Abstract
In this paper, a novel artificial neural network (ANN) based method dedicated to simultaneously estimating thermal conductivity and thermal diffusivity of CSP (concentrating solar power) plant receiver materials is presented. By monitoring the evolution of these two correlated thermophysical properties during aging cycles, CSP plants' cost efficiency could be maintained. The proposed method is based on the processing of experimental photothermal data using classification and estimation networks. All the networks are feedforward ANN trained with supervised learning algorithms. A pseudo random binary signal (PRBS) is used as excitation and the impact on performance of both the photothermal response length, which is used as model input, and the number of training examples has been evaluated. Of course, the networks' topology has been optimized, allowing the generalization ability to be controlled. Despite the lack of data, the results are promising. Mean relative errors are between 8% and 20%, and the main levers for improvement are identified. In this paper, the study deals with a large range of materials (polymers and metallic alloys).
Highlights
Thermophysical characterization of materials is an issue in various domains such as building design or energy plant optimization
The materials are submitted to strong climate conditions and maintaining the thermophysical properties is a key issue to ensure costefficiency of CSP plants: thermal conductivity is about the ability of a given material to transfer heat by conduction during a stationary phase, as a sunny day, whereas thermal diffusivity is about the ability of this material to respond to a sudden temperature change, for example caused by a cloudy day
We present the second part of the neural structure: the feedforward neural networks used to simultaneously estimate thermal diffusivity and thermal conductivity for both classes
Summary
Thermophysical characterization of materials is an issue in various domains such as building design (for estimating the insulating performance of a given structure) or energy plant optimization. Difficulties related to the correspondence between the model and experimental heat losses or the finite pulse excitation can be overcome These methods are not constrained by correlated properties, since one can exploit non-physical parameters or the possibilities these methods offer when it comes to find an adequate topology. During the past few years, several studies have focused on the use of machine learning tools for thermophysical characterization of materials from photothermal responses [20,21,22] In these papers thermal diffusivity and thermal conductivity are simultaneously estimated using side-by-side artificial neural networks or adaptive neuro-fuzzy inference systems (ANFIS).
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