Relative Importance of Parameters in Departure Procedure Design for LTO Noise, Emission, and Fuel Burn Minimization

Behere, Ameya; Kirby, Michelle; Mavris, Dimitri N.

doi:10.2514/6.2022-3916

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Accounting for the weather and elevation is important in aviation noise modeling and optimization [15]. The ambient temperature, for example, is one of the most dominant factors when modeling aviation noise [16,17].…”

Section: Aviation Noise-influencing Factorsmentioning

confidence: 99%

A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions

et al. 2023

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Aviation noise remains a key barrier to the sustainable growth of commercial aviation. The advent of emerging technologies, such as urban air mobility, and the renewed interest in commercial supersonic transport aircraft, has only further raised concerns over the resultant community noise exposure. The foundation of any noise mitigation effort is the ability to accurately model noise metrics over a wide range of scenarios. Aviation noise is influenced by a wide variety of factors, including aircraft type, payload weight, thrust settings, airport elevation, ambient weather, and flight trajectory. Traditional noise modeling paradigms rely on physics-based and empirical calculations, which are computationally expensive. Attempts at speeding up the computations with alternate models could deliver on speed or accuracy, but not both. Recent research has indicated that model order reduction techniques hold promise for transforming and greatly reducing the number of quantities that need to be modeled. Paired with surrogate modeling techniques, a rapid and accurate noise model can be generated. The research presented in this manuscript expands on the model order reduction method and develops a rapid noise surrogate model, which can account for the piloting actions, the ambient temperature, and airport elevation. The presented results indicate that the method works well with minimal error for most modeling scenarios. The results also outline avenues for improvement, such as using a different class of surrogate models or modeling additional training cases. The model developed in this research has numerous applications for multi-query applications, such as parametric trade-off analyses and optimization studies. With the inclusion of airport and aircraft parameters, the model enables the development of frameworks that optimize piloting actions for noise mitigation on the ground.

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Section: Aviation Noise-influencing Factorsmentioning

confidence: 99%

A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions

et al. 2023

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“…The Aviation Environmental Design Tool (AEDT) has been a cornerstone in aviation environmental impact assessment in the U.S., particularly at airports where The Aviation Environmental Design Tool (AEDT) has been a cornerstone in aviation environmental impact assessment in the U.S., particularly at airports where comprehensive aircraft operation and fleet mix data are readily available [11,12,17]. Researchers have explored strategies for optimizing aircraft departure and arrival procedures to enhance aviation environmental impact assessments, heavily relying on AEDT [18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Notably, Behere et al [30] pioneered a data-driven approach to refining noise assessments using AEDT and openly accessible surveillance data.…”

Section: Introductionmentioning

confidence: 99%

An Experimental and Analytical Approach to Evaluate Transponder-Based Aircraft Noise Monitoring Technology

Yang,

Mott

2024

Aerospace

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Aviation is a vital modern transportation sector connecting millions of passengers globally. Sustainable aviation development holds substantial community benefits, necessitating effective management of its environmental impacts. This paper addresses the need for an accurate and cost-effective aircraft noise monitoring model tailored to non-towered general aviation airports with limited resources for official air traffic data collection. The existing literature highlights a heavy reliance on air traffic data from control facilities in prevailing aircraft noise modeling solutions, revealing a disparity between real-world constraints and optimal practices. Our study presents a validation of a three-stage framework centered on a low-cost transponder unit, employing an innovative experimental and analytical approach to assess the model’s accuracy. An economical Automatic Dependent Surveillance-broadcast (ADS-B) receiver is deployed at Purdue University Airport (ICAO Code: KLAF) to estimate aircraft noise levels using the developed approach. Simultaneously, a physical sound meter is positioned at KLAF to capture actual acoustic noise levels, facilitating a direct comparison with the modeled data. Results demonstrate that the developed noise model accurately identifies aircraft noise events with an average error of 4.50 dBA. This suggests the viability of our low-cost noise monitoring approach as an affordable solution for non-towered general aviation airports. In addition, this paper discusses the limitations and recommendations for future research.

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“…Noise impacts are dependent on performance variations as well as weather conditions [17][18][19]. Noise can either be reduced or redistributed away from the community by varying performance parameters as a proxy for piloting commands [20].…”

Section: Introductionmentioning

confidence: 99%

Takeoff Ground Roll Analysis of Real-World Operations for Improved Noise Modeling

Bhanpato

Behere

Kirby

et al. 2023

AIAA SCITECH 2023 Forum

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The ability to quantify aviation environmental impacts accurately is one of the key enablers for sustainable aviation growth. The Aviation Environmental Design Tool (AEDT) offers the capability to model aircraft operations and quantify the associated environmental impacts in terms of emissions and noise metrics. In a departure, the takeoff ground roll segment has a large contribution to the overall noise footprint from the operation. Discrepancies between modeling assumptions and real-world operations during takeoff ground roll not only lead to inaccuracies in peak noise levels but also affect noise contours throughout the downstream departure operation. This study investigates the modeling assumptions for B737-800 and B737-900 takeoff ground roll at Hartsfield-Jackson Atlanta International Airport (KATL) using high-fidelity real-world flight and surface weather data. Aircraft trajectory, takeoff weight, thrust, and weather conditions are extracted from real-world operations for modeling. This study quantifies the performance and noise differences arising from real-world variability during takeoff ground roll. Additionally, the sensitivity of noise results to inaccuracies during takeoff ground roll is investigated. The outcome of this study is the identification of sources of noise differences arising from modeling assumptions and their relative importance for accurate representation of real-world operations, thereby enabling more accurate noise modeling.

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Relative Importance of Parameters in Departure Procedure Design for LTO Noise, Emission, and Fuel Burn Minimization

Cited by 3 publications

References 17 publications

A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions

A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions

An Experimental and Analytical Approach to Evaluate Transponder-Based Aircraft Noise Monitoring Technology

Takeoff Ground Roll Analysis of Real-World Operations for Improved Noise Modeling

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