The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kineti...The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kinetic energy,turbulent length scale,and vertical diffusivity coefficient for turbulent kinetic energy in the upper ocean.However,the accurate determination of its value remains a pressing scientific challenge.This study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E_(6).Through the integration of the information of the turbulent length scale equation into a physical-informed neural network(PINN),we achieved an accurate and physically meaningful inference of E_(6).Multiple cases were examined to assess the feasibility of PINN in this task,revealing that under optimal settings,the average mean squared error of the E_(6) inference was only 0.01,attesting to the effectiveness of PINN.The optimal hyperparameter combination was identified using the Tanh activation function,along with a spatiotemporal sampling interval of 1 s and 0.1 m.This resulted in a substantial reduction in the average bias of the E_(6) inference,ranging from O(10^(1))to O(10^(2))times compared with other combinations.This study underscores the potential application of PINN in intricate marine environments,offering a novel and efficient method for optimizing MY-type LT parameterization schemes.展开更多
Ocean information management is of great importance as it has been employed in many areas of ocean science and technology. However, the developments of Ocean Information Systems(OISs) often suffer from low efficiency ...Ocean information management is of great importance as it has been employed in many areas of ocean science and technology. However, the developments of Ocean Information Systems(OISs) often suffer from low efficiency because of repetitive work and continuous modifications caused by dynamic requirements. In this paper, the basic requirements of OISs are analyzed first, and then a novel platform DPOI is proposed to improve development efficiency and enhance software quality of OISs by providing off-the-shelf resources. In the platform, the OIS is decomposed hierarchically into a set of modules, which can be reused in different system developments. These modules include the acquisition middleware and data loader that collect data from instruments and files respectively, the database that stores data consistently, the components that support fast application generation, the web services that make the data from distributed sources syntactical by use of predefined schemas and the configuration toolkit that enables software customization. With the assistance of the development platform, the software development needs no programming and the development procedure is thus accelerated greatly. We have applied the development platform in practical developments and evaluated its efficiency in several development practices and different development approaches. The results show that DPOI significantly improves development efficiency and software quality.展开更多
基金The National Key Research and Development Program of China under contract No.2022YFC3105002the National Natural Science Foundation of China under contract No.42176020the project from the Key Laboratory of Marine Environmental Information Technology,Ministry of Natural Resources,under contract No.2023GFW-1047.
文摘The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kinetic energy,turbulent length scale,and vertical diffusivity coefficient for turbulent kinetic energy in the upper ocean.However,the accurate determination of its value remains a pressing scientific challenge.This study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E_(6).Through the integration of the information of the turbulent length scale equation into a physical-informed neural network(PINN),we achieved an accurate and physically meaningful inference of E_(6).Multiple cases were examined to assess the feasibility of PINN in this task,revealing that under optimal settings,the average mean squared error of the E_(6) inference was only 0.01,attesting to the effectiveness of PINN.The optimal hyperparameter combination was identified using the Tanh activation function,along with a spatiotemporal sampling interval of 1 s and 0.1 m.This resulted in a substantial reduction in the average bias of the E_(6) inference,ranging from O(10^(1))to O(10^(2))times compared with other combinations.This study underscores the potential application of PINN in intricate marine environments,offering a novel and efficient method for optimizing MY-type LT parameterization schemes.
基金supported in part by National Natural Science Foundation of China under grant No. 61170258 and 61379127National Ocean Public Benefit Research Foundation under grant No. 201305033-6 and 2011 05034-10+1 种基金Marine Renewable Energy Special Foundation under grant No. GHME2012ZC02Science and Technology Development Plan of Qingdao City under Grant No. 12-1-3-81-jh
文摘Ocean information management is of great importance as it has been employed in many areas of ocean science and technology. However, the developments of Ocean Information Systems(OISs) often suffer from low efficiency because of repetitive work and continuous modifications caused by dynamic requirements. In this paper, the basic requirements of OISs are analyzed first, and then a novel platform DPOI is proposed to improve development efficiency and enhance software quality of OISs by providing off-the-shelf resources. In the platform, the OIS is decomposed hierarchically into a set of modules, which can be reused in different system developments. These modules include the acquisition middleware and data loader that collect data from instruments and files respectively, the database that stores data consistently, the components that support fast application generation, the web services that make the data from distributed sources syntactical by use of predefined schemas and the configuration toolkit that enables software customization. With the assistance of the development platform, the software development needs no programming and the development procedure is thus accelerated greatly. We have applied the development platform in practical developments and evaluated its efficiency in several development practices and different development approaches. The results show that DPOI significantly improves development efficiency and software quality.
