Refine
Has Fulltext
- yes (164) (remove)
Document Type
- Article (72)
- Doctoral Thesis (56)
- Conference Proceeding (23)
- Preprint (6)
- Master's Thesis (5)
- Diploma Thesis (1)
- Habilitation (1)
Institute
- Institut für Strukturmechanik (ISM) (164) (remove)
Keywords
- Maschinelles Lernen (27)
- Computerunterstütztes Verfahren (22)
- OA-Publikationsfonds2020 (19)
- Architektur <Informatik> (17)
- Finite-Elemente-Methode (17)
- Machine learning (15)
- Angewandte Mathematik (13)
- Angewandte Informatik (12)
- CAD (10)
- machine learning (10)
- Optimierung (8)
- Computer Science Models in Engineering; Multiscale and Multiphysical Models; Scientific Computing (7)
- Erdbeben (7)
- Deep learning (6)
- OA-Publikationsfonds2022 (6)
- Wärmeleitfähigkeit (6)
- big data (6)
- Neuronales Netz (5)
- Peridynamik (5)
- Beton (4)
- Building Information Modeling (4)
- Isogeometric Analysis (4)
- Modellierung (4)
- Polymere (4)
- Strukturmechanik (4)
- finite element method (4)
- rapid visual screening (4)
- Batterie (3)
- Data, information and knowledge modeling in civil engineering; Function theoretic methods and PDE in engineering sciences; Mathematical methods for (robotics and) computer vision; Numerical modeling in engineering; Optimization in engineering applications (3)
- Fuzzy-Logik (3)
- Isogeometrische Analyse (3)
- Künstliche Intelligenz (3)
- Mehrskalenmodell (3)
- NURBS (3)
- OA-Publikationsfonds2018 (3)
- OA-Publikationsfonds2021 (3)
- Optimization (3)
- Peridynamics (3)
- Phasenfeldmodell (3)
- Schaden (3)
- Simulation (3)
- Strukturdynamik (3)
- Variational principle (3)
- artificial intelligence (3)
- artificial neural networks (3)
- damaged buildings (3)
- earthquake (3)
- earthquake safety assessment (3)
- random forest (3)
- support vector machine (3)
- Abaqus (2)
- Artificial neural network (2)
- Biodiesel (2)
- Bridges (2)
- Bruch (2)
- Bruchmechanik (2)
- Defekt (2)
- Dynamik (2)
- Elastizität (2)
- Erdbebensicherheit (2)
- FEM (2)
- Fahrleitung (2)
- Fehlerabschätzung (2)
- Fluid (2)
- Fotovoltaik (2)
- Fracture (2)
- Fracture mechanics (2)
- Intelligente Stadt (2)
- Internet of things (2)
- Mechanische Eigenschaft (2)
- Mehrgitterverfahren (2)
- Mehrskalenanalyse (2)
- Mikrokapsel (2)
- Modalanalyse (2)
- Multiscale modeling (2)
- Nanomechanik (2)
- Nanostrukturiertes Material (2)
- Nanoverbundstruktur (2)
- Nichtlineare Finite-Elemente-Methode (2)
- OA-Publikationsfonds2019 (2)
- Partielle Differentialgleichung (2)
- Phase-field modeling (2)
- Riss (2)
- Rissausbreitung (2)
- SHM (2)
- Schwingung (2)
- Staumauer (2)
- Tragfähigkeit (2)
- Transfer learning (2)
- Uncertainty (2)
- Unsicherheit (2)
- Vulnerability assessment (2)
- XFEM (2)
- buildings (2)
- clustering (2)
- continuum mechanics (2)
- crack (2)
- dams (2)
- data science (2)
- extreme learning machine (2)
- mathematical modeling (2)
- multiphase (2)
- multiscale (2)
- nanocomposite (2)
- optimization (2)
- reinforcement learning (2)
- smart cities (2)
- soft computing techniques (2)
- stochastic (2)
- urban morphology (2)
- variational principle (2)
- vulnerability assessment (2)
- wireless sensor networks (2)
- 2D/3D Adaptive Mesh Refinement (1)
- 3D printing (1)
- 3D reinforced concrete buildings (1)
- 3D-Druck (1)
- ANN modeling (1)
- Abbruch (1)
- Activation function (1)
- Adaptive Pushover (1)
- Adaptive central high resolution schemes (1)
- Adaptives System (1)
- Adaptives Verfahren (1)
- Aerodynamic Stability (1)
- Aerodynamic derivatives (1)
- Aerodynamik (1)
- Akkumulator (1)
- Algorithmus (1)
- Arc-direct energy deposition (1)
- Artificial Intelligence (1)
- Auswirkung (1)
- Autogenous (1)
- Autonomous (1)
- B-Spline (1)
- B-Spline Finite Elemente (1)
- B-spline (1)
- Battery (1)
- Battery development (1)
- Baustahl (1)
- Bayes (1)
- Bayes neuronale Netze (1)
- Bayes-Verfahren (1)
- Bayesian Inference, Uncertainty Quantification (1)
- Bayesian inference (1)
- Bayesian method (1)
- Bayesian neural networks (1)
- Bayes’schen Inferenz (1)
- Beam-to-column connection; semi-rigid; flush end-plate connection; moment-rotation curve (1)
- Berechnung (1)
- Beschleunigungsmessung (1)
- Beschädigung (1)
- Bildanalyse (1)
- Biomechanics (1)
- Biomechanik (1)
- Bodenmechanik (1)
- Bodentemperatur (1)
- Bornitrid (1)
- Bridge (1)
- Bridge aerodynamics (1)
- Bruchverhalten (1)
- Brustkorb (1)
- Brücke (1)
- Brückenbau (1)
- Bubble column reactor (1)
- Building safety assessment (1)
- CFD (1)
- Capsular clustering; Design of microcapsules (1)
- Carbon nanotubes (1)
- Catenary poles (1)
- Chirurgie (1)
- Cohesive surface technique (1)
- Collocation method (1)
- Computational fracture modeling (1)
- Computermodellierung des Bruchverhaltens (1)
- Computersimulation (1)
- Concrete (1)
- Concrete catenary pole (1)
- ContikiMAC (1)
- Continuous-Time Markov Chain (1)
- Continuum Mechnics (1)
- Control system (1)
- Cost-Benefit Analysis (1)
- Damage (1)
- Damage Identification (1)
- Damage accumulation (1)
- Damage identification (1)
- Damm (1)
- Damping (1)
- Dams (1)
