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 boundaryand 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 .
Temperature monitoring during the life time of heat source components in engineering systems becomes essential to guarantee the normal work and the working life of these components . However, prior methods, which mainly use the interpolate estimation to reconstruct the temperature field 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. 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 limited monitoring points unsupervisedly. First, we define the TFR-HSS task mathematically, and numerically model the task, and hence transform the task as an image-to-image regression problem. Then this work develops the deep reversible regression model which can better learn the physical information , especially over the boundary . 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 jointly learns the deep surrogate model with these losses unsupervisedly. Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness of the proposed method .
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ensure
guarantee
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even the long
the
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heat sources
components
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whole temperature field with the temperature value
temperature field
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Furthermore, limited number of labelled training samples are available for the training of deep models.
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the given
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temperature field reconstruction task of heat-source systems mathematically,
TFR-HSS task mathematically, and
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problem, and further transform the problem
task, and hence transform the task
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, based on the law of forward and backward propagation of deep models,
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near the boundaryand improve the reconstruction performance
, especially over the boundary
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joint
jointly
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and efficiency
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physics-informed deep reversible regression models for TFR-HSS task
method