TY - JOUR A1 - Kindrachuk, Vitaliy A1 - Titscher, Thomas A1 - Unger, Jörg F. T1 - A Fourier transformation-based method for gradient-enhanced modeling of fatigue N2 - A key limitation of the most constitutive models that reproduce a Degradation of quasi-brittle materials is that they generally do not address issues related to fatigue. One reason is the huge computational costs to resolve each load cycle on the structural level. The goal of this paper is the development of a temporal Integration scheme, which significantly increases the computational efficiency of the finite element method in comparison to conventional temporal integrations. The essential constituent of the fatigue model is an implicit gradient-enhanced formulation of the damage rate. The evolution of the field variables is computed as amultiscale Fourier series in time.On a microchronological scale attributed to single cycles, the initial boundary value problem is approximated by linear BVPs with respect to the Fourier coefficients. Using the adaptive cycle jump concept, the obtained damage rates are transferred to a coarsermacrochronological scale associated with the duration of material deterioration. The performance of the developedmethod is hence improved due to an efficient numerical treatment of the microchronological problem in combination with the cycle jump technique on the macrochronological scale. Validation examples demonstrate the convergence of the obtained solutions to the reference simulations while significantly reducing the computational costs. KW - Accelerated temporal integration KW - Fourier series KW - Gradient-enhanced fatigue model PY - 2018 DO - https://doi.org/10.1002/nme.5740 SN - 1097-0207 SN - 0029-5981 VL - 114 IS - 2 SP - 196 EP - 214 PB - Wiley AN - OPUS4-44008 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Titscher, Thomas A1 - van Dijk, T. A1 - Kadoke, Daniel A1 - Robens-Radermacher, Annika A1 - Herrmann, Ralf A1 - Unger, Jörg F. T1 - Bayesian model calibration and damage detection for a digital twin of a bridge demonstrator N2 - Using digital twins for decision making is a very promising concept which combines simulation models with corresponding experimental sensor data in order to support maintenance decisions or to investigate the reliability. The quality of the prognosis strongly depends on both the data quality and the quality of the digital twin. The latter comprises both the modeling assumptions as well as the correct parameters of these models. This article discusses the challenges when applying this concept to realmeasurement data for a demonstrator bridge in the lab, including the data management, the iterative development of the simulation model as well as the identification/updating procedure using Bayesian inference with a potentially large number of parameters. The investigated scenarios include both the iterative identification of the structural model parameters as well as scenarios related to a damage identification. In addition, the article aims at providing all models and data in a reproducibleway such that other researcher can use this setup to validate their methodologies. KW - Damage detection KW - Finite element analysis KW - Load identification KW - Model updating KW - Estimation KW - System identification KW - Variational Bayesian statistics PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-574496 DO - https://doi.org/10.1002/eng2.12669 SN - 2577-8196 SP - 1 EP - 27 PB - Wiley CY - Hoboken, NJ AN - OPUS4-57449 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Robens-Radermacher, Annika A1 - Held, Felix A1 - Coelho Lima, Isabela A1 - Titscher, Thomas A1 - Unger, Jörg F. T1 - Efficient identification of random fields coupling Bayesian inference and PGD reduced order model for damage localization N2 - One of the main challenges regarding our civil infrastructure is the efficient operation over their complete design lifetime while complying with standards and safety regulations. Thus, costs for maintenance or replacements must be optimized while still ensuring specified safety levels. This requires an accurate estimate of the current state as well as a prognosis for the remaining useful life. Currently, this is often done by regular manual or visual inspections within constant intervals. However, the critical sections are often not directly accessible or impossible to be instrumented at all. Model‐based approaches can be used where a digital twin of the structure is set up. For these approaches, a key challenge is the calibration and validation of the numerical model based on uncertain measurement data. The