Abstract

Surrogate models of temperature field calculation based on deep learning have gained popularity in recent years because it does not need to establish complex mathematical models. However, the existing models cannot generate the temperature field for different boundary conditions or thermal parameters. In addition, it is also challenging to generate the details of the complex temperature field. In this paper, we propose the Parameters-to-Temperature Generative Adversarial Network (PTGAN) to generate temperature field images with high-quality details for different thermal parameters. The PTGAN model mainly includes the temperature field generation module and the thermal parameter encoding module. Additionally, we use a joint loss function to improve the quality of the generated temperature field image. The temperature field of the armored vehicle is calculated by the computational fluid dynamics method to obtain data set to verify the proposed PTGAN. The results show that the temperature images generated by the PTGAN has high accuracy, and the average relative error is only 0.205%. The attempt to integrate thermal parameters into the temperature field image generation is successful. The temperature field database can be generated quickly and accurately, which is of great significance for the further integration of deep learning and heat transfer.

References

1.
Lin
,
D. T. W.
,
Yang
,
C.-Y.
,
Li
,
J.-C.
, and
Wang
,
C.-C.
,
2011
, “
Inverse Estimation of the Unknown Heat Flux Boundary With Irregular Shape Fins
,”
Int. J. Heat Mass Transfer
,
54
(
25–26
), pp.
5275
5285
.
2.
Nakamura
,
T.
,
Kamimura
,
Y.
,
Igawa
,
H.
, and
Morino
,
Y.
,
2014
, “
Inverse Analysis for Transient Thermal Load Identification and Application to Aerodynamic Heating on Atmospheric Reentry Capsule
,”
Aerosp. Sci. Technol.
,
38
, pp.
48
55
.
3.
Luchesi
,
V. M.
, and
Coelho
,
R. T.
,
2012
, “
An Inverse Method to Estimate the Moving Heat Source in Machining Process
,”
Appl. Therm. Eng.
,
45–46
, pp.
64
78
.
4.
Wang
,
X.
,
Tang
,
L.
,
Zang
,
X.
, and
Yao
,
M.
,
2012
, “
Mold Transient Heat Transfer Behavior Based on Measurement and Inverse Analysis of Slab Continuous Casting
,”
J. Mater. Process. Technol.
,
212
(
9
), pp.
1811
1818
.
5.
Peng
,
J.-Z.
,
Chen
,
S.
,
Aubry
,
N.
,
Chen
,
Z.
, and
Wu
,
W.-T.
,
2020
, “
Unsteady Reduced-Order Model of Flow Over Cylinders Based on Convolutional and Deconvolutional Neural Network Structure
,”
Phys. Fluids
,
32
(
12
), p.
123609
.
6.
Lucia
,
D. J.
,
Beran
,
P. S.
, and
Silva
,
W. A.
,
2004
, “
Reduced-order Modeling: New Approaches for Computational Physics
,”
Prog. Aerosp. Sci.
,
40
(
1–2
), pp.
51
117
.
7.
Rowley
,
C. W.
, and
Dawson
,
S. T. M.
,
2017
, “
Model Reduction for Flow Analysis and Control
,”
Annu. Rev. Fluid Mech.
,
49
(
1
), pp.
387
417
.
8.
Brunton
,
S. L.
, and
Noack
,
B. R.
,
2015
, “
Closed-Loop Turbulence Control: Progress and Challenges
,”
ASME Appl. Mech. Rev.
,
67
(
5
), p.
050801
.
9.
Benner
,
P.
,
Gugercin
,
S.
, and
Willcox
,
K.
,
2015
, “
A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
,”
SIAM Rev.
,
57
(
4
), pp.
483
531
.
10.
Akhtar
,
I.
,
Borggaard
,
J.
,
Burns
,
J. A.
,
Imtiaz
,
H.
, and
Zietsman
,
L.
,
2015
, “
Using Functional Gains for Effective Sensor Location in Flow Control: A Reduced-Order Modelling Approach
,”
J. Fluid Mech.
,
781
, pp.
622
656
.
11.
San
,
O.
, and
Maulik
,
R.
,
2018
, “
Machine Learning Closures for Model Order Reduction of Thermal Fluids
,”
Appl. Math. Model.
,
60
, pp.
681
710
.
12.
Isoz
,
M.
,
2019
, “
POD-DEIM Based Model Order Reduction for Speed-Up of Flow Parametric Studies
,”
Ocean Eng.
,
186
, p.
