基于中国科学院自主研发的第二代地球系统模式CAS-ESM2.0,本研究通过在陆面分量模式CoLM(Common Land Model)中引入植被水力模型以替换原有的经验性方案,开展了两组34年(1981~2014年)的AMIP(Atmospheric Model Intercomparison Project...基于中国科学院自主研发的第二代地球系统模式CAS-ESM2.0,本研究通过在陆面分量模式CoLM(Common Land Model)中引入植被水力模型以替换原有的经验性方案,开展了两组34年(1981~2014年)的AMIP(Atmospheric Model Intercomparison Project)数值模拟试验,探讨了植被水力方案的引入对中国夏季降水模拟的影响。结果表明,植被水力方案的引入能够显著降低CAS-ESM2.0模式对中国夏季降水气候态的模拟偏差,特别是显著改进了中国东部、青藏高原降水的低估,青藏高原以东的川西地区降水高估的偏差,同时也改善了夏季降水年际变率和极端大雨日数的模拟性能。进一步分析显示,植被水力方案的改进显著减小了土壤湿度在长江流域偏干、青藏高原偏湿的模式模拟偏差,降低了我国中东部以及青藏高原地表感热通量和潜热通量的模拟偏差,改善了模式对陆气相互作用过程的模拟能力。陆气相互作用的改进显著提升了模式对东亚季风环流的模拟,改进后的模式模拟的西北太平洋海平面气压的负偏差显著降低,有利于西南季风以及西北太平洋向我国东部的水汽输送,同时在对流层低层出现反气旋异常响应,有效改善了中国东部南风偏弱及水汽辐合偏弱的模拟偏差,使得我国东部降水负偏差显著减小。以上结果表明,包括植被水力过程的陆气相互作用的合理表述是改善东亚夏季降水模拟的重要途径之一。展开更多
利用实况资料和再分析资料,结合WRF(weather research and forecasting)模式对南通一次极端大风过程进行诊断分析及数值模拟。分析了该个例发生的天气形势背景和系统的水平、垂直结构,探究大风天气成因,并进一步对比不同参数化方案的模...利用实况资料和再分析资料,结合WRF(weather research and forecasting)模式对南通一次极端大风过程进行诊断分析及数值模拟。分析了该个例发生的天气形势背景和系统的水平、垂直结构,探究大风天气成因,并进一步对比不同参数化方案的模拟效果。结果表明:1)大风过程发生在高空深厚冷涡和地面强暖湿低压的环流背景下,上空存在不稳定层结和不稳定能量的累积;雷暴大风在12—13时经历了发展、成熟、消散3个阶段,飑线以碎块型的方式形成。2)3种微物理方案中,MG方案模拟出更大面积的层云、强回波和极端大风,模拟的最大地面阵风为44.47 m·s^(-1)。Lin方案较好地模拟出飑线的演变过程和垂直结构特征,模拟的最强上升气流达23.55 m·s^(-1),下沉气流达-13.21 m·s^(-1)。3)水平方向上,雷暴大风附近存在成熟的飑线地面中尺度系统,地面存在深厚冷池出流、变压梯度大值区和冷锋过境,它们共同促进了地面大风的生成。4)垂直方向上,对流单体上空高层辐散、低层辐合,存在强上升气流和水汽潜热释放;后侧的干空气蒸发和粒子的拖曳加强下沉运动,配合地面冷池出流和辐散气流,造成了极端大风天气。展开更多
Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.展开更多
Lead magnesium niobate-lead titanate(PMN-PT)piezoelectric single crystals are widely utilized due to their outstanding performance,with varying compositions significantly impacting their properties.While application o...Lead magnesium niobate-lead titanate(PMN-PT)piezoelectric single crystals are widely utilized due to their outstanding performance,with varying compositions significantly impacting their properties.While application of PMN-PT in high-power settings is rapidly evolving,material parameters are typically tested under low signal conditions(1 V),and effects of different PT(PbTiO_(3))contents on the performance of PMN-PT single crystals under high-power conditions remain unclear.This study developed a comprehensive high-power testing platform using the constant voltage method to evaluate performance of PMN-PT single crystals with different PT contents under high-power voltage stimulation.Using crystals sized at 10 mm×3 mm×0.5 mm as an example,this research explored changes in material parameters.The results exhibit that while trend of the parameter changes under high-power excitation was consistent across different PT contents,degree of the change varied significantly.For instance,a PMN-PT single crystal with 26%(in mol)PT content exhibited a 25%increase in the piezoelectric coefficient d_(31),a 13%increase in the elastic compliance coefficient s_(11)^(E),a 17%increase in the electromechanical coupling coefficient k_(31),and a 73%decrease in the mechanical quality factor Q_(m) when the power reached 7.90 W.As the PT content increased,the PMN-PT materials became more susceptible to temperature influences,significantly reducing the power tolerance and more readily reaching the depolarization temperatures.This led to loss of piezoelectric performance.Based on these findings,a clearer understanding of impact of PT content on performance of PMN-PT single crystals under high-power applications has been established,providing reliable data to support design of sensors or transducers using PMN-PT as the sensitive element.展开更多
