Computationally efficient High-Fidelity Generalized Method of Cells micromechanics via order-reduction techniques

2016 ◽  
Vol 156 ◽  
pp. 2-9 ◽  
Author(s):  
Trenton M. Ricks ◽  
Thomas E. Lacy ◽  
Evan J. Pineda ◽  
Brett A. Bednarcyk ◽  
Steven M. Arnold
Keyword(s):  
Order Reduction ◽  
High Fidelity ◽  
Computationally Efficient ◽  
Reduction Techniques ◽  
Generalized Method Of Cells

scholarly journals Solution of the Nonlinear High-Fidelity Generalized Method of Cells Micromechanics Relations via Order-Reduction Techniques

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Trenton M. Ricks ◽  
Thomas E. Lacy ◽  
Brett A. Bednarcyk ◽  
Annika Robens-Radermacher ◽  
Evan J. Pineda ◽  
...  
Keyword(s):  
Computational Efficiency ◽  
Order Reduction ◽  
Material Behavior ◽  
High Fidelity ◽  
Reduced Models ◽  
Reduction Techniques ◽  
Significant Difference ◽  
Generalized Method Of Cells ◽  
Unit Cells ◽  
Nylon 12

The High-Fidelity Generalized Method of Cells (HFGMC) is one technique, distinct from traditional finite-element approaches, for accurately simulating nonlinear composite material behavior. In this work, the HFGMC global system of equations for doubly periodic repeating unit cells with nonlinear constituents has been reduced in size through the novel application of a Petrov-Galerkin Proper Orthogonal Decomposition order-reduction scheme in order to improve its computational efficiency. Order-reduced models of an E-glass/Nylon 12 composite led to a 4.8–6.3x speedup in the equation assembly/solution runtime while maintaining model accuracy. This corresponded to a 21–38% reduction in total runtime. The significant difference in assembly/solution and total runtimes was attributed to the evaluation of integration point inelastic field quantities; this step was identical between the unreduced and order-reduced models. Nonetheless, order-reduced techniques offer the potential to significantly improve the computational efficiency of multiscale calculations.

Energy-Based Model Reduction Methodology for Automated Modeling

2010 ◽  
Vol 132 (6) ◽  
Author(s):  
Loucas S. Louca ◽  
Jeffrey L. Stein ◽  
Gregory M. Hulbert
Keyword(s):  
Model Reduction ◽  
Order Reduction ◽  
Process Models ◽  
Model Parameters ◽  
Automated Modeling ◽  
Reduced Models ◽  
System Models ◽  
Quarter Car Model ◽  
Reduction Techniques ◽  
Physical Phenomena

In recent years, algorithms have been developed to help automate the production of dynamic system models. Part of this effort has been the development of algorithms that use modeling metrics for generating minimum complexity models with realization preserving structure and parameters. Existing algorithms, add or remove ideal compliant elements from a model, and consequently do not equally emphasize the contribution of the other fundamental physical phenomena, i.e., ideal inertial or resistive elements, to the overall system behavior. Furthermore, these algorithms have only been developed for linear or linearized models, leaving the automated production of models of nonlinear systems unresolved. Other model reduction techniques suffer from similar limitations due to linearity or the requirement that the reduced models be realization preserving. This paper presents a new modeling metric, activity, which is based on energy. This metric is used to order the importance of all energy elements in a system model. The ranking of the energy elements provides the relative importance of the model parameters and this information is used as a basis to reduce the size of the model and as a type of parameter sensitivity information for system design. The metric is implemented in an automated modeling algorithm called model order reduction algorithm (MORA) that can automatically generate a hierarchical series of reduced models that are realization preserving based on choosing the energy threshold below which energy elements are not included in the model. Finally, MORA is applied to a nonlinear quarter car model to illustrate that energy elements with low activity can be eliminated from the model resulting in a reduced order model, with physically meaningful parameters, which also accurately predicts the behavior of the full model. The activity metric appears to be a valuable metric for automating the reduction of nonlinear system models—providing in the process models that provide better insight and may be more numerically efficient.