aim of this contribution is to increase the efficiency of model updating by using the advantage of model reduction (Proper Generalized Decomposition, PGD) and applying the derived method for efficient model identification of a random stiffness field of a real bridge.” KW - Model reduction KW - Model updating KW - Proper generalized decomposition KW - Random field KW - Variational Bayesian Inference PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-521275 DO - https://doi.org/10.1002/pamm.202000063 VL - 20 IS - 1 SP - e202000063 PB - Wiley Online Libary AN - OPUS4-52127 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Titscher, Thomas A1 - Unger, Jörg F. A1 - Oliver, J. ED - von Scheven, M. ED - Keip, M.-A. ED - Karajan, N. T1 - Cycle-by-cycle fatigue damage model for concrete N2 - Damage caused by stress concentrations in the complex mesoscopic geometry of concrete leads to continuous stress redistribution over the material’s life time. The presented fatigue damage model captures this by resolving each load cycle in a cycle-by-cycle time integration. The model extends a static damage model to failure caused by the (time dependent) strain amplitudes and, thus, allows calibrating the majority of the material’s parameters in static experiments. T2 - 7th GACM Colloquium on Computational Mechanics for Young Scientists from Academia and Industry CY - Stuttgart, Germany DA - 11.10.2017 KW - Cycle-by-cycle KW - Fatigue damage KW - Mesoscopic PY - 2017 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-436681 DO - https://doi.org/10.18419/opus-9334 VL - 7 SP - MS13, 440 EP - 443 PB - Institute for Structural Mechanics, University of Stuttgart CY - Stuttgart AN - OPUS4-43668 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Coelho Lima, Isabela A1 - Robens-Radermacher, Annika A1 - Titscher, Thomas A1 - Kadoke, Daniel A1 - Koutsourelakis, P.-S. A1 - Unger, Jörg F. T1 - Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forwardmodel's accuracy N2 - Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future ofmonitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results. KW - Variational inference KW - Proper generalized decomposition KW - Goal-oriented KW - Digital twin KW - Random field PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-555755 DO - https://doi.org/10.1007/s00466-022-02214-6 SN - 1432-0924 SP - 1 EP - 22 PB - Springer CY - Berlin AN - OPUS4-55575 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. A1 - Kindrachuk, Vitaliy A1 - Hirthammer, Volker A1 - Titscher, Thomas A1 - Pohl, Christoph T1 - The importance of multiphysics and multiscale modelling of concrete to understand its complex macroscopic properties N2 - Concrete is a complex material. Its properties evolve over time, especially at early age, and are dependent on environmental conditions, i.e. temperature and moisture conditions, as well as the composition of the material. This leads to a variety of macroscopic phenomena such as hydration/solidification/hardening, creep and shrinkage, thermal strains, damage and inelastic deformations. Most of these phenomena are characterized by specific set of model assumptions and often an additive decomposition of strains into elastic, plastic, shrinkage and creep components is performed. Each of these phenomena are investigated separately and a number of respective independent models have been designed. The interactions are then accounted for by adding appropriate correction factors or additional models for the particular interaction. This paper discusses the importance of reconsider even in the experimental phase the model assumptions required to generalize the experimental data into models used in design codes. It is especially underlined that the complex macroscopic behaviour of concrete is strongly influenced by its multiscale and multiphyscis nature and two examples (shrinkage and fatigue) of interacting phenomena are discussed. T2 - International RILEM Conference on Materials, Systems and Structures in Civil Engineering CY - Lyngby, Denmark DA - 22.08.2016 KW - Concrete KW - Multiscale KW - Multiphysics PY - 2016 VL - 1 SP - 115 EP - 124 PB - International RILEM Conference on Materials, Systems and Structures in Civil Engineering, Conference segment on COST TU1404 AN - OPUS4-38651 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Titscher, Thomas T1 - Implicit/explicit (IMPL-EX) integration of the gradient enhanced damage model N2 - Isotropic damage models are widely used for the finite element simulation of softening materials, e.g. in mesoscale simulations of concrete. Regularization techniques must be