Electromagnetic backscattering from one-dimensional drifting fractal sea surface I: Wave–current coupled model

2016 ◽  
Vol 25 (6) ◽  
pp. 064101 ◽  
Author(s):  
Tao Xie ◽  
Shang-Zhuo Zhao ◽  
William Perrie ◽  
He Fang ◽  
Wen-Jin Yu ◽  
...  
Keyword(s):  
Coupled Model ◽  
Sea Surface ◽  
One Dimensional ◽  
Electromagnetic Backscattering

Electromagnetic backscattering from one-dimensional drifting fractal sea surface II: Electromagnetic backscattering model

2016 ◽  
Vol 25 (7) ◽  
pp. 074102 ◽  
Author(s):  
Tao Xie ◽  
William Perrie ◽  
Shang-Zhuo Zhao ◽  
He Fang ◽  
Wen-Jin Yu ◽  
...  
Keyword(s):  
Sea Surface ◽  
One Dimensional ◽  
Electromagnetic Backscattering

scholarly journals Roles of initial ocean surface and subsurface states on successfully predicting 2006–2007 El Niño with an intermediate coupled model

Ocean Science ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 187-194 ◽  
Author(s):  
F. Zheng ◽  
J. Zhu
Keyword(s):  
El Niño ◽  
El Nino ◽  
Surface Condition ◽  
Coupled Model ◽  
Ocean Surface ◽  
Sea Surface ◽  
Initial Surface ◽  
El Niño Event ◽  
Successful Prediction ◽  
Intermediate Coupled Model

Abstract. The 2006–2007 El Niño event, an unusually weak event, was predicted by most models only after the warming in the eastern Pacific had commenced. In this study, on the basis of an El Niño prediction system, roles of the initial ocean surface and subsurface states on predicting the 2006–2007 El Niño event are investigated to determine conditions favorable for predicting El Niño growth and are isolated in three sets of hindcast experiments. The hindcast is initialized through assimilation of only the sea surface temperature (SST) observations to optimize the initial surface condition, only the sea level (SL) data to update the initial subsurface state, or both the SST and SL data. Results highlight that the hindcasts with three different initial states can all successfully predict the 2006–2007 El Niño event 1 year in advance and that the hindcast initialized by both the SST and SL data performs best. A comparison between the various sets of hindcast results further demonstrates that successful prediction is more significantly affected by the initial subsurface state than by the initial surface condition. The accurate initial surface state can trigger the easier prediction of the 2006–2007 El Niño, whereas a more reasonable initial subsurface state can contribute to improving the prediction in the growth of the warm event.

scholarly journals Computational modeling of the electromechanical response of a ventricular fiber affected by eccentric hypertrophy

2017 ◽  
Vol 8 (1) ◽  
pp. 185-209
Author(s):  
Fabrizio Del Bianco ◽  
Piero Colli Franzone ◽  
Simone Scacchi ◽  
Lorenzo Fassina
Keyword(s):  
Coupled Model ◽  
Electrical Current ◽  
Mechanoelectric Feedback ◽  
Isotonic Contraction ◽  
Strongly Coupled ◽  
One Dimensional ◽  
Electromechanical Responses ◽  
Eccentric Hypertrophy ◽  
Dimensional Finite Element ◽  
Simulation Results

AbstractThe aim of this work is to study the effects of eccentric hypertrophy on the electromechanics of a single myocardial ventricular fiber by means of a one-dimensional finite-element strongly-coupled model. The electrical current ow model is written in the reference configuration and it is characterized by two geometric feedbacks, i.e. the conduction and convection ones, and by the mechanoelectric feedback due to stretchactivated channels. First, the influence of such feedbacks is investigated for both a healthy and a hypertrophic fiber in case of isometric simulations. No relevant discrepancies are found when disregarding one or more feedbacks for both fibers. Then, all feedbacks are taken into account while studying the electromechanical responses of fibers. The results from isometric tests do not point out any notable difference between the healthy and hypertrophic fibers as regards the action potential duration and conduction velocity. The length-tension relationships show increased stretches and reduced peak values for tension instead. The tension-velocity relationships derived from afterloaded isotonic and quick- release tests depict higher values of contraction velocity at smaller afterloads. Moreover, higher maximum shortenings are achieved during the isotonic contraction. In conclusion, our simulation results are innovative in predicting the electromechanical behavior of eccentric hypertrophic fibers.

scholarly journals Verification of the Reliability of Offshore Wind Resource Prediction Using an Atmosphere–Ocean Coupled Model

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 254
Author(s):  
Minhyeop Kang ◽  
Kyungnam Ko ◽  
Minyeong Kim
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Prediction Models ◽  
Wrf Model ◽  
Coupled Model ◽  
Sea Surface ◽  
Offshore Wind ◽  
Wind Effect ◽  
Observation Data ◽  
Wind Resource

An atmosphere–ocean coupled model is proposed as an optimal numerical prediction method for the offshore wind resource. Meteorological prediction models are mainly used for wind speed prediction, with active studies using atmospheric models. Seawater mixing occurring at sea due to solar radiation and wind intensity can significantly change the sea surface temperature (SST), an important variable for predicting wind resources and energy production, considering its wind effect, within a short time. This study used the weather research forecasting and ocean mixed layer (WRF-OML) model, an atmosphere–ocean coupled model, to reflect time-dependent SST and sea surface fluxes. Results are compared with those of the WRF model, another atmospheric model, and verified through comparison with observation data of a meteorological mast (met-mast) at sea. At a height of 94 m, the wind speed predicted had a bias and root mean square error of 1.09 m/s and 2.88 m/s for the WRF model, and −0.07 m/s and 2.45 m/s for the WRF-OML model, respectively. Thus, the WRF-OML model has a higher reliability. In comparing to the met-mast observation data, the annual energy production (AEP) estimation based on the predicted wind speed showed an overestimation of 15.3% and underestimation of 5.9% from the WRF and WRF-OML models, respectively.

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