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Estimation and testing of linkages between forest structure and rainfall interception characteristics of a Robinia pseudoacacia plantation on China’s Loess Plateau

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Journal of Forestry Research 2022年第2期33卷 529-542页

作者： Changkun Ma Yi Luo Mingan Shao Xiaoxu Jia State Key Laboratory of Eco-Hydraulics in Northwest Arid Region Xian University of TechnologyXianChina Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100190China

Understanding the interaction between canopy structure and the parameters of interception loss is essential in predicting the variations in partitioning rainfall and water resources as affected by changes in canopy structure and in implementing water-based management in semiarid forest plantations.In this study,seasonal variations in rainfall interception loss and canopy storage capacity as driven by canopy structure were predicted and the linkages were tested using seasonal filed measurements.The study was conducted in nine 50 m×50 m Robinia pseudoacacia plots in the semiarid region of China’s Loess Plateau.Gross rain-fall,throughfall and stemflow were measured in seasons with and without leaves in 2015 and 2016.Results show that measured average interception loss for the nine plots were 17.9% and 9.4% of gross rainfall during periods with leaves (the growing season) and without leaves, respectively. Average canopy storage capacity estimated using an indirect method was 1.3 mm in the growing season and 0.2 mm in the leafless season. Correlations of relative interception loss and canopy storage capacity to canopy variables were highest for leaf/wood area index (LAI/WAI) and canopy cover, fol-lowed by bark area, basal area, tree height and stand density. Combined canopy cover, leaf/wood area index and bark area multiple regression models of interception loss and canopy storage capacity were established for the growing season and in the leafless season in 2015. It explained 97% and 96% of the variations in relative interception loss during seasons with and without leaves, respectively. It also explained 98% and 99% of the variations in canopy storage capacity during seasons with and without leaves, respectively. The empiri-cal regression models were validated using field data col-lected in 2016. The models satisfactorily predicted relative interception loss and canopy storage capacity during seasons with and without leaves. This study provides greater under-standing about the effects of changes in tree canopy struc

关键词： Rainfall interception loss Canopy storage Canopy structure regression models China’s Loess Plateau

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Quick identification of guidance law for an incoming missile using multiple-model mechanism

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Chinese Journal of Aeronautics 2022年第9期35卷 282-292页

作者： Yinhan WANG Shipeng FAN Jiang WANG Guang WU School of Aerospace Engineers Beijing Institute of TechnologyBeijing 10081China Beijing Aerospace Automatic Institute Beijing 100854China

A guidance law parameter identification model based on Gated Recurrent Unit(GRU)neural network is established. The scenario of the model is that an incoming missile(called missile)attacks a target aircraft(called aircraft) using Proportional Navigation(PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism(MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.

关键词： Gated recurrent unit Multiple-model mechanism Neural networks Parameter identification regression models

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A mesh optimization method using machine learning technique and variational mesh adaptation

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Chinese Journal of Aeronautics 2022年第3期35卷 27-41页

作者： Tingfan WU Xuejun LIU Wei AN Zenghui HUANG Hongqiang LYU MIITKey Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Computational mesh is an important ingredient that affects the accuracy and efficiency of CFD numerical simulation.In light of the introduced large amount of computational costs for many adaptive mesh methods,moving mesh methods keep the number of nodes and topology of a mesh unchanged and do not increase CFD computational expense.As the state-of-the-art moving mesh method,the variational mesh adaptation approach has been introduced to CFD calculation.However,quickly estimating the flow field on the updated meshes during the iterative algorithm is challenging.A mesh optimization method,which embeds a machine learning regression model into the variational mesh adaptation,is proposed.The regression model captures the mapping between the initial mesh nodes and the flow field,so that the variational method could move mesh nodes iteratively by solving the mesh functional which is built from the estimated flow field on the updated mesh via the regression model.After the optimization,the density of the nodes in the high gradient area increases while the density in the low gradient area decreases.Benchmark examples are first used to verify the feasibility and effectiveness of the proposed method.And then we use the steady subsonic and transonic flows over cylinder and NACA0012 airfoil on unstructured triangular meshes to test our method.Results show that the proposed method significantly improves the accuracy of the local flow features on the adaptive meshes.Our work indicates that the proposed mesh optimization approach is promising for improving the accuracy and efficiency of CFD computation.

关键词： CFD Flow field Machine learning Moving mesh method regression models Variational mesh adaptation

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Influence of urban morphological factors on building energy consumption combined with photovoltaic potential: A case study of residential blocks in central China

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建筑模拟(英文) 2023年第9期16卷 1777-1792页

作者： Shen Xu Mengcheng Sang Mengju Xie Feng Xiong Thushini Mendis Xingwei Xiang School of Architecture and Urban Planning Huazhong University of Science and TechnologyWuhanChina Hubei New Urbanization Engineering and Technology Research Center WuhanChina Department of Architecture and Built Environment University of Nottingham Ningbo ChinaNingboChina

Studies on urban energy have been growing in interest,and past research has mostly been focused on studies of urban solar potential or urban building energy consumption independently.However,holistic research on the combination of urban building energy consumption and solar potential at the urban block-scale is required in order to minimize energy use and maximize solar power generation simultaneously.The aim of this study is to comprehensively evaluate the impact of urban morphological factors on photovoltaic(PV)potential and building energy consumption.Firstly,58 residential blocks were classified into 6 categories by k-means clustering.Secondly,3 energy performance factors,which include the energy use intensity(EUI),the energy use intensity combined with PV potential(EUI-PV),and photovoltaic substitution rate(PSR)were calculated for these blocks.The study found that the EUI of the Small Length&High Height blocks was the lowest at around 30 kWh/(m^(2)·y),while the EUI-PV of the Small Length&Low Height blocks was the lowest at around 4.45 kWh/(m^(2)·y),and their PSR was the highest at 87%.regression modelling was carried out,and the study concluded that the EUI of residential blocks was mainly affected by shape factor,building density and floor area ratio,while EUI-PV and PSR were mainly affected by height and sky view factor.In this study,the results and developed methodology are helpful to provide recommendations and strategies for sustainable planning of residential blocks in central China.

关键词： urban morphological factors residential blocks building energy consumption photovoltaic potential regression models

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Rainfall erosivity and erosivity density through rainfall synthetic series for Sao Paulo State,Brazil:Assessment,regionalization and modeling

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International Soil and Water Conservation Research 2022年第3期10卷 355-370页

作者： David Bruno de Sousa Teixeira Roberto Avelino Cecílio Joao Paulo Bestete de Oliveira Laura Thebit de Almeida Gabrielle Ferreira Pires Department of Agricultural Engineering Federal University of ViçosaViçosaMG36570-900Brazil Department of Forest and Wood Sciences Federal University of Espírito SantoJer^onimo MonteiroES29550-000Brazil Federal Institute of Espírito Santo Campus AlegreAlegreES29520-000Brazil

Rainfall is the main cause of erosion of Brazilian soils,which makes assessing the rainfall erosivity factor(RE)and the erosivity density(ED)fundamental for soil and water conservation.Therefore,the objectives of this study were:i)to estimate the RE and ED for Sao Paulo State,Brazil,using synthetic series of pluviographic data;ii)to define homogeneous regions regarding rainfall erosivity;and iii)to generate regression models for rainfall erosivity estimates in each of the homogeneous regions.Synthetic series of pluviographic data were initially obtained on a sub-daily scale from the daily rainfall records of 696 rainfall gauges.The RE values were then estimated from the synthetic rainfall data,and ED was calculated from the relationship between erosivity and rainfall amounts.Monthly and annual maps for RE and ED were obtained.Hierarchical clustering analysis was used to define homogeneous regions in terms of rainfall erosivity,and regionalized regression models for estimating RE were generated.The results demonstrate high spatial variability of RE in Sao Paulo,where the highest annual values were observed in the coastal region.December to March concentrate approximately 60%of the intra-annual erosivity.The highest values of annual ED were observed in regions with intense agricultural activity.The definition of five homogeneous regions concerning the rainfall erosive potential evidenced distinct seasonal patterns of the spatial distribution of erosivity.Finally,the high predictive accuracy of the regionalized models obtained characterizes them as essential tools for reliable estimates of rainfall erosivity,and contribute to better soil conservation planning.

关键词： Soil erosion Stochastic weather generator Homogeneous regions Modified Fournier index regression models

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Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

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Computers, Materials & Continua 2022年第8期72卷 2305-2321页

作者： Abdelaziz A.Abdelhamid Sultan R.Alotaibi Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Computer Science College of Science and Human StudiesShaqra UniversitySaudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityEgypt

The design ofmicrostrip antennas is a complex and time-consuming process,especially the step of searching for the best design parameters.Meanwhile,the performance ofmicrostrip antennas can be improved usingmetamaterial,which results in a new class of antennas called metamaterial antenna.Several parameters affect the radiation loss and quality factor of this class of antennas,such as the antenna size.Recently,the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning,which presents a better alternative to simulation tools and trialand-error processes.However,the prediction accuracy depends heavily on the quality of the machine learning model.In this paper,and benefiting from the current advances in deep learning,we propose a deep network architecture to predict the bandwidth of metamaterial antenna.Experimental results show that the proposed deep network could accurately predict the optimal values of the antenna bandwidth with a tiny value of mean-square error(MSE).In addition,the proposed model is comparedwith current competing approaches that are based on support vector machines,multi-layer perceptron,K-nearest neighbors,and ensemble models.The results show that the proposed model is better than the other approaches and can predict antenna bandwidth more accurately.

关键词： Metamaterial antenna deep learning bandwidth prediction regression models

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通过运行数据的性能分析评估Mewis导流罩的效率

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Journal of Marine Science and Application 2022年第2期21卷 144-158页

作者： Anastasios Zalachoris Nikos Themelis School of Naval Architecture and Marine Engineering National Technical University of Athens 9 Iroon Polytechneiou15780AthensGreece

Τhe efficiency of a Mewis propeller duct by the analysis of ship operational data is examined.The analysis employs data collected with high frequency for a three-year period for two siter vessels,one of them fitted with a Mewis type duct.Our approach to the problem of identifying improvements in the operational performance of the ship equipped with the duct is two-fold.Firstly,we proceed with the calculation of appropriate Key Performance Indicators to monitor vessels performance in time for different operational periods and loading conditions.An extensive pre-processing stage is necessary to prepare a dataset free from datapoints that could impair the analysis,such as outliers,as well as the appropriate preparations for a meaningful KPI calculation.The second approach concerns the development of multiple linear regression problem for the prediction of main engine fuel oil consumption based on operational and weather parameters,such as ship’s speed,mean draft,trim,rudder angle and the wind speed.The aim is to quantify reductions due to the Mewis duct for several scenarios.Key results of the studies reveal a contribution of the Mewis duct mainly in laden condition,for lower speed range and in the long-term period after dry-docking.

关键词： Performance monitoring Energy saving device regression models Data processing Mewis duct

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An innovative model for predicting the displacement and rotation of column-tree moment connection under fire

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Frontiers of Structural and Civil Engineering 2021年第1期15卷 194-212页

作者： Mohammad Ali NAGHSH Aydin SHISHEGARAN Behnam KARAMI Timon RABCZUK Arshia SHISHEGARAN Hamed TAGHAVIZADEH Mehdi MORADI Department of Civil Engineering Isfahan University of TechnologyIsfahan 84156-83111Iran School of Civil Engineering School of Civil EngineeringIran University of Science and TechnologyTehran 13114-16846Iran Department of Civil Engineering International Institute of Earthquake Engineering and SeismologyTehran 19539-14453Iran Division of Computational Mechanics Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Civil Engineering Ton Duc Thang UniversityHo Chi Minh CityVietnam Department of Civil Engineering Islamic Azad UniversityTehran 16511-53311Iran Department of Civil Engineering Isfahan UniversityIsfahan 81746-73441Iran

In this study,we carried out nonlinear finite element simulations to predict the performance of a columntree moment connection(CTMC)under fire and static loads.We also conducted a detailed parameter study based on five input variables,including the applied temperature,number of flange bolts,number of web bolts,length of the beam,and applied static loads.The first variable is changed among seven levels,whereas the other variables are changed among three levels.Employing the Taguchi method for variables 2-5 and their levels,9 samples were designed for the parameter study,where each sample was exposed to 7 different temperatures yielding 63 outputs.The related variables for each output are imported for the training and testing of different surrogate models.These surrogate models include a multiple linear regression(MLR),multiple Ln equation regression(MLnER),an adaptive network-based fuzzy inference system(ANFIS),and gene expression programming(GEP).44 samples were used for training randomly while the remaining samples were employed for testing.We show that GEP outperforms MLR,MLnER,and ANFIS.The results indicate that the rotation and deflection of the CTMC depend on the temperature.In addition,the fire resistance increases with a decrease in the beam length;thus,a shorter beam can increase the fire resistance of the building.The numbers of flanges and web bolts slightly affect the rotation and displacement of the CTMCs at temperatures of above 400℃.

关键词： column-tree moment connection Finite element model parametric study fire regression models gene expression programming

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Unified methodology for site-characterization and sampling of highway runoff

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Frontiers of Environmental Science & Engineering 2010年第1期4卷 47-58页

作者： Jy S.WU Craig J.ALLAN Department of Civil and Environmental Engineering University of North Carolina at CharlotteCharlotteNC 28223USA 2 Department of Geography and Earth SciencesUniversity of North Carolina at CharlotteCharlotteNC 28223USA

Hydrology,roadway traffic conditions,and atmospheric deposition are three essential data categories for the planning and implementation of highway-runoff monitoring and characterization programs.Causal variables pertaining to each data category could be site specific but have been shown to correlate with runoff pollutant loads.These data categories were combined to derive statistical relationships for characterization and prioritization of the respective pollutant loads at highway runoff sites.Storm runoff data of total suspended solids(TSS),total dissolved solid(TDS),chemical oxygen demand(COD),total Kjeldahl nitrogen(TKN)and total phosphorus(TP)collected from three highway sites in Charlotte,North Carolina,USA,were used to illustrate the development of site-specific highway-runoff pollutant loading models.This unified methodology provides a basis for initial assessment of the pollutant-constituent loads from highway runoff using hydrologic component variables.Improved reliability is achievable when additional traffic and/or atmospheric component variables are incorporated into the basic hydrologic regression model.In addition,operational guidance is suggested for implementing highway-runoff monitoring programs that are subject to sampling and resources constraints.

关键词： highway runoff pollutant loads regression models non-point source pollution storm water permit

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Impact of Freeze-Thaw Cycles on Compressive Characteristics of Asphalt Mixture in Cold Regions

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Journal of Wuhan University of Technology(Materials Science) 2015年第4期30卷 703-709页

作者：司伟 LI Ning 马骉 REN Junping WANG Hainian HU Jian Key Laboratory for Special Area Highway Engineering of Ministry of Education Chang'an University The Second Highway Engineering of China Communications Construction Company Ltd.

Low average temperature, large temperature difference and continual freeze-thaw （F-T） cycles have significant impacts on mechanical property of asphalt pavement. F-T cycles test was applied to illustrate the mixtures＇ compressive characteristics. Exponential model was applied to analyze the variation of compressive characteristics with F-T cycles; Loss ratio model and Logistic model were used to present the deterioration trend with the increase of F-T cycles. ANOVA was applied to show the significant impact of F-T cycles and asphalt- aggregate ratio. The experiment results show that the compressive strength and resilient modulus decline with increasing F-T cycles; the degradation is sharp during the initial F-T cycles, after 8 F-T cycles it turns to gentle. ANOVA results show that F-T cycles, and asphalt-aggregate ratio have significant influence on the compressive characteristics. Exponential model, Loss ratio model and Logistic model are significantly fitting the test data from statistics view. These models well reflect the compressive characteristics of asphalt mixture degradation trend with increasing F-T cycles.

关键词： freeze-thaw cycles test compressive strength resilient modulus regression models

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