再生粗骨料透水混凝土性能影响因素研究与配合比优化设计探研

本文针对再生骨料透水混凝土在不同水胶比、孔隙率、硅灰掺量、矿渣粉掺量、砂率和钢纤维各个因素下,采用正交试验法和单一优化试验法,以最优配合比为目标,对抗压强度和透水系数进行选择,为耐久性研究及应用提供依据。实验结果表明:水胶比增加时,抗压强度减小;孔隙率对两者影响很小;硅灰掺量在0%-5%变化时,抗压强度增加,在5%-10%变化时,抗压强度下降,透水系数变化规律同样如此;矿粉掺量在0%-5%变化时,抗压强度变化程度大于透水系数;砂率对抗压强度的影响程度大于其透水系数;钢纤维的加入在一定程度上对抗压强度产生负面作用。

Improvement Mechanism of Adhesion Performance of Anti-stripping Agents and Coupling Agents on Asphalt-Aggregate Interface Based on Molecular Dynamics

This study examined the mechanisms for improving the adhesion performance of the asphalt-aggregate interface with two anti-stripping agents and two coupling agents.The investigation of contact behavior between various asphalt-aggregate surfaces was conducted using molecular dynamics(MD)simulations.The interaction energy and the relative concentration distribution were employed as the parameters to analyze the enhancement mechanisms of anti-stripping agents and coupling agents on the asphalt-aggregate interface.Results indicated that the adhesion at the asphalt-aggregate interface could be strengthened by both anti-stripping agents and coupling agents.Anti-stripping agents primarily improve adhesion through the reinforcement of electrostatic attraction,while coupling agents primarily upgrade adhesion by strengthening the van der Waals.Hence,the molecular dynamics modeling and calculation techniques presented in this study can be utilized to elucidate the development mechanism of the asphalt-aggregate interface through the use of anti-stripping agents and coupling agents.

Zonal Coupling Analysis Method of Seismic Response of Offshore Monopile Wind Turbine

The seismic safety of offshore wind turbines is an important issue that needs to be solved urgently.Based on a unified computing framework,this paper develops a set of seawater-seabed-wind turbine zoning coupling analysis methods.A 5 MW wind turbine and a site analysis model are established,and a seismic wave is selected to analyze the changes in the seismic response of offshore monopile wind turbines under the change of seawater depth,seabed wave velocity and seismic wave incidence angle.The analysis results show that when the seawater increases to a certain depth,the seismic response of the wind turbine increases.The shear wave velocity of the seabed affects the bending moment and displacement at the bottom of the tower.When the angle of incidence increases,the vertical displacement and the acceleration of the top of the tower increase in varying degrees.

A New Meta Heuristic Approach for Aircraft Landing Problem

A new meta-heuristic approach is proposed in this paper based on a new composite dispatching rule to tackle the aircraft landing problem(ALP).First,the ALP is modeled as a machine scheduling problem with the objective of minimizing the total penalty,i.e.,total weighted earliness plus total weighted tardiness.Second,a composite dispatching rule,minimized penalty with due dates and set-ups(MPDS),is presented to determine the landing sequence.Then,an efficient heuristic approach is proposed to solve the problem by integrating the MPDS rule and CPLEX solver.In the first stage,the landing sequence is established based on the proposed MPDS rule.In the second stage,landing time is optimized using CPLEX solver.Next,a new meta-heuristic strategy is introduced into the heuristic approach by conducting the local search from the potential landing sequences,which are generated by the proposed MPDS rule.Finally,the performance of the proposed approach is evaluated using a set of benchmark instances taken from the OR library.The results demonstrate the effectiveness and efficiency of the proposed approaches.

Key Technology in Multi UAV Conflict Detection and Resolution Strategy

Based on ADS-B surveillance data,this paper proposes a multi-unmanned aerial vehicle(multi-UAV)collision detection method based on linear extrapolation for ground-based UAV collision detection and resolution,thus to provide early warning of possible conflicts.To address the problem of multi-UAV conflict,the basic ant colony algorithm is introduced.The conflict simplification model of the traditional basic ant colony algorithm is optimized by adding a speed regulation strategy.A multi-UAV conflict resolution scheme is presented based on speed regulation and heading strategies.The ant colony algorithm is improved by adding angle information and a queuing system.The results show that the improved ant colony algorithm can provide multi-UAV joint escape routes for a multi-UAV conflict situation in airspace.Unlike the traditional ant colony algorithm,our approach converges to the optimization target.The time required for the calculation is reduced by 43.9%,and the total delay distance caused by conflict resolution is reduced by 58.4%.

Risk Assessment of Unmanned Aerial Vehicle Flight Based on Kmeans Clustering Algorithm

To quantify unmanned aerial vehicle(UAV)flight risks in low-altitude airspace,we analyze the factors of UAV flight risks from three aspects:flight conflict,flight environment,and traffic characteristics.The aerial risk index and ground risk index of the UAV are constructed,the index screening model and the UAV flight risk assessment model are established,and a UAV flight risk assessment model based on K-means clustering has been proposed.Meanwhile,numerical simulations show the proposed method can not only evaluate the UAV flight risks effectively,but also provide technical support for UAV risk management and control.

Identifying Anomaly Aircraft Trajectories in Terminal Areas Based on Deep Autoencoder and Its Application in Trajectory Clustering

Anomalous trajectory detection and traffic flow classification for complicated airspace are of vital importance to safety and efficiency analysis.Some researchers employed density-based unsupervised machine learning method to exploit these trajectories related to air traffic control(ATC)actions.However,the quality of position data and the tiny density difference between traffic flows in the terminal area make it particularly challenging.To alleviate these two challenges,this paper proposes a novel framework which combines robust deep auto-encoder(RDAE)model and density peak(DP)clustering algorithm.Specifically,the RDAE model is utilized to reconstruct denoising trajectory and identify anomaly trajectories in the terminal area by two different regularizations.Then,the nonlinear components captured by the encoder of RDAE are input in the DP algorithm to classify the global traffic flows.An experiment on a terminal airspace at Guangzhou Baiyun Airport(ZGGG)with anomaly label shows that the proposed combination can automatically capture non-conventional spatiotemporal traffic patterns in the aircraft movement.The superiority of RDAE and combination are also demonstrated by visualizing and quantitatively evaluating the experimental results.

Improved Whale Optimization Algorithm Based on Mirror Selection

Since traditional whale optimization algorithms have slow convergence speed,low accuracy and are easy to fall into local optimal solutions,an improved whale optimization algorithm based on mirror selection(WOA-MS)is proposed. Specific improvements includes:(1)An adaptive nonlinear inertia weight based on Branin function was introduced to balance global search and local mining.(2) A mirror selection method is proposed to improve the individual quality and speed up the convergence. By optimizing several test functions and comparing the experimental results with other three algorithms,this study verifies that WOA-MS has an excellent optimization performance.

Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.

Finite Element Modeling and Parametric Analysis of Pavement Dynamic Responses under Moving Vehicle Load

This paper intends to develop finite element models that can simulate vehicle load moving on pavement system and reflect the pavement response of vehicle and pavement interaction.We conduct parametric analysis considering the influences of asphalt concrete layer modulus and thickness,base layer modulus and thickness,and subgrade modulus on pavement surface displacement,frequency,and strain response.The analysis findings are fruitful.Both the displacement basin width and maximum value of dynamic surface displacements are larger than those of static surface displacements.The frequency is positively correlated with the pavement structure moduli,and negatively correlated with the pavement structure thicknesses.The shape of dynamic and static tensile strain is similar along the depth of the pavement structure.The maximum value of dynamic tensile strain is larger than that of static tensile strain.The frequency of entire pavement structure holds more significant influence than the surface displacement and strain do.The subgrade modulus has a significant effect on surface displacement,frequency and strain.

Bilevel Programming Model for Joint Scheduling of Arrival and Departure Flights Based on Traffic Scenario

In order to meet the needs of collaborative decision making,considering the different demands of air traffic control units,airlines,airports and passengers in various traffic scenarios,the joint scheduling problem of arrival and departure flights is studied systematically.According to the matching degree of capacity and flow,it is determined that the traffic state of arrival/departure operation in a certain period is peak or off-peak.The demands of all parties in each traffic state are analyzed,and the mathematical models of arrival/departure flight scheduling in each traffic state are established.Aiming at the four kinds of joint operation traffic scenarios of arrival and departure,the corresponding bi-level programming models for joint scheduling of arrival and departure flights are established,respectively,and the elitism genetic algorithm is designed to solve the models.The results show that:Compared with the first-come-firstserved method,in the scenarios of arrival peak&departure off-peak and arrival peak&departure peak,the departure flight equilibrium satisfaction is improved,and the runway occupation time of departure flight flow is reduced by 38.8%.In the scenarios of arrival off-peak&departure off-peak and departure peak&arrival off-peak,the arrival flight equilibrium delay time is significantly reduced,the departure flight equilibrium satisfaction is improved by 77.6%,and the runway occupation time of departure flight flow is reduced by 46.6%.Compared with other four kinds of strategies,the optimal scheduling method can better balance fairness and efficiency,so the scheduling results are more reasonable.

A Bilevel Programming Approach for Optimization of Airport Ground Movement

This paper proposes an optimization model for the airport ground movement problem(GMP)based on bilevel programming to address taxi conflicts on the airport ground and to improve the operating safety and efficiency.To solve GMP,an iterative heuristic algorithm is designed.Instead of separately investigating each problem,this model simultaneously coordinates and optimizes the aircraft routing and scheduling.A simulation test is conducted on Nanjing Lukou International Airport(NKG)and the results show that the bilevel programming model can clearly outperform the widely used first-come-first-service(FCFS)scheduling scheme in terms of aircraft operational time under the precondition of none conflict.The research effort demonstrates that with the reduced operating cost and the improved overall efficiency,the proposed model can assist operations of the airports that are facing increasing traffic demand and working at almost maximum capacity.

A Novel Aircraft Air Conditioning System with a Sterilization Unit by Ultra-High-Temperature Air Stream

An aircraft cabin is a narrow,closed-space environment.To keep the air quality in cabin healthy for passengers,especially during an epidemic such as SARS-CoV-2(or 2019-nCoV)in 2020,a novel aircraft air conditioning system,called the ultra-high-temperature instantaneous sterilization air conditioning system(UHTACS),is proposed.Based on the proposed system,a simulation of the UHT-ACS is analysed in various flight states.In the UHT-ACS,the mixing air temperature of return and bleed air can reach temperature up to 148.8°C,which is high enough to kill bacilli and viruses in 2一8 s.The supply air temperature of the UHT-ACS in a mixing cavity is about 12 C in cooling mode both on the ground and in the air.The supply air temperature is about 42 C in heating mode.Compared with the air conditioning systems(ACS)of traditional aircraft the supply air temperatures of the UHT-ACS in the mixing cavity are in good agreement with those of a traditional ACS with 60%fresh air and 40%return air.Furthermore the air temperature at the turbine outlet of the UHT-ACS is higher than that of a traditional ACS which will help to reduce the risk of icing at the outlet.Therefore the UHT-ACS can operate normally in various flight states.

Terminal Traffic Flow Prediction Method Under Convective Weather Using Deep Learning Approaches

In order to improve the accuracy and stability of terminal traffic flow prediction in convective weather,a multi-input deep learning(MICL)model is proposed.On the basis of previous studies,this paper expands the set of weather characteristics affecting the traffic flow in the terminal area,including weather forecast data and Meteorological Report of Aerodrome Conditions(METAR)data.The terminal airspace is divided into smaller areas based on function and the weather severity index(WSI)characteristics extracted from weather forecast data are established to better quantify the impact of weather.MICL model preserves the advantages of the convolution neural network(CNN)and the long short-term memory(LSTM)model,and adopts two channels to input WSI and METAR information,respectively,which can fully reflect the temporal and spatial distribution characteristics of weather in the terminal area.Multi-scene experiments are designed based on the real historical data of Guangzhou Terminal Area operating in typical convective weather.The results show that the MICL model has excellent performance in mean squared error(MSE),root MSE(RMSE),mean absolute error(MAE)and other performance indicators compared with the existing machine learning models or deep learning models,such as Knearest neighbor(KNN),support vector regression(SVR),CNN and LSTM.In the forecast period ranging from 30 min to 6 h,the MICL model has the best prediction accuracy and stability.

Air Route Network Generation Based on Traffic Assignment

Air route network is the carrier of air traffic flow,and traffic assignment is a method to verify the rationality of air route network structure.Therefore,air route network generation based on traffic assignment has been becoming the research focus of airspace programming technology.Based on link prediction technology and optimization theory,a bi-level programming model is established in the paper.The model includes an upper level of air route network generation model and a lower level of traffic assignment model.The air route network structure generation incorporates network topology generation algorithm based on link prediction technology and optimal path search algorithm based on preference,and the traffic assignment adopts NSGA-Ⅲalgorithm.Based on the Python platform NetworkX complex network analysis library,a network of 57 airports,383 nodes,and 635 segments within China Airspace Beijing and Shanghai Flight Information Regions and 187975 sorties of traffic are used to simulate the bilevel model.Compared with the existing air route network,the proposed air route network can decrease the cost by 50.624%,lower the flight conflict coefficient by 33.564%,and reduce dynamic non-linear coefficient by 7.830%.

A Review on Effects of Personalized Ventilation Systems on Air Quality and Thermal Comfort in Aircraft Cabin Mini-Environments

This study conducts an evaluation of air quality,dispersion of airborne expiratory pollutants and thermal comfort in aircraft cabin mini-environments using a critical examination of significant studies conducted over the last20 years.The research methods employed in these studies are also explained in detail.Based on the current literature,standard procedures for airplane personal ventilation and air quality investigations are defined for each study approach.Present study gaps are examined,and prospective study subjects for various research approaches are suggested.

Identifying Similar Operation Scenes for Busy Area Sector Dynamic Management

Air traffic controllers face challenging initiatives due to uncertainty in air traffic.One way to support their initiatives is to identify similar operation scenes.Based on the operation characteristics of typical busy area control airspace,an complexity measurement indicator system is established.We find that operation in area sector is characterized by aggregation and continuity,and that dimensionality and information redundancy reduction are feasible for dynamic operation data base on principle components.Using principle components,discrete features and time series features are constructed.Based on Gaussian kernel function,Euclidean distance and dynamic time warping(DTW)are used to measure the similarity of the features.Then the matrices of similarity are input in Spectral Clustering.The clustering results show that similar scenes of trend are not ideal and similar scenes of modes are good base on the indicator system.Finally,actual vertical operation decisions for area sector and results of identification are compared,which are visualized by metric multidimensional scaling(MDS)plots.We find that identification results can well reflect the operation at peak hours,but controllers make different decisions under the similar conditions before dawn.The compliance rate of busy operation mode and division decisions at peak hours is 96.7%.The results also show subjectivity of actual operation and objectivity of identification.In most scenes,we observe that similar air traffic activities provide regularity for initiatives,validating the potential of this approach for initiatives and other artificial intelligence support.

Calibration Method of Capacitive Soil Moisture Sensor

Soil properties and water content vary from place to place. The calibration method based on capacitive soil moisture and humidity sensor is carried out. The sensor readings are compared with the mass water content measured by the oven dried method,and the calibration formula of sensor reading and mass moisture content is established.Results show that the sensor reading has a good linear relationship with the mass water content measured by the oven dried method,and has high precision. It can calibrate the mass moisture content of the data obtained from the moisture migration test in the soil column.

Forecast of Air Traffic Controller Demand Based on SVR and Parameter Optimization

As the main body of air traffic control safety,the air traffic controller is an important part of the whole air traffic control system. According to the relevant data of civil aviation over the years,a mapping model between flight support sorties and air traffic controller demand is constructed by using the prediction algorithm of support vector regression(SVR) based on grid search and cross-validation. Then the model predicts the demand for air traffic controllers in seven regions. Additionally,according to the employment data of civil aviation universities,the future training scale of air traffic controller is predicted. The forecast results show that the average relative error of the number of controllers predicted by the algorithm is 1.73%,and the prediction accuracy is higher than traditional regression algorithms. Under the influence of the epidemic,the demand for air traffic controllers will decrease in the short term,but with the control of the epidemic,the demand of air traffic controllers will return to the pre-epidemic level and gradually increase. It is expected that the controller increment will be about 816 by 2028. The forecast results of the demand for air traffic controllers provide a theoretical basis for the introduction and training of medium and long-term air traffic controllers,and also provide method guidance and decision support for the establishment of professional reserve and dynamic control mechanism in the air traffic control system.

Collaboration Optimization of Flight Schedule in Beijing⁃Tianjin⁃Hebei Airport Group

The coordinated and integrated development of regional airport group system has been identified as an important research topic in the field of air traffic management in China.However,due to the clear limitation on airspace resources and severe traffic congestion,it is necessary to further study the problem of flight schedule coordination optimization for airport clusters.We take the Beijing-Tianjin-Hebei airport Group as an example and construct an optimization model of flight schedule with the minimum adjustment and delay.The design of the implementation algorithm is proposed.As demonstrated by the simulation results,the flight delay in the Beijing-Tianjin-Hebei multi-airport system is noticeably reduced by applying both the optimization model and the algorithm proposed in this paper.