Recent Submissions
Carrier-phase DNS of ignition and combustion of iron particles in a turbulent mixing layer
(2024) Luu, Tien Duc; Shamooni, Ali; Kronenburg, Andreas; Braig, Daniel; Mich, Johannes; Nguyen, Bich-Diep; Scholtissek, Arne; Hasse, Christian; Thäter, Gabriel; Carbone, Maurizio; Frohnapfel, Bettina; Stein, Oliver Thomas
Three-dimensional carrier-phase direct numerical simulations (CP-DNS) of reacting iron particle dust clouds in a turbulent mixing layer are conducted. The simulation approach considers the Eulerian transport equations for the reacting gas phase and resolves all scales of turbulence, whereas the particle boundary layers are modelled employing the Lagrangian point-particle framework for the dispersed phase. The CP-DNS employs an existing sub-model for iron particle combustion that considers the oxidation of iron to FeO and that accounts for both diffusion- and kinetically-limited combustion. At first, the particle sub-model is validated against experimental results for single iron particle combustion considering various particle diameters and ambient oxygen concentrations. Subsequently, the CP-DNS approach is employed to predict iron particle cloud ignition and combustion in a turbulent mixing layer. The upper stream of the mixing layer is initialised with cold particles in air, while the lower stream consists of hot air flowing in the opposite direction. Simulation results show that turbulent mixing induces heating, ignition and combustion of the iron particles. Significant increases in gas temperature and oxygen consumption occur mainly in regions where clusters of iron particles are formed. Over the course of the oxidation, the particles are subjected to different rate-limiting processes. While initially particle oxidation is kinetically-limited it becomes diffusion-limited for higher particle temperatures and peak particle temperatures are observed near the fully-oxidised particle state. Comparing the present non-volatile iron dust flames to general trends in volatile-containing solid fuel flames, non-vanishing particles at late simulation times and a stronger limiting effect of the local oxygen concentration on particle conversion is found for the present iron dust flames in shear-driven turbulence.
Mutual and thermal diffusivities in binary mixtures of polystyrene with dissolved N2 or CO2 by dynamic light scattering
(2024) Schmidt, Patrick S.; Jander, Julius H.; Alhadi, Fatima; Ratka, Marcel; Bonten, Christian; Klein, Tobias; Fröba, Andreas P.
In physical foaming processes of thermoplastics, a liquid mixture consisting of a molten polymer with a dissolved blowing agent is extruded through a die or injected into a mold. The morphology of the foam matrix strongly depends on the mutual diffusion coefficient D11 of the liquid polymer-blowing agent mixture. For a better understanding of the underlying physical mechanisms during the foaming process and the development of corresponding models, accurate knowledge of D11 is needed. This work reports on the simultaneous measurement of D11 and the thermal diffusivity a of liquid mixtures consisting of polystyrene melts with dissolved nitrogen (N2) or carbon dioxide (CO2) at mass fractions wsolute up to 0.003 or 0.02 and temperatures T between (433 and 533) K using dynamic light scattering (DLS). The determined D11 range is between (1 and 4) × 109 m2⋅s-1 and are slightly larger for the mixtures containing N2 at a given T and wsolute. D11 could be determined with an average expanded relative uncertainty of 18%. Considering all investigated state points and the achieved experimental uncertainties, both D11 and a are independent on the amount of dissolved gas, despite relatively large mole fractions of the dissolved blowing agents xsolute of 0.997 and 0.93.
