Non-Linear Analysis of Vehicle Dynamics (NAVDyn): A Reduced Order Model for Vehicle Handling Analysis

2000 ◽  
Author(s):  
Jon D. Demerly ◽  
Kamal Youcef-Toumi
Keyword(s):  
Vehicle Dynamics ◽  
Linear Analysis ◽  
Reduced Order Model ◽  
Order Model ◽  
Vehicle Handling ◽  
Reduced Order ◽  
Non Linear

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>

Non-linear PGD Reduced-order model for industrial finite element software

2021 ◽  
Author(s):  
Ronan Scanff ◽  
David Néron ◽  
Pierre Ladevèze ◽  
Philippe Barabinot ◽  
Frédéric Cugnon ◽  
...  
Keyword(s):  
Finite Element ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Finite Element Software ◽  
Non Linear

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.

scholarly journals Stabilized Reduced-Order Model of a Non-Linear Eddy Current Problem by a Gappy-POD Approach

2018 ◽  
Vol 54 (12) ◽  
pp. 1-8 ◽  
Author(s):  
MD. Rokibul Hasan ◽  
Laurent Montier ◽  
Thomas Henneron ◽  
Ruth V. Sabariego
Keyword(s):  
Eddy Current ◽  
Current Problem ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Non Linear ◽  
Eddy Current Problem ◽  
Gappy Pod
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