In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
文摘基于中国科学院自主研发的第二代地球系统模式CAS-ESM2.0,本研究通过在陆面分量模式CoLM(Common Land Model)中引入植被水力模型以替换原有的经验性方案,开展了两组34年(1981~2014年)的AMIP(Atmospheric Model Intercomparison Project)数值模拟试验,探讨了植被水力方案的引入对中国夏季降水模拟的影响。结果表明,植被水力方案的引入能够显著降低CAS-ESM2.0模式对中国夏季降水气候态的模拟偏差,特别是显著改进了中国东部、青藏高原降水的低估,青藏高原以东的川西地区降水高估的偏差,同时也改善了夏季降水年际变率和极端大雨日数的模拟性能。进一步分析显示,植被水力方案的改进显著减小了土壤湿度在长江流域偏干、青藏高原偏湿的模式模拟偏差,降低了我国中东部以及青藏高原地表感热通量和潜热通量的模拟偏差,改善了模式对陆气相互作用过程的模拟能力。陆气相互作用的改进显著提升了模式对东亚季风环流的模拟,改进后的模式模拟的西北太平洋海平面气压的负偏差显著降低,有利于西南季风以及西北太平洋向我国东部的水汽输送,同时在对流层低层出现反气旋异常响应,有效改善了中国东部南风偏弱及水汽辐合偏弱的模拟偏差,使得我国东部降水负偏差显著减小。以上结果表明,包括植被水力过程的陆气相互作用的合理表述是改善东亚夏季降水模拟的重要途径之一。
文摘利用实况资料和再分析资料,结合WRF(weather research and forecasting)模式对南通一次极端大风过程进行诊断分析及数值模拟。分析了该个例发生的天气形势背景和系统的水平、垂直结构,探究大风天气成因,并进一步对比不同参数化方案的模拟效果。结果表明:1)大风过程发生在高空深厚冷涡和地面强暖湿低压的环流背景下,上空存在不稳定层结和不稳定能量的累积;雷暴大风在12—13时经历了发展、成熟、消散3个阶段,飑线以碎块型的方式形成。2)3种微物理方案中,MG方案模拟出更大面积的层云、强回波和极端大风,模拟的最大地面阵风为44.47 m·s^(-1)。Lin方案较好地模拟出飑线的演变过程和垂直结构特征,模拟的最强上升气流达23.55 m·s^(-1),下沉气流达-13.21 m·s^(-1)。3)水平方向上,雷暴大风附近存在成熟的飑线地面中尺度系统,地面存在深厚冷池出流、变压梯度大值区和冷锋过境,它们共同促进了地面大风的生成。4)垂直方向上,对流单体上空高层辐散、低层辐合,存在强上升气流和水汽潜热释放;后侧的干空气蒸发和粒子的拖曳加强下沉运动,配合地面冷池出流和辐散气流,造成了极端大风天气。
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
基金Research and Development Project on Voltage Sensors by China Southern Power Grid Digital Research Institute(210000KK52220017)。
文摘Lead magnesium niobate-lead titanate(PMN-PT)piezoelectric single crystals are widely utilized due to their outstanding performance,with varying compositions significantly impacting their properties.While application of PMN-PT in high-power settings is rapidly evolving,material parameters are typically tested under low signal conditions(1 V),and effects of different PT(PbTiO_(3))contents on the performance of PMN-PT single crystals under high-power conditions remain unclear.This study developed a comprehensive high-power testing platform using the constant voltage method to evaluate performance of PMN-PT single crystals with different PT contents under high-power voltage stimulation.Using crystals sized at 10 mm×3 mm×0.5 mm as an example,this research explored changes in material parameters.The results exhibit that while trend of the parameter changes under high-power excitation was consistent across different PT contents,degree of the change varied significantly.For instance,a PMN-PT single crystal with 26%(in mol)PT content exhibited a 25%increase in the piezoelectric coefficient d_(31),a 13%increase in the elastic compliance coefficient s_(11)^(E),a 17%increase in the electromechanical coupling coefficient k_(31),and a 73%decrease in the mechanical quality factor Q_(m) when the power reached 7.90 W.As the PT content increased,the PMN-PT materials became more susceptible to temperature influences,significantly reducing the power tolerance and more readily reaching the depolarization temperatures.This led to loss of piezoelectric performance.Based on these findings,a clearer understanding of impact of PT content on performance of PMN-PT single crystals under high-power applications has been established,providing reliable data to support design of sensors or transducers using PMN-PT as the sensitive element.
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
