A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions
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....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/7/627 |
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| author | Howard Peng Jirat Bhanpato Ameya Behere Dimitri N. Mavris |
| author_facet | Howard Peng Jirat Bhanpato Ameya Behere Dimitri N. Mavris |
| author_sort | Howard Peng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T01:24:57Z |
| format | Article |
| id | doaj.art-e2e75d4fc32545249dddc5b817727b99 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T01:24:57Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-e2e75d4fc32545249dddc5b817727b992023-11-18T17:50:58ZengMDPI AGAerospace2226-43102023-07-0110762710.3390/aerospace10070627A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating ConditionsHoward Peng0Jirat Bhanpato1Ameya Behere2Dimitri N. Mavris3Aerospace Systems Design Laboratory, Georgia Institute of Technology, Atlanta, GA 30332-0150, USAAerospace Systems Design Laboratory, Georgia Institute of Technology, Atlanta, GA 30332-0150, USAAerospace Systems Design Laboratory, Georgia Institute of Technology, Atlanta, GA 30332-0150, USAAerospace Systems Design Laboratory, Georgia Institute of Technology, Atlanta, GA 30332-0150, USAAviation 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.https://www.mdpi.com/2226-4310/10/7/627aviation noise modelingaviation noise mitigationcommunity noise exposuredeparture modelingsustainable aviationmodel order reduction |
| spellingShingle | Howard Peng Jirat Bhanpato Ameya Behere Dimitri N. Mavris A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions Aerospace aviation noise modeling aviation noise mitigation community noise exposure departure modeling sustainable aviation model order reduction |
| title | A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions |
| title_full | A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions |
| title_fullStr | A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions |
| title_full_unstemmed | A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions |
| title_short | A Rapid Surrogate Model for Estimating Aviation Noise Impact across Various Departure Profiles and Operating Conditions |
| title_sort | rapid surrogate model for estimating aviation noise impact across various departure profiles and operating conditions |
| topic | aviation noise modeling aviation noise mitigation community noise exposure departure modeling sustainable aviation model order reduction |
| url | https://www.mdpi.com/2226-4310/10/7/627 |
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