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As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry, it has become imperative to monitor and mitigate these threats to ensure civil aviation safety. The eddy dissipation rate (EDR) has been established as the standard metric for quantifying turbulence in civil aviation. This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder (QAR) data. The detection of atmospheric turbulence is approached as an anomaly detection problem. Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events. Moreover, comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available. In summary, the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data, comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms. This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Wake re-categorization (RECAT) has been implemented to improve runway capacity, and consequently, aircraft arrival runway occupancy time has become a crucial factor influencing runway capacity. Accurate prediction of the runway occupancy time can assist controllers in determining aircraft separation, thereby enhancing the operational efficiency of the runway. In this study, the GA–PSO algorithm is utilized to optimize the Back Propagation neural network prediction model using Quick access recorder data from various domestic airports, achieving high-precision prediction. Additionally, the SHapley Additive explanation model is applied to quantify the effect of each characteristic parameter on the arrival runway occupancy time, resulting in the prediction of aircraft arrival runway occupancy time. This model can provide a foundation for improving runway operation efficiency and technical support for the design of airport runway and taxiway structure.

Accurate estimation of the fuel consumed during aircraft operation is key for determining the fuel load, reducing the airline operating cost, and mitigating environmental impacts. Aerodynamic parameters in current fuel consumption models are obtained from a static diagram extracted from the outcomes of wind tunnel experiments. Given that these experiments are performed in a lab setting, the parameters cannot be used to estimate additional fuel consumption caused by aircraft performance degradation. In addition, wind tunnel experiment results rarely involve the influence of crosswind on fuel consumption; thus, the results could be inaccurate when compared with field data. This study focuses on the departure climbing phase of aircraft operation and proposes a new fuel consumption model. In this model, the relationships between aerodynamic parameters are extracted by fitting quick access recorder (QAR) actual flight data, and the crosswind effect is also considered. Taking QAR data from two airports in China, the accuracy of the proposed model and its transferability are demonstrated. Applying the proposed model, the fuel saving of a continuous climb operation (CCO) compared with the traditional climb operation is further quantified. Finally, how aircraft mass, climbing angle, and different aircraft models could affect the fuel consumption of the climbing phase of aircraft operation is investigated. The proposed fuel consumption model fills gaps in the existing literature, and the method can be used for developing specific fuel consumption models for more aircraft types at other airports.

Purpose The purpose of this paper is to present the irregular deviation examination of flight control surfaces and the potential problem diagnosis of irregular deviations for the jet transport aircraft. A four-jet transport aircraft at transonic flight in cruise phase is the study case of the present article. Design/methodology/approach The standard lift-to-drag ratio (L/D) and flight dynamic models are established through flight data mining and the fuzzy logic modeling technique based on the flight data of quick access recorder available in the Flight Operations Quality Assurance (FOQA) program of the airlines. The irregular deviations of flight control surfaces are examined by the standard L/D model-predicted results through sensitivity analysis. The contribution values in L/D deficiency are predicted by the deviations and the L/D derivatives of all influencing variables in Taylor series expansion. The potential problems due to irregular deviations can be excavated by the flight dynamic models through the analysis of in-flight stability and controllability. Findings The magnitude of stabilizer angle to the deficiency of L/D is the largest among the four control surfaces and elevator is the second one through the judgment of contribution values in L/D deficiency. The stabilizer has irregular deviations with obvious endplay problems of jackscrew, as found in the present study. The stabilizer is suggested to have the unscheduled maintenance for the flight control rigging. Research limitations/implications The specific transport aircraft of the standard L/D model should be the best one in L/D performance among all transport aircraft in the fleet of the airlines. The present method is a new concept to monitor the irregular deviation of flight control surface. The study case of the four-jet transport aircraft at transonic flight in cruise phase is illustrated as the standard L/D mode. The required flight data of monitored flight is requested to eliminate the biases through compatibility checks. The flight data of study case in the present study is also illustrated as monitored flight data. Practical implications To diagnose the irregular deviations of flight control surface deflected angles with contributing to the L/D deficiency estimation is an innovation to improve the flight data analysis of FOQA program for airlines. If the irregular deviation problems of control surfaces can be fixed after rigging in maintenance, the goal of flight safety and aviation fuel saving will be achieved. Social implications The flight control surface rigging of unscheduled maintenance is not expected to coincide with an airline’s peak season or unavailable space in hangar. The optimal time of unscheduled maintenance for the flight control rigging will be easily decided through the correlations between excessive fuel cost and flight safety. Originality/value This method can be used to assist airlines to monitor irregular angular positions of flight control surfaces as a complementary tool for management to improve aviation safety, operation and operational efficiency.

High-plateau flight safety is an important research hotspot in the field of civil aviation transportation safety science. Complete and accurate high-plateau flight data are beneficial for effectively assessing and improving the flight status of civil aviation aircrafts, and can play an important role in carrying out high-plateau operation safety risk analysis. Due to various reasons, such as low temperature and low pressure in the harsh environment of high-plateau flights, the abnormality or loss of the quick access recorder (QAR) data affects the flight data processing and analysis results to a certain extent. In order to effectively solve this problem, an improved least squares support vector machines method is proposed. Firstly, the entropy weight method is used to obtain the index weights. Secondly, the principal component analysis method is used for dimensionality reduction. Finally, the data are fitted and repaired by selecting appropriate eigenvalues through multiple tests based on the LS-SVM. In order to verify the effectiveness of this method, the QAR data related to multiple real plateau flights are used for testing and comparing with the improved method for verification. The fitting results show that the error measurement index mean absolute error of the average error accuracy is more than 90%, and the error index value equal coefficient reaches a high fit degree of 0.99, which proves that the improved least squares support vector machines machine learning model can fit and supplement the missing QAR data in the plateau area through historical flight data to effectively meet application needs.