Flight Dataset Research Articles

Reynold number scaling of circulation growth on flapping wings

Insect scale flight is characterized by large amplitude motions and high frequencies, often described by the Strouhal number and reduced frequency. Previous studies have scaled force coefficients with Strouhal number using classical potential flow theories. However, these often include a Reynolds number term, despite the implicit inviscid assumptions in classical theory. In this study, we derive a scaling relationship between force coefficients and wing kinematics in order to investigate this apparent contradiction. The model is based on the work of von Kàrmàn and Sears and, as a consequence of this choice, also provides an estimate of circulation in the wake. We consider a Blasius-like stream function to model the circulation transported into the leading-edge vortex from the shear layer in two cases: one that scaled circulation flux by the Reynolds number and one that did not. This model was applied to an existing insect flight data set, consisting of 168 individual flights of 38 individual mountain pine beetles. The model accurately captures the relationship between force coefficient and wing kinematics, regardless of the stream function scaling with or without the Reynolds number. However, only the model that was allowed to scale with the Reynolds number was also able to predict the circulation in the wake.

System Identification for Small Flying-Wing Unmanned Aircraft Using Open-Loop and Closed-Loop Flight Data

Small unmanned aircraft systems (sUAS) face unique challenges in system identification due to their lightweight, small size, and increased sensitivity to wind and gusts. To address these challenges, flight test procedures for sUAS system identification using open-loop and closed-loop flight data are presented. The approaches are demonstrated through system identification of a small flying-wing UAS equipped with a low-cost Pixhawk autopilot. The open-loop method is demonstrated through identification of the longitudinal bare-airframe aircraft dynamics. Longitudinal frequency responses were estimated from the open-loop flight data, and a linear state space model was identified from the estimated frequency responses and supplemental trim data, which was used to better identify parameters related to the low-frequency phugoid mode. The identified phugoid mode matches well with low-frequency oscillations measured from flight data in the time domain. The closed-loop method is demonstrated through identification of the lateral-directional dynamics. This approach is shown to improve the signal-to-noise ratio and reduce deviation from the reference flight condition, which improves modeling results, and it can be used to simultaneously identify the bare-airframe, closed-loop, and broken-loop UAS dynamics from the same set of closed-loop flight data, reducing time and efforts spent flight testing. The identified lateral-directional model is compared with closed- and broken-loop frequency responses estimated from flight data. The results show excellent agreement, with all computed metrics, such as stability margins, matching within 10%, demonstrating the effectiveness of this method.

An integrated aircraft scheduling model based on flight delay propagation and improved column generation algorithm

PurposeThis paper aims to reduce flight delay propagation, improve flight punctuality rate and ensure aircraft maintenance opportunities by establishing an integrated aircraft scheduling model, aiming at minimizing the total propagated delay and direction operational cost.Design/methodology/approachIn this paper, flight data sets are obtained through automatic dependent detection broadcast. To accurately predict flight delay time, the flight delay prediction eXtreme gradient boosting model adds the data set obtained by random forest advance model learning and predicts the newly generated flight delays. Finally, based on the forecast results, the flight plan can be optimized and adjusted by using the improved column generation algorithm.FindingsIt is verified by the actual weekly planned operation data of an airline company, experiments show that the model established in this paper can reduce flight delay propagation by 30% in case tests and each aircraft has the opportunity to be repaired at the base airport.Originality/valueOptimize the aircraft scheduling plan, cover a wide range of data, not just a single route and airport, supplement the gap in the aircraft scheduling plan based on weather factors to predict flight delays.

