scholarly journals Multigap absorption inCaCu3Ti4O12and the prediction capability ofab initiomethods

2014 ◽  
Vol 90 (4) ◽  
Author(s):  
Francesco Ricci ◽  
Paola Alippi ◽  
Alessio Filippetti ◽  
Vincenzo Fiorentini
Keyword(s):  
Prediction Capability

Investigating prediction capability of HEC-1 and KINEROS kinematic wave runoff models

1994 ◽  
Vol 157 (1-4) ◽  
pp. 87-103 ◽  
Author(s):  
J.Obiukwu Duru ◽  
Allen T. Hjelmfelt
Keyword(s):  
Kinematic Wave ◽  
Prediction Capability

scholarly journals Applicability of Susceptibility Model for Rock and Loess Earthquake Landslides in the Eastern Tibetan Plateau

2021 ◽  
Vol 13 (13) ◽  
pp. 2546
Author(s):  
Xinyi Guo ◽  
Bihong Fu ◽  
Jie Du ◽  
Pilong Shi ◽  
Qingyu Chen ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Disaster Relief ◽  
Coupling Model ◽  
Scientific Data ◽  
Eastern Tibetan Plateau ◽  
Loess Landslide ◽  
Rock Landslide ◽  
Susceptibility Model ◽  
Prediction Capability ◽  
Better Than

It is crucial to explore a suitable landslide susceptibility model with an excellent prediction capability for rapid evaluation and disaster relief in seismic regions with different lithological features. In this study, we selected two typical seismic events, the Jiuzhaigou and Minxian earthquakes, which occurred in the Alpine karst and loess regions, respectively. Eight influencing factors and five models were chosen to calculate the susceptibility of landslide, including the information (I) model, certainty factor (CF) model, logistic regression (LR) model, I + LR coupling model, and CF + LR coupling model. Then, the accuracy and the landslide susceptibility distribution of these models were assessed by the area under curve (AUC) and distribution criteria. Finally, the model with high accuracy and good applicability for the rock landslide or loess landslide regions was optimized. Our results showed that the accuracy of the coupling model is higher than that of the single models. Except for the LR model, the landslide susceptibility distribution for the above-mentioned models is consistent with universal cognition. The coupling models are generally better than their single models. Among them, the I + LR model can obtain the best comprehensive results for assessing the distribution and accuracy of both rock and loess landslide susceptibility, which is helpful for disaster relief and policy-making, and it can also provide useful scientific data for post-seismic reconstruction and restoration.

scholarly journals The National Earth System Prediction Capability: Coordinating the Giant

2017 ◽  
Vol 98 (2) ◽  
pp. 239-252 ◽  
Author(s):  
Jessie C. Carman ◽  
Daniel P. Eleuterio ◽  
Timothy C. Gallaudet ◽  
Gerald L. Geernaert ◽  
Patrick A. Harr ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
The United States ◽  
Department Of Energy ◽  
Earth System ◽  
Numerical Weather ◽  
Interagency Coordination ◽  
Research Technology ◽  
Prediction Capability ◽  
The U.S

Abstract The United States has had three operational numerical weather prediction centers since the Joint Numerical Weather Prediction Unit was closed in 1958. This led to separate paths for U.S. numerical weather prediction, research, technology, and operations, resulting in multiple community calls for better coordination. Since 2006, the three operational organizations—the U.S. Air Force, the U.S. Navy, and the National Weather Service—and, more recently, the Department of Energy, the National Aeronautics and Space Administration, the National Science Foundation, and the National Oceanic and Atmospheric Administration/Office of Oceanic and Atmospheric Research, have been working to increase coordination. This increasingly successful effort has resulted in the establishment of a National Earth System Prediction Capability (National ESPC) office with responsibility to further interagency coordination and collaboration. It has also resulted in sharing of data through an operational global ensemble, common software standards, and model components among the agencies. This article discusses the drivers, the progress, and the future of interagency collaboration.

scholarly journals Edgewise Compressive Behavior of Composite Structural Insulated Panels with Magnesium Oxide Board Facings

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Łukasz Smakosz ◽  
Ireneusz Kreja ◽  
Zbigniew Pozorski
Keyword(s):  
Magnesium Oxide ◽  
Sandwich Panel ◽  
Failure Modes ◽  
Small Scale ◽  
Fe Model ◽  
Compressive Behavior ◽  
Axial Loads ◽  
Reliable Prediction ◽  
Structural Insulated Panels ◽  
Prediction Capability

