TY - CONF A1 - Titscher, Thomas A1 - Robens-Radermacher, Annika A1 - Unger, Jörg F. T1 - Model calibration and damage detection for a digital twin N2 - Numerical models are an essential tool in predicting and monitoring the behavior of civil structures. Inferring the model parameters is a challenging tasks as they are often measured indirectly and are affected by uncertainties. Digital twins couple those models with real-world data and can introduce additional, systematic sensor uncertainties related to the sensor calibration, i.e. uncertain offsets and calibration factors. In this work, the challenges of data processing, parameter identification, model selection and damage detection are explored using a lab-scale cable stayed bridge demonstrator. By combining force measurements in the cables with displacement measurements from both laser and stereo-photogrammetry systems, the elastic parameters of a three-dimensional finite element beam model are inferred. Depending on the number of sensors and the number of datasets used, parametrizing the sensor offsets and factors, leads to model with over 100 parameters. With a real-time solution of the problem in mind, a highly efficient analytical variational Bayesian approach is used to solve it within seconds. An analysis of the required assumptions and limitations of the approach, especially w.r.t. to the computed evidence, is provided by a comparison with dynamic nested sampling in a simplified problem. Finally, by inferring the value of additional damage parameters along the bridge, the method is successfully used to detect the location of an artificially introduced weak spot in the demonstrator bridge. T2 - ECCOMAS 2022 CY - Oslo, Norway DA - 05.06.2022 KW - Bayesian identification KW - Digital twin KW - Variational Bayesian KW - Damage detection PY - 2022 AN - OPUS4-55083 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Diercks, Philipp A1 - Veroy, K. A1 - Robens-Radermacher, Annika A1 - Unger, Jörg F. T1 - Multiscale modeling of heterogeneous structures based on a localized model order reduction approach N2 - Many of today’s problems in engineering demand reliable and accurate prediction of failure mechanisms of mechanical structures. Herein, it is necessary to take into account the heterogeneous structure on the lower scale, to capture the underlying physical phenomena. However, this poses a great challenge to the numerical solution as the computational cost is significantly increased by resolving the lower scale in the model. Moreover, in applications where scale separation as the basis of classical homogenization schemes does not hold, the influence of the lower scale on the upper scale has to be modelled directly. This work aims to develop an efficient concurrent methodology to model heterogeneous structures combining the variational multiscale method (VMM) [1] and model order reduction techniques (e. g. [2]). First, the influence of the lower scale on the upper scale can be taken into account following the additive split of the displacement field as in the VMM. Here, also a decomposition of the global domain into subdomains, each containing a fine grid discretization of the lower scale, is introduced. Second, reduced approximation spaces for the upper and lower scale solution are constructed by exploring possible solutions for each subdomain based on a representative unit cell. The local reduced spaces are designed such that local contributions of each subdomain can be coupled in a conforming way. Thus, the resulting global system is sparse and reduced in size compared to the direct numerical simulation, leading to a faster solution of the problem. The authors gratefully acknowledge financial support by the German Research Foundation (DFG), project number 394350870, and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC Grant agreement No. 818473). T2 - The 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) 2022 CY - Oslo, Norway DA - 05.06.2022 KW - Multiscale methods KW - Variational multiscale method KW - Domain decomposition KW - Model order reduction PY - 2022 AN - OPUS4-55117 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Robens-Radermacher, Annika A1 - Coelho Lima, Isabela A1 - Unger, Jörg F. T1 - Efficient model identification using a PGD forward model - Influence of surrogate accuracy and converergence approach N2 - There is a rising attention of using numerical models for effcient structural monitoring and ensuring the structure's safety. Setting up virtual models as twin for real structures requires a model identification process calculating the unknown model parameters, which mostly are only indirectly measurable. This is a computationally very costly inverse optimization process, which often makes it unfeasible for real applications. Effcient surrogate models such as reduced order models can be used, to overcome this limitation. But the influence of the model accuracy on the identification process has then to be considered. The aim is to automatically control the influence of the model's accuracy on the identification. Here, a variational Bayesian inference approach[3] is coupled with a reduced forward model using the Proper Generalized Decomposition (PGD) method. The