1,721,032 research outputs found
Combination of model reduction and adaptive subset simulation for structural reliability problems
No full textA safe and robust design is a key criterion when building a structure or a component. Ensuring this criterion can either be performed by fullfilling prescribed safety margins, or by using a full probabilistic approach with a computation of the failure probability. The latter approach is particularly well suited for complex Problems with an interaction of different physical penomena that can be described in a numerical model. The bottleneck in this approach is the computational effort. Sampling methods such as Markov chain Monte Carlo methods are often used to evaluate the system reliability. Due to small failure probabilities (e.g. 10^6) and complex physical models with already and extensive computational effort for a single set of parameters, these methods a prohibitively expensive. The focus of this contribution is to demonstrate the advantages of combining model reduction techniques within the concept a variance reducing adaptive sampling procedures. In the developed method, a modification of the adaptive subset simulation based on Papaioannou et al. 2015 is used and coupled with a limit state function based on Proper Generalized Decomposition (PGD) (Chinesta et al. 2011). In the subset simulation the failure probability is expressed as a product of larger conditional failure probabilities. The intermediate failure events are chosen as a decreasing sequence. Instead of solving each conditional probability with a Markov chain approach, an importance sampling approach is used. It is be shown that the accuracy of the estimation depends mainly on the number of samples in the last sub-problem. For model reduction, the PGD approach is used to solve the structural problem a priori for a given Parameter space (physical space plus all random parameters). The PGD approach results in an approximation of the problem output within a prescribed range of all input Parameters (load factor, material properties, ..). The approximation of the solution by a separated form allows an evaluation of the limit state function within the sampling algorithm with almost no cost. This coupled PGD – adaptive subset Simulation approach is used to estimate the failure probability of examples with different complexity. The convergence, the error propagation as well as the reduction in computational time is discussed
A Database for Analyzing Round Robin Data
No full textFAIR (findable, accessible, interoperable and reusable) data usage is one of the main principals that many of the research and funding organizations include in their strategic plans, which means that following the main principals of FAIR data is required in many research projects. The definition of data being FAIR is very general, and when implementing that for a specific application or project or even setting a standardized procedure within a working group, a company or a research community, many challenges arise. In this contribution, an overview about our experience with different methods, tools and procedures is outlined.
We begin with a motivation on potential use cases for the applications of FAIR data with increasing complexity starting from a reproducible research paper over collaborative projects with multiple participants such as Round-Robin tests up to data-based models within standardization codes, applications in machine learning or parameter estimation of physics-based simulation models.
In a second part, different options for structuring the data are discussed. On the one hand, this includes a discussion on how to define actual data structures and in particular metadata schema, and on the other hand, two different systems for storing the data are discussed. The first one is the openBIS system, which is an open-source Lab notebook and PostgreSQL based data management system. A second option are a semantic representations using RDF based ontologies for the domain of interest.
In a third section, requirements for workflow tools to automate data processing are discussed and their integration into reproducible data analysis is presented with an outlook on required information to be stored as metadata in the database.
Finally, the presented procedures are exemplarily demonstrated for the calibration of a temperature dependent constitutive model for additively manufactured mortar. Metadata schemata for a rheological measurement setup are derived and implemented in an openBIS database. After a short review of a potential numerical model predicting the structural build-up behaviour, the automatic workflow to use the stored data for model parameter estimation is demonstrated
Data provenance - from experimental data to trustworthy simulation models and standards
No full textData provenance - from experimental data to trust worthy simulation models and standards Jörg F. Unger, Annika Robens-Radermacher, Erik Tamsen Bundesanstalt für Materialforschung und -prüfung (BAM). Unter den Eichen 87, 12205 Berlin, Germany FAIR (findable, accessible, interoperable and reusable) data usage is one of the main principals that many of the research and funding organizations include in their strategic plans, which means that following the main principals of FAIR data is required in many research projects. The definition of data being FAIR is very general, and when implementing that for a specific application or project or even setting a standardized procedure within a working group, a company or a research community, many challenges arise. In this contribution, an overview about our experience with different methods, tools and procedures is outlined. We begin with a motivation on potential use cases for the applications of FAIR data with increasing complexity starting from a reproducible research paper over collaborative projects with multiple participants such as Round-Robin tests up to data-based models within standardization codes, applications in machine learning or parameter estimation of physics-based simulation models. In a second part, different options for structuring the data are discussed. On the one hand, this includes a discussion on how to define actual data structures and in particular metadata schema, and on the other hand, two different systems for storing the data are discussed. The first one is the open BIS system, which is an opensource Lab notebook and Postgre SQL based data management system. A second option are a semantic representations using RDF based ontologies for the domain of interest. In a third section, requirements for workflow tools to automate data processing are discussed and their integration into reproducible data analysis is presented with an outlook on required information to be stored as metadata in the database
Model calibration and damage detection for a digital twin
No full textNumerical 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
A hyper reduced domain decomposition approach for modeling nonlinear heterogeneous structures
No full textMany 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 often heterogeneous structure on the fine scale, to capture the underlying physical phenomena. However, an increase of accuracy by dissolving the fine scale inevitably leads to an increase in computational cost. In the context of multiscale simulations, the FE2 method is widely used. In a two-level computation, the fine scale is depicted by a boundary value problem for a representative volume element (RVE), which is then solved in each integration point of the macro scale to determine the macroscopic response. However, the FE2 approach in general is computationally expensive and problematic in the special case of concrete structures. Here rather large RVEs are necessary to sufficiently represent the meso-structure, such that separation of scales cannot be assumed.
