scholarly journals Time-domain non-linear aeroelastic analysis via a projection-based reduced-order model

2020 ◽  
Vol 124 (1281) ◽  
pp. 1798-1818 ◽  
Author(s):  
S. Lee ◽  
H. Cho ◽  
H. Kim ◽  
S.-J. Shin
Keyword(s):  
Flow Field ◽  
Time Domain ◽  
Reduced Order Model ◽  
Navier Stokes ◽  
Computational Time ◽  
Order Model ◽  
Navier Stokes Equation ◽  
Reduced Order ◽  
Non Linear ◽  
The Time Domain

ABSTRACTThe aeroelastic phenomenon of limit-cycle oscillations (LCOs) is analysed using a projection-based reduced-order model (PROM) and Navier–Stokes computational fluid dynamics (CFD) in the time domain. The proposed approach employs incompressible Navier–Stokes CFD to construct the full-order model flow field. A proper orthogonal decomposition (POD) of the snapshot matrix is conducted to extract the POD modes and corresponding temporal coefficients. The POD modes are directly projected to the incompressible Navier–Stokes equation to reconstruct the flow field efficiently. The methodology is applied to a plunging cylinder and an aerofoil undergoing LCOs. This scheme decreases the computational time while preserving the capability to predict the flow field accurately. The ROM is capable of reducing the computational time by at least 70% while maintaining the discrepancy within 0.1%. The causes of LCOs are also investigated. The scheme can be used to analyse non-linear aeroelastic phenomena in the time domain with reduced computational time.

Modal Reduced Order Model for Vision Sensing of IPMC Actuator

2006 ◽  
Vol 326-328 ◽  
pp. 1523-1526
Author(s):  
Il Kweon Oh ◽  
Seong Won Yeom ◽  
Dong Weon Lee
Keyword(s):  
Reduced Order Model ◽  
Mode Shapes ◽  
Computational Time ◽  
Transverse Displacement ◽  
Metal Composite ◽  
Motion Generation ◽  
Order Model ◽  
Lower Mode ◽  
Reduced Order ◽  
Vision Sensing

In order to control the IPMC (Ionic Polymer Metal Composite) actuators, it is necessary to use a vision sensing system and a reduced order model from the vision sensing data. In this study, the MROVS (Modal Reduced Order Vision Sensing) model using the least square method has been developed for implementation of the biomimetic motion generation. The simulated transverse displacement is approximated with a sum of the lower mode shapes of the cantilever beam. The NIPXI 1409 image acquisition board and CCD camera (XC-HR50) are used in the experimental setup. Present results show that the MROVS model can efficiently process the vision sensing of the biomimetic IPMC actuator with cost-effective computational time.

scholarly journals Precipitation Nowcasting using Data-driven Reduced-order Model

2021 ◽  
Author(s):  
Divya S Vidyadharan ◽  
Aaron Xavier ◽  
Blossom Treesa Bastian ◽  
Ajay Ragh ◽  
Naveen Chittilapilly
Keyword(s):  
Radar Reflectivity ◽  
Reduced Order Model ◽  
Motion Field ◽  
L1 Norm ◽  
Order Model ◽  
Linear Dynamics ◽  
Reduced Order ◽  
Squall Lines ◽  
Short Period ◽  
Non Linear

<div>Radar-based precipitation nowcasting refers to predicting rain for a short period of time using radar reflectivity images. For dynamic nowcasting, motion fields can be extrapolated using an approximate and localized reduced-order model. Motion field estimation based on traditional Horn-Schunck (HS) algorithm suffers from over-smoothing at discontinuities in non-rigid and dissolving texture present in precipitation nowcasting products. An attempt to preserve the discontinuities using an L1 norm formulation in HS led to the use of Total Variation L1 norm (TVL1). In this paper, we propose a radar-based precipitation nowcasting model with TVL1-based estimation of motion field and Sparse Identification of Non-linear Dynamics (SINDy)-based</div><div>estimation of non-linear dynamics. TVL1 is effective in preserving the edges especially in the case of the eye of typhoons and squall lines while estimating motion vectors. SINDy captures the non-linear dynamics and generates the subsequent update values for the motion field based on a reduced-order representation. Finally, the SINDy-generated ensemble of motion field is used along with</div><div>the radar reflectivity image for generating precipitation nowcasts. We evaluated the effectiveness of TVL1 in preserving edges while capturing the motion field from non-rigid surfaces. The performance of the proposed TVL1-SINDy model in nowcasting weather events such as Typhoons and Squall lines are evaluated using performance metrics such as Mean Absolute Error (MAE), and Critical Success Index (CSI). Experimental results show that the proposed nowcasting system demonstrates better performance compared to the benchmark nowcasting models with lower MAE, higher CSI at higher lead times.</div>

Precipitation Nowcasting using Data-driven Reduced-order Model

2021 ◽  
Author(s):  
Divya S Vidyadharan ◽  
Aaron Xavier ◽  
Blossom Treesa Bastian ◽  
Ajay Ragh ◽  
Naveen Chittilapilly
Keyword(s):  
Radar Reflectivity ◽  
Reduced Order Model ◽  
Motion Field ◽  
L1 Norm ◽  
Order Model ◽  
Linear Dynamics ◽  
Reduced Order ◽  
Squall Lines ◽  
Short Period ◽  
Non Linear

