The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learni...The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learning(ML)-based prediction method to study the evolution of the mechanical properties of Al-Li alloys in the marine environment.We obtained the mechanical properties of Al-Li alloy samples under uniaxial tensile deformation at different exposure times through Marine exposure experiments.We obtained the strain evolution by digital image correlation(DIC).The strain field images are voxelized using 2D-Convolutional Neural Networks(CNN)autoencoders as input data for Long Short-Term Memory(LSTM)neural networks.Then,the output data of LSTM neural networks combined with corrosion features were input into the Back Propagation(BP)neural network to predict the mechanical properties of Al-Li alloys.The main conclusions are as follows:1.The variation law of mechanical properties of2297-T8 in the Marine atmosphere is revealed.With the increase in outdoor exposure test time,the tensile elastic model of 2297-T8 changes slowly,within 10%,and the tensile yield stress changes significantly,with a maximum attenuation of 23.6%.2.The prediction model can predict the strain evolution and mechanical response simultaneously with an error of less than 5%.3.This study shows that a CNN/LSTM system based on machine learning can be built to capture the corrosion characteristics of Marine exposure experiments.The results show that the relationship between corrosion characteristics and mechanical response can be predicted without considering the microstructure evolution of metal materials.展开更多
This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neur...This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neural network methodology, the system was designed to produce a binary output that is subsequently classified into categories of low, medium, or high risk. A significant challenge encountered during the study was the identification and procurement of appropriate historical and forecasted marine weather data, which is integral to the model’s accuracy. Despite these challenges, the results of the study were startlingly optimistic, showcasing the model’s ability to predict with impressive accuracy. In conclusion, the developed forecasting tool not only offers promise in its immediate application but also sets a robust precedent for the adoption and adaptation of similar predictive systems in various analogous use cases in the marine environment and beyond.展开更多
Marine risers play a key role in the deep and ultra-deep water oil and gas production. The vortex-induced vibration （VIV） of marine risers constitutes an important problem in deep water oil exploration and productio...Marine risers play a key role in the deep and ultra-deep water oil and gas production. The vortex-induced vibration （VIV） of marine risers constitutes an important problem in deep water oil exploration and production. VIV will result in high rates of structural failure of marine riser due to fatigue damage accumulation and diminishes the riser fatigue life. In-service monitoring or full scale testing is essential to improve our understanding of V1V response and enhance our ability to predict fatigue damage. One ma- rine riser fatigue acoustic telemetry scheme is proposed and an engineering prototype machine has been developed to monitor deep and ultra-deep water risers＇ fatigue and failure that can diminish the riser fatigue life and lead to economic losses and eco-catastrophe. Many breakthroughs and innovation have been achieved in the process of developing an engineering prototype machine. Sea trials were done on the 6th generation deep-water drilling platform HYSY-981 in the South China Sea. The inclination monitoring results show that the marine riser fatigue acoustic telemetry scheme is feasible and reliable and the engineering prototype machine meets the design criterion and can match the requirements of deep and ultra-deep water riser fatigue monitoring. The rich experience and field data gained in the sea trial which provide much technical support for optimization in the engineering prototype machine in the future.展开更多
The atmospheric duct is a vital radio wave environment.Conventional methods of forecasting the atmospheric duct mainly include statistical analysis based on sounding observation data and mesoscale numerical model-base...The atmospheric duct is a vital radio wave environment.Conventional methods of forecasting the atmospheric duct mainly include statistical analysis based on sounding observation data and mesoscale numerical model-based prediction.The former can provide accurate duct information but is highly dependent on the acquisition of data sets.The latter is more practical but still lacks accuracy.This paper introduces machine learning to establish a novel meteorological parameter correction model for atmospheric duct prediction.In detail,using the weather research and forecasting(WRF)model data and spatiotemporal characteristics as input,sounding data as label and extreme gradient boosting(XGBoost)model for training,the meteorological parameter correction effect is the best,i.e.,the accuracy of forecast meteorological parameters is improved by about 65.4%.Combining the mapping relationship between meteorological parameters and corrected atmospheric refractive index(CARI),and the transition mechanism of CARI to duct parameters,a new duct forecasting mechanism is proposed.Due to the high efficiency of numerical model and the accuracy of sounding data,the new duct forecasting mechanism has excellent performance.By comparing the duct forecasting results,the forecasting accuracy of the new duct forecasting model is significantly higher than that of the mesoscale model.展开更多
介绍自主研发的12 000 k N船用构件力学性能测试平台,该平台可以实现对各种船用钢结构件的力学性能测试。采用模块化设计技术,研发了半潜式分段机身结构及多功能测试装置,探讨双缸同步加载、大液压缸防冲击等技术与方法;并介绍集系统控...介绍自主研发的12 000 k N船用构件力学性能测试平台,该平台可以实现对各种船用钢结构件的力学性能测试。采用模块化设计技术,研发了半潜式分段机身结构及多功能测试装置,探讨双缸同步加载、大液压缸防冲击等技术与方法;并介绍集系统控制与状态监测为一体的软件系统。结果表明:该平台技术先进、测量精度高、测量范围广、使用方便。展开更多
基金supported by the Southwest Institute of Technology and Engineering cooperation fund(Grant No.HDHDW5902020104)。
文摘The ocean is one of the essential fields of national defense in the future,and more and more attention is paid to the lightweight research of Marine equipment and materials.This study it is to develop a Machine learning(ML)-based prediction method to study the evolution of the mechanical properties of Al-Li alloys in the marine environment.We obtained the mechanical properties of Al-Li alloy samples under uniaxial tensile deformation at different exposure times through Marine exposure experiments.We obtained the strain evolution by digital image correlation(DIC).The strain field images are voxelized using 2D-Convolutional Neural Networks(CNN)autoencoders as input data for Long Short-Term Memory(LSTM)neural networks.Then,the output data of LSTM neural networks combined with corrosion features were input into the Back Propagation(BP)neural network to predict the mechanical properties of Al-Li alloys.The main conclusions are as follows:1.The variation law of mechanical properties of2297-T8 in the Marine atmosphere is revealed.With the increase in outdoor exposure test time,the tensile elastic model of 2297-T8 changes slowly,within 10%,and the tensile yield stress changes significantly,with a maximum attenuation of 23.6%.2.The prediction model can predict the strain evolution and mechanical response simultaneously with an error of less than 5%.3.This study shows that a CNN/LSTM system based on machine learning can be built to capture the corrosion characteristics of Marine exposure experiments.The results show that the relationship between corrosion characteristics and mechanical response can be predicted without considering the microstructure evolution of metal materials.
文摘This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neural network methodology, the system was designed to produce a binary output that is subsequently classified into categories of low, medium, or high risk. A significant challenge encountered during the study was the identification and procurement of appropriate historical and forecasted marine weather data, which is integral to the model’s accuracy. Despite these challenges, the results of the study were startlingly optimistic, showcasing the model’s ability to predict with impressive accuracy. In conclusion, the developed forecasting tool not only offers promise in its immediate application but also sets a robust precedent for the adoption and adaptation of similar predictive systems in various analogous use cases in the marine environment and beyond.
基金supported in part by the National Science and Technology Major Project of China (2011ZX 05026-001-06)the National Natural Science Foundation of China (51249005 60972153)
文摘Marine risers play a key role in the deep and ultra-deep water oil and gas production. The vortex-induced vibration （VIV） of marine risers constitutes an important problem in deep water oil exploration and production. VIV will result in high rates of structural failure of marine riser due to fatigue damage accumulation and diminishes the riser fatigue life. In-service monitoring or full scale testing is essential to improve our understanding of V1V response and enhance our ability to predict fatigue damage. One ma- rine riser fatigue acoustic telemetry scheme is proposed and an engineering prototype machine has been developed to monitor deep and ultra-deep water risers＇ fatigue and failure that can diminish the riser fatigue life and lead to economic losses and eco-catastrophe. Many breakthroughs and innovation have been achieved in the process of developing an engineering prototype machine. Sea trials were done on the 6th generation deep-water drilling platform HYSY-981 in the South China Sea. The inclination monitoring results show that the marine riser fatigue acoustic telemetry scheme is feasible and reliable and the engineering prototype machine meets the design criterion and can match the requirements of deep and ultra-deep water riser fatigue monitoring. The rich experience and field data gained in the sea trial which provide much technical support for optimization in the engineering prototype machine in the future.
基金supported by the National Natural Science Foundation of China(62071071,61790553,61871057).
文摘The atmospheric duct is a vital radio wave environment.Conventional methods of forecasting the atmospheric duct mainly include statistical analysis based on sounding observation data and mesoscale numerical model-based prediction.The former can provide accurate duct information but is highly dependent on the acquisition of data sets.The latter is more practical but still lacks accuracy.This paper introduces machine learning to establish a novel meteorological parameter correction model for atmospheric duct prediction.In detail,using the weather research and forecasting(WRF)model data and spatiotemporal characteristics as input,sounding data as label and extreme gradient boosting(XGBoost)model for training,the meteorological parameter correction effect is the best,i.e.,the accuracy of forecast meteorological parameters is improved by about 65.4%.Combining the mapping relationship between meteorological parameters and corrected atmospheric refractive index(CARI),and the transition mechanism of CARI to duct parameters,a new duct forecasting mechanism is proposed.Due to the high efficiency of numerical model and the accuracy of sounding data,the new duct forecasting mechanism has excellent performance.By comparing the duct forecasting results,the forecasting accuracy of the new duct forecasting model is significantly higher than that of the mesoscale model.
文摘介绍自主研发的12 000 k N船用构件力学性能测试平台,该平台可以实现对各种船用钢结构件的力学性能测试。采用模块化设计技术,研发了半潜式分段机身结构及多功能测试装置,探讨双缸同步加载、大液压缸防冲击等技术与方法;并介绍集系统控制与状态监测为一体的软件系统。结果表明:该平台技术先进、测量精度高、测量范围广、使用方便。