Therefore, the aim is to develop an efficient approach to modeling nonlinear heterogeneous structures using domain decomposition and reduced order modeling
Assessing Structural Failure in Extrusion-based 3D Concrete Printing Using Plasticity Models
No full text3D concrete printing (3DCP) brings automation in construction, reduces material usage, increases design flexibility, and eliminates the need for formwork. However, it is a complex process involving various parameters that are often defined by trial and error. This can lead to unforeseen failures during the print, such as buckling or yielding. Computational modeling can be used in the design stage to predict and prevent failure, during printing for real-time process control, and afterwards to assess how variations during printing affect the final structure.
The structural failure during the print is primarily governed by how concrete behaves at the material level, making the choice of constitutive model crucial. Plasticity models are commonly used to assess buildability, with the Mohr-Coulomb criterion being a widely used approach. However, its suitability for modeling fresh concrete for 3DCP, under such loading conditions and varying material properties is still an open research question. Furthermore, experimental studies have shown that fresh concrete exhibits non-linear behavior before failure, which is usually not considered in structural simulations of 3DCP.
This work investigates the influence of plasticity models on different structural failure modes observed in 3DCP, specifically elastic buckling and plastic collapse. The non-linear behavior of fresh concrete is accounted for by incorporating non-linear isotropic hardening into the plasticity models. A Von-Mises plasticity model and a Mohr-Coulomb model with a hyperbolic smooth approximation are implemented, both incorporating non-linear hardening. An objective stress rate formulation is adopted to consider geometric non-linearity for accurate buckling predictions. As freshly deposited layers structurate over time, an age-dependent model is implemented to capture the stiffness and strength evolution of printed layers. To simulate the layer-by-layer process, a pseudo-density-based activation method is used, allowing sequential activation of layers as printing progresses. Model parameters are identified for different ages using Bayesian inference via inverse finite element modeling by numerically replicating stress-strain data from uniaxial compression tests on samples at different ages. Printing simulations are conducted for thin-walled and cylindrical structures, demonstrating the influence of choice of plasticity model on buckling behavior and material failur
Multiscale modeling of heterogeneous structures based on a localized model order reduction approach
No full textMany of today’s problems in engineering demand reliable and accurate prediction of failure mechanisms of mechanical structures. Thus, it is necessary to take into account the heterogeneous structure on the smaller scale, to capture the underlying physical phenomena. However, this poses a great challenge to the numerical solution since the computational cost is significantly increased by resolving the smaller scale in the model. Moreover, in applications where scale separation as the basis of classical homogenization schemes does not hold, the influence of the smaller scale on the larger 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) and model order reduction techniques. First, the influence of the smaller scale on the larger 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 smaller scale, is introduced. Second, local reduced approximation spaces for the smaller scale solution are constructed by exploring possible solutions for each subdomain based on the concept of oversampling and the solution of the associated transfer operator problem. Herein, we propose to choose the training data based on the solution of a reduced global problem to incorporate the actual physical behaviour of the structure of interest and to extend it by random samples to ensure sufficient approximation capabilities in general. 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)
Stochastische Modellkalibrierung eines digitalen Zwillings
No full textMotivation:
Ein Digitaler Zwilling repräsentiert ein reales Objekt in der digitalen Welt.
Die Digitalen Zwillinge sind aus Daten und Modellen/Algorithmen aufgebaut
und kontinuierlich über Sensoren mit der realen Welt gekoppelt. Anwendung
z.B. Modellbewertung, Monitoring, Schadensdetektion
Efficient model updating in structural analysis using proper generalized decomposition
No full textThe proper generalized decomposition is combined with a Bayesian model updating to identify unknown material parameters for mechanical structures. A convergence analysis for the Bayesian updating procedure is finally presented
PGD in thermal transient problems with a moving heat source – a sensitivity study on factors affecting accuracy and efficiency
No full textThermal transient problems, essential in 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 the application, e. g. for model parameter estimation or online process control. Model order reduction, a solution to preserve accuracy while reducing complexity, is explored. This paper addresses challenges in developing a reduced model 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 examples 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
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