106083
.
13.
Weller
,
J.
,
Lombardi
,
E.
,
Bergmann
,
M.
, and
Iollo
,
A.
,
2010
, “
Numerical Methods for Low-Order Modeling of Fluid Flows Based on POD
,”
Int. J. Numer. Methods Fluids
,
63
(
2
), pp.
249
268
.
14.
Li
,
T.
,
Gao
,
Y.
,
Han
,
D.
,
Yang
,
F.
, and
Yu
,
B.
,
2020
, “
A Novel POD Reduced-Order Model Based on EDFM for Steady-State and Transient Heat Transfer in Fractured Geothermal Reservoir
,”
Int. J. Heat Mass Transfer
,
146
, p.
118783
.
15.
Mahapatra
,
P. S.
,
Chatterjee
,
S.
,
Mukhopadhyay
,
A.
,
Manna
,
N. K.
, and
Ghosh
,
K.
,
2016
, “
Proper Orthogonal Decomposition of Thermally-Induced Flow Structure in an Enclosure With Alternately Active Localized Heat Sources
,”
Int. J. Heat Mass Transfer
,
94
, pp.
373
379
.
16.
El Majd
,
B. A.
, and
Cordier
,
L.
,
2016
, “
New Regularization Method for Calibrated Pod Reduced-Order Models
,”
Math. Model. Anal.
,
21
(
1
), pp.
47
62
.
17.
Kim
,
M.
, and
Moon
,
J. H.
,
2022
, “
Deep Neural Network Prediction for Effective Thermal Conductivity and Spreading Thermal Resistance for Flat Heat Pipe
,”
Int. J. Numer. Methods Heat Fluid Flow
,
33
(
2
), pp.
437
455
.
18.
Deng
,
S.
, and
Hwang
,
Y.
,
2006
, “
Applying Neural Networks to the Solution of Forward and Inverse Heat Conduction Problems
,”
Int. J. Heat Mass Transfer
,
49
(
25–26
), pp.
4732
4750
.
19.
Lin
,
J.
,
Zhou
,
S.
, and
Guo
,
H.
,
2020
, “
A Deep Collocation Method for Heat Transfer in Porous Media: Verification From the Finite Element Method
,”
J. Energy Storage
,
28
, p.
101280
.
20.
Zhu
,
H.
,
Lian
,
W.
,
Lu
,
L.
,
Kamunyu
,
P.
,
Yu
,
C.
,
Dai
,
S.
, and
Hu
,
Y.
,
2017
, “
Online Modelling and Calculation for Operating Temperature of Silicon-Based PV Modules Based on BP-ANN
,”
Int. J. Photoenergy
,
2017
, pp.
1
13
.
21.
Dhillon
,
A.
, and
Verma
,
G. K.
,
2019
, “
Convolutional Neural Network: a Review of Models, Methodologies and Applications to Object Detection
,”
Prog. Artif. Intell.
,
9
(
2
), pp.
85
112
.
22.
Yu
,
H.
,
Li
,
G.
,
Su
,
L.
,
Zhong
,
B.
,
Yao
,
H.
, and
Huang
,
Q.
,
2020
, “
Conditional GAN Based Individual and Global Motion Fusion for Multiple Object Tracking in UAV Videos
,”
Pattern Recognit. Lett.
,
131
, pp.
219
226
.
23.
Yang
,
L.
,
Min
,
Z.
,
Yue
,
T.
,
Rao
,
Y.
, and
Chyu
,
M. K.
,
2019
, “
High Resolution Cooling Effectiveness Reconstruction of Transpiration Cooling Using Convolution Modeling Method
,”
Int. J. Heat Mass Transfer
,
133
, pp.
1134
1144
.
24.
Yang
,
L.
,
Wang
,
Q.
, and
Rao
,
Y.
,
2021
, “
Searching for Irregular Pin-Fin Shapes for High Temperature Applications Using Deep Learning Methods
,”
Int. J. Therm. Sci.
,
161
, p.
106746
.
25.
Li
,
Y.
,
Wang
,
H.
, and
Deng
,
X.
,
2019
, “
Image-Based Reconstruction for a 3D-PFHS Heat Transfer Problem by ReConNN
,”
Int. J. Heat Mass Transfer
,
134
, pp.
656
667
.
26.
Kim
,
D.-J.
,
Kim
,
S.-I.
, and
Kim
,
H.-S.
