OpenAP: An Open-Source Aircraft Performance Model for Air Transportation Studies and Simulations

Sun, Junzi; Ellerbroek, Joost

doi:10.3390/aerospace7080104

Cited by 78 publications

(55 citation statements)

References 16 publications

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“…SOPHIA is based to a large extent on purely physical laws, considers all forces of acceleration and uses the methodology published in [10]. Coefficients that cannot be estimated without aircraft-specific aerodynamic properties, such as drag polar and the maximum available thrust as a function of altitude and speed, are obtained from the open-source flight performance model OpenAp [11].…”

Section: State Of the Artmentioning

confidence: 99%

Impact of Chinese and European Airspace Constraints on Trajectory Optimization

et al. 2021

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Air traffic trajectory optimization is a complex, multidimensional and non-linear optimization problem and requires a firm focus on the essential criteria. The criteria cover operational, economical, environmental, political, and social factors and differ from continent to continent. Since air traffic is a transcontinental transport system, the criteria may also change during a single flight. Historic flight track data allow observation and assess real flights, to extract essential criteria and to derive optimization strategies to increase air traffic efficiency. Real flight track data from the Chinese and European air traffic show significant differences in the routing structure in both regions. For that reason, reference trajectories of historic ADS-B 24-h air traffic data in China and Europe have been extracted and analyzed regarding horizontal flight efficiency and the most restrictive criteria of trajectory optimization. We found that prohibited areas might be the most powerful reason to describe deviations from the great circle distance in the Chinese air traffic system. Atmospheric conditions, network requirements, aircraft types and flight planning procedures are similar in China and Europe and only have a minor impact on flight efficiency during the cruise phase. In a multi-criteria trajectory optimization of the extracted reference trajectories considering the weather, operational constraints and prohibited areas, we found that flown ground distances could be reduced by 255 km in the Chinese airspace and 2.3 km in the European airspace. The resultant reference trajectories can be used for further analysis to increase the efficiency of continental air traffic flows.

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Section: State Of the Artmentioning

confidence: 99%

Impact of Chinese and European Airspace Constraints on Trajectory Optimization

et al. 2021

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“…Back-propagation algorithm [43] is used to train the neural network by utilizing gradient based optimization methods using the loss function given in Equation 8: To prevent overfitting and improve generalization capability of the neural network, an additional regularization term can be applied to the total loss function in Equation 8. Either L 1 , or L 2 norm penalties, which is also the model complexity loss are given in Equations (9) and 10:…”

Section: Neural Network With Multi-layer Perceptrons (Mlp)mentioning

confidence: 99%

“…Machine Learning (ML) algorithms have been shown to produce models that capture actual physical processes in a wide range of engineering disciplines including aircraft performance modeling [7,8]. In that aspect, the most critical aspect in such flight performance modeling is to extract the actual fuel usage based on the flying conditions such as Mach number, altitude number and environmental conditions including disturbances such as wind [9]. As such, ML algorithms, are shown to be able to automatically extract complicated relationships from data [10].…”

Section: Introductionmentioning

confidence: 99%

Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling

Uzun

Demirezen²,

Tsourdos

2021

Aerospace

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This paper presents a physics-guided deep neural network framework to estimate fuel consumption of an aircraft. The framework aims to improve data-driven models’ consistency in flight regimes that are not covered by data. In particular, we guide the neural network with the equations that represent fuel flow dynamics. In addition to the empirical error, we embed this physical knowledge as several extra loss terms. Results show that our proposed model accomplishes correct predictions on the labeled test set, as well as assuring physical consistency in unseen flight regimes. The results indicate that our model, while being applicable to the aircraft’s complete flight envelope, yields lower fuel consumption error measures compared to the model-based approaches and other supervised learning techniques utilizing the same training data sets. In addition, our deep learning model produces fuel consumption trends similar to the BADA4 aircraft performance model, which is widely utilized in real-world operations, in unseen and untrained flight regimes. In contrast, the other supervised learning techniques fail to produce meaningful results. Overall, the proposed methodology enhances the explainability of data-driven models without deteriorating accuracy.

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“…The experimental environment utilized in the present paper was mainly based on the Bluesky simulator [ 34 ], which is an open-source air traffic control simulator using OpenAP [ 35 ] aircraft performance models. The training environment was set in ZUUU airport terminal aerospace, located at latitude 30.5635165° North, and longitude 103.939946° East, with the runway oriented at 22°.…”

Section: Numerical Experimentsmentioning

confidence: 99%

A Multi-Dimensional Goal Aircraft Guidance Approach Based on Reinforcement Learning with a Reward Shaping Algorithm

Yang

Liu

et al. 2021

Sensors

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Guiding an aircraft to 4D waypoints at a certain heading is a multi-dimensional goal aircraft guidance problem. [d=Zu]In order to improve the performance and solve this problem, this paper proposes a multi-layer RL approach.To enhance the performance, in the present study, a multi-layer RL approach to solve the multi-dimensional goal aircraft guidance problem is proposed. The approach [d=Zu]enablesassists the autopilot in an ATC simulator to guide an aircraft to 4D waypoints at certain latitude, longitude, altitude, heading, and arrival time, respectively. To be specific, a multi-layer RL [d=Zu]approach is proposedmethod to simplify the neural network structure and reduce the state dimensions. A shaped reward function that involves the potential function and Dubins path method is applied. [d=Zu]Experimental and simulation results show that the proposed approachExperiments are conducted and the simulation results reveal that the proposed method can significantly improve the convergence efficiency and trajectory performance. [d=Zu]FurthermoreFurther, the results indicate possible application prospects in team aircraft guidance tasks, since the aircraft can directly approach a goal without waiting in a specific pattern, thereby overcoming the problem of current ATC simulators.

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OpenAP: An Open-Source Aircraft Performance Model for Air Transportation Studies and Simulations

Cited by 78 publications

References 16 publications

Impact of Chinese and European Airspace Constraints on Trajectory Optimization

Impact of Chinese and European Airspace Constraints on Trajectory Optimization

Physics Guided Deep Learning for Data-Driven Aircraft Fuel Consumption Modeling

A Multi-Dimensional Goal Aircraft Guidance Approach Based on Reinforcement Learning with a Reward Shaping Algorithm

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