Machine Learning Models of C-17 Specific Range Using Flight Recorder Data

Fuel is a significant expense for the Air Force. The C-17 Globemaster eet accounts for a significant portion. Estimating the range of an aircraft based on its fuel consumption is nearly as old as flight itself. Consideration of operational energy and the related consideration of fuel efficiency is increasing. Meanwhile machine learning and data-mining techniques are on the rise. The old question, How far can my aircraft y with a given load cargo and fuel? has given way to How little fuel can I load into an aircraft and safely arrive at the destination? Specific range is a measure of efficiency that is fundamental in answering both questions, old and new. Predicting efficiency and consumption is key to decreasing unnecessary aircraft weight. Less weight means more efficient flight and less fuel consumption. Machine learning techniques were applied to flight recorder data to make fuel consumption predictions. Accurate predictions afford smaller fuel reserves, less weight, more efficient flight, and less fuel consumed overall. The accuracy of these techniques were compared and illustrated. A plan to incorporate these and other modeling techniques is proposed to realize immediate fuel cost savings and increase savings over time

Cost index (CI) and take-off mass (TOM) estimation using machine learning algorithms

The Cost Index (CI) and Take-off Mass (TOM) are two parameters that are very important in order to study the preferences on airlines operation. In the same way, these two parameters would allow to predict ground-based trajectories accurately. Nowadays, unfortunately, this information is not shared by the airlines, because this information is confidential as they help to define market strategies of the airline. The objective of this final degree project is to develop and evaluate an algorithm able to estimate CI and TOM from data of a flight trajectory, that could be collected by a conventional antenna (i.e. radar data or ADS-B), by using Machine Learning algorithms. The algorithm should be trained with data from the PEP (Performance Program Airbus). The data will be shaped by thousands of trajectories generated with different ranges of distances, TOM, CI and atmospheric conditions in order to establish the input training data for Machine Learning. Once the algorithm has been generated, to ensure its robustness, it will be tested with data containing noise where the influence of the parameters in the prediction would be evaluated. Finally, it will be validated with new aircraft trajectories from PEP. The ultimate goal of the final degree project is to check and perform the study with real flight data. To realize this, radar data will be obtained from the DDR2 platform of Eurocontrol. With some flights trajectories, we will study the values of CI and TOM used by several airlines with the Machine Learning algorithm previously trained. In conclusion, it has been demonstrated that for CI the most relevant input variable is the Mach Number because it is the most visible evidence given to the time-fuel cost relation. On the other hand, TOM is more related to the distance of the flight and flight levels (FL). When the prediction algorithm is applied to real cases flights, we observed that low-cost airlines and flag carriers use different strategies of CI. Even so, a single airline usually use the same CI for most of their routes, wasting the opportunity to optimize the costs of the route and all the advantages offered by the CI

Lentokoneen suoritusarvoparametrien selvittäminen käyttäen koneoppimisen periaatteita