- Data Mining (1)
- Data, information and knowledge modeling in civil engineering (1)
- Data-driven (1)
- Deal ii C++ code (1)
- Demolition (1)
- Design Spectra (1)
- Diskontinuumsmechanik (1)
- Diskrete-Elemente-Methode (1)
- Dissertation (1)
- Domain Adaptation (1)
- Dreidimensionales Modell (1)
- Druckluft (1)
- Dual phase steel (1)
- Dual-support (1)
- ELM (1)
- Earthquake (1)
- Electrochemical properties (1)
- Elektrochemische Eigenschaft (1)
- Elektrode (1)
- Elektrodenmaterial (1)
- Elektrostatische Welle (1)
- Empire XPU 8.01 (1)
- Energieeffizienz (1)
- Energiespeichersystem (1)
- Energiespeicherung (1)
- Entropie (1)
- Entwurf von Mikrokapseln (1)
- Erbeben (1)
- Erneuerbare Energien (1)
- Erweiterte Finite-Elemente-Methode (1)
- Explicit finite element method (1)
- Fachwerkbau (1)
- Fahrleitungsmast (1)
- Fatigue life (1)
- Fernerkung (1)
- Festkörpermechanik (1)
- Feststoff (1)
- Fiber Reinforced Composite (1)
- Finite Element Method (1)
- Finite Element Model (1)
- Flattern (1)
- Flexoelectricity (1)
- Fluid-Structure Interaction (1)
- Flutter (1)
- Fracture Computational Model (1)
- Full waveform inversion (1)
- Function theoretic methods and PDE in engineering sciences (1)
- Funktechnik (1)
- Fuzzy Logic (1)
- Fuzzy logic (1)
- Fuzzy-Regelung (1)
- Gasleitung (1)
- Gaussian process regression (1)
- Gebäude (1)
- Geoinformatik (1)
- Geometric Modeling (1)
- Geometric Partial Differential Equations (1)
- Geometrie (1)
- Geometry Independent Field Approximation (1)
- Geschwindigkeit (1)
- Gesundheitsinformationssystem (1)
- Gesundheitswesen (1)
- Gewebeverbundwerkstoff (1)
- Goal-oriented A Posteriori Error Estimation (1)
- Graphen (1)
- Graphene (1)
- Grauguss (1)
- Gravel-bed rivers (1)
- Grundwasser (1)
- Größenverhältnis (1)
- Guyed antenna masts (1)
- HPC (1)
- Healing (1)
- High-speed electric train (1)
- High-speed railway bridge (1)
- Hochbau (1)
- Holzkonstruktion (1)
- Homogenisieren (1)
- Homogenisierung (1)
- Homogenization (1)
- Hydraulic geometry (1)
- Hydrodynamik (1)
- Hydrological drought (1)
- Hyperbolic PDEs (1)
- IOT (1)
- Impact (1)
- Implicit (1)
- Incompressibility (1)
- Infrastructures (1)
- Ingenieurwissenschaften (1)
- Instandhaltung (1)
- Internet der Dinge (1)
- Internet der dinge (1)
- Internet of Things (1)
- Inverse Probleme (1)
- Inverse Problems (1)
- Inverse analysis (1)
- Inverse problems (1)
- Isogeometrc Analysis (1)
- K-nearest neighbors (1)
- KNN (1)
- Kapselclustern (1)
- Kaverne (1)
- Keramik (1)
- Kirchoff--love theory (1)
- Klüftung (1)
- Kohlenstoff Nanoröhre (1)
- Kohäsionsflächenverfahren (1)
- Kollokationsmethode (1)
- Konjugierte-Gradienten-Methode (1)
- Kontinuierliche Simul (1)
- Kontinuumsmechanik (1)
- Kosten-Nutzen-Analyse (1)
- Körper (1)
- Kühlkörper (1)
- Land surface temperature (1)
- Lebensdauerabschätzung (1)
- Lebenszyklus (1)
- Loading sequence (1)
- Local maximum entropy approximants (1)
- Lufttemperatur (1)
- Lösungsverfahren (1)
- M5 model tree (1)
- MATLAB (1)
- MDLSM method (1)
- Machine Learning (1)
- Markov-Kette mit stetiger Zeit (1)
- Marmara Region (1)
- Maschinenbau (1)
- Mass Tuned Damper (1)
- Material (1)
- Materialverhalten (1)
- Materialversagen (1)
- Mathematical methods for (robotics and) computer vision (1)
- Matlab (1)
- Mechanical properties (1)
- Mechanik (1)
- Membrane contactors (1)
- Mensch (1)
- Mesh Refinement (1)
- Meso-Scale (1)
- Messtechnik (1)
- Mikro-Scale (1)
- Mild steel (1)
- MoS2 (1)
- Model assessment (1)
- Model-free status monitoring (1)
- Modellbildung (1)
- Modellkalibrierung (1)
- Modezuordung (1)
- Molecular Dynamics Simulation (1)
- Molecular Liquids (1)
- Molekulardynamik (1)
- Molekülstruktur (1)
- Monte-Carlo-Integration (1)
- Monte-Carlo-Simulation (1)
- Morphologie (1)
- Motion-induced forces (1)
- Multi-criteria decision making (1)
- Multi-objective Evolutionary Optimization, Elitist Non- Dominated Sorting Evolution Strategy (ENSES), Sandwich Structure, Pareto-Optimal Solutions, Evolutionary Algorithm (1)
- Multi-scale modeling (1)
- Multiphysics (1)
- Muscle model (1)
- Muskel (1)
- NURBS geometry (1)
- Nachhaltigkeit (1)
- Nanocomposite materials (1)
- Nanofluid (1)
- Nanomaterial (1)
- Nanomaterials (1)
- Nanomechanical Resonators (1)
- Nanopore (1)
- Nanoporöser Stoff (1)
- Nanoribbons, thermal conductivity (1)
- Nanostructures (1)
- Nasskühlung (1)
- Naturkatastrophe (1)
- Navier–Stokes equations (1)
- Neuronales Lernen (1)
- Nichtlokale Operatormethode (1)
- Nitratbelastung (1)
- Nonlocal operator method (1)
- Numerical Simulation (1)
- Numerical Simulations (1)
- Numerical modeling in engineering (1)
- Numerische Berechnung (1)
- Numerische Mathematik (1)
- OA-Publikationsfonds2023 (1)
- Oberflächentemperatur (1)
- Oberleitungsmasten (1)
- Operante Konditionierung (1)
- Operational modal analysis (1)
- Operator energy functional (1)
- Optimization in engineering applications (1)
- Optimization problems (1)
- PDEs (1)
- PU Enrichment method (1)
- Parameteridentification (1)
- Parameteridentifikation (1)
- Partial Differential Equations (1)
- Passive damper (1)
- Phase field method (1)
- Phase field model (1)
- Phase-field model (1)
- Physics informed neural network (1)
- Physikalische Eigenschaft (1)
- Piezoelectricity (1)
- Polykristall (1)
- Polymer compound (1)
- Polymer nanocomposites (1)
- Polymers (1)
- Polymerverbindung (1)
- Polymorphie (1)
- Polynomial Splines over Hierarchical T-meshes (1)
- Potential problem (1)
- RC Buildings (1)
- RSSI (1)
- Railway bridges (1)
- Rainflow counting algorithm (1)
- Rapid Visual Assessment (1)
- Rapid Visual Screening (1)
- Recovery Based Error Estimator (1)
- Referenzfläche (1)
- Rehabilitation (1)
- Reliability Analysis (1)
- Reliability Theory (1)
- Renewable energy (1)
- Residual-based variational multiscale method (1)
- Resonator (1)
- Rotorblatt (1)
- Schadenerkennung (1)
- Schadensakkumulation (1)
- Schadensdetektionsverfahren (1)
- Schadenserkennung (1)
- Schadensmechanik (1)
- Schubspannung (1)
- Schwellenwert (1)
- Schwingungsanalyse (1)
- Schwingungsdämpfer (1)
- Schädigung (1)
- Schätztheorie (1)
- Seismic Vulnerability (1)
- Seismic risk (1)
- Selbstheilendem Beton (1)
- Selbstheilung (1)
- Self-healing concrete (1)
- Semi-active damper (1)
- Sensitivity (1)
- Sensitivitätsanalyse (1)
- Sensor (1)
- Sigmoid function (1)
- Simulationsprozess (1)
- Solar (1)
- Spannungs-Dehnungs-Beziehung (1)
- Sprödbruch (1)
- Stabilität (1)
- Stahlbau (1)
- Stahlbetonkonstruktion (1)
- Standsicherheit (1)
- Staudamm (1)
- Steifigkeit (1)
- Stiffness matrix (1)
- Stochastic Subspace Identification (1)
- Stochastic analysis (1)
- Stochastik (1)
- Stoffeigenschaft (1)
- Stress-strain curve (1)
- Strukturanalyse (1)
- Strukturoptimierung (1)
- Strömungsmechanik (1)
- Stütze (1)
- Super Healing (1)
- Surface effects (1)
- Sustainability (1)
- Sustainable production (1)
- System Identification (1)
- Systemidentifikation (1)
- TPOGS (1)
- Talsperre (1)
- Taylor Series Expansion (1)
- Taylor series expansion (1)
- Thermal Fluid-Structure Interaction (1)
- Thermal conductivity (1)
- Thermoelastic damping (1)
- Thermoelasticity (1)
- Thermoelastizität (1)
- Thin shell (1)
- Thorax (1)
- Tichonov-Regularisierung (1)
- Tikhonov regularization (1)
- Träger (1)
- Tsallis entropy (1)
- Uncertainty analysis (1)
- Unschärfequantifizierung (1)
- Variationsprinzip (1)
- Verbundwerkstoff (1)
- Vernetzung (1)
- Vesicle dynamics (1)
- Vesikel (1)
- Vortex Induced Vibration (1)
- Vulnerability (1)
- Wasserbau (1)
- Wave propagation (1)
- Wavelet (1)
- Wavelet based adaptation (1)
- Wechselwirkung (1)
- Werkstoff (1)
- Werkstoffdämpfung (1)
- Werkstoffprüfung (1)
- Wind Energy (1)
- Wind Turbines (1)
- Wind load (1)
- Windenergie (1)
- Windkraftwerk (1)
- Windlast (1)
- Windturbine (1)
- Zementbeton (1)
- Zustandsraummodell (1)
- Zuverlässigkeitsanalyse (1)
- Zuverlässigkeitstheorie (1)
- action recognition (1)
- adaptive neuro-fuzzy inference system (ANFIS) (1)
- adaptive pushover (1)
- adaptive simulation (1)
- ant colony optimization algorithm (ACO) (1)
- artificial neural network (1)
- atomistic simulation methods (1)
- automatic modal analysis (1)
- back-pressure (1)
- battery (1)
- beton (1)
- biodiesel (1)
- brittle fracture (1)
- buckling (1)
- building information modelling (1)
- capsular clustering (1)
- ceramics (1)
- circumferential contact length (1)
- classification (1)
- classifier (1)
- clear channel assessments (1)
- cluster density (1)
- cluster shape (1)
- cohesive elements (1)
- composite (1)
- computation (1)
- computational fluid dynamics (CFD) (1)
- computational hydraulics (1)
- concrete (1)
- congestion control (1)
- conjugate gradient method (1)
- continuum damage mechanics (1)
- coronary artery disease (1)
- crack detection (1)
- crack identification (1)
- cylindrical shell structures (1)
- damage (1)
- damage identification (1)
- decay experiments (1)
- deep learning (1)
- deep learning neural network (1)
- deep neural network (1)
- defects (1)
- diesel engines (1)
- dimensionality reduction (1)
- diskontinuum mechanics (1)
- dissimilarity measures (1)
- domain decomposition (1)
- dual-support (1)
- duty-cycles (1)
- earthquake damage (1)
- earthquake vulnerability assessment (1)
- effective properties (1)
- electromagnetic waves (1)
- energy consumption (1)
- energy dissipation (1)
- energy efficiency (1)
- energy form (1)
- energy, exergy (1)
- ensemble model (1)
- estimation (1)
- explicit time integration (1)
- extreme events (1)
- extreme pressure (1)
- finite element (1)
- firefly optimization algorithm (1)
- flow pattern (1)
- fog computing (1)
- food informatics (1)
- fractional-order control (1)
- full-waveform inversion (1)
- fused filament fabrication (1)
- fuzzy decision making (1)
- gas pipes (1)
- genetic algorithm (1)
- genetic programming (1)
- geoinformatics (1)
- gradient elasticity (1)
- grid-based (1)
- ground structure (1)
- ground water contamination (1)
- growth mode (1)
- gully erosion susceptibility (1)
- health (1)
- health informatics (1)
- heart disease diagnosis (1)
- heat sink (1)
- heterogeneous material (1)
- high-performance computing (1)
- human blob (1)
- human body proportions (1)
- hybrid machine learning (1)
- hybrid machine learning model (1)
- hybride Werkstoffe (1)
- hydraulic jump (1)
- hydrological model (1)
- hydrology (1)
- image processing (1)
- industry 4.0 (1)
- intergranular damage (1)
- inverse analysis (1)
- isogeometric analysis (1)
- isogeometric methods (1)
- jointed rock (1)
- least square support vector machine (LSSVM) (1)
- level set method (1)
- longitudinal dispersion coefficient (1)
- maschinelles Lernen (1)
- material failure (1)
- matrix-free (1)
- maximum stress (1)
- mehrphasig (1)
- microcapsule (1)
- mitigation (1)
- modal analysis (1)
- modal damping (1)
- modal parameter estimation (1)
- modal tracking (1)
- mode pairing (1)
- model updating (1)
- molecular dynamics (1)
- mortar method (1)
- multigrid (1)
- multigrid method (1)
- multiscale method (1)
- nanofluid (1)
- nanoreinforced composites (1)
- nanosheets (1)
- natural hazard (1)
- neural architecture search (1)
- neural networks (NNs) (1)
- nonlocal Hessian operator (1)
- nonlocal operator method (1)
- numerical methods (1)
- numerical modelling (1)
- operator energy functional (1)
- optimal sensor positions (1)
- optimale Sensorpositionierung (1)
- parameter identification (1)
- partical swarm optimization (1)
- passive control (1)
- peridynamics (1)
- phase field (1)
- phase field fracture method (1)
- photovoltaic (1)
- photovoltaic-thermal (PV/T) (1)
- physical activities (1)
- polymorphe Unschärfemodellierung (1)
- precipitation (1)
- prediction (1)
- predictive model (1)
- principal component analysis (1)
- public health (1)
- public space (1)
- quasicontinuum method (1)
- randomized spectral representation (1)
- rapid assessment (1)
- rapid classification (1)
- received signal strength indicator (1)
- recovery-based and residual-based error estimators (1)
- remote sensing (1)
- residential buildings (1)
- response surface methodology (1)
- rice (1)
- rivers (1)
- rule based classification (1)
- scalable smeared crack analysis (1)
- scale transition (1)
- seasonal precipitation (1)
- seismic assessment (1)
- seismic control (1)
- seismic hazard analysis (1)
- seismic risk estimation (1)
- seismic vulnerability (1)
- self healing concrete (1)
- self-healing concrete (1)
- signal processing (1)
- site-specific spectrum (1)
- smart sensors (1)
- smooth rectangular channel (1)
- smoothed particle hydrodynamics (1)
- soil temperature (1)
- solver (1)
- spatial analysis (1)
- spatiotemporal database (1)
- spearman correlation coefficient (1)
- square root cubature calman filter (1)
- standard deviation of pressure fluctuations (1)
- statistical analysis (1)
- statistical coeffcient of the probability distribution (1)
- stilling basin (1)
- stochastic subspace identification (1)
- structural control (1)
- structural dynamics (1)
- sugarcane (1)
- supervised learning (1)
- support vector regression (1)
- sustainability (1)
- tall buildings (1)
- thermal conductivity (1)
- three-dimensional truss structures (1)
- topology optimization (1)
- tuned mass damper (1)
- tuned mass dampers (1)
- type-3 fuzzy systems (1)
- urban health (1)
- urban sustainability (1)
- vibration-based damage identification (1)
- vibration-based methodology (1)
- water quality (1)
- wave propagation (1)
- wavelet transform (1)
- weak form (1)
- weighted residual method (1)
- wind turbine rotor blades (1)
- wireless sensor network (1)
- woven composites (1)
The numerical simulation of microstructure models in 3D requires, due to enormous d.o.f., significant resources of memory as well as parallel computational power. Compared to homogeneous materials, the material hetrogeneity on microscale induced by different material phases demand for adequate computational methods for discretization and solution process of the resulting highly nonlinear problem. To enable an efficient/scalable solution process of the linearized equation systems the heterogeneous FE problem will be described by a FETI-DP (Finite Element Tearing and Interconnecting - Dual Primal) discretization. The fundamental FETI-DP equation can be solved by a number of different approaches. In our approach the FETI-DP problem will be reformulated as Saddle Point system, by eliminating the primal and Lagrangian variables. For the reduced Saddle Point system, only defined by interior and dual variables, special Uzawa algorithms can be adapted for iteratively solving the FETI-DP saddle-point equation system (FETI-DP SPE). A conjugate gradient version of the Uzawa algorithm will be shown as well as some numerical tests regarding to FETI-DP discretization of small examples using the presented solution technique. Furthermore the inversion of the interior-dual Schur complement operator can be approximated using different techniques building an adequate preconditioning matrix and therewith leading to substantial gains in computing time efficiency.
The purpose of this study is to develop self-contained methods for obtaining smooth meshes which are compatible with isogeometric analysis (IGA). The study contains three main parts. We start by developing a better understanding of shapes and splines through the study of an image-related problem. Then we proceed towards obtaining smooth volumetric meshes of the given voxel-based images. Finally, we treat the smoothness issue on the multi-patch domains with C1 coupling. Following are the highlights of each part.
First, we present a B-spline convolution method for boundary representation of voxel-based images. We adopt the filtering technique to compute the B-spline coefficients and gradients of the images effectively. We then implement the B-spline convolution for developing a non-rigid images registration method. The proposed method is in some sense of “isoparametric”, for which all the computation is done within the B-splines framework. Particularly, updating the images by using B-spline composition promote smooth transformation map between the images. We show the possible medical applications of our method by applying it for registration of brain images.
Secondly, we develop a self-contained volumetric parametrization method based on the B-splines boundary representation. We aim to convert a given voxel-based data to a matching C1 representation with hierarchical cubic splines. The concept of the osculating circle is employed to enhance the geometric approximation, where it is done by a single template and linear transformations (scaling, translations, and rotations) without the need for solving an optimization problem. Moreover, we use the Laplacian smoothing and refinement techniques to avoid irregular meshes and to improve mesh quality. We show with several examples that the method is capable of handling complex 2D and 3D configurations. In particular, we parametrize the 3D Stanford bunny which contains irregular shapes and voids.
Finally, we propose the B´ezier ordinates approach and splines approach for C1 coupling. In the first approach, the new basis functions are defined in terms of the B´ezier Bernstein polynomials. For the second approach, the new basis is defined as a linear combination of C0 basis functions. The methods are not limited to planar or bilinear mappings. They allow the modeling of solutions to fourth order partial differential equations (PDEs) on complex geometric domains, provided that the given patches are G1
continuous. Both methods have their advantages. In particular, the B´ezier approach offer more degree of freedoms, while the spline approach is more computationally efficient. In addition, we proposed partial degree elevation to overcome the C1-locking issue caused by the over constraining of the solution space. We demonstrate the potential of the resulting C1 basis functions for application in IGA which involve fourth order PDEs such as those appearing in Kirchhoff-Love shell models, Cahn-Hilliard phase field application, and biharmonic problems.
Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40.
Due to an increased need for hydro-electricity, water storage, and flood protection, it is assumed that a series of new dams will be built throughout the world. Comparing existing design methodologies for arch-type dams, model-based shape optimization can effectively reduce construction costs and leverage the properties of construction materials. To apply the means of shape optimization, suitable variables need to be chosen to formulate the objective function, which is the volume of the arch dam here. In order to increase the consistency with practical conditions, a great number of geometrical and behavioral constraints are included in the mathematical model. An optimization method, namely Genetic Algorithm is adopted which allows a global search.
Traditional optimization techniques are realized based on a deterministic approach, which means that the material properties and loading conditions are assumed to be fixed values. As a result, the real-world structures that are optimized by these approaches suffer from uncertainties that one needs to be aware of. Hence, in any optimization process for arch dams, it is nec- essary to find a methodology that is capable of considering the influences of uncertainties and generating a solution which is robust enough against the uncertainties.
The focus of this thesis is the formulation and the numerical method for the optimization of the arch dam under the uncertainties. The two main models, the probabilistic model, and non-probabilistic models are intro- duced and discussed. Classic procedures of probabilistic approaches un- der uncertainties, such as RDO (robust design optimization) and RBDO (reliability-based design optimization), are in general computationally ex- pensive and rely on estimates of the system’s response variance and fail- ure probabilities. Instead, the robust optimization (RO) method which is based on the non-probabilistic model, will not follow a full probabilistic approach but works with pre-defined confidence levels. This leads to a bi-level optimization program where the volume of the dam is optimized under the worst combination of the uncertain parameters. By this, robust and reliable designs are obtained and the result is independent of any as- sumptions on stochastic properties of the random variables in the model.
The optimization of an arch-type dam is realized here by a robust optimiza- tion method under load uncertainty, where hydraulic and thermal loads are considered. The load uncertainty is modeled as an ellipsoidal expression. Comparing with any traditional deterministic optimization (DO) method, which only concerns the minimum objective value and offers a solution candidate close to limit-states, the RO method provides a robust solution against uncertainties.
All the above mentioned methods are applied to the optimization of the arch dam to compare with the optimal design with DO methods. The re- sults are compared and analyzed to discuss the advantages and drawbacks of each method.
In order to reduce the computational cost, a ranking strategy and an ap- proximation model are further involved to do a preliminary screening. By means of these, the robust design can generate an improved arch dam structure which ensures both safety and serviceability during its lifetime.
From the design experiences of arch dams in the past, it has significant practical value to carry out the shape optimization of arch dams, which can fully make use of material characteristics and reduce the cost of constructions. Suitable variables need to be chosen to formulate the objective function, e.g. to minimize the total volume of the arch dam. Additionally a series of constraints are derived and a reasonable and convenient penalty function has been formed, which can easily enforce the characteristics of constraints and optimal design. For the optimization method, a Genetic Algorithm is adopted to perform a global search. Simultaneously, ANSYS is used to do the mechanical analysis under the coupling of thermal and hydraulic loads. One of the constraints of the newly designed dam is to fulfill requirements on the structural safety. Therefore, a reliability analysis is applied to offer a good decision supporting for matters concerning predictions of both safety and service life of the arch dam. By this, the key factors which would influence the stability and safety of arch dam significantly can be acquired, and supply a good way to take preventive measures to prolong ate the service life of an arch dam and enhances the safety of structure.
Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80% of the points for training and 20% for validation). Two backpropagation-based methods, namely Levenberg–Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng–Robinson (PR) or Soave–Redlich–Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R2 = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R2 from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R2 = 0.9896 and RMSE = 0.0201.
Environmental and operational variables and their impact on structural responses have been acknowledged as one of the most important challenges for the application of the ambient vibration-based damage identification in structures. The damage detection procedures may yield poor results, if the impacts of loading and environmental conditions of the structures are not considered.
The reference-surface-based method, which is proposed in this thesis, is addressed to overcome this problem. In the proposed method, meta-models are used to take into account significant effects of the environmental and operational variables. The usage of the approximation models, allows the proposed method to simply handle multiple non-damaged variable effects simultaneously, which for other methods seems to be very complex. The input of the meta-model are the multiple non-damaged variables while the output is a damage indicator.
The reference-surface-based method diminishes the effect of the non-damaged variables to the vibration based damage detection results. Hence, the structure condition that is assessed by using ambient vibration data at any time would be more reliable. Immediate reliable information regarding the structure condition is required to quickly respond to the event, by means to take necessary actions concerning the future use or further investigation of the structures, for instance shortly after extreme events such as earthquakes.
The critical part of the proposed damage detection method is the learning phase, where the meta-models are trained by using input-output relation of observation data. Significant problems that may encounter during the learning phase are outlined and some remedies to overcome the problems are suggested.
The proposed damage identification method is applied to numerical and experimental models. In addition to the natural frequencies, wavelet energy and stochastic subspace damage indicators are used.
Renewable energy use is on the rise and these alternative resources of energy can help combat with the climate change. Around 80% of the world's electricity comes from coal and petroleum however, the renewables are the fastest growing source of energy in the world. Solar, wind, hydro, geothermal and biogas are the most common forms of renewable energy. Among them, wind energy is emerging as a reliable and large-scaled source of power production. The recent research and confidence in the performance has led to the construction of more and bigger wind turbines around the world. As wind turbines are getting bigger, a concern regarding their safety is also in discussion. Wind turbines are expensive machinery to construct and the enormous capital investment is one of the main reasons, why many countries are unable to adopt to the wind energy. Generally, a reliable wind turbine will result in better performance and assist in minimizing the cost of operation. If a wind turbine fails, it's a loss of investment and can be harmful for the surrounding habitat. This thesis aims towards estimating the reliability of an offshore wind turbine. A model of Jacket type offshore wind turbine is prepared by using finite element software package ABAQUS and is compared with the structural failure criteria of the wind turbine tower. UQLab, which is a general uncertainty quantification framework developed at ETH Zürich, is used for the reliability analysis. Several probabilistic methods are included in the framework of UQLab, which include Monte Carlo, First Order Reliability Analysis and Adaptive Kriging Monte Carlo simulation. This reliability study is performed only for the structural failure of the wind turbine but it can be extended to many other forms of failures e.g. reliability for power production, or reliability for different component failures etc. It's a useful tool that can be utilized to estimate the reliability of future wind turbines, that could result in more safer and better performance of wind turbines.
Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (σ*X), pressures with different non-exceedance probabilities (P*k%), and the statistical coefficient of the probability distribution (Nk%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for σ*X, and Nk%. The values of the Nk% coefficient indicated a single mean value for each probability.
In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Text
A novel combination of the ant colony optimization algorithm (ACO)and computational fluid dynamics (CFD) data is proposed for modeling the multiphase chemical reactors. The proposed intelligent model presents a probabilistic computational strategy for predicting various levels of three-dimensional bubble column reactor (BCR) flow. The results prove an enhanced communication between ant colony prediction and CFD data in different sections of the BCR.
The assessment of wind-induced vibrations is considered vital for the design of long-span bridges. The aim of this research is to develop a methodological framework for robust and efficient prediction strategies for complex aerodynamic phenomena using hybrid models that employ numerical analyses as well as meta-models. Here, an approach to predict motion-induced aerodynamic forces is developed using artificial neural network (ANN). The ANN is implemented in the classical formulation and trained with a comprehensive dataset which is obtained from computational fluid dynamics forced vibration simulations. The input to the ANN is the response time histories of a bridge section, whereas the output is the motion-induced forces. The developed ANN has been tested for training and test data of different cross section geometries which provide promising predictions. The prediction is also performed for an ambient response input with multiple frequencies. Moreover, the trained ANN for aerodynamic forcing is coupled with the structural model to perform fully-coupled fluid--structure interaction analysis to determine the aeroelastic instability limit. The sensitivity of the ANN parameters to the model prediction quality and the efficiency has also been highlighted. The proposed methodology has wide application in the analysis and design of long-span bridges.
Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of
the potential for these algorithms to provide fast, cheap, and accurate predictions of hydraulic geometry is lacking. This study provides the first quantification of this potential. Using at-a-station field data, predictions of flow depth, water-surface width and longitudinal water surface slope are made using three standalone data mining techniques -, Instance-based Learning (IBK), KStar, Locally Weighted Learning (LWL) - along with four types of novel hybrid algorithms in which the standalone models are trained with Vote, Attribute Selected
Classifier (ASC), Regression by Discretization (RBD), and Cross-validation Parameter Selection (CVPS) algorithms (Vote-IBK, Vote-Kstar, Vote-LWL, ASC-IBK, ASC-Kstar, ASC-LWL, RBD-IBK, RBD-Kstar, RBD-LWL, CVPSIBK, CVPS-Kstar, CVPS-LWL). Through a comparison of their predictive performance and a sensitivity analysis of the driving variables, the results reveal: (1) Shield stress was the most effective parameter in the prediction of all geometry dimensions; (2) hybrid models had a higher prediction power than standalone data mining models,
empirical equations and traditional machine learning algorithms; (3) Vote-Kstar model had the highest performance in predicting depth and width, and ASC-Kstar in estimating slope, each providing very good prediction performance. Through these algorithms, the hydraulic geometry of any river can potentially be predicted accurately and with ease using just a few, readily available flow and channel parameters. Thus, the results reveal that these models have great potential for use in stable channel design in data poor catchments, especially in developing nations where technical modelling skills and understanding of the hydraulic and sediment processes occurring in the river system may be lacking.
The point collocation method of finite spheres (PCMFS) is used to model the hyperelastic response of soft biological tissue in real time within the framework of virtual surgery simulation. The proper orthogonal decomposition (POD) model order reduction (MOR) technique was used to achieve reduced-order model of the problem, minimizing computational cost. The PCMFS is a physics-based meshfree numerical technique for real-time simulation of surgical procedures where the approximation functions are applied directly on the strong form of the boundary value problem without the need for integration, increasing computational efficiency. Since computational speed has a significant role in simulation of surgical procedures, the proposed technique was able to model realistic nonlinear behavior of organs in real time. Numerical results are shown to demonstrate the effectiveness of the new methodology through a comparison between full and reduced analyses for several nonlinear problems. It is shown that the proposed technique was able to achieve good agreement with the full model; moreover, the computational and data storage costs were significantly reduced.
This thesis presents the advances and applications of phase field modeling in fracture analysis. In this approach, the sharp crack surface topology in a solid is approximated by a diffusive crack zone governed by a scalar auxiliary variable. The uniqueness of phase field modeling is that the crack paths are automatically determined as part of the solution and no interface tracking is required. The damage parameter varies continuously over the domain. But this flexibility comes with associated difficulties: (1) a very fine spatial discretization is required to represent sharp local gradients correctly; (2) fine discretization results in high computational cost; (3) computation of higher-order derivatives for improved convergence rates and (4) curse of dimensionality in conventional numerical integration techniques. As a consequence, the practical applicability of phase field models is severely limited.
The research presented in this thesis addresses the difficulties of the conventional numerical integration techniques for phase field modeling in quasi-static brittle fracture analysis. The first method relies on polynomial splines over hierarchical T-meshes (PHT-splines) in the framework of isogeometric analysis (IGA). An adaptive h-refinement scheme is developed based on the variational energy formulation of phase field modeling. The fourth-order phase field model provides increased regularity in the exact solution of the phase field equation and improved convergence rates for numerical solutions on a coarser discretization, compared to the second-order model. However, second-order derivatives of the phase field are required in the fourth-order model. Hence, at least a minimum of C1 continuous basis functions are essential, which is achieved using hierarchical cubic B-splines in IGA. PHT-splines enable the refinement to remain local at singularities and high gradients, consequently reducing the computational cost greatly. Unfortunately, when modeling complex geometries, multiple parameter spaces (patches) are joined together to describe the physical domain and there is typically a loss of continuity at the patch boundaries. This decrease of smoothness is dictated by the geometry description, where C0 parameterizations are normally used to deal with kinks and corners in the domain. Hence, the application of the fourth-order model is severely restricted. To overcome the high computational cost for the second-order model, we develop a dual-mesh adaptive h-refinement approach. This approach uses a coarser discretization for the elastic field and a finer discretization for the phase field. Independent refinement strategies have been used for each field.
The next contribution is based on physics informed deep neural networks. The network is trained based on the minimization of the variational energy of the system described by general non-linear partial differential equations while respecting any given law of physics, hence the name physics informed neural network (PINN). The developed approach needs only a set of points to define the geometry, contrary to the conventional mesh-based discretization techniques. The concept of `transfer learning' is integrated with the developed PINN approach to improve the computational efficiency of the network at each displacement step. This approach allows a numerically stable crack growth even with larger displacement steps. An adaptive h-refinement scheme based on the generation of more quadrature points in the damage zone is developed in this framework. For all the developed methods, displacement-controlled loading is considered. The accuracy and the efficiency of both methods are studied numerically showing that the developed methods are powerful and computationally efficient tools for accurately predicting fractures.
Phase Field Modeling for Fracture with Applications to Homogeneous and Heterogeneous Materials
(2017)
The thesis presents an implementation including different applications of a variational-based approach for gradient type standard dissipative solids. Phase field model for brittle fracture is an application of the variational-based framework for gradient type solids. This model allows the prediction of different crack topologies and states. Of significant concern is the application of theoretical and numerical formulation of the phase field modeling into the commercial finite element software Abaqus in 2D and 3D. The fully coupled incremental variational formulation of phase field method is implemented by using the UEL and UMAT subroutines of Abaqus. The phase field method
considerably reduces the implementation complexity of fracture problems as it removes the need for numerical tracking of discontinuities in the displacement field that are characteristic of discrete crack methods. This is accomplished by replacing the sharp discontinuities with a scalar damage phase field representing the diffuse crack topology wherein the amount of diffusion is controlled by a regularization parameter. The nonlinear coupled system consisting of the linear momentum equation and a diffusion type equation governing the phase field evolution is solved simultaneously via a Newton-
Raphson approach. Post-processing of simulation results to be used as visualization
module is performed via an additional UMAT subroutine implemented in the standard Abaqus viewer.