influence of the model accuracy on the inference result is studied and measured. Therefore, besides the commonly used Bayes factor the Kullback-Leibler divergences between the predicted posterior pdfs are proposed. In an adaptive inference procedure, the surrogate's accuracy is iteratively increased, and the convergence of the posterior pdf is analysed. The proposed adaptive identification process is applied to the identification of spatially distributed damage modeled by a random eld for a simple example with synthetic data as well as a small, reinforced bridge with real measurement data. It is shown that the proposed criteria can mirror the influence of the model accuracy and can be used to automatically select a suffciently accurate surrogate model. T2 - The 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) 2022 CY - Oslo, Norway DA - 05.06.2022 KW - Model order reduction KW - Model identification KW - Bayes factor KW - PGD KW - Kullback-Leibner divergence PY - 2022 AN - OPUS4-55112 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Strobl, Dominic A1 - Robens-Radermacher, Annika A1 - Ghnatios, C. A1 - Pittner, Andreas A1 - Rethmeier, Michael A1 - Unger, Jörg F. T1 - PGD model with domain mapping of Bead-on-Plate weld simulation for wire arc additive manufacturing N2 - Numerical simulations are essential in predicting the behavior of systems in many engineering fields and industrial sectors. The development of accurate virtual representations of actual physical products or processes allows huge savings in cost and resources. In fact, digital twins would allow reducing the number of real, physical prototypes, tests, and experiments, thus also increasing the sustainability of the production processes and products’ lifetime. Standard numerical methods fail in providing real time simulations, especially for complex processes such as additive manufacturing applications. This work aims to build up a reduced order model for efficient wire arc additive manufacturing simulations by using the proper generalized decomposition (PGD) [1,2] method. Model order reduction is a popular concept to decrease the computational effort, where each evaluation of the reduced forward model is faster than evaluations using classical methods, even for complex models. The simulation of a moving heat source leads to a hardly separable parametric problem, which is solved by a new mapping approach [3]. Using this procedure, it is possible to create a simple separated representation of the forward model. In this contribution, a PGD model is derived for the first part of wire arc additive manufacturing: bead-on-plate weld. An excellent agreement with a standard finite element method is shown. The reduced model is further used in a model calibration set up, speeding up calibrations and ultimately leading to an optimized real-time simulation. T2 - The 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) 2022 CY - Oslo, Norway DA - 05.06.2022 KW - PGD KW - Model calibration KW - Hardly separable problem KW - Additive manufacturing PY - 2022 AN - OPUS4-55111 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Jafari, Abbas A1 - Titscher, Thomas A1 - Chatzi, E. A1 - Unger, Jörg F. T1 - Variational Bayesian inference of damage in concrete material using spatially-dense data N2 - Numerical simulators, such as finite element models, have become increasingly capable of predicting the behaviour of structures and components owing to more sophisticated underlying mathematical models and advanced computing power. A common challenge lies, however, in calibrating these models in terms of their unknown/uncertain parameters. When measurements exist, this can be achieved by comparing the model response against measured data. Besides uncertain model parameters, phenomena like damage can give rise to further uncertainties; in particular, quasi-brittle materials, like concrete, experience damage in a heterogeneous manner due to various imperfections, e.g. in geometry and boundary conditions. This hardens an accurate prediction of the damaged behaviour of real structures that comprise such materials. In this study, which draws from a data-driven approach, we use the force-version of the finite element model updating method (FEMU-F) to incorporate measured displacements into the identification of the damage parameters, in order to cope with heterogeneity. In this method, instead of conducting a forward evaluation of the model and comparing the model response (displacements) against the data, we impose displacements to the model and compare the resulting force residuals with measured reaction forces. To account for uncertainties in the measurement of displacements, we endow this approach with a penalty term, which reflects the discrepancy between measured and imposed displacements, where the latter is assumed as unknown random variables to be identified as well. A Variational Bayesian approach is used as an approximating tool for computing posterior parameters. The underlying damage model considered in this work is a gradient-enhanced damage model. We first establish the identification procedure through two virtual examples, where synthetic data (displacements) are generated over a certain spatially-dense