Abstract
The determination of thermal structure functions from transient thermal measurements using network identification by deconvolution is a delicate process as it is sensitive to noise in the measured data. Great care must be taken not only during the measurement process but also to ensure a stable implementation of the algorithm. In this paper, a method is presented that quantifies the absolute accuracy of network identification on the basis of different test structures. For this purpose, three measures of accuracy are defined. By these metrics, several variants of network identification are optimized and compared against each other. Performance in the presence of noise is analyzed by adding Gaussian noise to the input data. In the cases tested, the use of a Bayesian deconvolution provided the best results.
Highlights
Energies 2021, 14, 7068. https://In the field of thermal management, detailed finite element models and multiphysics simulations are developed to predict the behavior of a system with high accuracy
A suggestion is made how to rank accuracies of thermal structure functions and time constant spectra resulting from a specific implementation of network identification by deconvolution
The theory of network identification by deconvolution provides analytical equations to directly obtain the parameters of an equivalence network of a Foster-type RC-ladder, see Figure 1
Summary
In the field of thermal management, detailed finite element models and multiphysics simulations are developed to predict the behavior of a system with high accuracy. The question remains as to which the most suitable procedure is Responding to these challenges, a method for verifying the accuracy of implementations was presented by Szalai and Székely [11]. An approach by Codecasa uses a multi-point moment matching technique [28] to calculate the Foster network from the thermal response. In this way, it is possible to generate the thermal structure function directly from the spatial distribution of the thermal resistances and the thermal capacitances of a three-dimensional thermal model. A suggestion is made how to rank accuracies of thermal structure functions and time constant spectra resulting from a specific implementation of network identification by deconvolution. Typical variants of the method are systematically analyzed and improved with respect to their accuracy
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