In the same context, we propose a simple yet effective algorithm to initiate and propagate cracks in 2D geometries which is independent of both particular constitutive laws and specific element technology and dimension. It consists of a localization limiter in the form of the screened Poisson equation with, optionally, local mesh refinement. A staggered scheme for standard equilibrium and screened Cauchy equations is used. The remeshing part of the algorithm consists of a sequence of mesh subdivision and element erosion steps. Element subdivision is based on edge split operations using a
given constitutive quantity (either damage or void fraction). Mesh smoothing makes use of edge contraction as function of a given constitutive quantity such as the principal stress or void fraction. To assess the robustness and accuracy of this algorithm, we use both quasi-brittle benchmarks and ductile tests.
Furthermore, we introduce a computational approach regarding mechanical loading in microscale on an inelastically deforming composite material. The nanocomposites material of fully exfoliated clay/epoxy is shaped to predict macroscopic elastic and fracture related material parameters based on their fine–scale features. Two different configurations of polymer nanocomposites material (PNCs) have been studied. These configurations are fully bonded PNCs and PNCs with an interphase zone formation between the matrix and the clay reinforcement. The representative volume element of PNCs specimens with different clay weight contents, different aspect ratios, and different
interphase zone thicknesses are generated by adopting Python scripting. Different constitutive models are employed for the matrix, the clay platelets, and the interphase zones. The brittle fracture behavior of the epoxy matrix and the interphase zones material are modeled using the phase field approach, whereas the stiff silicate clay platelets of the composite are designated as a linear elastic material. The comprehensive study investigates the elastic and fracture behavior of PNCs composites, in addition to predict Young’s modulus, tensile strength, fracture toughness, surface energy dissipation, and cracks surface area in the composite for different material parameters, geometry, and interphase zones properties and thicknesses.
The vibration control of the tall building during earthquake excitations is a challenging task due to their complex seismic behavior. This paper investigates the optimum placement and properties of the Tuned Mass Dampers (TMDs) in tall buildings, which are employed to control the vibrations during earthquakes. An algorithm was developed to spend a limited mass either in a single TMD or in multiple TMDs and distribute them optimally over the height of the building. The Non-dominated Sorting Genetic Algorithm (NSGA – II) method was improved by adding multi-variant genetic operators and utilized to simultaneously study the optimum design parameters of the TMDs and the optimum placement. The results showed that under earthquake excitations with noticeable amplitude in higher modes, distributing TMDs over the height of the building is more effective in mitigating the vibrations compared to the use of a single TMD system. From the optimization, it was observed that the locations of the TMDs were related to the stories corresponding to the maximum modal displacements in the lower modes and the stories corresponding to the maximum modal displacements in the modes which were highly activated by the earthquake excitations. It was also noted that the frequency content of the earthquake has significant influence on the optimum location of the TMDs.
PARAMETER IDENTIFICATION OF MESOSCALE MODELS FROM MACROSCOPIC TESTS USING BAYESIAN NEURAL NETWORKS
(2010)
In this paper, a parameter identification procedure using Bayesian neural networks is proposed. Based on a training set of numerical simulations, where the material parameters are simulated in a predefined range using Latin Hypercube sampling, a Bayesian neural network, which has been extended to describe the noise of multiple outputs using a full covariance matrix, is trained to approximate the inverse relation from the experiment (displacements, forces etc.) to the material parameters. The method offers not only the possibility to determine the parameters itself, but also the accuracy of the estimate and the correlation between these parameters. As a result, a set of experiments can be designed to calibrate a numerical model.
This study contributes to the identification of coupled THM constitutive model parameters via back analysis against information-rich experiments. A sampling based back analysis approach is proposed comprising both the model parameter identification and the assessment of the reliability of identified model parameters. The results obtained in the context of buffer elements indicate that sensitive parameter estimates generally obey the normal distribution. According to the sensitivity of the parameters and the probability distribution of the samples we can provide confidence intervals for the estimated parameters and thus allow a qualitative estimation on the identified parameters which are in future work used as inputs for prognosis computations of buffer elements. These elements play e.g. an important role in the design of nuclear waste repositories.
Paraffin Nanocomposites for Heat Management of Lithium-Ion Batteries: A Computational Investigation
(2016)
Lithium-ion (Li-ion) batteries are currently considered as vital components for advances in mobile technologies such as those in communications and transport. Nonetheless, Li-ion batteries suffer from temperature rises which sometimes lead to operational damages or may even cause fire. An appropriate solution to control the temperature changes during the operation of Li-ion batteries is to embed batteries inside a paraffin matrix to absorb and dissipate heat. In the present work, we aimed to investigate the possibility of making paraffin nanocomposites for better heat management of a Li-ion battery pack. To fulfill this aim, heat generation during a battery charging/discharging cycles was simulated using Newman’s well established electrochemical pseudo-2D model. We couple this model to a 3D heat transfer model to predict the temperature evolution during the battery operation. In the later model, we considered different paraffin nanocomposites structures made by the addition of graphene, carbon nanotubes, and fullerene by assuming the same thermal conductivity for all fillers. This way, our results mainly correlate with the geometry of the fillers. Our results assess the degree of enhancement in heat dissipation of Li-ion batteries through the use of paraffin nanocomposites. Our results may be used as a guide for experimental set-ups to improve the heat management of Li-ion batteries.