Abstract
ABSTRACTIn this article an optimal sensor placement problem for a thermo-elastic solid body model is considered. Temperature sensors are placed in a near-optimal way so that their measurements allow an accurate prediction of the thermally induced displacement of a point of interest (POI). Low-dimensional approximations of the transient thermal field are used which allows for efficient calculations. Four model order reduction (MOR) methods are applied and subsequently compared with respect to the accuracy of the estimated POI displacement and the location of the sensors obtained.
Highlights
This article is concerned with an optimal sensor placement problem for thermo-elastic solid body models
The main focus of this article was to compare the performance of different model order reduction (MOR) methods with respect to the sensor placement objective and the prediction quality of the induced displacement estimation using simulated measurements at the optimized sensor positions
Comparing the simulation results based on the data used as the proper orthogonal decomposition (POD) training scenario and a fixed model size of r = 20, the POD approach showed the best performance with respect to the experimental design objective as well as the estimation accuracy of the tool centre point (TCP) displacement
Summary
This article is concerned with an optimal sensor placement problem for thermo-elastic solid body models. To put this work into perspective, model order reduction techniques are frequently used in sensor placement problems involving PDEs, e.g. reaction–diffusion or convection–diffusion problems (Alonso, Frouzakis, and Kevrikidis 2004; Alonso et al 2004; Armaou and Demetriou 2006; Green 2006; García et al 2007) as well as fluid dynamics applications (Mokhasi and Rempfer 2004; Cohen, Siegel, and McLaughlin 2006; Willcox 2006; Yildirim, Chryssostomidis, and Karniadakis 2009), where POD is often the method of choice In another class of applications involving mechanical deformation of large structures (Kammer 1991; Yi, Li, and Gu 2011; Meo and Zumpano 2005; Sun and Büyüköztürk 2015), modal analysis is employed as a reduction technique. The objective in this article is to predict the mechanical displacement induced by that temperature field This approach changes the metric with respect to which the gain of information is measured; see Körkel et al (2008), Koevoets et al (2007), and Herzog and Riedel (2015).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