To obtain fuel consumption reductions in margin of 5 %, at most, the functions that provide the performance parameters to the fuel consumption optimization problem require enhanced accuracy. The aircraft parameters used in calculation of the consumption of fuel during flight are usually provided in table form. Thus, their utilization in computer software calculations requires application of statistical methods. This thesis explores the usage of machine learning methods in modelling of the data to obtain more accurate models. The data tables are presented in the Aircraft Flight Manual. The datasets used in this thesis are Thrust Specific Fuel Consumption (TSFC) and Cruise Fuel Flow (CFF). In this study, we select three candidate algorithms for analysis. The Enhanced Adaptive Regression Through Hinges (EARTH) algorithm, based on a trademarked Multivariate Adaptive Regression Splines (MARS) algorithm, Random Forest Regression (RFR) and Kernel Ridge Regression (KRR) are each used to analyze both datasets. An initial analysis gives insight to the algorithm, while a parameter optimization is conducted to obtain the optimal parameters for each algorithm. Additionally, the datasets are divided into training and testing sets in the optimization phase to reduce the effect of overfitting. With the optimal parameter combinations established, the machine learning models are validated using validation plots. The optimal algorithm is proposed for both datasets according to the accuracy of the prediction. Also, the computational time required for each algorithm is evaluated, but it is not a deciding factor in algorithm selection, due to the nature of the problem. The KRR algorithm is found to not accurately model the dataset with chosen kernel, Radial Basis Function (RBF). Moreover, the optimal parameters obtained from the analysis for RFR render the algorithm used to deviate from accurately representing RFR. With these limitations, and the fact that EARTH algorithm modelled both datasets most accurately, EARTH is proposed as the optimal algorithm for these datasets.Jotta saavutetaan 5 % marginaalissa olevia polttoainesäästöjä, vaaditaan polttoaineen kulutuksen optimoinnin suoritusarvoparametrifunktioissa suurta tarkkuutta. Lentokoneen polttoaineen kulutuksen suoritusarvoparametrit annetaan usein taulukkomuodossa. Tästä johtuen, niiden hyödyntäminen tietokonelaskelmissa vaati tilastotieteen menetelmien käyttöä. Tässä työssä tutkitaan koneoppimenetelmien käyttöä datan mallintamisessa tarkempien mallien saamiseksi. Käytetyt datataulukot on esitelty lentokäsikirjassa (Aircraft Flight Manual, AFM). Työn datasetit koostuvat työntövoimakohtaisesta polttoaineenkulutuksesta (Thrust Specific Fuel Consumption, TSFC) ja matkalennon polttoaineenkulutuksesta (Cruise Fuel Flow, CFF). Työssä valittiin kolme algoritmia analyysiin. Datasetit analysoidaan RandomForest -regressiolla (Random Forest Regression, RFR), Kernel Ridge -regressiolla (Kernel Ridge Regression, KRR) ja EARTH-algoritmilla (Enhanced Adaptive Regression Through Hinges), joka pohjautuu patentoituun MARS-algoritmiin (Multivariate Adaptive Regression Splines). Alustava analyysi antaa tietoa algoritmien toiminnasta ja parametrien optimoinnilla selvitetään jokaiselle algoritmille optimikombinaatio parametreista. Lisäksi datasetit jaetaan koulutus ja testaus setteihin, jolla vähennetään ylisovittamisen (overfitting) vaikutusta. Kun optimaaliset yhdistelmät parametreille on selvitetty, validoidaan koneoppimalli kuvaajilla. Lopuksi molemmille dataseteille suositellaan algoritmia ennusteen tarkkuuden perusteella. Laskenta-aika algoritmien välillä tarkastellaan, mutta sitä ei pidetä ratkaisevan tekijänä. Analyysissä huomattiin, että KRR-algoritmi ei mallinna dataa oikein valitulla kantafunktiolla (Radial Basis Function, RBF). Myös RFR:n optimaalisissa parametreissa huomattiin ongelmia, niiden muuttaessa käytetyn algoritmin toimintaa niin, että se ei enää mallintanut dataa kuten RFR:n todellisuudessa kuuluisi. Näiden rajoitusten ja EARTH-algoritmin paremman tarkkuuden johdosta, EARTH:ia suositellaan käytettäväksi näiden datasettien mallintamisessa

Turbofan Engine Behaviour Forecasting using Flight Data and Machine Learning Methods

The modern gas turbine engine widely used for aircraft propulsion is a complex integrated system which undergoes deterioration during operation due to the degradation of its gas path components. This dissertation outlines the importance of Engine Condition Monitoring (ECM) for a more efficient maintenance planning. Different ML approaches are compared with the application of predicting engine behaviour aiming at finding the optimal time for engine removal. The selected models were OLS, ARIMA, NeuralProphet, and Cond-LSTM. Long operating and maintenance history of two mature CF6-80C2 turbofan engines were used for the analysis, which allowed for the identification of the impact of different factors on engine performance. These factors were also considered when training the ML models, which resulted in models capable of performing prediction under specified operation and flight conditions. The Machine Learning (ML) models provided forecasting of the Exhaust Gas Temperature (EGT) parameter at take-off phase. Cond-LSTM is shown to be a reliable tool for forecasting engine EGT with a Mean Absolute Error (MAE) of 7.64?, allowing for gradual performance deterioration under specific operation type. In addition, forecasting engine performance parameters has shown to be useful for identifying the optimal time for performing important maintenance action, such as engine gas path cleaning. This thesis has shown that engine removal forecast can be more precise by using sophisticated trend monitoring and advanced ML methods.O moderno motor de turbina a gás amplamente utilizado para propulsão de aeronaves é um sistema integrado complexo que sofre deterioração durante a operação devido à degradação de seus componentes do percurso do gás. Esta dissertação destaca a importância da monitorização da condição do motor para um planejamento de manutenção mais eficiente. Diferentes abordagens de Machine Learning (ML) são comparadas visando a aplicação de previsão do comportamento do motor com o objetivo de encontrar o momento ideal para a remoção do motor. Os modelos selecionados foram OLS, ARIMA, NeuralProphet e Cond-LSTM. O longo histórico de operação e manutenção de dois motores turbofan CF6-80C2 maduros foi usado para a análise, o que permitiu a identificação do impacto de diferentes fatores no desempenho do motor. Esses fatores também foram considerados no treinamento dos modelos de ML, o que resultou em modelos capazes de realizar a previsão em operação e condições de voo especificadas. Os modelos ML forneceram previsão do parâmetro Exhaust Gas Temperature (EGT) na fase de decolagem. O Cond-LSTM demonstrou ser uma ferramenta confiável para previsão do EGT do motor com um erro absoluto médio de 7,64 ?, permitindo a deterioração gradual do desempenho sob um tipo específico de operação. Além disso, a previsão dos parâmetros de desempenho do motor tem se mostrado útil para identificar o momento ideal para realizar ações de manutenção importantes, como a limpeza do percurso do gás do motor. Esta tese mostrou que a previsão de remoção do motor pode ser mais precisa usando um monitoramento sofisticado de tendências e métodos avançados de ML

Unveiling airline preferences for pre-tactical route forecast through machine learning. An innovative system for ATFCM pre-tactical planning support

In this work we describe a novel approach for the prediction of the flight plan to be sent by airspace users during the pre-tactical phase of Air Traffic Flow and Capacity Management (ATFCM). The proposed approach uses machine learning algorithms to extract airspace user preferences in terms of route characteristics, allowing the prediction of new routes not observed during the model training phase. We present the results obtained from applying this approach to short and medium range KLM flights for 52 weeks. Results show that the proposed solution is robust, scalable and capable of reducing the number of wrong predictions provided by the current Network Manager operational solution by 24.3% (4.5% increment on accuracy).Manuel Mateos´ PhD is funded by the 1st SESAR ENGAGE KTN Call for PhDs and is developed in collaboration between Nommon and the Technical University of Catalonia. This PhD study has received funding from the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 783287.Peer ReviewedPostprint (published version

Data-driven estimation of flights’ hidden parameters

This paper presents a data-driven methodology for the estimation of flights’ hidden parameters, combining mechanistic and AI/ML models. In the context of this methodology the paper studies several AI/ML methods and reports on evaluation results for estimating hidden parameters, in terms of mean absolute error. In addition to the estimation of hidden parameters themselves, this paper examines how these estimations affect the prediction of KPIs regarding the efficiency of flights using a mechanistic model. Results show the accuracy of the proposed methods and the benefits of the proposed methodology. Indeed, the results show significant advances of data-driven methods to estimate hidden parameters towards predicting KPIs.This work has received funding from SESAR Joint Undertaking (JU) within SIMBAD project under grant agreement No 894241. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the SESAR JU members other than the UnionPeer ReviewedPostprint (author's final draft

Machine learning for aircraft trajectory prediction: a solution for pre-tactical air traffic flow management

Pla de Doctorats Industrials de la Generalitat de Catalunya(English) The goal of air traffic flow and capacity management (ATFCM) is to ensure that airport and airspace capacity meet traffic demand while optimising traffic flows to avoid exceeding the available capacity when it cannot be further increased. In Europe, ATFCM is handled by EUROCONTROL, in its role of Network Manager (NM), and comprises three phases: strategic, pre-tactical, and tactical. This thesis is focused on the pre-tactical phase, which covers the six days prior to the day of operations. During the pre-tactical phase, few or no flight plans (FPLs) have been filed by airspace users (AUs) and the only flight information available to the NM are the so-called flight intentions (FIs), consisting mainly of flight schedules. Trajectory information becomes available only when the AUs send their FPLs. This information is required to ensure a correct allocation of resources in coordination with air navigation service providers (ANSPs). To forecast FPLs before they are filed by the AUs, the NM relies on the PREDICT tool, which generates traffic forecasts for the whole European Civil Aviation Conference (ECAC) area according to the trajectories chosen by the same or similar flights in the recent past, without taking advantage of the information on AU choices encoded in historical data. The goal of the present PhD thesis is to develop a solution for pre-tactical traffic forecast that improves the predictive performance of the PREDICT tool while being able to cope with the entire set of flights in the ECAC network in a computationally efficient manner. To this end, trajectory forecasting approaches based on machine learning models trained on historical data have been explored, evaluating their predictive performance. In the application of machine learning techniques to trajectory prediction, three fundamental methodological choices have to be made: (i) approach to trajectory clustering, which is used to group similar trajectories in order to simplify the trajectory prediction problem; (ii) model formulation; and (iii) model training approach. The contribution of this PhD thesis to the state of the-art lies in the first two areas. First, we have developed a novel route clustering technique based on the area comprised between two routes that reduces the required computational time and increases the scalability with respect to other clustering techniques described in the literature. Second, we have developed, tested and evaluated two new modelling approaches for route prediction. The first approach consists in building and training an independent machine learning model for each origin destination (OD) pair in the network, taking as inputs different variables available from FIs plus other variables related to weather and to the number of regulations. This approach improves the performance of the PREDICT model, but it also has an important limitation: it does not consider changes in the airspace structure, thus being unable to predict routes not available in the training data and sometimes predicting routes that are not compatible with the airspace structure. The second approach is an airline-based approach, which consists in building and training a model for each airline. The limitations of the first model are overcome by considering as input variables not only the variables available from the FIs and the weather, but also airspace restrictions and route characteristics (e.g., route cost, length, etc.). The airline-based approach yields a significant improvement with respect to PREDICT and to the OD pair-based model, achieving a route prediction accuracy of 0.896 (versus PREDICT’s accuracy of 0.828), while being able to deal with the full ECAC network within reasonable computational time. These promising results encourage us to be optimistic about the future implementation of the proposed system.(Català) L’objectiu de la gestió de demanda i capacitat de trànsit aeri (ATFCM per les sigles en anglès) és garantir que la capacitat aeroportuària i de l’espai aeri satisfacin la demanda de trànsit mentre s’optimitzen els fluxos per evitar excedir la capacitat disponible quan aquesta no es pot augmentar més. A Europa, l’ATFCM està a càrrec d’EUROCONTROL, i consta de tres fases: estratègica, pre-tàctica i tàctica. Aquesta tesi se centra en la pre-tàctica, que inclou els sis dies previs al dia d’operacions. Durant la fase pre-tàctica, els de l'espai aeri han presentat pocs o cap pla de vol i l’única informació sobre els vols disponible són els anomenats intencions de vol (principalment els horaris). La informació de la trajectòria només està disponible quan els usuaris envien els seus pla. Aquesta informació és necessària per assegurar una assignació correcta de recursos en coordinació amb els proveïdors de serveis de. Per predir els plans abans que siguin presentats, EUROCONTROL es recolza en l'eina PREDICT, que genera prediccions de trànsit d'acord amb les trajectòries escollides per vols similars el passat recent, sense aprofitar la informació sobre les decisions en dades històriques. L'objectiu de la present tesi doctoral és millorar l'exercici predictiu de l'eina PREDICT mitjançant el desenvolupament d'una eina que pugui gestionar tots els vols a Europa de manera eficient. Per fer-ho, s’han explorat diferents enfocaments de predicció de trajectòries basats en models d’aprenentatge automàtic entrenats amb dades històriques, avaluant l’exercici de la predicció. A l’hora d’aplicar les tècniques d’aprenentatge automàtic per a la predicció de trajectòries, s’han identificat tres eleccions metodològiques fonamentals: (i) el clustering de trajectòries, que s’utilitza per agrupar trajectòries similars per simplificar el problema de predicció de trajectòries; (ii) la formulació del model d’aprenentatge automàtic; i (iii) l’aproximació seguida per entrenar el model. La contribució d’aquesta tesi doctoral a l’estat de l’art es troba a les dues primeres àrees. Primer, hem desenvolupat una nova tècnica de clustering de rutes, basada en l’àrea compresa entre dues rutes, que redueix el temps computacional requerit i augmenta l’escalabilitat respecte a altres tècniques de clustering descrites a la literatura. En segon lloc, hem