<div>Radar-based precipitation nowcasting refers to predicting rain for a short period of time using radar reflectivity images. For dynamic nowcasting, motion fields can be extrapolated using an approximate and localized reduced-order model. Motion field estimation based on traditional Horn-Schunck (HS) algorithm suffers from over-smoothing at discontinuities in non-rigid and dissolving texture present in precipitation nowcasting products. An attempt to preserve the discontinuities using an L1 norm formulation in HS led to the use of Total Variation L1 norm (TVL1). In this paper, we propose a radar-based precipitation nowcasting model with TVL1-based estimation of motion field and Sparse Identification of Non-linear Dynamics (SINDy)-based</div><div>estimation of non-linear dynamics. TVL1 is effective in preserving the edges especially in the case of the eye of typhoons and squall lines while estimating motion vectors. SINDy captures the non-linear dynamics and generates the subsequent update values for the motion field based on a reduced-order representation. Finally, the SINDy-generated ensemble of motion field is used along with</div><div>the radar reflectivity image for generating precipitation nowcasts. We evaluated the effectiveness of TVL1 in preserving edges while capturing the motion field from non-rigid surfaces. The performance of the proposed TVL1-SINDy model in nowcasting weather events such as Typhoons and Squall lines are evaluated using performance metrics such as Mean Absolute Error (MAE), and Critical Success Index (CSI). Experimental results show that the proposed nowcasting system demonstrates better performance compared to the benchmark nowcasting models with lower MAE, higher CSI at higher lead times.</div>

scholarly journals Nonlinear reduced order model of high aspect ratio rectangular wing plate

2018 ◽  
Vol 7 (4.13) ◽  
pp. 195-201
Author(s):  
Thinesh C ◽  
M Y Harmin
Keyword(s):  
Leading Edge ◽  
Reduced Order Model ◽  
Computational Time ◽  
Plate Model ◽  
Order Model ◽  
Reduced Order ◽  
Nonlinear Properties ◽  
First Case ◽  
Trailing Edges ◽  
Nonlinear Deformations

This paper presents a Combined Modal Finite Element (CMFE) approach to develop a Nonlinear Reduced Order Model (NROM) in order to characterize the nonlinear properties of the wing plate model. The wing plate model is subjected to three types of loading cases. The first case considers a uniformly distributed loading on the whole wing plate model for describing the bending deflection; the second case considers a uniformly distributed loading on both leading and trailing edges with one of them of an opposite direction for describing the twisting deflection; the third case considers the loading on the leading edge for describing a combination of bending-twisting deflection. The accuracy of the results is represented in the form of mean error, the standard deviation of the error and the percentage of error. From the findings, the NROMs are able to predict the nonlinear deformations of the wing plate with a minimal computational time and reasonably good accuracy. The results also indicate the importance of the selection modes when conducting the analysis.  

On the Parallelization of a Harmonic Balance Compressible Navier-Stokes Solver for Wind Turbine Aerodynamics

2011 ◽  
Author(s):  
Adrian Jackson ◽  
M. Sergio Campobasso ◽  
Mohammad H. Baba-Ahmadi
Keyword(s):  
Flow Field ◽  
Domain Decomposition ◽  
Wind Turbine ◽  
Time Domain ◽  
Harmonic Balance ◽  
Unsteady Aerodynamics ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Navier Stokes ◽  
The Time Domain

The paper discusses the parallelization of a novel explicit harmonic balance Navier-Stokes solver for wind turbine unsteady aerodynamics. For large three-dimensional problems, the use of a standard MPI parallelization based on the geometric domain decomposition of the physical domain may require an excessive degree of partitioning with respect to that needed when the same aerodynamic analysis is performed with the time-domain solver. This occurrence may penalize the parallel efficiency of the harmonic balance solver due to excessive communication among MPI processes to transfer halo data. In the case of the harmonic balance analysis, the necessity of further grid partitioning may arise because the memory requirement of each block is higher than for the time-domain analysis: it is that of the time-domain analysis multiplied by a variable proportional to the number of complex harmonics used to represent the sought periodic flow field. A hybrid multi-level parallelization paradigm for explicit harmonic balance Navier-Stokes solvers is presented, which makes use of both distributed and shared memory parallelization technologies, and removes the need for further domain decomposition with respect to the case of the time-domain analysis. The discussed parallelization approaches are tested on the multigrid harmonic balance solver being developed by the authors, considering various computational configurations for the CFD analysis of the unsteady flow field past the airfoil of a wind tubine blade in yawed wind.

Reduced-Order Model of Unsteady Flows in a Turbomachinery Fan Modeled Using a Novel Proper Orthogonal Decomposition

2019 ◽  
Author(s):  
Elizabeth H. Krath ◽  
Forrest L. Carpenter ◽  
Paul G. A. Cizmas ◽  
David A. Johnston
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Reference Conditions ◽  
Stable Solution ◽  
Orthogonal Decomposition ◽  
Reduced Order Model ◽  
Computational Time ◽  
Order Model ◽  
Reduced Order ◽  
Proper Orthogonal ◽  
Model Rotor

Abstract This paper presents a novel, more efficient reduced-order model based on the proper orthogonal decomposition (POD) for the prediction of flows in turbomachinery. To further reduce the computational time, the governing equations were written as a function of specific volume instead of density. This allowed for the pre-computation of the coefficients of the system of ordinary differential equations that describe the reduced-order model. A penalty method was developed to implement time-dependent boundary conditions and achieve a stable solution for the reduced-order model. Rotor 67 was used as a validation case for the reduced-order model, which was tested for both on- and off-reference conditions. This reduced-order model was shown to be more than 10,000 times faster than the full-order model.

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