BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Talks.cam//talks.cam.ac.uk// X-WR-CALNAME:Talks.cam BEGIN:VEVENT SUMMARY:Estimating operational loading conditions from wave measurements u sing Kalman filters - Andre Dalmora (CEA/Saclay) DTSTART:20230616T090000Z DTEND:20230616T093000Z UID:TALK202537@talks.cam.ac.uk DESCRIPTION:In leading-edge industrial applications\, assessing structural integrity is an important aspect of safety requirements. Structural Healt h Monitoring (SHM) proposes to use sensors and signal processing units in situ. One of the most attractive SHM techniques relies on ultrasonic guide d waves. Operational conditions can change wave propagation and therefore affect the interpretation of recorded signals [1]. Modeling and simulation can be helpful tools for the design or the reliability assessment of SHM solutions. In [2]\, we have proposed a wave propagation model to take into account effects of operational conditions such as internal stresses cause d by mechanical loading.\nIn this work\, we develop a strategy for estimat ing these load-induced (large) deformations from ultrasonic measurements. In the context of least-squares optimization\, we minimize the difference between measurements and the observed direct model. Among the available me thods there exist variational methods\, such as Full Waveform Inversion [3 ]\, and sequential approaches\, such as Kalman Filtering. As the linearize d aspect of the direct model leads to an unwieldy tangent dynamics\, tange nt-free methods are preferable. After reducing the dimension of the parame tric space by decomposing the deformation on selected eigenmodes of a stat ic problem\, we apply iteratively the Reduced-Order Unscented Kalman Filte r [4] as estimation method. We will show how this method can be understood as a gradient free alternative to an iterated Levenberg-Marquardt based m inimisation of the least squares functional. Moreover it allows to launch multiple direct solvers in parallel leading to an efficient exploration of the parametric space and sensitivity analysis w.r.t. extended sets of eig enmodes. We illustrate the proposed inversion strategy by applying it to r ealistic cases.\n[1] Gorgin\, R. et al. 2020. &ldquo\;Environmental and Op erational Conditions Effects on Lamb Wave Based Structural Health Monitori ng Systems: A Review.&rdquo\; Ultrasonics.[2] Dalmora\, A. et al. 2022. &l dquo\;A Generic Numerical Solver for Modeling the Influence of Stress Cond itions on Guided Wave Propagation for SHM Applications.&rdquo\; 49th Annua l Review of Progress in Quantitative Nondestructive Evaluation.[3] Virieux \, J. et al. 2014. &ldquo\;An Introduction to Full Waveform Inversion.&rdq uo\; In Encyclopedia of Exploration Geophysics\, Geophysical References Se ries.[4] Moireau\, P. and Chapelle\, D. 2011. &ldquo\;Reduced-Order Unscen ted Kalman Filtering with Application to Parameter Identification in Large -Dimensional Systems.&rdquo\; ESAIM: Control\, Optimisation and Calculus o f Variations.\nAndré\; Luiz Dalmora1\,2\,3\, Alexandre Imperiale1\, Sé\;bastien Imperiale2\,3\, Philippe Moireau2\,3\n1 \;Universit& eacute\; Paris-Saclay\, CEA\, List\, F-91120\, Palaiseau\, France2 \;P roject-Team M3DISIM\, Inria Saclay-Ile-de-France\, Inria\, 91128 Palaiseau \, France3 LMS\, É\;cole Polytechnique\, CNRS\, Institut Polytechniq ue de Paris\, 91128 Palaiseau\, France\n \;\n \; LOCATION:Seminar Room 2\, Newton Institute END:VEVENT END:VCALENDAR