Two Approaches to Optimal Sensor Locations

Abstract

Accurate modeling of thermal systems depends upon the determination of the material properties and the surface heat transfer coefficients. These parameters are frequently estimated from temperatures measured within the system or on the surface or from measured surface heat fluxes. Because of sensor errors or lack of sensitivity, the measurements may lead to erroneous estimates of the parameters. These errors can be ameliorated if the sensors are placed at points of maximum sensitivity. This paper describes two methods to optimize sensor locations: one to account for signal error, the other to consider interacting parameters. The methods are based upon variants of the normalized Fisher information matrix and are shown to be equivalent in some cases, but to predict differing sensor locations under other conditions, usually transient.

Heat-Transfer Coefficient and In-Cavity Pressure at the Casting-Die Interface during High-Pressure Die Casting of the Magnesium Alloy AZ91D

“…[17,22,23] Recently, a pyrometric technique has been employed to measure the casting surface temperature. In parallel, an analysis of the effect of thermocouple dynamics, their assembly in the die, and their locations from the die surface on the accuracy of HTC has been reported.…”

Recommendations and guidelines for the performance of accurate heat transfer measurements in rapid forming processes

“…This refers to the error in the interfacial heat exchange determination due to inaccuracies in the chain of measurements: the accuracy of the temperature measurement; the accuracy of the thermocouple location; and the accuracy of the thermal property data. Some authors [40][41][42] have worked on optimising the experiments in order to minimise the impact of those inaccuracies. The strategy they advise is to locate the sensor as close to the surface as possible, to reduce the sampling rate and to use a large dimension for the B-spline extrapolation (or equivalently the number of future instants, ntf).…”

Magnetic Material Characterization Using an Inverse Problem Approach

“…S k = σ 2 n and Alifanov et al, 1995), (Fadale et al, 1995b). Thus, under these conditions, the Fisher information matrix (F) reduces to the classical form:…”