@article{BarzSeligerMarxetal., author = {Barz, Tilman and Seliger, Dominik and Marx, Klemens and Sommer, Andreas and Walter, Sebastian F. and Bock, Hans Georg and Koerkel, Stefan}, title = {State and state of charge estimation for a latent heat storage}, series = {Control Engineering Practice}, volume = {72}, journal = {Control Engineering Practice}, publisher = {Pergamon-Elsevier d.}, doi = {10.1016/j.conengprac.2017.11.006}, pages = {151 -- 166}, abstract = {A nonlinear state observer is designed for a thermal energy storage with solid/liquid phase change material (PCM). Using a physical 2D dynamic model, the observer reconstructs transient spatial temperature fields inside the storage and estimates the stored energy and the state of charge. The observer has been successfully tested with a lab-scale latent heat storage with a single pass tube bundle and the phase change material located in a shell around each tube. It turns out that the observer robustly tracks the real process data with as few as four internal PCM temperature sensors. © 2017 Elsevier Ltd. All rights reserved.}, language = {en} } @article{SchmidtPotschkaKoerkeletal., author = {Schmidt, Andreas and Potschka, Andreas and K{\"o}rkel, Stefan and Bock, Hans Georg}, title = {Derivate-extended pod reduced-order modelling for parameter estimation}, series = {SIAM Journal on Scientific Computing}, volume = {35}, journal = {SIAM Journal on Scientific Computing}, number = {6}, publisher = {SIAM}, address = {Philadelphia, Pa.}, issn = {1095-7197}, doi = {10.1137/120896694}, abstract = {In this article we consider model reduction via proper orthogonal decomposition (POD) and its application to parameter estimation problems constrained by parabolic PDEs. We use a first discretize then optimize approach to solve the parameter estimation problem and show that the use of derivative information in the reduced-order model is important. We include directional derivatives directly in the POD snapshot matrix and show that, equivalently to the stationary case, this extension yields a more robust model with respect to changes in the parameters. Moreover, we propose an algorithm that uses derivative-extended POD models together with a Gauss--Newton method. We give an a posteriori error estimate that indicates how far a suboptimal solution obtained with the reduced problem deviates from the solution of the high dimensional problem. Finally we present numerical examples that showcase the efficiency of the proposed approach.}, language = {en} }