1,721,079 research outputs found

    Evaluation of Reliable Confidence Bounds for Sensor Calibration Curves

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    When calibrating a sensor, the evaluation of the uncertainty of the estimated function is at least as important as the numeric values of the function itself. The calibration process usually involves a least-squares fitting, performed, for example for a thermocouple, by comparing some voltage readings with corresponding temperatures from a reference thermometer. The aim of this work is to encourage the use of improved algorithms, based on an extended minimization criterion, which can overcome some limitations of the classic method

    The Role of Adsorption and Phase Change Phenomena in the Thermophysical Characterization of Moist Porous Materials

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    Phase change phenomena in moist porous media with low liquid content, the typical condition of a porous body at ambient conditions and far from the contact of liquid water, are controlled by the shape of the adsorption isotherms and by the effective liquid-vapor thermodynamic condition within the pores. Usually, heat and mass transfer models are developed under the assumption of thermal and hygrometric equilibrium. This gives rise to an expression of the evaporation source that is too complex in view of the dynamic identification of thermophysical and transport properties of a porous material. In this study, the hypothesis of hygrometric equilibrium is dropped. The phase change rate is considered proportional to the amount of local nonequilibrium through an appropriate delay coefficient. This approach leads to a simple representation of the process and makes manageable the formulation of a coupled heat and mass transfer inverse problem. A comparison with a first group of experiments performed with an open-pore light insulating material (expanded perlite board) confirms the suitability of the proposed approach. However, the analysis shows that, for this material, phase change occurs not far from the hygrometric equilibrium

    Kalman Smoothing Technique Applied to the Inverse Heat Conduction Problem

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    The time-dependent surface heat flux at one boundary of a one-dimensional system is reconstructed by using the Kalman smoothing technique, given the initial temperature distribution and the time-temperature history at an interior location. The study makes a parametric investigation and analyzes the behavior of two finite-difference schemes. The numerical results show the very good peiformance of the proposed technique, which provides a comprehensive way for using future temperature measurements. Although in this study attention is devoted to the one-dimensional linear problem, the algorithm can be generalized for the stochastic nonlinear multidimensional case

    Influence of Sensor Calibration Uncertainty in the Inverse Heat Conduction Problem (IHCP)

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    In the inverse heat conduction problem (IHCP) of finding the temperature-dependent thermophysical properties of materials, the evaluation of the confidence region of the unknown functions is at least as important as the numeric values of the function itself. The uncertainty of the reconstructed properties arises from various disturbances affecting the measurement instrumentation and from an imprecise mathematical description of the experiment. The aim of this work is to consider, besides the effect of random noises superposed onto the signals, the bias arising from the calibration of the sensors. By conceiving the calibration process as an integral part of the experiment it is possible to quantify the effect of the above bias and to obtain better estimates and correct confidence regians for the reconstructed properties
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