Aircraft Performance Model Research Articles

Modeling Civil Aviation Emissions with Actual Flight Trajectories and Enhanced Aircraft Performance Model

Aviation emissions are continuously increasing along with the rapid development of air transportation, and results in the deterioration in regional air quality and the global climate. Accurate emission estimation is of great importance for relevant policies promotion and the sustainable development of the environment. Previous studies focused on the total emissions of a flight and lacked high precision in both spatial and temporal resolutions, especially aviation activities near ground. In this research, we propose an open-sourced emission calculation framework based on actual flight trajectories (TrajEmission), which calculates both the ground and airborne emissions simultaneously according to the configuration parameters, trajectory characteristics, and ambient conditions. We compare the emission results with five emission inventory methods. The results indicate that pollutant (nitrogen oxides, carbon monoxide, and unburned hydrocarbons) emissions in the landing and takeoff (LTO) cycle might usually be underestimated due to a lack of trajectory-based methods. In addition, in the overall results, the method based on the great circle route leads to an overestimation of 56.8% of pollutant emissions compared to the method based on actual routes. We also investigate the extent to which other factors could influence the emission results. To summarize, the TrajEmission framework can build inventories for the whole process of flight movements with high spatial–temporal resolutions and provide solid data support for environmental science and other related fields.

Compilation and Applications of an Open-Source Dataset on Global Air Traffic Flows and Carbon Emissions

The study of the environmental transition of the aviation sector calls for prospective traffic scenarios. Detailed traffic and emissions inventories are often needed to refine the available analyses and to enable the simulation of regionalised scenarios. In the past studies, these are generally based on commercial, proprietary traffic data, making their dissemination problematic and reducing the reproducibility of the science produced. Open-source alternatives do exist but with limited geographical coverage. This study bridges this gap by presenting an innovative open-source dataset detailing 2019's global air traffic flows and associated CO2 emissions. A comprehensive approach that compiles diverse flight data sources is presented. The remaining data gaps are addressed by constructing a route network through systematic Wikipedia parsing and by estimating the related traffic using socio-economic data. Then, an aircraft performance model to estimate CO2 emissions is implemented. This methodology promises reinforced reproducibility and broader data accessibility in aviation environmental research. Several reference datasets are used to evaluate the accuracy of the open-source dataset. Despite various levels of accuracy for individual routes, major traffic flows are well estimated at the country and continental levels, albeit with room for refinement to ensure consistent data reliability. To facilitate the exploration of the dataset, the AeroSCOPE tool has been developed. To initiate research prospects, use cases of this dataset are proposed, concerning the network potential of electric and hydrogen-powered aircraft and inequalities in air transport.

Path Planning for Fixed-Wing Unmanned Aerial Vehicles: An Integrated Approach with Theta* and Clothoids

Unmanned Aerial Vehicles (UAVs) have emerged as a compelling alternative to manned operations, offering the capability to navigate hazardous environments without risks for human operators. Despite their potential, optimizing UAV missions in complex and unstructured environments remains a pivotal challenge. Path planning becomes a crucial aspect to increase mission efficiency, although it is inherently complex due to various factors such as obstacles, no-fly zones, non-cooperative aircraft, and flight mechanics limitations. This paper presents a path-planning technique for fixed-wing unmanned aerial vehicles (UAVs) based on the Theta* algorithm. The approach introduces innovative features, such as the use of Euler spiral, or clothoids, to serve as connection arcs between nodes, mitigating trajectory discontinuities. The design of clothoids can be linked to the aircraft performance model, establishing a connection between curvature constraints and the specific characteristics of the vehicle. Furthermore, to lower the computational burden, the implementation of an adaptive exploration distance and a vision cone was considered, reducing the number of explored solutions. This methodology ensures a seamless and optimized flight path for fixed-wing UAVs operating in static environments, showcasing a noteworthy improvement in trajectory smoothness. The proposed methodology has been numerically evaluated in several complex test cases as well as in a real urban scenario to prove its effectiveness.