desenvolupat, provat i avaluat dos nous enfocaments de modelatge per a la predicció de rutes. El primer enfocament consisteix a construir i entrenar un model d’aprenentatge automàtic independent per a cada parell de d'aeroports a la xarxa, prenent com a entrades diferents variables disponibles de les intencions més altres variables relacionades amb el clima i el nombre de regulacions. Aquest enfocament millora el rendiment del model PREDICT, però també té una limitació important: no considera canvis en l’estructura de l’espai aeri, per la qual cosa no podeu predir rutes que no estan disponibles a les dades d’entrenament i, de vegades, podeu predir rutes que no són compatibles amb l’estructura de l’espai aeri. El segon enfocament, basat en les aerolínies, consisteix a construir i entrenar un model independent per a cada aerolínia. Les limitacions del primer model se superen en considerar com a variables d’entrada no només les variables disponibles dels intencions i el clima, sinó també les restriccions de l’espai aeri i les característiques de la ruta (p. ex., cost de la ruta, longitud, etc.). L’enfocament basat en aerolínies produeix una millora significativa respecte a PREDICT i al model basat en parells d'aeroports, aconseguint una precisió de predicció de ruta del 0,896 (comparant amb la precisió de PREDICT del 0,828), alhora que el problema pot escalar a tota l'àrea al complet amb un temps de computació raonable.(Español) El objetivo de la gestión de demanda y capacidad de tráfico (ATFCM por sus siglas en inglés) es garantizar que la capacidad aeroportuaria y del espacio aéreo satisfagan la demanda de tráfico mientras se optimizan los flujos para evitar exceder la capacidad disponible cuando esta no se puede aumentar más. En Europa, el ATFCM está a cargo de EUROCONTROL y consta de tres fases: estratégica, pre-táctica y táctica. Esta tesis se centra en la pre-táctica, que abarca los seis días previos al día de operaciones. Durante la fase pre-táctica, los usuarios del espacio aéreo han presentado pocos o ningún plan de vuelo y la única información sobre los vuelos disponible para EUROCONTROL son las llamados Intenciones de vuelo, que consisten principalmente en los horarios. La trayectoria está disponible sólo cuando los usuarios envían sus planes. Esta información es necesaria para asegurar una correcta asignación de recursos en coordinación con los provedores de servicios de navegación aérea de los distintos estados. Para predecir los FPLs antes de que sean presentados, EUROCONTROL se apoya en la herramienta PREDICT, que genera predicciones de tráfico de acuerdo las trayectorias elegidas por vuelos similares en el pasado reciente, sin aprovechar la información sobre las decisiones en datos históricos. El objetivo de la presente tesis doctoral es mejorar el desempeño predictivo de la herramienta PREDICT mediante el desarrollo de una herramienta que pueda gestionar todos los vuelos en Europa de una forma eficiente. Para ello, se han explorado diferentes enfoques de predicción de trayectorias basados en modelos de aprendizaje automático. A la hora de aplicar las técnicas de aprendizaje automático para predicción de trayectorias, se han identificado tres elecciones metodológicas fundamentales: (i) el clustering de trayectorias, que se utiliza para agrupar trayectorias similares a fin de simplificar el problema de predicción de trayectorias; (ii) la formulación del modelo de aprendizaje automático; y (iii) la aproximación seguida para entrenar el modelo. La contribución de esta tesis doctoral al estado del arte se encuentra en las dos primeras áreas. Primero, hemos desarrollado una novedosa técnica de clustering de rutas, basada en el área comprendida entre dos rutas, que reduce el tiempo computacional requerido y aumenta la escalabilidad con respecto a otras técnicas de clustering en la literatura. En segundo lugar, hemos desarrollado, probado y evaluado dos nuevos enfoques de modelado para la predicción de rutas. El primer enfoque consiste en construir y entrenar un modelo de aprendizaje automático independiente para cada par de aeropuertos en la red, tomando como entradas diferentes variables disponibles de las intenciones de vuelo más otras variables relacionadas con la meteorología y el número de regulaciones. Este enfoque mejora el rendimiento del modelo PREDICT, pero también tiene una limitación importante: no considera cambios en la estructura del espacio aéreo, por lo que no xvii puede predecir rutas que no están disponibles en los datos de entrenamiento y, a veces, puede predecir rutas que no son compatibles con el estructura del espacio aéreo. El segundo enfoque, basado en las aerolíneas, consiste en construir y entrenar un modelo independiente para cada aerolínea. Las limitaciones del primer modelo se superan al considerar como variables de entrada no solo las variables disponibles de las FIs y la meteorología, sino también las restricciones del espacio aéreo y las características de la ruta (p. ej., coste de la ruta, longitud, etc.). El enfoque basado en aerolíneas produce una mejora significativa con respecto a PREDICT y al modelo basado en pares de aeropuertos, logrando una precisión de predicción de ruta de 0,896 (frente a la precisión de PREDICT de 0,828), a la vez que puede lidiar con toda la red en un tiempo de computación razonable. Estos prometedores resultados nos animan a ser optimistas sobre una futura implementación del sistema propuesto.Ciència i tecnologies aeroespacial

Estimating fuel-efficient air plane trajectories using machine learning

Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather, aerodynamic drag and air traffic within that virtual cube. There are several constraints like simultaneous flight separation rules, weather conditions like air temperature, pressure, humidity, wind speed and direction that pose a real challenge for calculating optimal flight trajectories. To validate the proposed methodology, a case analysis was undertaken within Indian airspace. The flight routes were simulated for four different air routes within Indian airspace. The experiment results observed a seven percent reduction in drag values on the predicted path, hence indicates reduction in carbon footprint and better fuel economy