1,721,041 research outputs found
A reduced order model for the simulation of mooring cable dynamics
No full textIn this paper the feasibility of a reduced order model (ROM) for thehydroelastic analysis of mooring lines is analysed. The local response of a piece of cableis studied through high delity uid structure interaction (FSI) simulations. The high delity model is built by coupling a computational structural dynamics (CSD) solver witha computational uid dynamics (CFD) solver using the approach of software components.The ROM is designed in such a way that it can be added to any beam element from astandard CSD solver. From the outside only the beam degrees of freedoms (DOFs) canbe seen, the ROM DOFs are all internal.The local response of the cable is analysed andthe feasibility of the ROM is discusse
Explicable hyper-reduced order models on nonlinearly approximated solution manifolds of compressible and incompressible Navier-Stokes equations
No full textA slow decaying Kolmogorov n-width of the solution manifold of a parametric partial differential equation precludes the realization of efficient linear projection-based reduced-order models. This is due to the high dimensionality of the reduced space needed to approximate with sufficient accuracy the solution manifold. To solve this problem, neural networks, in the form of different architectures, have been employed to perform accurate nonlinear regressions of the solution manifolds. However, the majority of the implementations are non-intrusive black-box surrogate models and only a part of them perform dimension reduction from the number of degrees of freedom of the discretized parametric models to a latent dimension. We present a new intrusive and explicable methodology for reduced-order modeling that employs neural networks for the solution manifold approximation but that does not discard the physical and numerical models underneath in the predictive/online stage. We will focus on autoencoders used to compress further the dimensionality of linear approximants of solution manifolds, achieving in the end a nonlinear dimension reduction. After having obtained an accurate nonlinear approximant, we seek for the solutions on the latent manifold with the residual-based nonlinear least-squares Petrov-Galerkin method, opportunely hyper-reduced in order to be independent of the number of degrees of freedom. New adaptive hyper-reduction strategies are developed along with the employment of local nonlinear approximants. We test our methodology on two nonlinear time dependent parametric benchmarks involving a supersonic flow past a NACA airfoil with changing Mach number and an incompressible turbulent flow around the Ahmed body with changing slant angle
Non-linear Manifold Reduced-Order Models with Convolutional Autoencoders and Reduced Over-Collocation Method
No full textFinite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equations
Full text linkIn this work a stabilised and reduced Galerkin projection of the incompressible unsteady Navier–Stokes equations for moderate Reynolds number is presented. The full-order model, on which the Galerkin projection is applied, is based on a finite volumes approximation. The reduced basis spaces are constructed with a POD approach. Two different pressure stabilisation strategies are proposed and compared: the former one is based on the supremizer enrichment of the velocity space, and the latter one is based on a pressure Poisson equation approach
A hybrid reduced-order model for segregated fluid-structure interaction solvers in an ALE approach at high Reynolds number
No full textThis study introduces a first step for constructing a hybrid reduced-order models (ROMs) for segregated fluid-structure interaction in an Arbitrary Lagrangian-Eulerian (ALE) approach at a high Reynolds number using the Finite Volume Method (FVM). The ROM is driven by proper orthogonal decomposition (POD) with hybrid techniques that combines the classical Galerkin projection and two data-driven methods (radial basis networks, and neural networks/ long short term memory). Results demonstrate the ROM's ability to accurately capture the physics of fluid-structure interaction phenomena. This approach is validated through a case study focusing on flow-induced vibration (FIV) of a pitch-plunge airfoil at a high Reynolds number (Re=107)
A comparative study about the effects of linear, weakly and fully nonlinear wave models on the dynamic response of offshore wind turbines
No full textThe present work aims at comparing the dynamic response of a fixed–bottom offshore wind turbine subjected to the combined wind–waves action employing different nonlinear irregular wave kinematic models. To this purpose, linear, second–order and fully nonlinear models are implemented in the hydrodynamic module of a global hydro-aero-elastic solver. All the wave models are based on the potential flow assumption. The fully nonlinear wave kinematics is reproduced both on the full simulation time and, in order to save com- putational time, only on some space-time subdomains within a domain decomposition strategy. This approach permits achieving a much higher accuracy in the assessment of the hydrodynamic loads keeping the global computational effort similar to the one required by linear or weakly nonlinear models. The paper represents a preliminary investigation aimed at establishing to what extend the second–order wave model can efficiently capture the system response even when the system is exposed to moderate sea states. Moreover, a comparison between the four wave models seems to reveal that some resonant oscillations of the tower are triggered by nonlinear components higher than the 2nd–order. Hydrodynamic loads associated to the four wave models are coupled with aerodynamic loads acting on the rotor of a 5-MW wind turbine. Hydro-aero-elastic calculations are performed using the NREL open-source software FAST
Generative models for the deformation of industrial shapes with linear geometric constraints: Model order and parameter space reductions
No full textReal-world applications of computational fluid dynamics often involve the evaluation of quantities of interest for several distinct geometries that define the computational domain or are embedded inside it. For example, design optimization studies require the realization of response surfaces from the parameters that determine the geometrical deformations to relevant outputs to be optimized. In this context, a crucial aspect to be addressed are the limited resources at disposal to computationally generate different geometries or to physically obtain them from direct measurements. This is the case for patient-specific biomedical applications for example. When additional linear geometrical constraints need to be imposed, the computational costs increase substantially. Such constraints include total volume conservation, barycenter location and fixed moments of inertia. We develop a new paradigm that employs generative models from machine learning to efficiently sample new geometries with linear constraints. A consequence of our approach is the reduction of the parameter space from the original geometrical parametrization to a low-dimensional latent space of the generative models. Crucial is the assessment of the quality of the distribution of the constrained geometries obtained with respect to physical and geometrical quantities of interest. Non-intrusive model order reduction is enhanced since smaller parametric spaces are considered. We test our methodology on two academic test cases: a mixed Poisson problem on the 3d Stanford bunny with fixed barycenter deformations and the multiphase turbulent incompressible Navier-Stokes equations for the Duisburg test case with fixed volume deformations of the naval hull
Assessment of URANS and LES Methods in Predicting Wake Shed Behind a Vertical Axis Wind Turbine
Full text linkIn order to shed light on the Vertical-Axis Wind Turbines (VAWT) wake
characteristics, in this paper we present high-fidelity CFD simulations of the
flow around an exemplary H-shaped VAWT turbine, and we propose to apply Proper
Orthogonal Decomposition (POD) to the computed flow field in the near wake of
the rotor. The turbine under consideration was widely studied in previous
experimental and computational investigations. In the first part of the study,
multiple Reynolds-Averaged Navier-Stokes (RANS) simulations were performed at
the Tip Speed Ratio (TSR) of peak power coefficient, to select the most
accurate turbulence model with respect to available data. In the following
step, further RANS numerical simulations were performed at different TSRs to
compare the power coefficient against experimental data. Then, Large Eddy
Simulation (LES) was applied for multiple TSR conditions. The spatial and
temporal POD modes along with modal energy for the RANS and LES results were
extracted, and the performance of the turbulence models was assessed. Also, an
interpretation of the POD modes with respect to the flow structures was given
to highlight the most significant time and length scales of the predictions
considering the different dynamical levels of approximations of the
computational models.Comment: 15 Page
Hybrid Data-Driven Closure Strategies for Reduced Order Modeling
Full text linkIn this paper, we propose hybrid data-driven ROM closures for fluid flows.
These new ROM closures combine two fundamentally different strategies: (i)
purely data-driven ROM closures, both for the velocity and the pressure; and
(ii) physically based, eddy viscosity data-driven closures, which model the
energy transfer in the system. The first strategy consists in the addition of
closure/correction terms to the governing equations, which are built from the
available data. The second strategy includes turbulence modeling by adding eddy
viscosity terms, which are determined by using machine learning techniques. The
two strategies are combined for the first time in this paper to investigate a
two-dimensional flow past a circular cylinder at Re=50000. Our numerical
results show that the hybrid data-driven ROM is more accurate than both the
purely data-driven ROM and the eddy viscosity ROM.Comment: arXiv admin note: text overlap with arXiv:2205.1511
Coupled dynamic simulations of offshore wind turbines using linear, weakly and fully nonlinear wave models: the limitations of the second-order wave theory
No full textThe present work investigates the dynamic response of a fixed–bottom offshore wind turbine subjected to the combined wind-waves action employing different nonlinear wave kinematic models. Linear, 2nd-order and fully nonlinear models are imple- mented in the hydrodynamic module of a global hydro-aero-servo-elastic solver. All the wave models are based on the potential flow assumption. A first study of the structural response is performed in regular waves with increasing steepness considering the turbine both in parked condition and in power production. A more realistic simulation is then carried out with irregular waves and turbulent wind. Hydrodynamic loads associated to the three wave models are coupled with aerodynamic loads acting on the rotor of a 5-MW wind turbine. Hydro-aero-elastic calculations are performed using the NREL software FAST. The paper shows that from wave steep- ness ka = 0.1 on the 2nd-order model becomes inaccurate. It underestimates the structural loads as well as the resonant oscillations of the tower caused by the higher-order components
- …
