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- 7.7 Modellierung und Simulation (23) (entfernen)
Eingeladener Vortrag
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Concrete has a long history in the construction industry and is currently one of the most widely used building materials. Especially precast concrete elements are frequently utilized in construction projects for standardized applications, increasing the quality of the composite material, as well as reducing the required building time. Despite the accumulated knowledge, continuous research and development in this field is essential due to the complexity of the composite combined with the ever-growing number of applications and requirements. Especially in view of global climate change, design aspects as CO2 emissions and resource efficiency require new mix designs and optimization strategies. A result of the material’s high complexity and heterogeneity on multiple scales is that utilizing the full potential with changing demands is highly challenging, even for the established industry. We propose a framework based on an ontology, which automatically combines experimental data with numerical simulations. This not only simplifies experimental knowledge transfer, but allows the model calibration and the resulting simulation predictions to be reproducible and interpretable. This research shows a way towards a more performance oriented material design. Within this talk we present our workflow for an automated simulation of a precast element, demonstrating the interaction of the ontology and the finite element simulation. We show the automatic calibration of our early-age concrete model [1, 2], to improve the prediction of the optimal time for the removal of the form work.
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.
Beton ist weltweit einer der wichtigsten Konstruktionswerkstoffe und zeichnet sich durch eine enorme Anpassungsfähigkeit an sich verändernde Anforderungen aus. Damit verbunden ist eine hohe und kontinuierlich zunehmende Komplexität hinsichtlich der Ausgangsstoffe, Rezepturen und des Herstellungsprozesses. Folglich setzt eine Ausschöpfung des technischen und umweltbezogenen Potenzials der Betonbauweise höchste Expertise bei den Einzelakteuren der Bauindustrie voraus.
Numerical simulations are essential in predicting the behavior of systems in many engineering fields and industrial sectors. The development of accurate virtual representations of actual physical products or processes (also known as digital twins) allows huge savings in cost and resources. In fact, digital twins would allow reducing the number of real, physical prototypes, tests, and experiments, thus also increasing the sustainability of production processes and products’ lifetime. Standard numerical methods fail in providing real time simulations, especially for complex processes such as additive manufacturing applications.
This work aims to use a reduced order model for efficient wire arc additive manufacturing simulations, calibrations and real-time process control. Model reduction, e.g. the proper generalized decomposition [1,2] method, is a popular concept to decrease the computational effort. A new mapping approach [3] was applied to simulate a moving heat source with the proper generalized decomposition. Using this procedure even complex models can be simulated in real-time. The physical model is later on calibrated with the use of a stochastic model updating process and the reduced order model, leading to an optimized real-time simulation.
In this contribution, a proper generalized decomposition model for a bead-on-plate wire arc additive manufacturing is presented. It is also coupled with a stochastic model updating process identifying the heat source characteristics as well as the boundary conditions of the transient thermal problem, whereas the heat source shape is simulated using a Goldak heat source
The aim of the project LeBeDigital is to present opportunities of digitalization for concrete applications and show a way towards a performance oriented material design.
Due to the high complexity of the manufacturing process of concrete and the range of parameters affecting the effective composite properties, a global optimization is challenging.
Currently, most optimization is only carried out on a narrow scope related to the respective players, e.g. a mix optimization for a target strength, or a design optimization for minimum weight, using a given mix. To enable a path toward a full global optimization
requires a reproducible chain of data, accessible for all contributors.
We propose a framework based on an ontology, which automatically combines experimental data with numerical simulations. This not only simplifies experimental knowledge transfer, but allows the model calibration and the resulting simulation predictions to be
reproducible and interpretable. In addition to an optimized set of parameters, this setup allows to study the quality and uncertainty of the data and models, as well as giving information about optimal experiments to improve the data set.
We will present the proposed optimization workflow, using the example of a precast concrete element. The contribution will focus on the workflow and challenges of an interoperable FEM formulation.
With increasing focus on industrialized processing, investigating, understanding, and modelling the structural build-up of cementitious materials becomes more important. The structural build-up governs the key property of fresh printable materials -- buildability -- and it influences the mechanical properties after the deposition. The structural build-up rate can be adjusted by optimization of the mixture composition and the use of concrete admixtures. Additionally, it is known, that the environmental conditions, i.e. humidity and temperature have a significant impact on the kinetic of cement hydration and the resulting hardened properties, such as shrinkage, cracking resistance etc. In this study, small amplitude oscillatory shear (SAOS) tests are applied to examine the structural build-up rate of cement paste subject to different temperatures under controlled humidity. The results indicate significant influences of the ambient temperature on the intensity of the re-flocculation (Rthix) rate, while the structuration rate (Athix) is almost not affected. A bi-linear thixotropy model extended by temperature dependent parameters coupled with a linear viscoelastic material model is proposed to simulate the mechanical behaviour considering the structural build-up during the SAOS test.
With increasing focus on industrialized processing, investigating, understanding, and modelling the structural build-up of cementitious materials becomes more important. The structural build-up governs the key property of fresh printable materials -- buildability -- and it influences the mechanical properties after the deposition. The structural build-up rate can be adjusted by optimization of the mixture composition and the use of concrete admixtures. Additionally, it is known, that the environmental conditions, i.e. humidity and temperature have a significant impact on the kinetic of cement hydration and the resulting hardened properties, such as shrinkage, cracking resistance etc. In this study, small amplitude oscillatory shear (SAOS) tests are applied to examine the structural build-up rate of cement paste subject to different temperatures under controlled humidity. The results indicate significant influences of the ambient temperature on the intensity of the re-flocculation (Rthix) rate, while the structuration rate (Athix) is almost not affected. A bi-linear thixotropy model extended by temperature dependent parameters coupled with a linear viscoelastic material model is proposed to simulate the mechanical behaviour considering the structural build-up during the SAOS test
Concrete has a long history in the construction industry and is currently one of the most widely used building materials. Unfortunately, the concrete industry has a significant impact on the environment by contributing to about 9% of the total anthropogenic greenhouse gas (GHG) emissions. Concrete is a highly complex composite material. However, the main source of concrete's GHG emissions is the cement. This leads to two main strategies when trying to reduce the environmental impact. The first is to reduce the cement within the concrete mix. This can be done by substituting it using additives or increasing the amount of aggregates. Usually this will lead to decreased material properties, like compressive strength or stiffness. The second option is to reduce the amount of required concrete by optimizing the topology of the structure. However, this might require higher compressive strength. In addition, there are other properties like to workability which need to be considered. All in all, this leads to a highly complex optimization problem, which requires the estimation of effective concrete properties, based on the mixture as input to a predictive simulation.
We present an automated workflow framework which combines experimental data with simulations, calibrates the simulation and performs the desired optimization. This workflow includes classical FE models, design guidelines based on model codes, as well as data driven methods. The chosen example is a beam, for which the concrete mixture is optimized to reduce GHG emissions. The first step is an estimation of material parameters, based on experimental data. This includes measures of stochastic distribution, allowing the quantification of the quality of the estimated parameters. The second step is the optimization. It takes into account constraints like the loading capacity after 28 days, the maximum allowed temperature during cement hydration and the maximum time till demoulding. The applied models include a Mori-Tanaka-based homogenization method to estimate effective concrete parameters, an FE simulation including the evolution of the concrete compressive strength and stiffness, the temperature field, displacements, and stress. This research shows a way towards a more performance-oriented material design.
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.
FenicsXConcrete
(2023)