Article
Meteorology & Atmospheric Sciences
Yuejian Zhu, Bing Fu, Bo Yang, Hong Guan, Eric Sinsky, Wei Li, Jiayi Peng, Xianwu Xue, Dingchen Hou, Xin-Zhong Liang, Sanghoon Shin
Summary: The Global Ensemble Forecast System version 12 (GEFSv12) has been implemented into National Centers For Environmental Prediction operations since September 2020, which improved forecast skills in many categories by increasing horizontal resolution, ensemble members, and extended forecasts. The improvements were achieved through upgrades in model resolution, data assimilation, and stochastic schemes. Coupled GEFS experiments further improved sub-seasonal forecast skill by coupling atmospheric, land surface, ocean, ice, and wave models, reducing forecast uncertainties and improving correlation.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2023)
Article
Construction & Building Technology
Zheng Li, Jing Luo, Minjuan He, Guirong He, Yongliang Sun
Summary: Beam-to-column connections play a crucial role in the lateral performance of post-and-beam timber structures, especially under fire scenarios where little research has been conducted on their mechanical behavior; an advanced nonlinear numerical model and analytical approach were developed to predict the fire performance of such connections, providing effective tools for practical engineering applications.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Mechanics
Chen Zhang, Yushu Li, Biao Jiang, Ruigang Wang, Yilun Liu, Liyong Jia
Summary: A method combining finite element analysis (FEA) and machine learning is studied to predict mechanical properties of composite laminates, by establishing FEA models and using artificial neural network (ANN) and random forest (RF) models to learn virtual samples, predicting mechanical properties. The predicted results are consistent with FEA values, demonstrating the effectiveness of this method.
COMPOSITE STRUCTURES
(2022)
Article
Engineering, Aerospace
Jing Liu, Meng Wang, Shu Li
Summary: This work demonstrates the use of Latin Hypercube Sampling and Proper Orthogonal Decomposition in combination with a Radial Basis Function model for vehicle prediction coupled fluid-thermal-structure. The data-driven method proposed in this paper showed good efficiency in predicting vehicle coupled fluid-thermal-structure based on proper orthogonal decomposition and radial basis function.
Article
Engineering, Geological
Chenyao Guo, Qiang Zhao, Jingwei Wu, Hang Li, Haoyu Yang, Zhe Wu
Summary: This study develops a coupled model to investigate the chemical clogging and permeability coefficient of geotextile envelopes. The results demonstrate that the saturation index and solution flow rate are key factors affecting the chemical clogging and permeability of geotextile envelopes.
GEOTEXTILES AND GEOMEMBRANES
(2022)
Article
Energy & Fuels
Jingwen Yan, Donghao Jin, Xin Liu, Chaoqun Zhang, Heyang Wang
Summary: Tube overheating is a major cause of boiler tube failures, resulting in significant replacement and maintenance costs. To accurately predict waterwall tube temperature, a coupled model combining a three-dimensional CFD model and a one-dimensional hydrodynamic model was developed. This model can consider all key parameters affecting waterwall tube temperature and solve issues caused by steam distribution and uneven heat absorption. It provides the capability to predict the detailed distribution of tube temperature under different operating parameters and locate areas at risk of tube overheating.
Article
Energy & Fuels
Wei Li, Yi Xie, Yangjun Zhang, Kuining Lee, Jiangyan Liu, Lisa Mou, Bin Chen, Yunlong Li
Summary: A dynamic coupled electro-thermal model is proposed, considering the impact of state of charge, inner temperature, and current flux on resistance and heat generation rate, suitable for prismatic batteries. Experimental results show that the model can accurately describe thermal behavior under different conditions and be used for temperature prediction under dynamic current conditions.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Engineering, Civil
Xinggan Lu, Kun Jiang, ShenShen Cheng, DongJian Su, Hao Wang
Summary: This paper designed a typical pyrotechnic device using a thin-walled circular tube as an energy absorber to isolate pyroshock, and established a coupled numerical model linking propellant combustion and finite element analysis. The shock experiment for the typical pyrotechnic device verified the correctness of the coupled model, and the investigation of the buffer performance of the thin-walled circular tube showed a significant reduction in shock overload. The study also found that the energy absorption rates of thin-walled circular tubes are similar, while other buffer performance and stability vary significantly.
THIN-WALLED STRUCTURES
(2022)
Article
Fisheries
Qiaofeng Ma, Yunkuan Han, Yanbin Xi, Jingming Huang, Zhaojun Sheng, Ruijin Zhang
Summary: The study developed an individual-based model for the Yesso scallop larvae, incorporating biological characteristics at different stages and temperature effects to predict their spatiotemporal distribution in the Northern Yellow Sea and Bohai Sea. Verification of the model showed that residual currents caused by tide interaction, wind, and thermohaline effects drive the Yesso scallop larvae from spawning areas to natural seedling areas, providing important guidance for natural seedling collection.
Article
Environmental Sciences
Wei Wang, Jia Liu, Chuanzhe Li, Yuchen Liu, Fuliang Yu
Summary: The study evaluated the potential of the WRF model and its 3DVar module in improving rainfall-runoff prediction accuracy, showing that assimilating radar reflectivity and observations can enhance initial conditions. The coupled atmospheric-hydrologic systems provide more accurate flood forecasts, with the grid-based Hebei model offering the most stable predictions.
Article
Engineering, Mechanical
Xue-Kun Wen, Jun-Hang Jiang, Wei Liu, Chao-Qing Dai
Summary: Using the extended physics-informed neural network, predictions for seven types of vector solitons in the coupled mixed derivative nonlinear Schrodinger equation are made. The results confirm the effectiveness of the physical neural network in solving the NLSE and reveal specific error patterns in different soliton solutions. The study also explores methods to improve parameter prediction for the model. These findings provide valuable references for the study of optical soliton transmission processes through machine learning.
NONLINEAR DYNAMICS
(2023)
Article
Geosciences, Multidisciplinary
Ruizi Shi, Fanghua Xu, Li Liu, Zheng Fan, Hao Yu, Hong Li, Xiang Li, Yunfei Zhang
Summary: This article describes the implementation of a coupling between a global forecast model and a wave model and investigates the effects of ocean surface waves on the air-sea interface. Through experiments and comparisons with observational data, it is found that the interaction between waves and the atmosphere in the new framework significantly improves the prediction of various parameters related to sea surface temperature, air temperature, mixed layer depth, wind speed, and wave height.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2022)
Article
Environmental Sciences
Xianqi Zhang, Zhiwen Zheng
Summary: This paper proposes a coupled model based on CEEMD and NAR model for predicting suspended sediment concentrations in the lower reaches of the Yellow River. Experimental results show that the model has good stability and high prediction accuracy.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Thermodynamics
Donghao Jin, Jingwen Yan, Xin Liu, Chaoqun Zhang, Heyang Wang
Summary: This paper proposes a coupled combustion and hydrodynamic model to predict the tube temperature distributions of the platen superheater in a 600 MW boiler. The model simulates the gas flow in the furnace using a three-dimensional CFD model and the steam flow in the tubes using a one-dimensional hydrodynamic model. The results are in good agreement with the measured values and incorporate the critical effects of gas thermal deviation and steam flow maldistribution.
Article
Engineering, Industrial
Fuhui Shen, Sebastian Munstermann, Junhe Lian
Summary: This study investigates the numerical prediction of forming limits by combining the Marciniak-Kuczynski localization criterion with an evolving non-associated plasticity model. The results show that both the flow potential and yield locus play a critical role in determining the final localization of the material under study.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
(2021)