TY - JOUR A1 - Robens-Radermacher, Annika A1 - Unger, Jörg F. T1 - Efficient structural reliability analysis by using a PGD model in an adaptive importance sampling schema N2 - One of the most important goals in civil engineering is to guarantee the safety of the construction. Standards prescribe a required failure probability in the order of 10−4 to 10−6. Generally, it is not possible to compute the failure probability analytically. Therefore, many approximation methods have been developed to estimate the failure probability. Nevertheless, these methods still require a large number of evaluations of the investigated structure, usually finite element (FE) simulations, making full probabilistic design studies not feasible for relevant applications. The aim of this paper is to increase the efficiency of structural reliability analysis by means of reduced order models. The developed method paves the way for using full probabilistic approaches in industrial applications. In the proposed PGD reliability analysis, the solution of the structural computation is directly obtained from evaluating the PGD solution for a specific parameter set without computing a full FE simulation. Additionally, an adaptive importance sampling scheme is used to minimize the total number of required samples. The accuracy of the failure probability depends on the accuracy of the PGD model (mainly influenced on mesh discretization and mode truncation) as well as the number of samples in the sampling algorithm. Therefore, a general iterative PGD reliability procedure is developed to automatically verify the accuracy of the computed failure probability. It is based on a goal-oriented refinement of the PGD model around the adaptively approximated design point. The methodology is applied and evaluated for 1D and 2D examples. The computational savings compared to the method based on a FE model is shown and the influence of the accuracy of the PGD model on the failure probability is studied. KW - Reliability KW - Probability of failure KW - Adaptive importance sampling KW - Reduced order models KW - Proper Generalized Decomposition PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-510345 DO - https://doi.org/10.1186/s40323-020-00168-z VL - 7 SP - Article number: 29 PB - SpringerOpen AN - OPUS4-51034 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Titscher, Thomas A1 - Unger, Jörg F. T1 - Efficient higher-order cycle jump integration of a continuum fatigue damage model N2 - Simulating high-cycle fatigue with continuum models offers the possibility to model stress-redistributions, consider 3Dstress states and simplifies extensions to multi-physics problems. The computational cost of conventional cycle-by-cycle time integrations is reduced by reformulating the fatigue problem as an ordinary differential equation for the material state and solving it with high-order adaptive time integration schemes. The computational cost of calculating the Change of the material state in one cycle is further reduced by a high-order fatigue-specific time integration. The approach is exemplarily demonstrated for a fatigue extension of the implicit gradient-enhanced damage model in 3D and compared to experimental Wöhler lines. KW - Cycle jump KW - Temporal multiscale KW - Fatigue modeling KW - Continuum damage mechanics KW - Regularized damage models PY - 2020 DO - https://doi.org/10.1016/j.ijfatigue.2020.105863 VL - 141 SP - 105863 PB - Elsevier Ltd. AN - OPUS4-51101 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Stochastische Modellkalibrierung eines digitalen Zwillings N2 - Ein Digitaler Zwilling repräsentiert ein reales Objekt in der digitalen Welt. Die Digitalen Zwillinge sind aus Daten und Modellen/Algorithmen aufgebaut und kontinuierlich über Sensoren mit der realen Welt gekoppelt. Anwendung z.B. Modellbewertung, Monitoring, Schadensdetektion. T2 - DVM-Online-Workshop "Grundlagen und Beispiele zur Digitalisierung für die Materialforschung und -prüfung" CY - Online meeting DA - 20.10.2020 KW - Modellkalibrierung KW - Stochastischen Verfahren KW - Realtime model updating KW - Demonstrator PY - 2020 AN - OPUS4-52175 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Robens-Radermacher, Annika A1 - Held, Felix A1 - Coelho Lima, Isabela A1 - Titscher, Thomas A1 - Unger, Jörg F. T1 - Efficient identification of random fields coupling Bayesian inference and PGD reduced order model for damage localization N2 - One of the main challenges regarding our civil infrastructure is the efficient operation over their complete design lifetime while complying with standards and safety regulations. Thus, costs for maintenance or replacements must be optimized while still ensuring specified safety levels. This requires an accurate estimate of the current state as well as a prognosis for the remaining useful life. Currently, this is often done by regular manual or visual inspections within constant intervals. However, the critical sections