Generation of Parametric Climb Trajectories considering Operational Inputs for Aircraft Engine Thrust Extraction

For aircraft engines costs related to maintenance, repair \& overhaul make up a great proportion of the overall direct operating costs. As the aviation sector is about to face substantial technological shifts towards hybrid-electric and all-electric propulsion, tools are required to model engine operating costs and their strong interdependence to operational factors. This study presents a method for adapting the parametric climb trajectory generation of the aircraft performance model OpenAP for considering operational inputs of flight distance and ambient conditions. Flight data of Airbus A320 operated in North America are analysed for the characteristic climb parameters. The data is used to train a XGBoost machine learning model in order to link the operational inputs to the trajectory parameters. The results show, that the model is able to represent global trends in the data while staying within the limits of the original model. However, the model shows some singularities, which could be addressed by parameter tuning and expanding the data base. Eventually, the generated trajectories differ from the default trajectory of the original model, such that the average thrust per segment varies in the range of ±20% to ±10%.

Aerodynamic model adjustment for an accurate flight performance representation using a large operational flight data base

A novel process to determine an aircraft performance model from operational flight data with limited a priori knowledge is developed. The given big data problem is solved by application of fundamental engineering knowledge and a specific data evaluation strategy. The resulting smart data approach is fundamentally different from existing deep learning methods to solve such big data problems. A given aerodynamic model is updated to represent the characteristics of an Airbus A320neo aircraft based on a given large database of operational flights. The updated aerodynamic model implementation for one specific flap/slat configuration is exemplarily compared to the information available from flight data and the results are discussed in terms of model quality.

Modeling of aircraft performance parameters with metaheuristic methods to achieve specific excess power contours using energy maneuverability method

The energy method, which deals with the total energy of the aircraft (sum of potential energy and kinetic energy), is frequently used in the climb analysis of high performance aircraft. Within the scope of this study, by using this energy approach; a new method is presented to obtain specific excess power contours (Ps), which show the performance limits of the aircraft, present the altitude and speed combinations at which they can fly at different specific excess powers, and help determine the trajectory corresponding to the minimum time to climb without the need for any mathematical operation. In the method presented for the B737-800 aircraft; aircraft performance model consisting of aerodynamic model, thrust, and fuel flow rate models was created and Ps contours implementing the energy maneuverability method were obtained by using this model. The data used in the study are real thrust and flight data record (FDR) data. For the models, the cuckoo search algorithm (CSA) method, which is relatively new but has proven itself in many challenging optimization problems, is used. Particle swarm optimization (PSO), a different metaheuristic method, was used to validate CSA models. In all of the optimization processes made using the Matlab program, very accurate results were accomplished with both metaheuristic methods.

OpenAP.top: Open Flight Trajectory Optimization for Air Transport and Sustainability Research

Trajectory optimization has been an active area of research for air transport studies for several decades. But almost all flight optimizers proposed in the literature remain close-sourced, which presents a major disadvantage for the advancement of scientific research. This optimization depends on aircraft performance models, emission models, and operational constraints. In this paper, I present a fully open trajectory optimizer, OpenAP.top, which offers researchers easy access to the complex but efficient non-linear optimal control approach. Full flights can be generated without specifying flight phases, and specific flight segments can also be independently created. The optimizer adapts to meteorological conditions and includes conventional fuel and cost index objectives. Based on global warming and temperature potentials, its climate objectives form the basis for climate optimal air transport studies. The optimizer’s performance and uncertainties under different factors like varying mass, cost index, and wind conditions are analyzed. Overall, this new optimizer brings a high performance for optimal trajectory generations by providing four-dimensional and wind-enabled full-phase optimal trajectories in a few seconds.

