TY - GEN A1 - Andrés Arcones, Daniel A1 - Diercks, Philipp A1 - Robens-Radermacher, Annika A1 - Rosenbusch, Sjard Mathis A1 - Tamsen, Erik A1 - Tyagi, Divyansh A1 - Unger, Jörg F. T1 - FenicsXConcrete N2 - FenicsXConcrete is a Python package for the simulation of mechanical problems. The general PDE solving software FEniCSx is extended with classes describing experimental setups, mechanical problems, thermo-mechanical problems, additive manufacturing and sensors. KW - FEM KW - Fenics KW - Concrete modelling PY - 2023 UR - https://github.com/BAMresearch/FenicsXConcrete DO - https://doi.org/10.5281/zenodo.7780757 PB - Zenodo CY - Geneva AN - OPUS4-59121 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Diercks, Philipp A1 - Gläser, D. A1 - Lünsdorf, O. A1 - Selzer, M. A1 - Flemisch, B. A1 - Unger, Jörg F. T1 - Evaluation of tools for describing, reproducing and reusing scientific workflows N2 - 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 WfMSswere 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:// this http URL), 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. KW - FAIR KW - Reproducibility, scientific workflow KW - Tool comparison KW - Workflow management PY - 2024 UR - https://arxiv.org/abs/2211.06429 DO - https://doi.org/10.48550/arXiv.2211.06429 PB - Arxiv; Cornell Tech CY - New York, NY AN - OPUS4-59804 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Diercks, Philipp A1 - Gläser, D. A1 - Unger, Jörg F. A1 - Crusoe, M. R. A1 - Huber, S. T1 - BAMresearch/NFDI4IngScientificWorkflowRequirements: Initial version N2 - Software-driven scientific workflows are often characterized by a complex interplay of various pieces of software executed in a particular order. The output of a computational step may serve as input to a subsequent computation, which requires them to be processed sequentially with a proper mapping of outputs to inputs. Other computations are independent of each other and can be executed in parallel. Thus, one of the main tasks of a workflow tool is a proper and efficient scheduling of the individual processing steps. Each processing step, just as the workflow itself, typically processes some input and produces output data. Apart from changing the input data to operate on, processing steps can usually be configured by a set of parameters to change their behavior. Moreover, the behavior of a processing step is determined by its source code and/or executable binaries/packages that are called within it. Beyond this, the computation environment not only has a significant influence on its behavior, but is also crucial in order for the processing step to work at all. The environment includes the versions of the interpreters or compilers, as well as all third-party libraries and packages that contribute to the computations carried out in a processing step. KW - Zenodo KW - 7790634 PY - 2023 DO - https://doi.org/10.5281/zenodo.7790633 PB - Zenodo CY - Geneva AN - OPUS4-59687 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Diercks, Philipp A1 - Veroy, K. A1 - Robens-Radermacher, Annika A1 - Unger, Jörg F. T1 - Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction N2 - In this paper, a methodology for fine scale modeling of large scale linear elastic structures is proposed, which combines the variational multiscale method, domain decomposition and model order reduction. The influence of the fine scale on the coarse scale is modelled by the use of an additive split of the displacement field, addressing applications without a clear scale separation. Local reduced spaces are constructed bysolving an oversampling problem with random boundary conditions. Herein, we inform the boundary conditions by a global reduced problem and compare our approach using physically meaningful correlated samples with existing approaches using uncorrelated samples. The local spaces are designed such that the local contribution of each subdomain can be coupled in a conforming way, which also preserves the sparsity pattern of standard finite element assembly procedures. Several numerical experiments show the accuracy and efficiency of the method, as well as its potential to reduce the size of the local spaces and the number of training samples compared to the uncorrelated sampling KW - Multiscale methods KW - Variational multiscale method KW - Localized model order reduction KW - Proper orthogonal PY - 2024 UR - https://arxiv.org/abs/2201.10374 DO - https://doi.org/10.48550/arXiv.2201.10374 PB - Arxiv; Cornell Tech CY - New York, NY AN - OPUS4-59803 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Robens-Radermacher, Annika A1 - Strobl, Dominic T1 - PGDrome N2 - A FEniCS based python module of the Proper Generalized Decomposition (PGD) method. KW - Reduced Order Model KW - Proper Generalized Decomposition KW - Fenics PY - 2023 DO - https://doi.org/10.5281/zenodo.10075334 PB - Zenodo CY - Geneva AN - OPUS4-59134 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Strobl, Dominic A1 - Unger, Jörg F. A1 - Chady, G. A1 - Klawoon, Alexander A1 - Pittner, Andreas A1 - Rethmeier, Michael A1 - Robens-Radermacher, Annika T1 - Efficient bead-on-plate weld model for parameter estimation towards effective wire arc additive manufacturing simulation N2 - Despite the advances in hardware and software techniques, standard numerical methods fail in providing real-time simulations, especially for complex processes such as additive manufacturing applications. A real-time simulation enables process control through the combination of process monitoring and automated feedback, which increases the flexibil- ity and quality of a process. Typically, before producing a whole additive manufacturing structure, a simplified experiment in form of a bead-on- plate experiment is performed to get a first insight into the process and to set parameters suitably. In this work, a reduced order model for the transient thermal problem of the bead-on-plate weld simulation is devel- oped, allowing an efficient model calibration and control of the process. The proposed approach applies the proper generalized decomposition (PGD) method, a popular model order reduction technique, to decrease the computational effort of each model evaluation required multiple times in parameter estimation, control and optimization. The welding torch is modeled by a moving heat source, which leads to difficulties separating space and time, a key ingredient in PGD simulations. A novel approach for separating space and time is applied and extended to 3D problems allowing the derivation of an efficient separated representation of the tem- perature. The results are verified against a standard finite element model showing excellent agreement. The reduced order model is also leveraged in a Bayesian model parameter estimation setup, speeding up calibrations and ultimately leading to an optimized real-time simulation approach for welding experiment using synthetic as well as real measurement data. KW - Ermüdung PY - 2023 DO - https://doi.org/10.5281/zenodo.7456813 PB - Zenodo CY - Geneva AN - OPUS4-59651 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -