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Thermal transient problems, essential for modeling applications like welding and additive metal manufacturing, are characterized by a dynamic evolution of temperature. Accurately simulating these phenomena is often computationally expensive, thus limiting their applications, for example for model parameter estimation or online process control. Model order reduction, a solution to preserve the accuracy while reducing the computation time, is explored. This article addresses challenges in developing reduced order models using the proper generalized decomposition (PGD) for transient thermal problems with a specific treatment of the moving heat source within the reduced model. Factors affecting accuracy, convergence, and computational cost, such as discretization methods (finite element and finite difference), a dimensionless formulation, the size of the heat source, and the inclusion of material parameters as additional PGD variables are examined across progressively complex examples. The results demonstrate the influence of these factors on the PGD model’s performance and emphasize the importance of their consideration when implementing such models. For thermal example, it is demonstrated that a PGD model with a finite difference discretization in time, a dimensionless representation, a mapping for a moving heat source, and a spatial domain non-separation yields the best approximation to the full order model.
Simulation-based digital twins have emerged as a powerful tool for evaluating the mechanical response of bridges. As virtual representations of physical systems, digital twins can provide a wealth of information that complements traditional inspection and monitoring data. By incorporating virtual sensors and predictive maintenance strategies, they have the potential to improve our understanding of the behavior and performance of bridges over time. However, as bridges age and undergo regular loading and extreme events, their tructural characteristics change, often differing from the predictions of their initial design. Digital twins must be continuously adapted to reflect these changes. In this article, we present a Bayesian framework for updating simulation-based digital twins in the context of bridges. Our approach integrates information from measurements to account for inaccuracies in the simulation model and quantify uncertainties. Through its implementation and assessment, this work demonstrates the potential for digital twins to provide a reliable and up-to-date representation of bridge behavior, helping to inform decision-making for maintenance and management.
In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing.
Typically, these components have to be combined in arbitrarily complex workflows to address a specific research question. In order for peer researchers to understand, reproduce and (re)use the findings of a scientific publication, several challenges have to be addressed. For instance, the employed workflow has to be automated and information on all used software must be available for a reproduction of the results. Moreover, the results must be traceable and the workflow documented and readable to allow for external verification and greater trust.
In this paper, existing workflow management systems (WfMSs) are discussed regarding their suitability for describing, reproducing and reusing scientific workflows. To this end, a set of general requirements for WfMSs were deduced from user stories that we deem relevant in the domain of computational science and engineering. On the basis of an exemplary workflow implementation, publicly hosted at GitHub (https://github.com/BAMresearch/NFDI4IngScientificWorkflowRequirements), a selection of different WfMSs is compared with respect to these requirements, to support fellow scientists in identifying the WfMSs that best suit their requirements.
Structural build-up describes the stability and early-age strength development of fresh mortar used in 3D printing. lt is influenced by several factors, i.e. the composition of the print able material, the printing regime, and the ambient conditions. The existing modelling approaches for structural build-up usually define the model parameters for a specific material composition with out considering the influence of the ambient conditions. The goal of this contribution is to explicitly include the temperature dependency in the modelling approach. Temperature changes have signifi cant impact on the structural build-up process: an increase of the temperature leads to a faster dissol ution of cement phases and accelerates hydration. The proposed extended model includes temperature dependency using the Arrhenius theory. The new model parameters are successfully calibrated based on Viskomat measurement data using Bayesian inference. Furthermore, a higher impact of the temperature in the re-flocculation as in the structuration stage is observed.
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.”
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.
Die Brücken im Netz der Bundesverkehrswege sind überwiegend in einem ausreichenden bis guten Zustand. Allerdings steigt der Unterhalts- und Sanierungsaufwand aufgrund des inzwischen hohen Alters vieler Brücken sowie des ständig wachsenden Schwerlastverkehrs. Techniken zur Einschätzung der verbleibenden Lebensdauer von Brücken sowie zur dauerhaften Beobachtung des Tragverhaltens bzw. des Erfolges von Sanierungsmaßnahmen werden daher für den sicheren und wirtschaftlichen Betrieb dringend benötigt. Zur Evaluierung dafür geeigneter holistischer Ansätze wurde in der BAM das Projekt BLEIB - Bewertung, Lebensdauerprognose und Instandsetzung von Brückenbauwerken - ins Leben gerufen.
Ein zentrales Ergebnis des Projektes ist eine extern vorgespannte Stahlbetonbrücke als Zweifeldträger mit einer Gesamtlänge von 24 m, die für den Test verschiedenster Sensorsysteme, zur Validierung numerischer Modelle und zur Erprobung von Sanierungs- und Verstärkungsmaßnahmen entwickelt wurde. Für die Simulation unterschiedlicher Schädigungsgrade kann die Vorspannung der Brücke variiert werden. Die Brücke wird mit beweglichen Gewichten belastet und über einen Shaker zum Schwingen angeregt.
Das Brückenmodell wurde bewusst geschädigt, indem die Vorspannung der Struktur erstmalig schrittweise bis auf null reduziert wurde. Unter der Eigenlast verformte sich die Brücke, wodurch eine Rissbildung im Beton einsetzte. Die Zugspannung, die zuvor durch die Vorspannung aufgenommen wurde, übernahm Schritt für Schritt der Beton. Als die Zugspannungen die relativ geringe Zugfestigkeit des Betons überstiegen, begann dieser zu reißen und die schlaffe Bewehrung der Struktur nahm die Spannungen auf. Dieser Versuch wurde unter anderem von Schallemissionsmessungen begleitet. Der Rissbildungsprozess konnte damit, bei gleichzeitiger Aufzeichnung der Vorspannung, früh detektiert und die Risse geortet werden. Die Ergebnisse korrelieren gut mit den Ergebnissen der stereophotogrammetrischen Verformungsmessungen der Struktur.