Developing a Cost-Effective Assessment Method for Noise Impacts at Non-Towered Airports: A Case Study at Purdue University

Aircraft noise is considered as one of the environmental impacts that most adversely affects airport development. Aircraft noise estimation tools such as the Aviation Environmental Design Tool (AEDT) are widely used by airports to mitigate local concerns for airport development. Aircraft operations and fleet mix information play important roles in aircraft performance modeling for noise prediction. However, such information is difficult to obtain at almost 85% of the 3,348 National Plan of Integrated Airport Systems (NPIAS) airports, which lack full-time air traffic control facilities and personnel. Given the increasing percentage of equipped automatic dependent surveillance-broadcast (ADS-B) aircraft in the U.S. national airspace, this paper presents a cost-effective noise estimation approach for non-towered general aviation (GA) airports. Instead of deploying physical sound meters or relying on the local air traffic control facilities, the authors collected aircraft positional data and fleet mix information based on ADS-B broadcasts. Advanced aircraft performance model parameters are constructed using four-dimensional aircraft position and a kinematic point-mass model. Aircraft-specific noise levels are interpolated based on the EUROCONTROL Aircraft Noise and Performance (ANP) database. To obtain the final noise level, two adjustments are applied to the interpolated noise values. The test results show the proposed approach can help non-towered airports predict the noise impact on any selected locations near the airport cost-effectively. Recommendations and opportunities for future investigation are also included.

Global Civil Aviation Emissions Estimates for 2017–2020 Using ADS-B Data

Aviation is a growing source of atmospheric emissions impacting the Earth’s climate and air quality. Comprehensive assessments of the environmental impact of this industry require up-to-date, spatially resolved, and speciated emissions inventories. We develop and evaluate the first such estimate of global emissions from aircraft operations for the years 2017–2020. Aircraft activity data, based on flights registered by networks of aircraft Automatic Dependent Surveillance–Broadcast (ADS-B) telemetry receivers, are used together with the Base of Aircraft Data (BADA) 3.15 aircraft performance model and the International Civil Aviation Organization Engine Emissions Databank to estimate spatially resolved fuel burn and emissions of , , (), (), CO, unburnt hydrocarbons (HC), and nonvolatile particulate matter (nvPM). We calculate that 937 Tg of and 4.62 Tg of were emitted by aircraft in 2019, and quantify the evolution of the fleet average emission indices over time. Owing to impacts from COVID-19, we estimate a 48% lower fuel burn, resulting in 463 Tg less and 2.29 Tg less emitted in 2020 than what would be otherwise expected. We conclude that ADS-B is a viable source of data to generate global emissions estimates in a timely and transparent manner for monitoring and assessing aviation’s atmospheric impacts.

Hybrid Solid Oxide Fuel Cell/Gas Turbine Model Development for Electric Aviation

A thermodynamic model was developed and validated to analyze a high-performance solid oxide fuel cell and gas turbine (SOFC-GT) hybrid power system for electric aviation. This study used a process simulation software package (ProMax) to study the role of SOFC design and operation on the feasibility and performance of the hybrid system. Standard modules, including compressor, turbine, heat exchanger, reforming reactor, and combustor were used from the ProMax tool suite while a custom module was created to simulate the SOFC stack. The model used an SOFC test data set as an input. Additional SOFC stack performance effects, such as pressure, temperature, and utilization of air and fuel, were added from open source data. System performance predictors were SOFC specific power, fuel-to-electricity conversion efficiency, and hybrid system efficiency. Using these input data and predictors, a static thermodynamic performance model was created that can be modified for different system configurations and operating conditions. Prior to creating the final aircraft performance model, initial demonstration models were developed to validate output results. We used the NASA SOFC model as a benchmark, which was created with their Numerical Propulsion System Simulator (NPSS) software framework. Our output results matched within 1% of both the NASA model and open source SOFC performance data. With confidence gained in the accuracy of this model, a 1-MW SOFC-GT hybrid power system was constructed for an aircraft propulsion concept. Overall hybrid system efficiencies of > 75% FTE were observed during standard 36,000 feet cruise flight conditions.

Fuel flow rate modeling for descent using cuckoo search algorithm: a case study for point merge system procedure at Istanbul airport

PurposeThe purpose of this paper is to create a new fuel flow rate model using cuckoo search algorithm (CSA) for the descending stage of the flight.Design/methodology/approachUsing the actual flight data record data of the B737-800 aircraft, a new fuel flow rate model has been developed for this aircraft type. The created model is to predict the fuel flow rate with high accuracy depending on the altitude and true airspeed. In addition, the CSA fuel flow rate model was used to calculate the fuel consumption for the point merge system, which is used for combining the initial approach to the final approach at Istanbul Airport, the largest airport of Turkey.FindingsAs a result of the analysis, the correlation coefficient value is found as 0.996858 for Flight 1, 0.998548 for Flight 2, 0.995363 and 0.997351 for Flight 3 and Flight 4, respectively. The values that are so close to 1 indicate that the model predicts the real fuel flow rate data with high accuracy.Practical implicationsThis model is considered to be useful in air traffic management decision support systems, aircraft performance models, models used for trajectory prediction and strategies used by the aviation community to reduce fuel consumption and related emissions.Originality/valueThe importance of this study lies in the fact that to the best of the authors’ knowledge, it is the first fuel flow rate model developed using CSA for the descent stage in the existing literature; the data set used is real values.

Aircraft Trajectory Prediction With Enriched Intent Using Encoder-Decoder Architecture

Aircraft trajectory prediction is a challenging problem in air traffic control, especially for conflict detection. Traditional trajectory predictors require a variety of inputs such as flight-plans, aircraft performance models, meteorological forecasts, etc. Many of these data are subjected to environmental uncertainties. Further, limited information about such inputs, especially the lack of aircraft tactical intent, makes trajectory prediction a challenging task. In this work, we propose a deep learning model that performs trajectory prediction by modeling and incorporating aircraft tactical intent. The proposed model adopts the encoder-decoder architecture and makes use of the convolutional layer as well as Gated Recurrent Units (GRUs). The proposed model does not require explicit information about aircraft performance and wind data. Results demonstrate that the provision of enriched aircraft intent, together with appropriate model design, could improve the prediction error up to 30% at a prediction horizon of 10 minutes (from 4.9 nautical miles to 3.4 nautical miles). The model also guarantees the mean error growth rate with increasing look-ahead time to be lower than 0.2 nautical miles per minute. In addition, the model offers a very low variance in the prediction, which satisfies the variance-standard specified by EUROCONTROL (EU Organization for Safety and Navigation of Air Traffic) for trajectory predictors. The proposed model also outperforms the state-of-the-art trajectory prediction model, where the Root Mean Square Error (RMSE) is reduced from 0.0203 to 0.0018 for latitude prediction, and from 0.0482 to 0.0021 for longitude prediction in a single prediction step of 15 seconds look-ahead. We showed that the pre-trained model on ADS-B data maintains its high performance, in terms of cross-track and along-track errors, when being validated in the Bluesky Air Traffic Simulator. The proposed model would significantly improve the performance of conflict detection systems where such trajectory prediction models are needed.

Multiobjective Four-Dimensional Trajectory Synergetic Optimization Based on Congestion Prediction and NSGA3-SA

Synergetic trajectory planning of flights is one of the important goals of trajectory-based operation (TBO), and it is also a method to further improve the utilization of airspace resources with the increasing number of flights in recent years. In order to plan the four-dimensional trajectory (4DT) pre-tactically and comprehensively, match the flight traffic with airspace capacity, reduce congestion, potential conflicts, and fuel consumption thus improving the efficiency of flights, this paper conducts a method for synergetic trajectory planning in the en-route phase from the perspective of airlines. Firstly, the aircraft performance model, aircraft fuel consumption model, and atmospheric model are constructed according to the base of aircraft data (BADA3.11), and an airspace congestion prediction model is constructed based on the historical flow data of airspace. Secondly, a multi-objective synergetic trajectory planning model is established, and a solution method based on the non-dominated sorting genetic algorithm and simulated annealing algorithm (NSGA3-SA) for the problem of synergetic trajectory planning is designed. The simulation shows that the optimization model and the solution algorithm of NSGA3-SA can reduce fuel consumption by about 4.5% compared to the original flight plans and has a good effect on reducing congestion and avoiding conflicts. The running time of the NSGA3-SA can meet the operational requirements of the pre-tactical trajectory planning. The multi-objective optimization model and the solution algorithm proposed in this paper have great value for the research of flight plan optimization.

