Temperature monitoring during the life time of heat-source components in engineering systems becomes essential to ensure the normal work and even the long working life of the heat sources. However, prior methods, which mainly use the interpolate estimation  , require large amounts of temperature tensors for an accurate estimation.  To solve this problem, this work develops a novel physics-informed deep surrogate models for temperature field reconstruction  . First, we defines the temperature field reconstruction task of heat-source systems  . Then,  this work develops the deep surrogate model mapping for the proposed task . Finally, considering the physical properties of heat transfer , this work proposes four different losses and joint learns the deep surrogate model with these losses  . Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep surrogate models for temperature field reconstruction .
Temperature monitoring during the life time of heat source components in engineering systems becomes essential to ensure the normal work and even the long working life of these heat sources. However, prior methods, which mainly use the interpolate estimation to reconstruct the whole temperature field with the temperature value from limited monitoring points , require large amounts of temperature tensors for an accurate estimation. This may decrease the availability and reliability of the system and sharply increase the monitoring cost. Furthermore, limited number of labelled training samples are available for the training of deep models. To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with the given limited monitoring points unsupervisedly . First, we define the temperature field reconstruction task of heat-source systems mathematically, numerically model the problem, and further transform the problem as an image-to-image regression problem . Then, based on the law of forward and backward propagation of deep models, this work develops the deep reversible regression model which can better learn the physical information near the boundary and improve the reconstruction performance . Finally, considering the physical characteristics of heat conduction as well as the boundary conditions , this work proposes the physics-informed reconstruction loss including four training losses and joint learns the deep surrogate model with these losses unsupervisedly . Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep reversible regression models for TFR-HSS task .
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heat-source
heat source
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the
these
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to reconstruct the whole temperature field with the temperature value from limited monitoring points
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This may decrease the availability and reliability of the system and sharply increase the monitoring cost. Furthermore, limited number of labelled training samples are available for the training of deep models.
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surrogate
reversible regression
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of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with the given limited monitoring points unsupervisedly
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defines
define
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mathematically, numerically model the problem, and further transform the problem as an image-to-image regression problem
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based on the law of forward and backward propagation of deep models,
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surrogate model mapping for the proposed task
reversible regression model which can better learn the physical information near the boundary and improve the reconstruction performance
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properties of heat transfer
characteristics of heat conduction as well as the boundary conditions
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four different
the physics-informed reconstruction loss including four training
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unsupervisedly
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surrogate models for temperature field reconstruction
reversible regression models for TFR-HSS task