Edgewise compression response of a composite structural insulated panel (CSIP) with magnesium oxide board facings was investigated. The discussed CSIP is a novel multifunctional sandwich panel introduced to the housing industry as a part of the wall, floor, and roof assemblies. The study aims to propose a computational tool for reliable prediction of failure modes of CSIPs subjected to concentric and eccentric axial loads. An advanced numerical model was proposed that includes geometrical and material nonlinearity as well as incorporates the material bimodularity effect to achieve accurate and versatile failure mode prediction capability. Laboratory tests on small-scale CSIP samples of three different slenderness ratios and full-scale panels loaded with three different eccentricity values were carried out, and the test data were compared with numerical results for validation. The finite element (FE) model successfully captured CSIP’s inelastic response in uniaxial compression and when flexural action was introduced by eccentric loads or buckling and predicted all failure modes correctly. The comprehensive validation showed that the proposed approach could be considered a robust and versatile aid in CSIP design.

Temperature Prediction at the Blow End of the Converter via Control Model

2014 ◽  
Vol 1025-1026 ◽  
pp. 298-301
Author(s):  
Alexandre Furtado Ferreira
Keyword(s):  
Mathematical Model ◽  
Dynamic Control ◽  
Control Model ◽  
Mass Balances ◽  
Temperature Prediction ◽  
Oxygen Blow ◽  
Model Based ◽  
Gas Control ◽  
Prediction Capability

In the present work, a technique and model for temperature prediction at the blow end are briefly discussed, along with their limitations and perspectives for application. As a result of this analysis, a mathematical model based in heat and mass balances has been developed with a view to evaluating the possibility of improving this prediction capability. The study here presented focuses the development of a semi-dynamic control model in the LD-KGC converter (Linz-Donawitz-Kawasaki Gas Control Converter). The control model enables one to predict the temperature of the blow end by solving both the energy and mass equations. The inputs to the control model are the load data of the LD-KGC converter at the blow beginning and the collected data by the lance to 89% of oxygen blow. The results obtained in the present work were compared to the data measured in steelmaking. The semi-dynamic control model results agree well with data for LD-KGC converters.

Evaluation of prediction capability, robustness, and sensitivity in non-linear landslide susceptibility models, Guantánamo, Cuba

2011 ◽  
Vol 37 (4) ◽  
pp. 410-425 ◽  
Author(s):  
C. Melchiorre ◽  
E.A. Castellanos Abella ◽  
C.J. van Westen ◽  
M. Matteucci
Keyword(s):  
Landslide Susceptibility ◽  
Non Linear ◽  
Prediction Capability

Capability of the Bayesian Forecasting Method to Predict Field Time Series

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Nicolò Gatta ◽  
Mauro Venturini ◽  
Lucrezia Manservigi ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Field Data ◽  
Single Step ◽  
Bayesian Forecasting ◽  
Virtual Sensors ◽  
Average Prediction Error ◽  
Prediction Scheme ◽  
Forecasting Method ◽  
Prediction Capability

This paper addresses the challenge of forecasting the future values of gas turbine measureable quantities. The final aim is the simulation of “virtual sensors” capable of producing statistically coherent measurements aimed at replacing anomalous observations discarded from the time series. Among the different available approaches, the Bayesian forecasting method (BFM) adopted in this paper uses the information held by a pool of observations as knowledge base to forecast the values at a future state. The BFM algorithm is applied in this paper to Siemens field data to assess its prediction capability, by considering two different approaches, i.e., single-step prediction (SSP) and multistep prediction (MSP). While SSP predicts the next observation by using true data as base of knowledge, MSP uses previously predicted data as base of knowledge to perform the prediction of future time steps. The results show that BFM single-step average prediction error can be very low, when filtered field data are analyzed. On the contrary, the average prediction error achieved in case of BFM multistep prediction is remarkably higher. To overcome this issue, the BFM single-step prediction scheme is also applied to clusters of time-wise averaged data. In this manner, an acceptable average prediction error can be achieved by considering clusters composed of 60 observations.

Sign in / Sign up

Export Citation Format

Share Document