skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Sensor placement for calibration of spatially varying model parameters

; ;
Publication Date:
Sponsoring Org.:
OSTI Identifier:
Grant/Contract Number:
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of Computational Physics
Additional Journal Information:
Journal Volume: 343; Journal Issue: C; Related Information: CHORUS Timestamp: 2018-01-12 09:34:33; Journal ID: ISSN 0021-9991
Country of Publication:
United States

Citation Formats

Nath, Paromita, Hu, Zhen, and Mahadevan, Sankaran. Sensor placement for calibration of spatially varying model parameters. United States: N. p., 2017. Web. doi:10.1016/
Nath, Paromita, Hu, Zhen, & Mahadevan, Sankaran. Sensor placement for calibration of spatially varying model parameters. United States. doi:10.1016/
Nath, Paromita, Hu, Zhen, and Mahadevan, Sankaran. 2017. "Sensor placement for calibration of spatially varying model parameters". United States. doi:10.1016/
title = {Sensor placement for calibration of spatially varying model parameters},
author = {Nath, Paromita and Hu, Zhen and Mahadevan, Sankaran},
abstractNote = {},
doi = {10.1016/},
journal = {Journal of Computational Physics},
number = C,
volume = 343,
place = {United States},
year = 2017,
month = 8

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on May 10, 2018
Publisher's Accepted Manuscript

Save / Share:
  • In this study the authors investigated the effects of spatial variability of soil contaminants on cost calculations for soil remediation. Most cost models only provide a single figure, whereas spatial variability is one of the sources to contribute to the uncertainty. A cost model is applied to a study site of 19 ha containing a former gasworks in the Rotterdam harbor. The site was contaminated by heavy metals, PAH and mineral oil. Two sets of environmental thresholds were applied, one for identifying the severeness of contamination and one to decide upon the future use of excavated soil. Three remediation scenariosmore » were compared. Geostatistical simulations were applied, both on individual contaminants and on indicator variables derived from these. As it turns out, spatial uncertainty causes 2--5% uncertainty in the final cost estimates. Another source of uncertainty is the direction of application of the cost model: a least-case approach starts with the lowest threshold value, followed by increasingly higher values, whereas a worst-case approach starts with the highest threshold value followed by decreasing values. Using a worst-case approach yielded cost estimates that were 6--8% higher than cost estimates by a least-case approach. The authors concluded that 8--13% of the uncertainty in cost estimates could be explained by spatial variation of soil contaminants and lithology.« less
  • This paper concentrates on the optimal sensor placement problem in ambient vibration based structural health monitoring. More specifically, the paper examines the covariance of estimated parameters during system identification using auto-regressive and moving average vector (ARMAv) model. By utilizing the discrete-time steady state Kalman filter, this paper realizes the structure's finite element (FE) model under broad-band white noise excitations using an ARMAv model. Based on the asymptotic distribution of the parameter estimates of the ARMAv model, both a theoretical closed form and a numerical estimate form of the covariance of the estimates are obtained. Introducing the information entropy (differential entropy)more » measure, as well as various matrix norms, this paper attempts to find a reasonable measure to the uncertainties embedded in the ARMAv model estimates. Thus, it is possible to select the optimal sensor placement that would lead to the smallest uncertainties during the ARMAv identification process. Two numerical examples are provided to demonstrate the methodology and compare the sensor placement results upon various measures.« less
  • The selective catalytic reduction (SCR) is a technology used for reducing NO x emissions in the heavy-duty diesel (HDD) engine exhaust. In this study, the spatially resolved capillary inlet infrared spectroscopy (Spaci-IR) technique was used to study the gas concentration and NH 3 storage distributions in a SCR catalyst, and to provide data for developing a SCR model to analyze the axial gaseous concentration and axial distributions of NH 3 storage. A two-site SCR model is described for simulating the reaction mechanisms. The model equations and a calculation method was developed using the Spaci-IR measurements to determine the NH 3more » storage capacity and the relationships between certain kinetic parameters of the model. Moreover, a calibration approach was then applied for tuning the kinetic parameters using the spatial gaseous measurements and calculated NH3 storage as a function of axial position instead of inlet and outlet gaseous concentrations of NO, NO 2, and NH 3. The equations and the approach for determining the NH 3 storage capacity of the catalyst and a method of dividing the NH 3 storage capacity between the two storage sites are presented. It was determined that the kinetic parameters of the adsorption and desorption reactions have to follow certain relationships for the model to simulate the experimental data. Finally, the modeling results served as a basis for developing full model calibrations to SCR lab reactor and engine data and state estimator development as described in the references (Song et al. 2013a, b; Surenahalli et al. 2013).« less
  • Many computer assisted surgery systems are based on intraoperative x-ray images. To achieve reliable and accurate results these images have to be calibrated concerning geometric distortions, which can be distinguished between constant distortions and distortions caused by magnetic fields. Instead of using an intraoperative calibration phantom that has to be visible within each image resulting in overlaying markers, the presented approach directly takes advantage of the physical background of the distortions. Based on a computed physical model of an image intensifier and a magnetic field sensor, an online compensation of distortions can be achieved without the need of an intraoperativemore » calibration phantom. The model has to be adapted once to each specific image intensifier through calibration, which is based on an optimization algorithm systematically altering the physical model parameters, until a minimal error is reached. Once calibrated, the model is able to predict the distortions caused by the measured magnetic field vector and build an appropriate dewarping function. The time needed for model calibration is not yet optimized and takes up to 4 h on a 3 GHz CPU. In contrast, the time needed for distortion correction is less than 1 s and therefore absolutely acceptable for intraoperative use. First evaluations showed that by using the model based dewarping algorithm the distortions of an XRII with a 21 cm FOV could be significantly reduced. The model was able to predict and compensate distortions by approximately 80% to a remaining error of 0.45 mm (max) (0.19 mm rms)« less
  • This note reviews the extensively adopted equations used as models of hot-wire anemometric sensors. An unified formal form of the mathematical model of a hot-wire anemometric sensor with otherwise defined parameters is proposed. Those parameters, static and dynamic, have simple physical interpretation and can be easily determined. They show directly the range of sensor application. They determine the metrological properties of the given sensor in the actual medium. Hence, the parameters' values might be ascribed to each sensor in the given medium and be quoted in manufacturers' catalogues, supplementing the sensor specifications. Because of their simple physical interpretation, those parametersmore » allow the direct comparison of the fundamental metrological properties of various sensors and selection of the optimal sensor for the given research measurement application. The parameters are also useful in modeling complex hot-wire systems.« less