Simulation and Flight Test Environments for the TASAR Traffic Aware Planner

Barney, Terique L.; Underwood, Matthew C.; Buck, Bill K.

doi:10.2514/6.2019-3614

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Therefore, a need was identified to thoroughly test the functionality of the software in a similar hardware architecture to that of the partner airline's aircraft. Information regarding test apparatus and environments used to evaluate TAP prior to testing on the HU-25A can be found in reference [18].…”

Section: Introductionmentioning

confidence: 99%

In-Flight Evaluation of the Traffic Aware Planner on the NASA HU-25A Guardian Aircraft

Underwood

Lewis

Barney

2019

AIAA Aviation 2019 Forum

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NASA's Traffic Aware Planner (TAP) software is a research-prototype decision support tool that provides pilots with time-and fuel-saving route recommendations that optimize their current trajectory. The software runs on a first-of-a-kind system architecture onboard three aircraft in revenue service conducting operational evaluations with a major domestic airline. Therefore, significant NASA-internal testing is required prior to releasing the software to the partner airline. This paper describes a flight test plan that exercises the functionality of the TAP software in a representative operational environment, describes the system architecture developed and implemented for the NASA Langley HU-25A Guardian aircraft to support the test objectives, presents outcomes of the flight test campaign, and discusses use cases that demonstrate the value of flight testing for this activity.

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Section: Introductionmentioning

confidence: 99%

In-Flight Evaluation of the Traffic Aware Planner on the NASA HU-25A Guardian Aircraft

Underwood

Lewis

Barney

2019

AIAA Aviation 2019 Forum

Self Cite

View full text Add to dashboard Cite

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Small aircraft flight trajectory optimisation using a multidisciplinary approach

Rostami,

Bardin,

Neufeld

et al. 2024

Aeronaut. j.

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In a competitive market, airlines continually seek solutions that can reduce their operational costs. Flight path optimisation is a commonly pursued approach to this but requires a large amount of data about the flight environment including the weather information, the aircraft performance and the air traffic control (ATC) requirements. Existing programmes require the user to provide this aircraft performance data in advance and are incapable of generating the information on their own. In this study, using a multidisciplinary approach and numerical optimisations, a novel standalone flight path optimiser (SAFPO) solution is proposed and developed to choose the best flight path for a flight between two points in accordance with the cost objectives. SAFPO uses its own performance calculator, predefined ATC routes, and known weather information to find the optimum flight path which minimises fuel consumption and/or flight time. The aerodynamic characteristics of the aircraft are determined using a validated semi-empirical programme called MAPLA, previously developed for light aircraft analysis. Furthermore, the optimisation process consists of a multidisciplinary-feasible (MDF) framework that employs a genetic algorithm (GA) optimiser. The resulting performance characteristics of the aircraft and the optimisation process are compared with the actual information provided within the flight manual of a Beechcraft Baron G58 aircraft. The optimisation results show that SAFPO can be used to make advances in the daily operations of small and local airlines suffering from a lack of aircraft performance data and help them to choose the scenario that best accomplishes their cost objectives.

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Simulation and Flight Test Environments for the TASAR Traffic Aware Planner

Cited by 2 publications

References 9 publications

In-Flight Evaluation of the Traffic Aware Planner on the NASA HU-25A Guardian Aircraft

In-Flight Evaluation of the Traffic Aware Planner on the NASA HU-25A Guardian Aircraft

Small aircraft flight trajectory optimisation using a multidisciplinary approach

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