scholarly journals Rapid Evaluation for Position-Dependent Dynamics of a 3-DOF PKM Module

2014 ◽  
Vol 6 ◽  
pp. 238928 ◽  
Author(s):  
Hai-wei Luo ◽  
Hui Wang ◽  
Jun Zhang ◽  
Qi Li
Keyword(s):  
Natural Frequencies ◽  
Mode Shapes ◽  
Parallel Kinematic Machine ◽  
Computationally Efficient ◽  
Element Analysis ◽  
Reduction Techniques ◽  
Modal Reduction ◽  
Governing Differential Equation ◽  
Parallel Kinematic ◽  
Analytical System

Based on the substructure synthesis and modal reduction technique, a computationally efficient elastodynamic model for a fully flexible 3-RPS parallel kinematic machine (PKM) tool is proposed, in which the frequency response function (FRF) at the end of the tool can be obtained at any given position throughout its workspace. In the proposed elastodynamic model, the whole system is divided into a moving platform subsystem and three identical RPS limb subsystems, in which all joint compliances are included. The spherical joint and the revolute joint are treated as lumped virtual springs with equal stiffness; the platform is treated as a rigid body and the RPS limbs are modelled with modal reduction techniques. With the compatibility conditions at interfaces between the limbs and the platform, an analytical system governing differential equation is derived. Based on the derived model, the position-dependent dynamic characteristics such as natural frequencies, mode shapes, and FRFs of the 3-RPS PKM are simulated. The simulation results indicate that the distributions of natural frequencies throughout the workspace are strongly dependant on mechanism's configurations and demonstrate an axial-symmetric tendency. The following finite element analysis and modal tests both validate the analytical results of natural frequencies, mode shapes, and the FRFs.

scholarly journals Assessing the Sensitivity of Stall-Regulated Wind Turbine Power to Blade Design Using High-Fidelity Computational Fluid Dynamics

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Andrea G. Sanvito ◽  
Giacomo Persico ◽  
M. Sergio Campobasso
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Field Testing ◽  
Turbine Rotor ◽  
Operating Conditions ◽  
High Fidelity ◽  
Blade Design ◽  
Computationally Efficient ◽  
Wind Speeds ◽  
Wide Range

Abstract This study provides a novel contribution toward the establishment of a new high-fidelity simulation-based design methodology for stall-regulated horizontal axis wind turbines. The aerodynamic design of these machines is complex, due to the difficulty of reliably predicting stall onset and poststall characteristics. Low-fidelity design methods, widely used in industry, are computationally efficient, but are often affected by significant uncertainty. Conversely, Navier–Stokes computational fluid dynamics (CFD) can reduce such uncertainty, resulting in lower development costs by reducing the need of field testing of designs not fit for purpose. Here, the compressible CFD research code COSA is used to assess the performance of two alternative designs of a 13-m stall-regulated rotor over a wide range of operating conditions. Validation of the numerical methodology is based on thorough comparisons of novel simulations and measured data of the National Renewable Energy Laboratory (NREL) phase VI turbine rotor, and one of the two industrial rotor designs. An excellent agreement is found in all cases. All simulations of the two industrial rotors are time-dependent, to capture the unsteadiness associated with stall which occurs at most wind speeds. The two designs are cross-compared, with emphasis on the different stall patterns resulting from particular design choices. The key novelty of this work is the CFD-based assessment of the correlation among turbine power, blade aerodynamics, and blade design variables (airfoil geometry, blade planform, and twist) over most operational wind speeds.

Comparative Study of Convolution and Order Reduction Techniques for Blackbox Macromodeling Using Scattering Parameters

2011 ◽  
Vol 1 (10) ◽  
pp. 1642-1650 ◽  
Author(s):  
José E. Schutt-Aine ◽  
Patrick Goh ◽  
Yidnekachew Mekonnen ◽  
Jilin Tan ◽  
Feras Al-Hawari ◽  
...  
Keyword(s):  
Comparative Study ◽  
Order Reduction ◽  
Scattering Parameters ◽  
Reduction Techniques
Sign in / Sign up

Export Citation Format

Share Document