are often not directly accessible or impossible to be instrumented at all. Model‐based approaches can be used where a digital twin of the structure is set up. For these approaches, a key challenge is the calibration and validation of the numerical model based on uncertain measurement data. The aim of this contribution is to increase the efficiency of model updating by using the advantage of model reduction (Proper Generalized Decomposition, PGD) and applying the derived method for efficient model identification of a random stiffness field of a real bridge.” KW - Model reduction KW - Model updating KW - Proper generalized decomposition KW - Random field KW - Variational Bayesian Inference PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-521275 DO - https://doi.org/10.1002/pamm.202000063 VL - 20 IS - 1 SP - e202000063 PB - Wiley Online Libary AN - OPUS4-52127 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pohl, Christoph A1 - Smilauer, V. A1 - Unger, Jörg F. T1 - A three-phase transport model for high-temperature concrete simulations validated with X-ray CT data N2 - Concrete exposure to high temperatures induces thermo-hygral phenomena, causing water phase changes, buildup of pore pressure and vulnerability to spalling. In order to predict these phenomena under various conditions, a three-phase transport model is proposed. The model is validated on X-ray CT data up to 320 ◦C, showing good agreement of the temperature profiles and moisture changes. A dehydration description, traditionally derived from thermogravimetric analysis, was replaced by a formulation based on data from neutron radiography. In addition, treating porosity and dehydration evolution as independent processes, previous approaches do not fulfil the solid mass balance. As a consequence, a new formulation is proposed that introduces the porosity as an independent variable, ensuring the latter condition. KW - Concrete KW - Porous media KW - Spalling KW - Dehydration KW - Moisture transport KW - Heat transfer KW - Pore pressure KW - Porosity KW - Finite elements PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-532840 UR - https://doi.org/10.5281/zenodo.4890635 DO - https://doi.org/10.3390/ma14175047 SN - 1996-1944 VL - 14 IS - 17 SP - 1 EP - 21 PB - MDPI CY - Basel AN - OPUS4-53284 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Digital Twins for materials and structures N2 - A safe and robust performance is a key criterion when building and maintaining structures and components. Ensuring this criterion at different stages of the lifetime can be supported by applying continuous monitoring concepts. The latter usually can serve multiple purposes, including the determination of material parameters for the design phase, the evaluation of the actual loading/environmental conditions (instead of using conservative estimates that are usually larger) and evaluating or predicting the true performance of the structure (thus decreasing the model bias). In this context, a digital twin of the structure has many benefits. In addition, it allows to introduce virtual sensors to “measure” sensor information that is e.g. inaccessible or unmeasureable. In the limit, the remaining useful life of a structure can be interpreted as a property that can be “measured” indirectly via the numerical model in combination with real sensor data. In order to efficiently use monitoring techniques in the context of a digital twin, it is important to consider the complete chain of information including the choice of sensors, the data processing and structuring, the modelling assumptions, the numerical simulation and finally the stochastic nature of the model prediction. In this presentation, challenges in this context are discussed with a specific focus on Bayesian model updating of the digital twin, accounting for both parameter updates as well as model bias that results from the limitations of modelling assumption. A bottleneck in this approach is the computational effort related to sampling methods such as Markov chain Monte Carlo methods that require many evaluations of the forward model. An alternative to the expensive computation of the forward model for updating the digital twin is the combination with model reduction techniques such as the Proper General Decomposition. The results are illustrated for several examples and scales, ranging from digitals twin for material tests in the lab over lab scale structural digital twins up to damage identification in field experiments. T2 - 1st International Workshop on Metrology for virtual measuring instruments CY - Berlin, Germany DA - 21.09.2021 KW - Digital twin for monitoring KW - Digital Twins for materials and structures PY - 2021 AN - OPUS4-53879 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Digital Twins for monitoring purposes N2 - In materials and component research, artificial intelligence methodologies will lead to massive upheavals in the coming years. The