Modelling piston engine aircraft performance using pilot's operating handbook

With the growth of air traffic, airspace design proves to be difficult with current tools, aircraft models and airspace simulations. An increase in traffic causes proportional growth in general aviation traffic, and from this point onward, general aviation traffic can no longer be overseen. This paper presents a new model for general aviation airplanes, powered by normally aspirated piston engines, enabling simple but accurate simulations. The modelling process is done by utilizing the data available in the pilot's operating handbook. This model, compared to the current traditional aircraft performance models, enables more precise and flexible simulations. Within the results chapter, an example of the modelling process is shown upon an example airplane. Results are precise and validated against the before-mentioned pilot's operating handbook. With the new model, general aviation can be included in simulations for airspace design and other applications. Future research should be focused on modelling the pre-charged piston engines and airplanes in non-clean configuration, allowing widening of the airplane databases and simulations through terminal phases of fight.

Minimax and Multi-Criteria Selection of Representative Model Portfolios for Complex Systems Analysis

The analysis of complex systems typically requires a large and diverse ensemble of models to confront the richness and complexity of the problem. Nevertheless, a common development and validation process of each individual model can be rigorous and time consuming. Because of limited time and resources, it is sometimes unrealistic to model every individual object in the population to capture the complex phenomena of the system. One such challenge arises in aerospace engineering when only a very limited number of aircraft performance models can be built to cover the aircraft fleet of the world for the environmental impact modeling of air transportation system. This paper considers the problem of selecting a small proportion of representative models to sufficiently cover and represent the population for more efficient and accurate systems analysis. At the intersection of unsupervised data mining and high-dimensional data analysis, the proposed methodology uses minimax and multi-criteria considerations to select a representative model portfolio at each budget level. Applying on the case study of representative aircraft models for environmental impact modeling, the benefits of the proposed method are demonstrated through both data visualization and quantitative metrics in multiple experiments. Potential limitations, extensions, and broader impacts of the method are also thoroughly discussed.

Community noise assessment of urban air mobility vehicle operations using the FAA Aviation Environmental Design Tool

In contrast to most commercial air traffic today, vehicles serving the urban air mobility (UAM) market are anticipated to operate in communities close to the public at large. The approved model for assessing environmental impact of air traffic actions in the United States, the Federal Aviation Administration's Aviation Environmental Design Tool (AEDT), does not support analysis of such operations due to a combined lack of a UAM aircraft performance model and aircraft noise data. This paper discusses the initial development of a method to assess the acoustic impact of UAM fleet operations on the community using AEDT and demonstrates its use for representative UAM operations. In particular, methods were developed using fixed-point flight profiles and user-supplied noise data in a manner that avoids unwanted behavior in AEDT. A set of 32 routes in the Dallas-Ft. Worth area were assessed for single and multiple (fleet) operations for two concept vehicles.

AIRNOISEUAM: An Urban Air Mobility Noise-Exposure Prediction Tool

A new software tool called AIRNOISEUAM is introduced that models the noise exposure of Urban Air Mobility (UAM) operations. Given relevant UAM aircraft performance models, mission profiles, and Noise-Power-Distance data, AIRNOISEUAM predicts the noise exposure footprint for receptors on the ground. The performance of AIRNOISEUAM using a Robinson R66 helicopter model and a six-passenger quadrotor model, and a diverse set of scenarios from NASA’s UAM human-in-the-loop simulations is compared to that of the industry-standard tool with the same inputs. The predicted noise exposure results from both tools are found to be nearly identical. AIRNOISEUAM offers a fast-time, flexible interface and modular design to facilitate the dynamic requirements of the aviation research community.

