As a new type of marine structure,floating breakwater can provide suitable water area for coastal residents.In this paper,a multi-module floating breakwater with three cylinders was designed.According to the character...As a new type of marine structure,floating breakwater can provide suitable water area for coastal residents.In this paper,a multi-module floating breakwater with three cylinders was designed.According to the characteristics of each module,the elastic connector was created.The cabins with functions such as living,generating electricity and entertainment were arranged.A linear spring constrained design wave(LSCDW)method for strength analysis of floating marine structures with multi-module elastic connections was proposed.The numerical model was verified by 1:50 similarity ratio in the test tank.According to the analysis of design wave and extreme wave conditions,considering the mooring loads and environmental loads and connector loads,the overall strength of breakwater was analyzed by LSCDW method.These studies can provide new insights and theoretical guidance for the design of multi-module floating structures.展开更多
Accurate estimations of grain output in the agriculturally important region of Northeast China are of great strategic significance for guaranteeing food security.New prediction models for maize and rice yields are bui...Accurate estimations of grain output in the agriculturally important region of Northeast China are of great strategic significance for guaranteeing food security.New prediction models for maize and rice yields are built in this paper based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index.The year-to-year increment is first forecasted and then the original yield value is obtained by adding the historical yield of the previous year.The multivariate linear prediction model of maize shows good predictive ability,with a low normalized root-mean-square error（NRMSE）of 13.9%,and the simulated yield accounts for 81%of the total variance of the observation.To improve the performance of the multivariate linear model,a combined forecasting model of rice is built by considering the weight of the predictors.The NRMSE of the model is 12.9%and the predicted rice yield explains 71%of the total variance.The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models.It is inferred that the statistical models established here by applying year-to-year increment approach could make rational prediction for the maize and rice yield in Northeast China before harvest.The present study may shed new light on yield prediction in advance by use of antecedent large-scale climate signals adequately.展开更多
基金the National Natural Science Foundation of China(No.52071161)。
文摘As a new type of marine structure,floating breakwater can provide suitable water area for coastal residents.In this paper,a multi-module floating breakwater with three cylinders was designed.According to the characteristics of each module,the elastic connector was created.The cabins with functions such as living,generating electricity and entertainment were arranged.A linear spring constrained design wave(LSCDW)method for strength analysis of floating marine structures with multi-module elastic connections was proposed.The numerical model was verified by 1:50 similarity ratio in the test tank.According to the analysis of design wave and extreme wave conditions,considering the mooring loads and environmental loads and connector loads,the overall strength of breakwater was analyzed by LSCDW method.These studies can provide new insights and theoretical guidance for the design of multi-module floating structures.
基金Supported by the National Natural Science Foundation of China(41210007 and 41421004)Basic Research and Operation Fund of Chinese Academy of Meteorological Sciences(2016Y007)
文摘Accurate estimations of grain output in the agriculturally important region of Northeast China are of great strategic significance for guaranteeing food security.New prediction models for maize and rice yields are built in this paper based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index.The year-to-year increment is first forecasted and then the original yield value is obtained by adding the historical yield of the previous year.The multivariate linear prediction model of maize shows good predictive ability,with a low normalized root-mean-square error（NRMSE）of 13.9%,and the simulated yield accounts for 81%of the total variance of the observation.To improve the performance of the multivariate linear model,a combined forecasting model of rice is built by considering the weight of the predictors.The NRMSE of the model is 12.9%and the predicted rice yield explains 71%of the total variance.The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models.It is inferred that the statistical models established here by applying year-to-year increment approach could make rational prediction for the maize and rice yield in Northeast China before harvest.The present study may shed new light on yield prediction in advance by use of antecedent large-scale climate signals adequately.