set of points over the domain. The procedure is then validated on an experimental case-study; namely a 3-point bending experiment with displacement measurements resulting from a digital image correlation (DIC) analysis. T2 - MSE 2022 CY - Online meeting DA - 27.09.2022 KW - Heterogeneity KW - Gradient damage KW - Model updating KW - Variational Bayesian KW - Concrete PY - 2022 AN - OPUS4-56625 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Tamsen, Erik A1 - Unger, Jörg F. T1 - Towards an automatic optimization framework for performance oriented precast concrete design N2 - The aim of the project LeBeDigital is to present opportunities of digitalization for concrete applications and show a way towards a performance oriented material design. Due to the high complexity of the manufacturing process of concrete and the range of parameters affecting the effective composite properties, a global optimization is challenging. Currently, most optimization is only carried out on a narrow scope related to the respective players, e.g. a mix optimization for a target strength, or a design optimization for minimum weight, using a given mix. To enable a path toward a full global optimization requires a reproducible chain of data, accessible for all contributors. We propose a framework based on an ontology, which automatically combines experimental data with numerical simulations. This not only simplifies experimental knowledge transfer, but allows the model calibration and the resulting simulation predictions to be reproducible and interpretable. In addition to an optimized set of parameters, this setup allows to study the quality and uncertainty of the data and models, as well as giving information about optimal experiments to improve the data set. We will present the proposed optimization workflow, using the example of a precast concrete element. The contribution will focus on the workflow and challenges of an interoperable FEM formulation. T2 - Material Science and Engineering, MSE Congress 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Performance oriented concrete design KW - Ontology KW - Optimization workflow PY - 2022 AN - OPUS4-56208 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Tamsen, Erik A1 - Unger, Jörg F. T1 - Towards an automatic optimization framework for performance oriented precast concrete design N2 - Concrete has a long history in the construction industry and is currently one of the most widely used building materials. Especially precast concrete elements are frequently utilized in construction projects for standardized applications, increasing the quality of the composite material, as well as reducing the required building time. Despite the accumulated knowledge, continuous research and development in this field is essential due to the complexity of the composite combined with the ever-growing number of applications and requirements. Especially in view of global climate change, design aspects as CO2 emissions and resource efficiency require new mix designs and optimization strategies. A result of the material’s high complexity and heterogeneity on multiple scales is that utilizing the full potential with changing demands is highly challenging, even for the established industry. We propose a framework based on an ontology, which automatically combines experimental data with numerical simulations. This not only simplifies experimental knowledge transfer, but allows the model calibration and the resulting simulation predictions to be reproducible and interpretable. This research shows a way towards a more performance oriented material design. Within this talk we present our workflow for an automated simulation of a precast element, demonstrating the interaction of the ontology and the finite element simulation. We show the automatic calibration of our early-age concrete model [1, 2], to improve the prediction of the optimal time for the removal of the form work. T2 - The 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS) 2022 CY - Oslo, Norway DA - 05.06.2022 KW - Performance oriented concrete design KW - Early-age concrete KW - Precast concrete KW - Ontology KW - Optimization workflow PY - 2022 AN - OPUS4-55302 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Meng, Birgit A1 - Pirskawetz, Stephan A1 - Tamsen, Eric A1 - Unger, Jörg F. T1 - Betondesign digital - Potenziale für das Bauwesen N2 - Beton ist weltweit einer der wichtigsten Konstruktionswerkstoffe und zeichnet sich durch eine enorme Anpassungsfähigkeit an sich verändernde Anforderungen aus. Damit verbunden ist eine hohe und kontinuierlich zunehmende Komplexität hinsichtlich der Ausgangsstoffe, Rezepturen und des Herstellungsprozesses. Folglich setzt eine Ausschöpfung des technischen und umweltbezogenen Potenzials der Betonbauweise höchste Expertise bei den Einzelakteuren der Bauindustrie voraus. T2 - MatFo2022 „Vom Material zur Innovation: Digital, Neutral, Vital“ CY - Cologne, Germany DA - 14.11.2022 KW - Beton KW - Zement KW - Digitalisierung KW - Ontologien PY - 2022 UR - https://www.werkstofftechnologien.de/veranstaltungen/matfo2022-vom-material-zur-innovation-digital-neutral-vital AN - OPUS4-59140 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -