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Adaptive Selection of Relevant Sensors in a Network for Unknown Mobile Heating Flux Estimation

An original and attractive method of choosing sensors in a fixed network is proposed in order to identify online the density of heat flow from a mobile source. The system state (temperature) is described by a set of partial differential equations issued from an energy balance. Inverse heat conduction problems based on such valid mathematical model are ill posed and the choice of relevant sensors is crucial to enhance the identification algorithm. Strategies based on optimal sensors location are generally built on observations taken at fixed positions and considering a supposed nominal state. The proposed new approach is based on the iterative regularization of the minimization problem resulting from the inverse problem. Such an online regulation technique provides relevant information on the evolution of the sensitivity distributions and the best sensor locations can be updated. The main results are illustrated on a complex thermal situation in two-dimensional geometry and it is shown which sensors should be chosen over time when the identification of the unknown mobile heating flux is carried out online.