation of boundary condition on the furnace inner wall based on precise integration BEM without iteration publication-title: Int. J. Heat Mass Transf. – volume: 137 start-page: 106270 year: 2022 ident: b0070 article-title: Three-dimensional rapid visualization of flame temperature field via compression and noise reduction of light field imaging publication-title: Int. Commun. Heat Mass Transf. – volume: 2 start-page: 148 issue: 2 year: 2013 ident: 10.1016/j.applthermaleng.2023.120237_b0085 article-title: Turbine blade temperature calculation and life estimation – a sensitivity analysis publication-title: Propul. Power Res. doi: 10.1016/j.jppr.2013.04.004 – volume: 91 start-page: 808 year: 2015 ident: 10.1016/j.applthermaleng.2023.120237_b0150 article-title: Online heat flux estimation using artificial neural network as a digital filter approach publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2015.08.010 – volume: 875 start-page: R2 year: 2019 ident: 10.1016/j.applthermaleng.2023.120237_b0075 article-title: Online in Situ Prediction of 3-D Flame Evolution from Its History 2-D Projections via Deep Learning publication-title: J. Fluid Mech. doi: 10.1017/jfm.2019.545 – volume: 85 start-page: 147 year: 2017 ident: 10.1016/j.applthermaleng.2023.120237_b0010 article-title: An inverse problem of finding the time-dependent thermal conductivity from boundary data publication-title: Int. Commun. Heat Mass doi: 10.1016/j.icheatmasstransfer.2017.05.009 – volume: 122 start-page: 823 year: 2018 ident: 10.1016/j.applthermaleng.2023.120237_b0030 article-title: Estimation of boundary condition on the furnace inner wall based on precise integration BEM without iteration publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2018.02.039 – volume: 26 start-page: 1515 issue: 14-15 year: 2006 ident: 10.1016/j.applthermaleng.2023.120237_b0105 article-title: A three-dimensional inverse problem in estimating the internal heat flux of housing for high speed motors publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2005.12.009 – volume: 104 start-page: 171 year: 2007 ident: 10.1016/j.applthermaleng.2023.120237_b0045 article-title: Regularization of inverse boundary design radiative heat transfer problems publication-title: J. Quant. Spectrosc. Radiat. Transf. doi: 10.1016/j.jqsrt.2006.09.001 – volume: 134 start-page: 185 year: 2019 ident: 10.1016/j.applthermaleng.2023.120237_b0125 article-title: A novel adaptive approximate Bayesian computation method for inverse heat conduction problem publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2019.01.002 – volume: 105 start-page: 65 year: 2016 ident: 10.1016/j.applthermaleng.2023.120237_b0035 article-title: An inverse method for estimating heat sources in a high speed spindle publication-title: Appl. Therm. Eng doi: 10.1016/j.applthermaleng.2016.05.123 – start-page: 1 year: 2022 ident: 10.1016/j.applthermaleng.2023.120237_b0080 article-title: Reconstruction of surface laser power and internal temperature of biological tissue during laser-induced thermal therapy publication-title: Numer. Heat Transf., Part A: Appl. – volume: 54 start-page: 549 issue: 2 year: 2013 ident: 10.1016/j.applthermaleng.2023.120237_b0115 article-title: Three-dimensional conjugate heat transfer in partitioned enclosures: Determination of geometrical and thermal properties by an inverse method publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2013.02.040 – ident: 10.1016/j.applthermaleng.2023.120237_b0180 – ident: 10.1016/j.applthermaleng.2023.120237_b0050 – ident: 10.1016/j.applthermaleng.2023.120237_b0145 doi: 10.1108/HFF-11-2020-0684 – volume: 87 start-page: 496 year: 2014 ident: 10.1016/j.applthermaleng.2023.120237_b0015 article-title: Forward and inverse solutions of a conductive, convective and radiative cylindrical porous fin publication-title: Energ. Conver. Manage. doi: 10.1016/j.enconman.2014.06.096 – volume: 142 start-page: 200 issue: 1 year: 2005 ident: 10.1016/j.applthermaleng.2023.120237_b0055 article-title: Non-destructive inverse method for determination of irregular internal temperature distribution in PEMFCs publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2004.11.019 – volume: 137 start-page: 106270 year: 2022 ident: 10.1016/j.applthermaleng.2023.120237_b0070 article-title: Three-dimensional rapid visualization of flame temperature field via compression and noise reduction of light field imaging publication-title: Int. Commun. Heat Mass Transf. doi: 10.1016/j.icheatmasstransfer.2022.106270 – ident: 10.1016/j.applthermaleng.2023.120237_b0175 doi: 10.2514/6.2020-1530 – ident: 10.1016/j.applthermaleng.2023.120237_b0195 – volume: 63 start-page: 158 issue: 1 year: 2014 ident: 10.1016/j.applthermaleng.2023.120237_b0025 article-title: Determination of transient temperature and heat flux on the surface of a reactor control rod based on temperature measurements at the interior points publication-title: Appl. Therm. Eng doi: 10.1016/j.applthermaleng.2013.10.066 – volume: 197 start-page: 117392 year: 2021 ident: 10.1016/j.applthermaleng.2023.120237_b0135 article-title: Identification of the effective heat capacity–temperature relationship and the phase change hysteresis in PCMs by means of an inverse heat transfer problem solved with metaheuristic methods publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2021.117392 – volume: 49 start-page: 86 issue: 1 year: 2010 ident: 10.1016/j.applthermaleng.2023.120237_b0100 article-title: Inverse estimation for unknown fouling-layer profiles with arbitrary geometries on the inner wall of a forced-convection duct publication-title: Int. J. Therm. Sci. doi: 10.1016/j.ijthermalsci.2009.06.005 – volume: 195 start-page: 117174 year: 2021 ident: 10.1016/j.applthermaleng.2023.120237_b0020 article-title: Stability analysis of the inverse heat transfer problem in the optimization of the machining process publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2021.117174 – volume: 67 start-page: 1 year: 2015 ident: 10.1016/j.applthermaleng.2023.120237_b0040 article-title: Geometry estimation for the inner surface in a furnace wall made of functionally graded materials publication-title: Int. Commun. Heat Mass doi: 10.1016/j.icheatmasstransfer.2015.06.012 – volume: 5 start-page: 275 issue: 3 year: 1982 ident: 10.1016/j.applthermaleng.2023.120237_b0095 article-title: Efficient sequential solution of the nonlinear inverse heat conduction problem publication-title: Numer. Heat Transf. doi: 10.1080/10407788208913448 – volume: 241 start-page: 118762 year: 2022 ident: 10.1016/j.applthermaleng.2023.120237_b0130 article-title: A Bayesian model to solve a two-dimensional inverse heat transfer problem of gas turbine discs publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2022.118762 – volume: 61 start-page: 85 issue: 2 year: 2012 ident: 10.1016/j.applthermaleng.2023.120237_b0060 article-title: Inverse Estimation of Surface Temperature Induced by a Moving Heat Source in a 3-D Object Based on Back Surface Temperature with Random Measurement Errors publication-title: Numer. Heat Transf. Part A Appl. doi: 10.1080/10407782.2012.644166 – ident: 10.1016/j.applthermaleng.2023.120237_b0005 doi: 10.1201/9781003155157 – volume: 201 start-page: 117819 year: 2022 ident: 10.1016/j.applthermaleng.2023.120237_b0065 article-title: A novel parametric level set method coupled with Tikhonov regularization for tomographic laser absorption reconstruction publication-title: Appl. Therm. Eng. doi: 10.1016/j.applthermaleng.2021.117819 – volume: 33 start-page: 1291 year: 2006 ident: 10.1016/j.applthermaleng.2023.120237_b0120 article-title: Training based, moving digital filter method for real time heat flux function estimation publication-title: Int. Commun. Heat Mass Transf. doi: 10.1016/j.icheatmasstransfer.2006.08.013 – ident: 10.1016/j.applthermaleng.2023.120237_b0090 doi: 10.1007/978-94-015-8480-7 – year: 2015 ident: 10.1016/j.applthermaleng.2023.120237_b0140 article-title: Convolutional LSTM network: A machine learning approach for precipitation nowcasting publication-title: Adv. Neural Inf. Proces. Syst. – ident: 10.1016/j.applthermaleng.2023.120237_b0185 – ident: 10.1016/j.applthermaleng.2023.120237_b0170 doi: 10.1016/j.applthermaleng.2022.119406 – ident: 10.1016/j.applthermaleng.2023.120237_b0165 – ident: 10.1016/j.applthermaleng.2023.120237_b0190 – volume: 54 start-page: 2782 issue: 13-14 year: 2011 ident: 10.1016/j.applthermaleng.2023.120237_b0110 article-title: A decentralized fuzzy inference method for solving the two-dimensional steady inverse heat conduction problem of estimating boundary condition publication-title: Int. J. Heat Mass Transf. doi: 10.1016/j.ijheatmasstransfer.2011.01.032 – volume: 170 start-page: 107149 year: 2021 ident: 10.1016/j.applthermaleng.2023.120237_b0160 article-title: Experimental and artificial neural network based study on the heat transfer and flow performance of ZnO-EG/water nanofluid in a mini-channel with serrated fins publication-title: Int. J. Therm. Sci. doi: 10.1016/j.ijthermalsci.2021.107149 – year: 2007 ident: 10.1016/j.applthermaleng.2023.120237_b0155 |
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