Modular and Multimodal Generative Adversarial Imitation Learning for Modeling Flight Trajectories

We aim to imitate the execution of modular tasks by exploiting unsegmented trajectories that demonstrate the execution of these tasks. This is challenging since the execution of tasks follows different modes (i.e., patterns of behavior), which may exist in various mixtures within subtasks, and the identification of trajectories’ modules (i.e., subtrajectories executing subtasks) may not be easy. This paper addresses the modularity of trajectories in conjunction with multimodality toward imitating the execution of aircraft trajectories. It proposes an imitation learning framework for the aircraft trajectory prediction problem, which segments demonstrated aircraft trajectories into subtrajectories corresponding to flight phases. This facilitates disentangling modes and learning a mixture of policies per flight phase. While trajectories are segmented using domain-specific rules, a mixture of policies per flight phase is learned by a generative multimodal imitation learning method. This modular approach enables accurate prediction of both modes and subtrajectories, which finally results in predicting the evolution of the aircraft state across the whole trajectory in a compositional way. Experiments using a real-world dataset of long flights show the potential of the proposed framework to disentangle multimodal trajectories in real-world settings and predict trajectories with high accuracy, in comparison to methods that do not exploit subtrajectories.¶

Topological data analysis in air traffic management: The shape of big flight data sets.

Analyzing flight trajectory data sets poses challenges due to the intricate interconnections among various factors and the high dimensionality of the data. Topological Data Analysis (TDA) is a way of analyzing big data sets focusing on the topological features this data sets have as point clouds in some metric space. Techniques as the ones that TDA provides are suitable for dealing with high dimensionality and intricate interconnections. This paper introduces TDA and its tools and methods as a way to derive meaningful insights from ATM data. Our focus is on employing TDA to extract valuable information related to airports. Specifically, by utilizing persistence landscapes (a potent TDA tool) we generate footprints for each airport. These footprints, obtained by averaging over a specific time period, are based on the deviation of trajectories and delays. We apply this method to the set of Spanish' airports in the Summer Season of 2018. Remarkably, our results align with the established Spanish airport classification and raise intriguing questions for further exploration. This analysis serves as a proof of concept, showcasing the potential application of TDA in the ATM field. While previous works have outlined the general applicability of TDA in aviation, this paper marks the first comprehensive application of TDA to a substantial volume of ATM data. Finally, we present conclusions and guidelines to address future challenges in the ATM domain.

A fast-aware multi-target response prediction approach and its application in aeronautical engineering

A fast-aware multi-target response prediction approach and its application in aeronautical engineering

Quantum-Accelerated Flight Selection: Probing Grover's Algorithm and Quantum Device Efficiency

Current flight search platforms primarily consider four essential factors when planning a trip: departure/arrival dates, as well as the origin and destination locations. However, when additional parameters are added to this search, the problem shifts from a simple to a complex search, as the engine must sift through a massive dataset of flights, including information on airlines, flight routes, fees, and more. To address this challenge and improve flight search efficiency amidst data-intensive and resource-demanding environments, this paper proposes the use of Grover's search algorithm. This algorithm is demonstrated as the optimal solution for searches with increased constraints. The paper highlights the practical application of Grover's search algorithm across three datasets of assorted sizes, as well as various quantum hardware and simulators available in the NISQ era. Furthermore, this paper provides an in-depth understanding of the complexity of quantum circuit design, including the key phases of state encoding and amplitude amplification. The effectiveness of these approaches is evaluated through analysis of execution times and quantum measurement results. The aim is to showcase the potential of quantum computing in revolutionizing real-world search tasks, particularly in the realm of flight selection.

Experimental backward integration for state-dependent differential Riccati equation (SDDRE): A case study on flapping-wing flying robot

Experimental backward integration for state-dependent differential Riccati equation (SDDRE): A case study on flapping-wing flying robot

SLKIR: A framework for extracting key information from air traffic control instructions Using small sample learning

In air traffic control (ATC), Key Information Recognition (KIR) of ATC instructions plays a pivotal role in automation. The field's specialized nature has led to a scarcity of related research and a gap with the industry's cutting-edge developments. Addressing this, an innovative end-to-end deep learning framework, Small Sample Learning for Key Information Recognition (SLKIR), is introduced for enhancing KIR in ATC instructions. SLKIR incorporates a novel Multi-Head Local Lexical Association Attention (MHLA) mechanism, specifically designed to enhance accuracy in identifying boundary words of key information by capturing their latent representations. Furthermore, the framework includes a task focused on prompt, aiming to bolster the semantic comprehension of ATC instructions within the core network. To overcome the challenges posed by category imbalance in boundary word and prompt discrimination tasks, tailored loss function optimization strategies are implemented, effectively expediting the learning process and boosting recognition accuracy. The framework's efficacy and adaptability are demonstrated through experiments on two distinct ATC instruction datasets. Notably, SLKIR outperforms the leading baseline model, W2NER, achieving a 3.65% increase in F1 score on the commercial flight dataset and a 12.8% increase on the training flight dataset. This study is the first of its kind to apply small-sample learning in KIR for ATC and the source code of SLKIR will be available at: https://github.com/PANPANKK/ATC_KIR.