,
2022
, “
Thermal Simulation Trained Deep Neural Networks for Fast and Accurate Prediction of Thermal Distribution and Heat Losses of Building Structures
,”
Appl. Therm. Eng.
,
202
, p.
117908
.
27.
Chen
,
J.
,
Zhu
,
F.
,
Han
,
Y.
, and
Chen
,
C.
,
2021
, “
Fast Prediction of Complicated Temperature Field Using Conditional Multi-Attention Generative Adversarial Networks (CMAGAN)
,”
Expert Syst. Appl.
,
186
, p.
115727
.
28.
Kuang
,
X.
,
Zhu
,
J.
,
Sui
,
X.
,
Liu
,
Y.
,
Liu
,
C.
,
Chen
,
Q.
, and
Gu
,
G.
,
2018
, “
Thermal Infrared Colorization via Conditional Generative Adversarial Network
,”
Infrared Phys. Technol.
,
107
, p.
103338
.
29.
Lai
,
S.
,
You
,
F.
,
Gong
,
H.
, and
Zhao
,
Y.
,
2019
, “
Fusion Image Style Transfer Network
,”
J. Phys. Conf. Ser. J. Phys. Conf. Ser.
,
1302
(
3
), p.
032002
.
30.
Reed
,
S.
,
Akata
,
Z.
,
Yan
,
X.
,
Logeswaran
,
L.
,
Schiele
,
B.
, and
Lee
,
H.
,
2016
, “
Generative Adversarial Text to Image Synthesis
,”
Proceedings of The 33rd International Conference on Machine Learning
,
New York
,
June 19–24
, PMLR, pp.
1060
1069
.
31.
Mok
,
T. C. W.
, and
Chung
,
A. C. S.
,
2018
, “
Learning Data Augmentation for Brain Tumor Segmentation With Coarse-to-Fine Generative Adversarial Networks
,
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop
,
Granada, Spain
,
Sept. 16
,
Springer International Publishing
, pp.
70
80
.
32.
Karnewar
,
A.
, and
Wang
,
O.
,
2020
, “
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks
,”
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
,
Seattle, WA
,
June 13–19
, pp.
7799
7808
.
33.
Pihlgren
,
G. G.
,
Sandin
,
F.
, and
Liwicki
,
M.
,
2020
, “
Improving Image Autoencoder Embeddings with Perceptual Loss
,”
2020 International Joint Conference on Neural Networks (IJCNN)
,
Glasgow, UK
,
July 19–24
,
IEEE
, pp.
1
7
.
34.
Luo
,
J.
,
Huang
,
J.
, and
Li
,
H.
,
2020
, “
A Case Study of Conditional Deep Convolutional Generative Adversarial Networks in Machine Fault Diagnosis
,”
J. Intell. Manuf.
,
32
(
2
), pp.
407
425
.
35.
Frolov
,
S.
,
Hinz
,
T.
,
Raue
,
F.
,
Hees
,
J.
, and
Dengel
,
A.
,
2021
, “
Adversarial Text-to-Image Synthesis: A Review
,”
Neural Netw.
,
144
, pp.
187
209
.
36.
Johnson J
,
A. A.
, and
Fei-Fei
,
L.
,
2016
, “
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
,”
European Conference on Computer Vision
,
Amsterdam, The Netherlands
,
Oct. 11–14
,
Springer International Publishing
, pp.
694
711
.
37.
Ledig
,
C.
,
Theis
,
L.
,
Huszár
,
F.
,
Caballero
,
J.
,
Cunningham
,
A.
,
Acosta
,
A.
,
Aitken
,
A.
, et al
,
2017
, “
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
,”
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
, pp.
4681
4690
.
38.
Xuan
,
Y.
,
2004
,
Infrared Characterizations of Ground Targets and Backgrounds
,
National Defence Industry Press in Beijing
,
Beijing, China
, pp.
20
32
.
39.
Lin
,
Q.
,
2019
, “
Research on the Effects of Droplets and Particles on Thermal Radiative Characteristics of Vehicles and Credibility Evaluation Method for Thermal Radiation Model
,”
Doctoral dissertation
,
Nanjing University of Science and Technology
,
Nanjing, China
, pp.
141
142
.
40.
Chang
,
T.
,
Zhan
,
L.
,
Tan
,
W.
, and
Li
,
S.
,
2017
, “
Optimization of Curing Process for Polymer-Matrix Composites Based on Orthogonal Experimental Method
,”
Fibers Polym.
,
18
, pp.
148
154
.
You do not currently have access to this content.