@inproceedings{58763, abstract = {{Utilizing data is crucial for economic success, but a lack of interoperability and concerns about the misuse of ones own data are hindering the cross-organizational use of data. Dataspaces provide the infrastructure necessary to integrate heterogeneous data sources within an organization or ecosystem, enabling seamless data interaction and interoperability. In addition, data spaces strengthen data sovereignty through their decentralized nature, which enables organizations to effectively control and manage their data. However, challenges persist in managing the complexity and dynamic nature of dataspaces, requiring significant resources and technical expertise. The decentralized nature leads to a large and diverse number of stakeholders, who need to agree on the use and scope of a dataspace. Modeling is a common approach to cope with technical complexity and heterogeneous stakeholders. In this paper, we propose a version of SysML and a corresponding method that focus on the modelling of data spaces. We provide a dataspace modelling method to unify the understanding of dataspaces and scope among all stakeholders to simplify the design and development process.}}, author = {{Kulkarni, Pranav Jayant and Zerbin, Julian and Koldewey, Christian and Bernijazov, Ruslan and Dumitrescu, Roman}}, booktitle = {{2024 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)}}, keywords = {{Dataspaces, Modelling, SysML, Gaia-X, System Specification}}, location = {{Sharjah, United Arab Emirates }}, publisher = {{IEEE}}, title = {{{Using SysML as a Modelling Language for Dataspaces}}}, doi = {{10.1109/ictmod63116.2024.10878227}}, year = {{2025}}, } @article{62937, abstract = {{Sandwich packings are assembled from two conventional structured packings with different geometrical surface areas stacked alternatingly within a separation column. When operated under partially flooded conditions, they provide significant mass transfer improvement compared to common structured packings. In this work, a rate-based model including novel mass transfer correlations is presented and validated using a comprehensive experimental database for the reactive absorption of CO2 into aqueous monoethanolamine. The proposed rate-based approach is capable of accounting for axial dispersion, thereby enabling the evaluation of the effect of liquid-phase backmixing on the mass transfer performance. The validated rate-based model is used to evaluate the separation performance of sandwich packings. Compared with structured packings, up to 10 % higher mass transfer rates are obtained.}}, author = {{Franke, Patrick and Schubert, Markus and Hampel, Uwe and Kenig, Eugeny Y.}}, issn = {{0009-2509}}, journal = {{Chemical Engineering Science}}, keywords = {{Sandwich packings Structured packings Rate-based approach Model validation Ultra-fast X-ray tomography}}, publisher = {{Elsevier BV}}, title = {{{A rate-based model for reactive separation columns with sandwich packings}}}, doi = {{10.1016/j.ces.2025.122681}}, volume = {{321}}, year = {{2025}}, } @inproceedings{62078, abstract = {{Fiber reinforced plastics (FRP) exhibit strongly non-linear deformation behavior. To capture this in simulations, intricate models with a variety of parameters are typically used. The identification of values for such parameters is highly challenging and requires in depth understanding of the model itself. Machine learning (ML) is a promising approach for alleviating this challenge by directly predicting parameters based on experimental results. So far, this works mostly for purely artificial data. In this work, two approaches to generalize to experimental data are investigated: a sequential approach, leveraging understanding of the constitutive model and a direct, purely data driven approach. This is exemplary carried out for a highly non-linear strain rate dependent constitutive model for the shear behavior of FRP.The sequential model is found to work better on both artificial and experimental data. It is capable of extracting well suited parameters from the artificial data under realistic conditions. For the experimental data, the model performance depends on the composition of the experimental curves, varying between excellently suiting and reasonable predictions. Taking the expert knowledge into account for ML-model training led to far better results than the purely data driven approach. Robustifying the model predictions on experimental data promises further improvement. }}, author = {{Gerritzen, Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}}, booktitle = {{ECCM21 - Proceedings of the 21st European Conference on Composite Materials}}, isbn = {{978-2-912985-01-9}}, keywords = {{Direct parameter identification, Machine learning, Convolutional neural networks, Strain rate dependency, Fiber reinforced plastics, woven composites, segmentation, synthetic training data, x-ray computed tomography}}, pages = {{1252–1259}}, publisher = {{European Society for Composite Materials (ESCM)}}, title = {{{Direct parameter identification for highly nonlinear strain rate dependent constitutive models using machine learning}}}, doi = {{10.60691/yj56-np80}}, volume = {{3}}, year = {{2024}}, } @inproceedings{46451, abstract = {{New technologies and materials carry significant potential for sustainable production and use of products. As an example, Additive Manufacturing technologies and materials promise lightweight design and energy efficient use of parts. Exhausting the full potential requires: a) consideration of uncertainties with respect to future capabilities, and b) upgradeable design guidelines to cover advancements consistently. The proposed approach merges concepts of Design-for-X with foresight algorithms of Scenario-Technique to derive actionable knowledge. It is validated by an application in the field of Additive Manufacturing, namely Metal Fused Deposition Modelling. Engineers benefit from the intuitive access to heterogeneous types of sustainability related information.