Weather Design Considerations for the TASAR Traffic Aware Planner

Lewis, Timothy A.; Burke, Kelly; Ubderwood, Matthew C.; Wing, David J.

doi:10.2514/6.2019-3616

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…Initial flights from August 2017 through March 2018 successfully tested the integration of the TAP system with the HU-25A Guardian, and also collected several hours of data from the prototype AWRP tool. From May through July 2018, flights assessed the performance of the TAP software near convective weather, demonstrated the ability to ingest airborne weather radar data from AWRP in real-time and use it in trajectory optimization (discussed further in [21]). Flight tests during this time period also tested the ability to connect to a ground-based trajectory optimization tool designed for airline dispatchers running at NASA Ames Research Center.…”

Section: A Overview Of Flight Evaluation Resultsmentioning

confidence: 99%

“…These data include now-cast convective weather avoidance polygons, forecast convective weather avoidance polygons, convective SIGnificant METeorological (SIGMET) data, and turbulence SIGMETs. It is designed to operate on a Linux operating systems [21].…”

Section: Software System Architecture Overviewmentioning

confidence: 99%

“…On these early flights, ARINC 708 outputs from the airborne weather radar were connected to a Ballard ARINC 708 USB Adapter that allowed for a laptop computer to receive and process these data. Discussed further in [21], a prototype tool known as the Airborne Weather Radar Processor (AWRP) ingested these data from the weather radar and used them to build avoidance polygons for the TAP software. This prototype tool provided avoidance polygons to the TAP software on one flight at the end of March 2018.…”

Section: Airborne Weather Radar Research System Configurationsmentioning

confidence: 99%

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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: A Overview Of Flight Evaluation Resultsmentioning

confidence: 99%

Section: Software System Architecture Overviewmentioning

confidence: 99%

Section: Airborne Weather Radar Research System Configurationsmentioning

confidence: 99%

See 1 more Smart Citation

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

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“…The most common approach for obtaining continuous wind information in current flight planning systems is via a linear interpolation method. For example, the Traffic Aware Planner (TAP) employs a linear interpolation method to provide continuous Rapid Refresh (RAP) wind data to the in-flight re-planning system [10]. The Aviation Environmental Design Tool (AEDT) uses a linear interpolation method with the Modern-Era Retrospective analysis for Research and Applications-2 (MERRA-2) wind data to calculate fuel consumption in the simulation environment [11].…”

Section: Fig 3 Gfs Eastward Wind Visualization By Panoplymentioning

confidence: 99%

Supervised Machine Learning-based Wind Prediction to Enable Real-Time Flight Path Planning

Kim

Zhang

Briceño

et al. 2021

AIAA Scitech 2021 Forum

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Many research groups have been committed to developing numerical models for weather forecasts. The models are currently used to predict weather patterns and trends in the aviation industry. In particular, pilots receive wind information predicted by the models and use the forecast to not only calculate how much fuel is needed for a flight but also optimize flight routes by seeking favorable winds. One potential issue is that the models provide relatively coarse wind information in both space and time, which potentially leads to inaccurate calculation of fuel consumption. This research aims to yield a continuous wind prediction model by combining a supervised learning algorithm with the Inverse Distance Weighting technique. Specifically, this research compares three different supervised learning algorithms that include Gaussian Process, Multi-Layer Perceptron, and Support Vector Machine to identify the most appropriate algorithm. The selected algorithm is then compared to a linear interpolation method that is widely used in current flight planning systems for obtaining continuous wind information. A case study is performed with the real Delta Airlines flight 1944 to evaluate the proposed methodology. The results show that 1) the Support Vector Machine provides a better wind prediction compared to the other models, 2) the supervised learning-based regression method performs better than the linear interpolation method in wind predictions, and 3) there are 16 seconds of difference between the real flight (12,117 seconds) and the simulated flight (12,101 seconds) for the cruise portion, indicating that the proposed methodology generates valid results as long as input wind data is provided accurately.

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“…A review of the available atmospheric data, their sources and their integration in a prototype route optimisation tool developed by NASA, called the Traffic Aware Planner (TAP) application, is presented in ref. [45].…”

Section: Introductionmentioning

confidence: 99%

New flight plan optimisation method utilising a set of alternative final point arrival time targets (RTA constraints)

Dancila

Botez

2021

Aeronaut. j.

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This study investigates a new aircraft flight trajectory optimisation method, derived from the Non-dominated Sorting Genetic Algorithm II method used for multi-objective optimisations. The new method determines, in parallel, a set of optimal flight plan solutions for a flight. Each solution is optimal (requires minimum fuel) for a Required Time of Arrival constraint from a set of candidate time constraints selected for the final waypoint of the flight section under optimisation. The set of candidate time constraints is chosen so that their bounds are contiguous, i.e. they completely cover a selected time domain. The proposed flight trajectory optimisation method may be applied in future operational paradigms, such as Trajectory-Based Operations/free flight, where aircraft do not need to follow predetermined routes. The intended application of the proposed method is to support Decision Makers in the planning phase when there is a time constraint or a preferred crossing time at the final point of the flight section under optimisation. The Decision Makers can select, from the set of optimal flight plans, the one that best fits their criteria (minimum fuel burn or observes a selected time constraint). If the Air Traffic Management system rejects the flight plan, then they can choose the next best solution from the set without having to perform another optimisation. The method applies for optimisations performed on lateral and/or vertical flight plan components. Seven proposed method variants were evaluated, and ten test runs were performed for each variant. For five variants, the worst results yielded a fuel burn less than 90kg (0.14%) over the ‘global’ optimum. The worst variant yielded a maximum of 321kg (0.56%) over the ‘global’ optimum.

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Weather Design Considerations for the TASAR Traffic Aware Planner

Cited by 7 publications

References 12 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

Supervised Machine Learning-based Wind Prediction to Enable Real-Time Flight Path Planning

New flight plan optimisation method utilising a set of alternative final point arrival time targets (RTA constraints)

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