employed to obtain a physically meaningful fracture energy upon mesh refinement. In regularized local damage models the strains localize in single elements allowing them to represent weak or strong discontinuities. In implicit integration schemes, these models can exhibit convergence Problems caused by an ill-conditioned tangent stiffness. This corresponds to the loss of ellipticity of the local rate equilibrium equations. Oliver et al. developed the implicit/explicit (IMPL-EX) integration scheme which overcomes These problems in local damage models. The internal damage driving variable is extrapolated based on previous implicitly determined values. This provides two main benefits: First, it always results in a symmetric positive semi-definite algorithmic stiffness matrix which precludes ill-posedness. Second, the system becomes incrementally linear and converges in one Newton-Raphson iteration. Even though the IMPL-EX algorithm, like explicit algorithms in general, requires smaller time steps than implicit schemes to obtain the same accuracy, it leads to a computational speedup. The gradient enhanced damage model by Peerlings is a nonlocal damage model that provides the regularization by limiting the curvature of the damage-driving strains. These models do not lose their ellipticity. However, structural instabilities often require tiny time steps and many iterations to obtain convergence. Here, the second aspect of the IMPL-EX scheme reduces the computational costs. This is shown in simulations of the complex geometry of concrete mesostructures, where only the gradient enhanced matrix material and linear elastic aggregates are considered. With regard to future mesoscale simulations, the remaining component of the mesoscopic structure, the interfacial transition zone and its degradation, has to be included. This adds a local damage model to the nonlocal problem. Thus, an IMPL-EX implementation has to be provided for both models to benefit from the increase of robustness and performance. T2 - ECCOMAS Congress 2016 CY - Crete, Greece DA - 05.06.2016 KW - Concrete KW - Mesoscale KW - IMPL-EX PY - 2016 AN - OPUS4-38671 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Titscher, Thomas T1 - Cycle-by-cycle fatigue damage model for concrete N2 - Damage caused by stress concentrations in the complex mesoscopic geometry of concrete leads to continuous stress redistribution over the material's life time. The presented fatigue damage model captures this by resolving each load cycle in a cycle-by-cycle time integration. The model extends a static damage model to failure caused by the (time dependent) strain amplitudes and, thus, allows calibrating the majority of the material's parameters in static experiments. T2 - 7th GACM Colloquium on Computational Mechanics CY - Stuttgart, Germany DA - 11.10.2017 KW - Fatigue KW - Damage model KW - Cycle-by-cycle integration PY - 2017 AN - OPUS4-43612 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Titscher, Thomas T1 - Concrete mesostructure geometry modelling with growing and colliding hard spheres T2 - 11th World Congress on Computational Mechanics (WCCM XI); 5th European Conference on Computational Mechanics (ECCM V); 6th European Conference on Computational Fluid Dynamics (ECFD VI) CY - Barcelona, Spanien DA - 2014-07-20 PY - 2014 AN - OPUS4-32897 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Titscher, Thomas A1 - Unger, Jörg F. T1 - Application of molecular dynamics simulations for the generation of dense concrete mesoscale geometries N2 - The problem of polydisperse sphere packings is applied to concrete mesoscale geometries in finite sized specimens. Realistic sphere diameter distributions are derived from concrete grading curves. An event-driven molecular dynamics simulation using growing particles is introduced. Compared to the widely used random sequential addition algorithm, it reaches denser aggregate packings and saves computation time at high volume fractions. A minimal distance between particles strongly influences the maximum aggregate content. It is essential to obtain undistorted elements when meshing the geometry for finite element simulations. The algorithm maximizes this value and produces meshable concrete mesostructures with more than 70% aggregate content. KW - Polydisperse sphere packing KW - Mesoscale geometry KW - Concrete mesostructure KW - Molecular dynamics PY - 2015 DO - https://doi.org/10.1016/j.compstruc.2015.06.008 SN - 0045-7949 SN - 0366-7138 VL - 158 SP - 274 EP - 284 PB - Pergamon Press CY - Oxford AN - OPUS4-34190 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -