Multi-objective Collaborative Optimization for Scheduling Aircraft Landing on Closely Spaced Parallel Runways Based on Genetic Algorithms

A scheduling model of closely spaced parallel runways for arrival aircraft was proposed,with multi-objections of the minimum flight delay cost,the maximum airport capacity,the minimum workload of air traffic controller and the maximum fairness of airlines′scheduling.The time interval between two runways and changes of aircraft landing order were taken as the constraints.Genetic algorithm was used to solve the model,and the model constrained unit delay cost of the aircraft with multiple flight tasks to reduce its delay influence range.Each objective function value or the fitness of particle unsatisfied the constrain condition would be punished.Finally,one domestic airport hub was introduced to verify the algorithm and the model.The results showed that the genetic algorithm presented strong convergence and timeliness for solving constraint multi-objective aircraft landing problem on closely spaced parallel runways,and the optimization results were better than that of actual scheduling.     

Sectorization Model of Terminal Airspace with Arrival and Departure Separation

Terminal airspace(TMA)is the airspace centering several military and civil aviation airports with complex route structure,limited airspace resources,traffic flow,difficult management and considerable airspace complexity.A scientific and rational sectorization of TMA can optimize airspace resources,and sufficiently utilize the control of human resources to ensure the safety of TMA.The functional sectorization model was established based on the route structure of arriving and departing aircraft as well as controlling requirements.Based on principles of sectorization and topological relations within a network,the arrival and departure sectorization model was established,using tree based ant colony algorithm(ACO)searching.Shanghai TMA was taken as an example to be sectorizaed,and the result showed that this model was superior to traditional ones when arrival and departure routes were separated at dense airport terminal airspace.     

WHAT DRIVES BUSINESS AND LEISURE AIR PASSENGER TRANSPORT DEMAND

The back-propagation neural network ( BPN ) is used to explore what the most influential variables are to drive business and leisure air passenger travel from Japan to Taiwan.The variables are systematically identified , evaluated and analyzed in detail.The results reveal that some factors affect both leisure and business air passenger transport , and the others only affect one of them.Flights from Tokyo to Taipei and average hotel rate in Taiwan are the two most important factors for forecasting the demand of leisure air passenger transport , while variables related to business activities have more effect on the demand forecast of business air passenger transport.By using BPN , a forecasting model that considers actual market segments is established , and the results show that it is an accurate tool to forecast air transport demand.     

A Hybrid Method Combining Improved K-means Algorithm with BADA Model for Generating Nominal Flight Profiles

A high-precision nominal flight profile,involving controllers′intentions is critical for 4Dtrajectory estimation in modern automatic air traffic control systems.We proposed a novel method to effectively improve the accuracy of the nominal flight profile,including the nominal altitude profile and the speed profile.First,considering the characteristics of trajectory data,we developed an improved K-means algorithm.The approach was to measure the similarity between different altitude profiles by integrating the space warp edit distance algorithm,thereby to acquire several fitted nominal flight altitude profiles.This approach breaks the constraints of traditional K-means algorithms.Second,to eliminate the influence of meteorological factors,we introduced historical gridded binary data to determine the en-route wind speed and temperature via inverse distance weighted interpolation.Finally,we facilitated the true airspeed determined by speed triangle relationships and the calibrated airspeed determined by aircraft data model to extract a more accurate nominal speed profile from each cluster,therefore we could describe the airspeed profiles above and below the airspeed transition altitude,respectively.Our experimental results showed that the proposed method could obtain a highly accurate nominal flight profile,which reflects the actual aircraft flight status.     

Departure Trajectory Design Based on Pareto Ant Colony Algorithm

Due to the ever-increasing air traffic flow,the influence of aircraft noise around the airport has become significant.As most airlines are trying to decrease operation cost,stringent requirements for more simple and efficient departure trajectory are on a rise.Therefore,a departure trajectory design was established for performancebased navigation technology,and a multi-objective optimization model was developed,with constraints of safety and noise influence,as well as optimization targets of efficiency and simplicity.An improved ant colony algorithm was then proposed to solve the optimization problem.Finally,an experiment was conducted using the Lanzhou terminal airspace operation data,and the results showed that the designed departure trajectory was feasible and efficient in decreasing the aircraft noise influence.     

SIMULATION OF BLEED AIR BEHAVIOR DURING AIRCRAFT IN FLIGHT BASED ON FLOWMASTER

Bleed air system is one of the most important components of air management system(AMS).It acts as transfer pipes responsible for air supply at high temperature and pressure.The thermal and flow performance of the bleed air system is a key issue for the design of AMS since the characteristics of air source have a great influence on the anti-ice system,the environmental control system and other downstream system in need of high temperature pressurized air.Based on the one-dimensional lumped parameter technology,a computer analysis model of bleed air system is developed in order to analyze the thermal and flow behaviors of the nodal points in the pipeline network.The simulation are performed with a given flight assignment using the analysis model,and the results verify that the system meets the design requirements.     

Analysis on Potential Conflict Frequency of Intersected Air Routes in Terminal Airspace Design

In order to obtain accurate conflict risks in terminal airspace design, the concept and calculation model of potential conflict frequency for intersected routes are proposed. Conflict frequency is represented by the product of horizontal conflict frequency and vertical conflict probability. The horizontal conflict frequency is derived from the probability density distribution of conflicts in a period of time. Based on the recorded radar trajectory data, the concept and model of ROUTE distance are proposed, and the probability density function of aircraft height at a specified ROUTE distance is deduced by kernel density estimation. Furthermore, vertical conflict probability and its horizontal distribution are achieved. Examples of three intersected arrival and departure route design schemes are studied. Compared with scheme 1, the conflict frequency values of the other two improved schemes decrease to 53 % and 24%, respectively. The results show that the model can quantify potential conflict frequency of intersec ted routes.     

Modeling and Algorithm Application of Weapon Assignment System

In order to improve weapon assignment(WA)accuracy in real scenario,an artificial neural network(ANN)model is built to calculate real-time weapon kill probabilities.Considering the WA characteristic,each input representing one assessment index should be normalized properly.Therefore,the modified WA model is oriented from constant value to dynamic computation.Then an improved invasive weed optimization algorithm is applied to solve the WA problem.During search process,local search is used to improve the initial population,and seed reproduction is redefined to guarantee the mutation from multipoint to single point.In addition,the idea of vaccination and immune selection in biology is added into optimization process.Finally,simulation results verify the model′s rationality and effectiveness of the proposed algorithm.     

MULTI-OBJECTIVE PROGRAMMING FOR AIRPORT GATE REASSIGNMENT

To improve the efficiency of gate reassignment and optimize the plan of gate reassignment,the concept of disruption management is introduced,and a multi-objective programming model for airport gate reassignment is proposed.Considering the interests of passengers and the airport,the model minimizes the total flight delay,the total passengers′walking distance and the number of flights reassigned to other gates different from the planned ones.According to the characteristics of the gate reassignment,the model is simplified.As the multi-objective programming model is hard to reach the optimal solutions simultaneously,a threshold of satisfactory solutions of the model is set.Then a simulated annealing algorithm is designed for the model.Case studies show that the model decreases the total flight delay to the satisfactory solutions,and minimizes the total passengers′walking distance.The least change of planned assignment is also reached.The results achieve the goals of disruption management.Therefore,the model is verified to be effective.     

Air Traffic Operation Complexity Analysis Based on Metrics System

In order to quantitatively analyze air traffic operation complexity,multidimensional metrics were selected based on the operational characteristics of traffic flow.The kernel principal component analysis method was utilized to reduce the dimensionality of metrics,therefore to extract crucial information in the metrics.The hierarchical clustering method was used to analyze the complexity of different airspace.Fourteen sectors of Guangzhou Area Control Center were taken as samples.The operation complexity of traffic situation in each sector was calculated based on real flight radar data.Clustering analysis verified the feasibility and rationality of the method,and provided a reference for airspace operation and management.     

APPLICATION OF HYBRID GENETIC ALGORITHM IN AEROELASTIC MULTIDISCIPLINARY DESIGN OPTIMIZATION OF LARGE AIRCRAFT

The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.The program of genetic algorithm is developed by the authors while the gradient-based algorithm borrows from the modified method for feasible direction in MSC/NASTRAN software.In the hybrid algorithm,the genetic algorithm is used to perform global search to avoid to fall into local optima,and then the excellent individuals of every generation optimized by the genetic algorithm are further fine-tuned by the modified method for feasible direction to attain the local optima and hence to get global optima.Moreover,the application effects of hybrid genetic algorithm in aeroelastic multidisciplinary design optimization of large aircraft wing are discussed,which satisfy multiple constraints of strength,displacement,aileron efficiency,and flutter speed.The application results show that the genetic/gradient-based hybrid algorithm is available for aeroelastic optimization of large aircraft wings in initial design phase as well as detailed design phase,and the optimization results are very consistent.Therefore,the design modifications can be decreased using the genetic/gradient-based hybrid algorithm.     

Flight Schedule Recovery under Uncertain Airport Capacity

Airlines adjust their flight schedules to satisfy more stringent airport capacity constraints caused by inclement weather or other unexpected disruptions.The problem will be more important and complicated if uncertain disruptions occur in hub airports.A two-stage stochastic programming model was established to deal with the realtime flight schedule recovery and passenger re-accommodation problem.The first-stage model represents the flight re-timing and re-fleeting decision in current time period when capacity information is deterministic,while the second-stage recourse model evaluates the passenger delay given the first-stage solutions when one future scenario is realized.Aiming at the large size of the problem and requirement for quick response,an algorithmic framework combining the sample average approximation and heuristic method was proposed.The computational results indicated of that the proposed method could obtain solutions with around 5% optimal gaps,and the computing time was linearly positive to the sample size.     

Estimation of Standard Operation Time of Flight Legs Based on Clustering and Probability Analysis

A clustering algorithm and a probability statistics method were applied to different phases of a flight to analyze operation time during aircraft ground taxiing and airborne flight.And the clustering pattern,distribution characteristics and dynamically changing rules of the two phases were identified.Further,an estimate method was established to measure operation time of flight legs,with creative steps of calculating individual segment separately and then integrating them accordingly.The method can both objectively and dynamically measure operation time,and accurately reflect real situation.It helps to better utilize airport slot resources and provides a strong support for air traffic flow management when scheduling flight plan in strategic and pre-tactic phases.     

Modeling and Dynamic Analysis for the Foundation of a Blade-Rotor System

In order to achieve the model-based fault monitoring and diagnosis,an accurate model for the rotor system is necessary to locate and quantify faults.Since the dynamic characteristics of a blade-rotor system is influenced by foundation flexibility,the modeling and dynamic analyses on the foundation were sequentially investigated.Firstly,the effect of element size on the model convergence was investigated using the forward difference quotient as the slope of the frequency difference,which found that the model converged when the element size refined to 4mm.Secondly,a modal analysis and a harmonic response analysis were performed to obtain the dynamic characteristics of the foundation structure.Finally,an optimization to the foundation utilizing an additional stiffener was conducted to reduce the foundation response and make the critical speed far away from the working frequency band of 20—50Hz.     

ADAPTIVE FLIGHT CONTROL SYSTEM OF ARMED HELICOPTER USING WAVELET NEURAL NETWORK METHOD

A discussion is devoted to the design of an adaptive flight control system of the armed helicopter using wavelet neural network method. Firstly, the control loop of the attitude angle is designed with a dynamic inversion scheme in a quick loop and a slow loop. respectively. Then, in order to compensate the error caused by dynamic inversion, the adaptive flight control system of the armed helicopter using wavelet neural network method is put forward, so the BP wavelet neural network and the Lyapunov stable wavelet neural network are used to design the helicopter flight control system. Finally, the typical maneuver flight is simulated to demonstrate its validity and effectiveness. Result proves that the wavelet neural network has an engineering practical value and the effect of WNN is good.     

Quadcopter UAV Modeling and Automatic Flight Control Design

The mathematical model of quadcopter-unmanned aerial vehicle(UAV)is derived by using two approaches:One is the Newton-Euler approach which is formulated using classical mechanics;and other is the Euler-Lagrange approach which describes the model in terms of kinetic(translational and rotational)and potential energy.The proposed quadcopter′s non-linear model is incorporated with aero-dynamical forces generated by air resistance,which helps aircraft to exhibits more realistic behavior while hovering.Based on the obtained model,the suitable control strategy is developed,under which two effective flight control systems are developed.Each control system is created by cascading the proportional-derivative(PD)and T-S fuzzy controllers that are equipped with six and twelve feedback signals individually respectively to ensure better tracking,stabilization,and response.Both proposed flight control designs are then implemented with the quadcopter model respectively and multitudinous simulations are conducted using MATLAB/Simulink to analyze the tracking performance of the quadcopter model at various reference inputs and trajectories.     

Multi-objective Optimization of Differential Steering System of Electric Vehicle with Motorized Wheels

A differential steering system is presented for electric vehicle with motorized wheels and a dynamic model of three-freedom car is built. Based on these models, the quantitative expressions of the road feel, sensitivity, and operation stability of the steering are derived. Then, according to the features of multi-constrained optimization of multi-objective function, a multi-island genetic algorithm （MIGA） is designed. Taking the road feel and the sensi- tivity of the steering as optimization objectives and the operation stability of the steering as a constraint, the system parameters are optimized. The simulation results show that the system optimized with MIGA can improve the steering road feel, and guarantee the operation stability and steering sensibility.     

Parameter Optimization Method for Gaussian Mixture Model with Data Evolution

To learn from evolutionary experimental data points effectively,an evolutionary Gaussian mixture model based on constraint consistency（EGMM）is proposed and the corresponding method of parameter optimization is presented.Here,the Gaussian mixture model（GMM）is adopted to describe the data points,and the differences between the posterior probabilities of pairwise points under the current parameters are introduced to measure the temporal smoothness.Then,parameter optimization of EGMM can be realized by evolutionary clustering.Compared with most of the existing data analysis methods by evolutionary clustering,both the whole features and individual differences of data points are considered in the clustering framework of EGMM.It decreases the algorithm sensitivity to noises and increases the robustness of evaluated parameters.Experimental result shows that the clustering sequence really reflects the shift of data distribution,and the proposed algorithm can provide better clustering quality and temporal smoothness.     

Simulation of Air Source Heat Pump with Refrigerant Injection for Heating Operation

An air source heat pump(ASHP)with refrigerant injection is proposed for the air conditioning system of electric vehicles(EVs),especially for efficient heating in cold winter,when there is no wasted heat of engines.The simulation model is built with the framework of two-phase fluid network,where the compressor is separated as two compressors and the economizer is treated as two heat exchangers in the injection path and the main refrigerant path.With the validated simulation model,the heating performance is analyzed,and the results show that the coefficient of performance(COP)of ASHP with refrigerant injection is higher than 1.4 and the discharge temperature is less than 100℃ when the outdoor temperature is-20℃.The above performance ensures that the air conditioning system and EVs can operate normally with high efficiency even in the cold winter,which is much helpful for the practicability of EVs.     

Identification of Time-Varying Modal Parameters for Thermo- Elastic Structure Subject to Unsteady Heating

A time-varying modal parameter identification method combined with Bayesian information criterion （BIC） and grey correlation analysis （GCA） is presented for a kind of thermo-elastic structures with sparse natural frequencies and subject to an unsteady temperature field. To demonstrate the method, the thermo-elastic structure to be identified is taken as a simply-supported beam with an axially movable boundary and subject to both random excitation and an unsteady temperature field, and the dynamic outputs of the beam are first simulated as the meas- ured data for the identification. Then, an improved time-varying autoregressive （TVAR） model is generated from the simulated input and output of the system. The time-varying coefficients of the TVAR model are expahded as a finite set of time basis functions that facilitate the time-varying coefficients to be time invariant. According to the BIC for preliminarily determining the scope of the order number, the grey system theory is introduced to determine the order of TVAR and the dimension of the basis functions simultaneously via the absolute grey correlation degree （AGCD）. Finally, the time-varying instantaneous frequencies of the system are estimated by using the recursive least squares method. The identified results are capable of tracking the slow time-varying natural frequencies with high accuracy no matter for noise-free or noisy estimation.