Reliability Analysis with Proportional Hazard Model in Aeronautics

Reliability analysis is a specific field of statistics that studies failure time and its probability on a system. This branch of statistics takes an important place in many domains, especially in aeronautics. One of the most commonly used methods of reliability analysis is the hazard function which is defined for a specific instance in time, given the information up to that moment. Commonly, the most used parametric proportional hazard model is Weibull proportional hazard model. Weibull distribution makes restrictive assumptions of the baseline hazard function like monotonicity and gives average values, which can cover a large type of failures due to aircraft age and fatigue in life operation. The present work deals with an unusual phenomena, especially in the aircraft entrance in a turbulence area, see Zbrozek (Weather R Meteorol Soc 13(7):215–227, 1958). This type of situation can lead to the need of increasing–decreasing proportional hazard models, taking into account harsh conditions during the flight. The functions which could model these non-monotone phenomena are scarce in literature but remain very crucial due to their impact on flight safety. In this paper, we propose a new baseline hazard function using extreme values theory in proportional hazard model and we adjusted the flexible Weibull increasing–decreasing baseline hazard function proposed by Park* and Park (Commun Stat Theory Methods 47(4):767–778, 2018) to the proposed baseline hazard function. This function could be implemented in control system of navigation particularly the take-off and landing phase and used as a support tool for pilot or aviation controller running turbulence area. The newly suggested function is non-monotone and will be named generalized extreme values baseline hazard function and we prove that this function satisfies hazard properties and could be used as a tool in the prediction of system operations. An application on aircraft flights is done and the effects of environmental factors are studied in aircraft performance modeling with the proposed model.

Research on Method of Trajectory Prediction in Aircraft Flight Based on Aircraft Performance and Historical Track Data

Traditional 4D trajectory prediction based on aircraft performance models and flight procedures does not consider control handover rules. Meanwhile, method based on historical data mining cannot accurately couple with real-time conditions such as weather and also cause computational efficiency problems. This project collected a large amount of historical data to form a control experience database and mined the historical database to obtain control experience and flight intention. On the basis of the traditional aircraft performance model, this paper puts forward the aircraft maneuver mode using strategy and introduces the high-altitude wind information from the weather information into the aircraft 4D model to optimize the aircraft 4D trajectory calculation model. By comparing the flight forecast time with the real crossing time, it is found that the average error of the improved 4D forecast crossing time is less than 5% of the flight time, which is obviously better than that before optimization. It is proved that the optimized method based on historical track data is effective and reliable, and the accuracy of 4D track prediction is improved greatly.

Modeling of fuel flow-rate of commercial aircraft for the descent flight using particle swarm optimization

PurposeThe purpose of this paper is to create a new fuel flow rate model for the descent phase of the flight using particle swarm optimization (PSO).Design/methodology/approachA new fuel flow rate model was developed for the descent phase of the B737-800 aircraft, which is frequently used in commercial air transport using PSO method. For the analysis, the actual flight data records (FDRs) data containing the fuel flow rate, speed, altitude, engine speed, time and many more data were used. In this regard, an empirical formula has been created that gives real fuel flow rate values as a function of altitude and true airspeed. In addition, in the fuel flow rate predictions made for the descent phase of the specified aircraft, a different model has been created that can be used without any optimization process when FDR data are not available for a specific aircraft take-off weight condition.FindingsThe error analysis applied to the models showed that both models predict real fuel flow rate values with high precision.Practical implicationsBecause of the high accuracy of the PSO model, it is thought to be useful in air traffic management, decision support systems, models used for trajectory prediction, aircraft performance models, strategies used to reduce fuel consumption and emissions because of fuel consumption.Originality/valueThis study is the first fuel flow rate model for descent flight using PSO algorithm. The use of real FDR data in the analysis shows the originality of this study.