A Reduced-Order Model of Boiling Water Reactor Linear Dynamics

1986 ◽  
Vol 75 (1) ◽  
pp. 15-22 ◽  
Author(s):  
José March-Leuba
Keyword(s):  
Reduced Order Model ◽  
Boiling Water ◽  
Boiling Water Reactor ◽  
Order Model ◽  
Linear Dynamics ◽  
Reduced Order ◽  
Water Reactor

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>

Investigating the Stability Characteristics of Natural-Circulation Boiling Water Reactors Using Root Loci of a Reduced-Order Model

2001 ◽  
Vol 136 (3) ◽  
pp. 301-314 ◽  
Author(s):  
Robert Zboray ◽  
Wilhelmus J. M. de Kruijf ◽  
Tim H. J. J. van der Hagen ◽  
Hugo van Dam
Keyword(s):  
Natural Circulation ◽  
Reduced Order Model ◽  
Boiling Water ◽  
Boiling Water Reactors ◽  
Order Model ◽  
Reduced Order ◽  
Water Reactors ◽  
The Stability

Burnup characteristic of CERMET and TRISO fuel particles used in Boiling Water Reactor with Pebble-Bed Coated Particles

2014 ◽  
Vol 2014 (1) ◽  
pp. 17-22
Author(s):  
Abdelfettah Benchrif ◽  
◽  
Abdelouahed Chetaine ◽  
Hamid Amsil ◽  
◽  
...  
Keyword(s):  
Boiling Water ◽  
Boiling Water Reactor ◽  
Pebble Bed ◽  
Water Reactor ◽  
Coated Particles ◽  
Triso Fuel ◽  
Fuel Particles
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