Abstract
Engineering thermal management (ETM) is one of the critical tasks for quality control and system surveillance in many industries, and acquiring the temperature field and its evolution is a prerequisite for efficient thermal management. By harnessing the sensing data from sensor networks, an unprecedented opportunity has emerged for an accurate estimation of the temperature field. However, limited resources of sensor deployment and computation capacity pose a great challenge while modeling the spatiotemporal dynamics of the temperature field. This paper presents a novel temperature field estimation approach to describe the dynamics of a temperature field by combining a physics-specific model and a spatiotemporal Gaussian process. To reduce the computational burden while dealing with a large set of spatiotemporal data, we employ a tapering covariance function and develop an associated parameter estimation procedure. We introduce a case study of grain storage to show the effectiveness and efficiency of the proposed approach.
Highlights
Engineering thermal management (ETM) is one of the critical tasks for quality control and system surveillance in many industries
Conventional methods for field estimation are mostly achieved by the simulation of physical models, in which sensing observations collected from distributed sensor networks are not considered
Spatiotemporal dynamics of research haspaper, focused data auncertainty quantification a data temperature fields based on grain storage sensor networks by combining a physics-specific model and uncertainty quantification approach proposed by Lermusiaux is for estimating temperature fields in spatiotemporal processes
Summary
Engineering thermal management (ETM) is one of the critical tasks for quality control and system surveillance in many industries. Conventional methods for field estimation are mostly achieved by the simulation of physical models, in which sensing observations collected from distributed sensor networks are not considered. These methods may lead to a discrepancy between model predictions and observations because of stochastic dynamics and uncertainties in the temperature fields. A data uncertainty considering a species transfer model, in which data of ambient temperature and humidity, wind quantification approach proposed by Lermusiaux is for estimating temperature fields in the ocean [24]. Spatiotemporal dynamics of research haspaper, focused data auncertainty quantification a data temperature fields based on grain storage sensor networks by combining a physics-specific model and uncertainty quantification approach proposed by Lermusiaux is for estimating temperature fields in spatiotemporal processes.
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