Data-driven multivariate regression-based anomaly detection and recovery of unmanned aerial vehicle flight data

Abstract Flight data anomaly detection is crucial to ensuring the safe operation of unmanned aerial vehicles (UAVs) and has been extensively studied. However, the accurate modeling and analysis of flight data is challenging due to the influence of random noise. Meanwhile, existing methods are often inadequate in parameter selection and feature extraction when dealing with large-scale and high-dimensional flight data. This paper proposes a data-driven multivariate regression-based framework considering spatio-temporal correlation for UAV flight data anomaly detection and recovery, which integrates the techniques of correlation analysis (CA), one-dimensional convolutional neural network and long short-term memory (1D CNN-LSTM), and error filtering (EF), named CA-1DCL-EF. Specifically, correlation analysis is first performed on original UAV flight data to select parameters with correlation to reduce the model input and avoid the negative impact of irrelevant parameters on the model. Next, a regression model based on 1D CNN-LSTM is designed to fully extract the spatio-temporal features of UAV flight data and realize parameter mapping. Then, to overcome the effect of random noise, a filtering technique is introduced to smooth the errors to improve the anomaly detection performance. Finally, two common anomaly types are injected into real UAV flight datasets to verify the effectiveness of the proposed method.

GPS-free roll and pitch estimation through pressure altitude aided inertial navigation system for a jet aircraft

GPS-free roll and pitch estimation through pressure altitude aided inertial navigation system for a jet aircraft

A novel intelligent approach for flight delay prediction

Flight delay prediction is one of the most significant components of intelligent aviation systems that may spread throughout the whole aviation network and cause multi-billion-dollar losses faced by airlines and airports, it is quickly becoming an important research issue to improve airport and airline performance. Thus this paper proposed an effective algorithm called Flight Delay Path Previous-based Machine Learning (FDPP-ML) capable of improved prediction of individual flight delay minutes using regression models to an up level of accuracy. As aviation system connectivity presents complex spatial–temporal correlations, machine learning approaches have addressed flight delay prediction by using complex flight or weather features, or private information for specific airports and airlines that are hard to obtain, In contrast, the proposed FDPP-ML improved prediction based only on basic flight schedule features even with wide flight networks. The FDPP-ML consists of a novel algorithm with a supervised learning model, which works on reshaping datasets and creates two new features the main feature is previous flight delay (PFD) for flight paths, there is a strong relationship between departure and arrival delay, and vice versa for the same flight path, which increases the strength of the training model based on historical data. For target future flights, the algorithm works on inheriting the predicted flight delay to the next flight on the same flight path and repeats this process to end the prediction forecast horizon. The proving of approach effectiveness by using a wide network of US flight arrival and departure flights containing 366 airports and 10 airlines with various metrics accuracies of regression, and explanatory the impacts on various forecast horizons 2, 6, and 12 h for future flights. The FDPP-ML outperforms traditional training models by using machine and deep learning models and improving model accuracy in 10 models with an average of up to 39% in MAE, and 42% in MSE in a forecast horizon of 2 h. Finally, providing airport and airline analysis further reveals that can improve prediction than traditional training models for the individual busiest airports "Core 30" with an average of 35% in MAE and 42% in MSE respectively, and for the busiest 10 airlines with an average of 36% in MAE and 47% in MSE respectively. The findings of this study may offer informative recommendations to airport regulators and aviation authorities for developing successful air traffic control systems for enhanced flight delay prediction to flight operational effectiveness, not only over the US flight network but with wide worldwide flight networks if a dataset of flights existed.

Performance Model Identification of the General Electric CF34-8C5B1 Turbofan Using Neural Networks

This paper presents a methodology developed at the Laboratory of Applied Research in Active Controls, Avionics, and Aeroservoelasticity to identify a performance model of the CF34-8C5B1 turbofan engine powering the CRJ-700 regional jet aircraft from simulated flight data using artificial neural networks (ANNs). For this purpose, a qualified virtual research simulator was used to conduct different types of flight tests and to collect engine data under a wide range of operating conditions. The collected data were then used to create a comprehensive database for the training of the ANN model. This process was performed using the Bayesian regularization algorithm available in the MATLAB Neural Networks Toolbox, followed by a study to identify the optimal network architecture, namely, the number of layers and the number of neurons. The validation of the methodology was accomplished by comparing the model predictions with a set of flight data collected with the flight simulator for different flight conditions and flight regimes including takeoff, climb, cruise, and descent. The results showed that the model was able to predict the engine performance in terms of fan speed, core speed, inlet turbine temperature, net thrust, and fuel flow with less than 5% relative error.

Estimation of Longitudinal and Lateral Aerodynamic Derivatives From Flight Data Using Maximum Likelihood Method

The paper presents the estimation of longitudinal and lateral (lateral-directional) aerodynamic derivatives from real flight data of Hansa-3 aircraft using Maximum Likelihood (ML) method. The application of the ML method to the flight data of an air vehicle requires the postulation of correct flight dynamic formulations having accurate description of aerodynamic model. The application of the ML method to the flight data with either the measurement noise or the process noise has been accepted as a standard approach for the estimation of the aircraft parameters. A large number of longitudinal and lateral flight data sets were gathered during the flight testing of Hansa-3 aircraft through the execution of controlled maneuvers using elevator and aileron/rudder control inputs respectively. The acquired flight data in compatible form generated for various control input forms has been analyzed during the estimation of aerodynamic derivatives using Maximum Likelihood method. The results obtained in terms of aerodynamic derivatives are reasonably accurate and have been presented in tabular and graphical form. The paper highlights the various factors affecting the estimation.

Flight departure delay forecasting

The most accurate methods to predict delays have become essential to mitigate their generation and proliferation (and prevent their spread to other airports in the network). Given that both the delay time dependence function and the input variables on which it depends are not fully known, the objective of this research is to predict the delays in the departure of scheduled commercial flights through a methodology that uses predictive tools based on machine learning/deep learning (ML/DL), with supervised training in regression, based on the available flight datasets. The novel contribution of this work is, first, to make a comparison of the predictions in terms of means and statistical variance of the different ML/DL models implemented (ten in total) and, secondly, to determine the coefficients of the importance of the features or flight attributes. Using ML methods known as permutation importance, it is possible to rank the importance of flight attributes by their influence in determining the delay time and reduce the problem of selecting the most important flight attributes. The data for the analysis was obtained from the Colombian airport system for the year 2018. From the results obtained, it is worth mentioning that the model that presents the best performance is the Ensemble, or combination method of Random Forest Regressor models, configured with 2,000 trees within the forest, with an acceptable prediction range measured with the root-mean-square-error (RMSE) metric of 16 to 33 minutes (prediction of time of the flight departure delay) depending on the scenario analysed.

An adaptive law based online system identification of cropped delta unmanned aerial vehicles from flight tests

An adaptive law based online system identification of cropped delta unmanned aerial vehicles from flight tests

Structure, Resilience and Evolution of the European Air Route Network From 2015 to 2018

In spite of the dynamic nature of air transport, air route networks, i.e. the backbone used to organise aircraft flows, are expected to be mostly static, with small changes occasionally being introduced to improve the efficiency and resilience of the system. By leveraging a large data set of European flights comprising years 2015 to 2018, we analyse its structure and evolution from the perspective of complex networks, with the aim of firstly describing it, and secondly to confirm its static nature. Results depict a highly dynamic system, with major topological changes happening at the end of 2017. Peripheral links are usually more vulnerable, due to the lack of effective reroutings, as well as central regions; additionally, the overall resilience of the network is almost constant throughout time, in spite of an increase in traffic. We further test several hypotheses regarding the design considerations driving such evolution. Beyond specific operational insights, these results highlight the importance of taking into account the evolution of this network in the study of traffic flows.

