scholarly journals Estimation of Transport Trajectory and Residence Time in Large River–Lake Systems: Application to Poyang Lake (China) Using a Combined Model Approach

Water ◽  
2015 ◽  
Vol 7 (10) ◽  
pp. 5203-5223 ◽  
Author(s):  
Yunliang Li ◽  
Jing Yao
Keyword(s):  
Residence Time ◽  
Poyang Lake ◽  
Large River ◽  
Combined Model ◽  
Lake Systems ◽  
Model Approach

scholarly journals Hydrological evidence and causes of seasonal low water levels in a large river-lake system: Poyang Lake, China

2016 ◽  
Vol 47 (S1) ◽  
pp. 24-39 ◽  
Author(s):  
Jing Yao ◽  
Qi Zhang ◽  
Yunliang Li ◽  
Mengfan Li
Keyword(s):  
Yangtze River ◽  
Water Resource Management ◽  
Poyang Lake ◽  
Large River ◽  
Water Levels ◽  
The Yangtze River ◽  
Lake System ◽  
Spatial Differences ◽  
Northern Portion ◽  
Lake Systems

Seasonal variations in local catchments and connected rivers lead to complex hydrological behaviours in river-lake systems. Poyang Lake is a seasonally dynamic lake with frequent low levels in spring and autumn, which may be triggered by the local catchment and Yangtze River. Based on two typical years, a hydrodynamic model combined with long term hydrological observations was applied to quantify the spatiotemporal impacts of the local catchment and Yangtze River on spring and autumn low water levels in Poyang Lake. As a first attempt, this study explored the spatial differences of the two influences. Simulation results showed that the contributions of the catchment and the Yangtze River were approximately 70% and 30% in spring 1963, and 5% and 95% in autumn 2006, respectively. The area of catchment influence was mainly distributed in channels and southern floodplains, with relatively uniform water levels. The area impacted by the Yangtze River mainly spanned from the northern portion of the waterway to the central lake, with strong spatial variability. This study focused on two typical years; however, the results can be extended to explain common hydrological phenomena and improve future strategies of water resource management in this river-lake system.

Towards A Combined Model for On-Line and Real Conferences

2011 ◽  
pp. 293-306
Author(s):  
Pedro Isaías
Keyword(s):  
Web 2.0 ◽  
Lessons Learned ◽  
Combined Model ◽  
On Line ◽  
Model Approach

This chapter presents a combined model for on-line and real conferences. The chapter introduces Web 2.0 and its importance. Then, using Web 2.0 in real and virtual conferences is discussed since Web 2.0 can make a difference is supporting such a conference model. A past on-line event is analysed and evaluated in order to have lessons learned and make recommendations towards this proposal. The combined model approach is presented and detailed in its components and the importance of Web 2.0 elements is discussed.

Real-time simulation of hydrodynamic and scalar transport in large river-lake systems

2020 ◽  
Vol 582 ◽  
pp. 124531 ◽  
Author(s):  
Dechao Hu ◽  
Shiming Yao ◽  
Chengkun Duan ◽  
Songping Li
Keyword(s):  
Real Time ◽  
Large River ◽  
Scalar Transport ◽  
Real Time Simulation ◽  
Time Simulation ◽  
Lake Systems

A Combined Model Approach for Estimating T0

2019 ◽  
Author(s):  
Marjorie Erickson
Keyword(s):  
Fracture Toughness ◽  
Master Curve ◽  
Reference Temperature ◽  
Initiation Toughness ◽  
Correlation Model ◽  
Combined Model ◽  
Best Estimate ◽  
Curve Model ◽  
Asme Code ◽  
Model Approach

Abstract The current best-estimate model describing the fracture toughness of ferritic steels is the Master Curve methodology standardized in ASTM E1921. Shortly following standardization by ASTM, efforts were undertaken to incorporate this best-estimate model into the framework of the ASME Code to reduce the conservatisms resulting from use of a reference temperature based on the nil-ductility temperature (RTNDT) to index the plane strain fracture initiation toughness (KIc). The reference temperature RTT0, which is based on the ASTM E1921-defined T0 value, was introduced in ASME Code Cases N-629 (replaced by Code Case N-851) and N-631 to replace RTNDT for indexing the ASME KIc curve. Efforts are continuing within the ASME Code to implement direct use of the Master Curve model; using the T0 reference temperature to index an elastic-plastic, KJc fracture toughness curve. Transitioning to a direct T0-based fracture toughness assessment methodology requires the availability of T0 estimates for all materials to be assessed. The historical Charpy and NDT-based regulatory approach to characterizing toughness for reactor pressure vessel (RPV) steels results in a lack of T0 values for a large population of the US nuclear fleet. The expense of the fracture toughness testing required to estimate a valid T0 value makes it unlikely that T0 will ever be widely available. Since direct implementation of best-estimate, fracture toughness models in codes and regulatory actions requires an estimate of T0 for all materials of interest it is necessary to develop an alternative means of estimating T0. A project has been undertaken to develop a combined model approach to estimating T0 from data that may include limited elastic-plastic fracture toughness KJc, Charpy, tensile, ductile initiation toughness, arrest toughness, and/or nil-ductility temperature data. Using correlations between these properties and T0 a methodology for combining estimates of T0 from several sources of data was developed. T0 estimates obtained independently from the Master Curve model, the Simple T28J correlation model, and a more complex Charpy correlation model were combined using the Mixture Probability Density Function (PDF) method to provide a single estimate for T0. Using this method, the individual T0 estimates were combined using weighting factors that accounted for sample size and individual model accuracy to optimize the accuracy and precision of the combined T0 estimate. Combining weighted estimates of T0 from several sources of data was found to provide a more refined estimate of T0 than could be obtained from any of the models alone.

The Combined Model Approach for Assessment of in Vivo Insulin Secretion Rates

1994 ◽  
Vol 27 (1) ◽  
pp. 307-308
Author(s):  
Richard M. Watanabe ◽  
Garry M. Steil ◽  
Richard N. Bergman ◽  
Yolanta Kruszynska
Keyword(s):  
Insulin Secretion ◽  
Combined Model ◽  
Secretion Rates ◽  
Model Approach
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