}}, author = {{Gräßler, Iris and Mozgova, Iryna and Pottebaum, Jens and Ott, Manuel and Jung, Philipp and Hesse, Philipp}}, booktitle = {{17th CIRP Conference on Intelligent Computation in Manufacturing Engineering}}, keywords = {{Design-for-X, Scenario-Technique, sustainability, uncertainty, Life-Cycle Engineering, Additive Manufacturing, Circular Economy}}, location = {{Gulf of Naples}}, pages = {{549--554}}, publisher = {{Elsevier}}, title = {{{Handling of uncertainties in the design of sustainable Additive Manufacturing products by merging Design-for-X and Scenario-Technique}}}, doi = {{10.1016/j.procir.2024.08.238}}, volume = {{126}}, year = {{2024}}, } @article{52204, author = {{Genovese, Matteo and Schlüter, Alexander and Scionti, Eugenio and Piraino, Francesco and Corigliano, Orlando and Fragiacomo, Petronilla}}, issn = {{0360-3199}}, journal = {{International Journal of Hydrogen Energy}}, keywords = {{Hydrogen economy, Green hydrogen, Power-to-X, Hydrogen-to-X, Sector coupling}}, number = {{44}}, pages = {{16545--16568}}, publisher = {{Elsevier BV}}, title = {{{Power-to-hydrogen and hydrogen-to-X energy systems for the industry of the future in Europe}}}, doi = {{10.1016/j.ijhydene.2023.01.194}}, volume = {{48}}, year = {{2023}}, } @article{35728, abstract = {{Technological developments such as Cloud Computing, the Internet of Things, Big Data and Artificial Intelligence continue to drive the digital transformation of business and society. With the advent of platform-based ecosystems and their potential to address complex challenges, there is a trend towards greater interconnectedness between different stakeholders to co-create services based on the provision and use of data. While previous research on digital transformation mainly focused on digital transformation within organizations, it is of growing importance to understand the implications for digital transformation on different layers (e.g., interorganizational cooperation and platform ecosystems). In particular, the conceptualization and implications of public data spaces and related ecosystems provide promising research opportunities. This special issue contains five papers on the topic of digital transformation and, with the editorial, further contributes by providing an initial conceptualization of public data spaces' potential to foster innovative progress and digital transformation from a management perspective.}}, author = {{Beverungen, Daniel and Hess, Thomas and Köster, Antonia and Lehrer, Christiane}}, issn = {{1019-6781}}, journal = {{Electronic Markets}}, keywords = {{Digital transformation, Public data spaces, Digital platforms, GAIA-X}}, pages = {{493--501}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{From private digital platforms to public data spaces: implications for the digital transformation}}}, doi = {{10.1007/s12525-022-00553-z}}, volume = {{32}}, year = {{2022}}, } @inproceedings{12918, abstract = {{The test for small delay faults is of major importance for predicting potential early life failures or wearout problems. Typically, a faster-than-at-speed test (FAST) with sev¬eral different frequencies is used to detect also hidden small delays, which can only be propagated over short paths. But then the outputs at the end of long paths may no longer reach their stable values at the nominal observation time and must be considered as unknown (X-values). Thus, test response compaction for FAST must be extremely flexible to cope with high X-rates, which also vary with the test frequencies. Stochastic compaction introduced by Mitra et al. is controlled by weighted pseudo-random signals allowing for easy adaptation to varying conditions. As demonstrated in previous work, the pseudo-random control can be optimized for high fault efficiency or X-reduction, but a given target in fault efficiency cannot be guaranteed. To close this gap, a hybrid space compactor is introduced in this paper. It is based on the observation that many faults are lost in the compaction of relatively few critical test patterns. For these critical patterns a deterministic compaction phase is added to the test, where the existing compactor structure is re-used, but controlled by specifically determined control vectors. }}, author = {{Maaz, Mohammad Urf and Sprenger, Alexander and Hellebrand, Sybille}}, booktitle = {{50th IEEE International Test Conference (ITC)}}, keywords = {{Faster-than-at-speed test, BIST, DFT, Test response compaction, Stochastic compactor, X-handling}}, location = {{Washington, DC, USA}}, pages = {{1--8}}, publisher = {{IEEE}}, title = {{{A Hybrid Space Compactor for Adaptive X-Handling}}}, year = {{2019}}, } @inproceedings{9974, abstract = {{The integrated modeling of behavior and reliability in system development delivers a model-based approach for reliability investigation by taking into account the dynamic system behavior as well as the system architecture at different phases of the development process. This approach features an automated synthesis of a reliability model out of a behavior model enabling for the closed loop modeling of degradation of the system and its (dynamic) behavior. The approach is integrated into the development process following Systems Engineering. It is based on standard models used in model-based development methodologies i.e. SysML or Matlab/Simulink. In addition to the theoretical description of the necessary steps the procedure is validated by an application example at two stages of the development process.}}, author = {{Hentze, Julian and Kaul, Thorben and Grässler, Iris and Sextro, Walter}}, booktitle = {{ICED17, 21st International conference on enginieering design}}, keywords = {{Design for X (DfX), Product modelling / models, Robust design, Systems Engineering (SE), Reliability}}, pages = {{385--394}}, title = {{{Integrated modeling og behavior and reliability in system development}}}, year = {{2017}}, }