Software product line testing : a systematic literature review
(2024) Agh, Halimeh; Azamnouri, Aidin; Wagner, Stefan
A Software Product Line (SPL) is a software development paradigm in which a family of software products shares a set of core assets. Testing has a vital role in both single-system development and SPL development in identifying potential faults by examining the behavior of a product or products, but it is especially challenging in SPL. There have been many research contributions in the SPL testing field; therefore, assessing the current state of research and practice is necessary to understand the progress in testing practices and to identify the gap between required techniques and existing approaches. This paper aims to survey existing research on SPL testing to provide researchers and practitioners with up-to-date evidence and issues that enable further development of the field. To this end, we conducted a Systematic Literature Review (SLR) with seven research questions in which we identified and analyzed 118 studies dating from 2003 to 2022. The results indicate that the literature proposes many techniques for specific aspects (e.g., controlling cost/effort in SPL testing); however, other elements (e.g., regression testing and non-functional testing) still need to be covered by existing research. Furthermore, most approaches are evaluated by only one empirical method, most of which are academic evaluations. This may jeopardize the adoption of approaches in industry. The results of this study can help identify gaps in SPL testing since specific points of SPL Engineering still need to be addressed entirely.
Using automatic model calibration for 3D morphological simulations : a case study of the Bodendorf reservoir flushing
(2024) Shoarinezhad, Vahid; Olsen, Nils Reidar Bøe; Wieprecht, Silke; Haun, Stefan
Reservoir sedimentation poses a significant challenge to water resource management. Improving the lifespan and productivity of reservoirs requires appropriate sediment management strategies, among which flushing operations have become more prevalent in practice. Numerical modeling offers a cost-effective approach to assessing the performance of different flushing operations. However, calibrating highly parametrized morphological models remains a complex task due to inherent uncertainties associated with sediment transport processes and model parameters. Traditional calibration methods require laborious manual adjustments and expert knowledge, hindering calibration accuracy and efficiency and becoming impractical when dealing with several uncertain parameters. A solution is to use optimization techniques that enable an objective evaluation of the model behavior by expediting the calibration procedure and reducing the issue of subjectivity. In this paper, we investigate bed level changes as a result of a flushing event in the Bodendorf reservoir in Austria by using a three-dimensional numerical model coupled with an optimization algorithm for automatic calibration. Three different sediment transport formulae (Meyer-Peter and Müller, van Rijn, and Wu) are employed and modified during the calibration, along with the roughness parameter, active layer thickness, volume fraction of sediments in bed, and the hiding-exposure parameter. The simulated bed levels compared to the measurements are assessed by several statistical metrics in different cross-sections. According to the goodness-of-fit indicators, the models using the formulae of van Rijn and Wu outperform the model calculated by the Meyer-Peter and Müller formula regarding bed patterns and the volume of flushed sediments.
Semi-explicit integration of second order for weakly coupled poroelasticity
(2024) Altmann, R.; Maier, R.; Unger, B.
We introduce a semi-explicit time-stepping scheme of second order for linear poroelasticity satisfying a weak coupling condition. Here, semi-explicit means that the system, which needs to be solved in each step, decouples and hence improves the computational efficiency. The construction and the convergence proof are based on the connection to a differential equation with two time delays, namely one and two times the step size. Numerical experiments confirm the theoretical results and indicate the applicability to higher-order schemes.
Improved a posteriori error bounds for reduced port-Hamiltonian systems
(2024) Rettberg, Johannes; Wittwar, Dominik; Buchfink, Patrick; Herkert, Robin; Fehr, Jörg; Haasdonk, Bernard
Projection-based model order reduction of dynamical systems usually introduces an error between the high-fidelity model and its counterpart of lower dimension. This unknown error can be bounded by residual-based methods, which are typically known to be highly pessimistic in the sense of largely overestimating the true error. This work applies two improved error bounding techniques, namely (a) a hierarchical error bound and (b) an error bound based on an auxiliary linear problem , to the case of port-Hamiltonian systems. The approaches rely on a secondary approximation of (a) the dynamical system and (b) the error system. In this paper, these methods are adapted to port-Hamiltonian systems. The mathematical relationship between the two methods is discussed both theoretically and numerically. The effectiveness of the described methods is demonstrated using a challenging three-dimensional port-Hamiltonian model of a classical guitar with fluid–structure interaction.
Higher-order iterative decoupling for poroelasticity
(2024) Altmann, Robert; Mujahid, Abdullah; Unger, Benjamin
For the iterative decoupling of elliptic–parabolic problems such as poroelasticity, we introduce time discretization schemes up to order five based on the backward differentiation formulae. Its analysis combines techniques known from fixed-point iterations with the convergence analysis of the temporal discretization. As the main result, we show that the convergence depends on the interplay between the time step size and the parameters for the contraction of the iterative scheme. Moreover, this connection is quantified explicitly, which allows for balancing the single error components. Several numerical experiments illustrate and validate the theoretical results, including a three-dimensional example from biomechanics.
Dictionary-based online-adaptive structure-preserving model order reduction for parametric Hamiltonian systems
(2024) Herkert, Robin; Buchfink, Patrick; Haasdonk, Bernard
Classical model order reduction (MOR) for parametric problems may become computationally inefficient due to large sizes of the required projection bases, especially for problems with slowly decaying Kolmogorov n -widths. Additionally, Hamiltonian structure of dynamical systems may be available and should be preserved during the reduction. In the current presentation, we address these two aspects by proposing a corresponding dictionary-based, online-adaptive MOR approach. The method requires dictionaries for the state-variable, non-linearities, and discrete empirical interpolation (DEIM) points. During the online simulation, local basis extensions/simplifications are performed in an online-efficient way, i.e., the runtime complexity of basis modifications and online simulation of the reduced models do not depend on the full state dimension. Experiments on a linear wave equation and a non-linear Sine-Gordon example demonstrate the efficiency of the approach.
Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems
(2024) Ehring, Tobias; Haasdonk, Bernard
Numerical methods for the optimal feedback control of high-dimensional dynamical systems typically suffer from the curse of dimensionality. In the current presentation, we devise a mesh-free data-based approximation method for the value function of optimal control problems, which partially mitigates the dimensionality problem. The method is based on a greedy Hermite kernel interpolation scheme and incorporates context knowledge by its structure. Especially, the value function surrogate is elegantly enforced to be 0 in the target state, non-negative and constructed as a correction of a linearized model. The algorithm allows formulation in a matrix-free way which ensures efficient offline and online evaluation of the surrogate, circumventing the large-matrix problem for multivariate Hermite interpolation. Additionally, an incremental Cholesky factorization is utilized in the offline generation of the surrogate. For finite time horizons, both convergence of the surrogate to the value function and for the surrogate vs. the optimal controlled dynamical system are proven. Experiments support the effectiveness of the scheme, using among others a new academic model with an explicitly given value function. It may also be useful for the community to validate other optimal control approaches.
Metadata management in virtual product development to enable cross-organizational data analytics
(2024) Ziegler, Julian; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)
Due to the advancing digitalization, companies are increasingly adopting computer-aided technologies. Especially in product development, computer-aided technologies enable a gradual shift from physical to virtual prototypes. This shift towards virtual product development includes design, simulation, testing, and optimization of products, and reduces costs and time needed for these tasks. Companies with strong activities in the field of virtual product development generate large amounts of heterogeneous data and wish to mine these data for knowledge. In this context, metadata is a key enabler for data discovery, data exploration, and data analyses but often neglected.
The diversity in the structure and formats of virtual product development data makes it difficult for domain experts to analyze them. Domain experts struggle with this task because such engineering data are not sufficiently described with metadata. Moreover, data in companies are often isolated in data silos and difficult to explore by domain experts. This calls for an adequate data and metadata management that is able to cope with the significant data heterogeneity in virtual product development, and that empowers domain experts to discover and access data for further analyses.
This thesis identifies previously unsolved challenges for a data and metadata management that is tailored to virtual product development and makes three contributions. First, a metadata model that provides a connected view on all data, metadata, and work activities of virtual product development projects. A prototypical implementation of this metadata model is already being applied to a real-world use case of an industry partner. Based on this foundation, the second contribution uses this metadata model to enable feature engineering with domain experts as part of data analyses projects. Going further, data analyses can directly use the metadata structure to provide added value without having to access the large amounts of product data. To this end, the third contribution utilizes the metadata structure itself to enable a novel approach to process discovery for product development projects. Thus, process structures in development projects can be analyzed with little effort, e.g., to identify good or inefficient processes in development projects.