Prediction of departure flight delays through the use of predictive tools based on machine learning/deep learning algorithms

AbstractThe objective of this research is to predict the delays in the departure of scheduled commercial flights through a methodology that uses predictive tools based on machine learning/deep learning (ML/DL), with supervised training in regression, based on the available flight datasets. Since the novel contribution of this work is, first, to make the comparison of the predictions in terms of means and statistical variance of the different ML/DL models implemented and, second, to determine the coefficients of the importance of the features or flight attributes, using ML methods known as permutation importance, it is possible to rank the importance of flight attributes by their influence in determining the delay time and reduce the problem of selecting the most important flight attributes. From the results obtained, it is worth mentioning that the model that presents the best performance is the ensemble or combinatorial method of random forest regressor models, with an acceptable prediction range (measured with the root-mean-square-error).

Sectional Leading Edge Vortex Lift and Drag Coefficients of Autorotating Samaras

Autorotating samaras such as Sycamore seeds are capable of descending at exceptionally slow speeds and the secret behind this characteristic is attributed to a flow mechanism known as the leading edge vortex (LEV). A stable LEV is known to increase the maximum lift coefficient attainable at high angles of attack and recent studies of revolving and flapping wings have proposed suitable lift and drag coefficient models to characterise the aerodynamic forces of the LEV. For the samara, however, little has been explored to properly test the suitability of these low-order lift and drag coefficient models in describing the aerodynamic forces produced by the samara. Thus, in this paper, we aim to analyse the use of two proposed aerodynamic models, namely, the normal force and Polhamus models, in describing the sectional aerodynamic lift of a samara that is producing a LEV. Additionally, we aim to quantify the aerodynamic parameters that can describe the lift and drag of the samara for a range of wind speed conditions. To achieve this, the study first examined the samara flight data available in the literature, and from it, the profiles of the lift coefficient curves were investigated. Subsequently, a numerical Blade Element-Momentum model (BEM) of the autorotating samara encompassing different lift profiles was developed and validated against a comprehensive set of samara flight data, which were measured from wind tunnel experiments conducted at the University of Bristol for three different Sycamores. The results indicated that both the normal force and Polhamus lift models combined with the normal force drag can be used to describe the two-dimensional lift characteristics of a samara exhibiting an LEV. However, the normal force model appeared to be more suitable, since the Polhamus relied on many assumptions. The results also revealed that the aerodynamic force parameters can vary with windspeed and with the samara wing characteristics, as well as along the span of the samara wing. Values of the lift curve slope, zero-lift drag coefficient, and maximum lift coefficient are predicted and presented for different samaras. The study also showed that the low-order BEM model was able to generate a good agreement with the experimental measurements in the prediction of both rotational speed and thrust. Such a validated BEM model can be used for the initial design of bio-inspired rotors for micro-air vehicles.

Study of Delay Prediction in the US Airport Network

In modern business, Artificial Intelligence (AI) and Machine Learning (ML) have affected strategy and decision-making positively in the form of predictive modeling. This study aims to use ML and AI to predict arrival flight delays in the United States airport network. Flight delays carry severe social, environmental, and economic impacts. Deploying ML models during the process of operational decision-making can help to reduce the impact of these delays. A literature review and critical appraisal were carried out on previous studies and research relating to flight delay prediction. In the literature review, the datasets used, selected features, selected algorithms, and evaluation tools used in previous studies were analyzed and influenced the decisions made in the methodology for this study. Data for this study comes from two public sets of domestic flight and weather data from 2017. Data are processed and split into training, validation, and testing data. Subsequently, these ML models are evaluated and compared based on performance metrics obtained using the testing data. The predictive model with the best performance (in choosing between logistic regression, random forest, the gradient boosting machine, and feed-forward neural networks) is the gradient boosting machine.