TY - JOUR A1 - Barz, Tilman A1 - Seliger, Dominik A1 - Marx, Klemens A1 - Sommer, Andreas A1 - Walter, Sebastian F. A1 - Bock, Hans Georg A1 - Koerkel, Stefan T1 - State and state of charge estimation for a latent heat storage JF - Control Engineering Practice N2 - 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. KW - Heat conduction in cylindrical shell KW - Kalman filter KW - Latent heat thermal energy storage (LHTES) KW - MODEL KW - Nonlinear state observer KW - OF-THE-ART KW - Orthogonal collocation KW - PCM KW - PERFORMANCE ENHANCEMENT TECHNIQUES KW - PHASE-CHANGE MATERIAL KW - POLYETHYLENE KW - Reduced model KW - simulation KW - Solidification KW - State of charge (SOC) KW - SYSTEMS KW - THERMAL-ENERGY STORAGE Y1 - 2018 U6 - https://doi.org/10.1016/j.conengprac.2017.11.006 VL - 72 SP - 151 EP - 166 PB - Pergamon-Elsevier d. ER - TY - JOUR A1 - Schmidt, Andreas A1 - Potschka, Andreas A1 - Körkel, Stefan A1 - Bock, Hans Georg T1 - Derivate-extended pod reduced-order modelling for parameter estimation JF - SIAM Journal on Scientific Computing N2 - 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. KW - proper orthogonal decomposition KW - parameter estimation KW - error estimates Y1 - 2013 U6 - https://doi.org/10.1137/120896694 SN - 1095-7197 SN - 1064-8275 VL - 35 IS - 6 PB - SIAM CY - Philadelphia, Pa. ER -