processes of material development, material processing, lifetime prediction and material characterization will change significantly. By combining AI methods and new forms of knowledge representation, the data-based management of product life cycles will take on new qualities. To address this emerging field of research Fraunhofer IWM set up the online workshop »AI Methods for Fatigue Behavior Assessment and Component Lifetime Prediction« T2 - Workshop “AI for materials fatigue assessment and machine component lifetime prediction” CY - Online meeting DA - 24.11.2021 KW - Digital twin KW - Model calibration KW - Parameter estimation KW - Ontology for model calibration PY - 2021 AN - OPUS4-53846 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Digital twins for monitoring purposes - uncertainty, model bias and model order reduction N2 - A safe and robust performance is a key criterion when building and maintaining structures and component. Ensuring this criterion at different stages of the lifetime can be supported by applying continuous monitoring concepts. The latter usually can serve multiple purposes, including the determination of material parameters for the design phase, the evaluation of the actual loading/environmental conditions (instead of using conservative estimates that are usually larger) and evaluating or predicting the true performance of the structure (thus decreasing the model bias). In this context, a digital twin of the structure has many benefits. It allows to introduce virtual sensors to “measure” sensor information that is e.g. inaccessible or unmeasureable. In order to efficiently use monitoring techniques in the context of a digital twin, it is important to consider the complete chain of information including the choice of sensors, the data processing and structuring, the modelling assumptions, the numerical simulation and finally the stochastic nature of the model prediction. In this presentation, challenges in this context are discussed with a specific focus on Bayesian model updating of the digital twin, accounting for both parameter updates as well as model bias that results from the limitations of modelling assumption. A bottleneck in this approach is the computational effort related to sampling methods such as Markov chain Monte Carlo methods that require many evaluations of the forward model. An alternative to the expensive computation of the forward model for updating the digital twin is the combination with model reduction techniques such as the Proper General Decomposition [1, 2]. The results are illustrated for several examples and scale, ranging from digitals twin for material tests in the lab over lab scale structural digital twins up to damage identification in field experiments. T2 - MMLDT-CSET 2021 Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology CY - San Diego, CA, USA DA - 26.09.2021 KW - Digital twin for monitoring purposes KW - Model bias KW - Model order reduction PY - 2021 AN - OPUS4-53881 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Stochastische Modellkalibrierung eines digitalen Zwillings N2 - Motivation: Ein Digitaler Zwilling repräsentiert ein reales Objekt in der digitalen Welt. Die Digitalen Zwillinge sind aus Daten und Modellen/Algorithmen aufgebaut und kontinuierlich über Sensoren mit der realen Welt gekoppelt. Anwendung z.B. Modellbewertung, Monitoring, Schadensdetektion. T2 - VIPO Symposium 2021 CY - Bauhaus-Universität Weimar DA - 09.07.2021 KW - Digitaler Zwilling KW - Modellkalibrierung KW - Modelanpassung KW - Bayesian Inferenz PY - 2021 AN - OPUS4-53063 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Gerist, S. A1 - R. Maheri, M. A1 - Unger, Jörg F. T1 - Damage Detection of a Scaled Steel Frame Offshore Structure Using an Improved Imperialist Competitive Algorithm N2 - In this paper, the imperialist competitive optimization algorithm is improved by damage functions to detect damage in a model steel frame test structure for offshore applications. A finite element model of the test structure is developed, validated and updated using the proposed method. As there are much more design variables, which are related to the stiffness of each finite element than the measured mode shapes, the problem is underdetermined. Therefore, damage functions are used to regularize the problem and decrease the number of design variables. A new objective function is proposed for the algorithm using the mode shapes and their l1 norm. The first ten measured mode shapes are used to solve the problem. It is shown that the proposed method is capable of predicting the damage locations with acceptable accuracy. KW - Damage detection KW - Imperialist competitive algorithm KW - Steel frames KW - Offshore structure KW - Damage functions PY - 2021 DO - https://doi.org/10.1007/s40996-021-00743-3 SN - 2228-6160 VL - 9 SP - 1 EP - 15 PB - Springer Verlag CY - Schweiz AN - OPUS4-53709 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -