Zobraziť v EDS

CNN-LSTM Assisted Multi-Objective Aerodynamic Optimization Method for Low-Reynolds-Number Micro-UAV Airfoils.

Uložené v:
Podrobná bibliografia
Názov: CNN-LSTM Assisted Multi-Objective Aerodynamic Optimization Method for Low-Reynolds-Number Micro-UAV Airfoils.
Autori: Peng, Jinzhao, Li, Enying, Wang, Hu
Zdroj: Aerospace (MDPI Publishing); Jan2026, Vol. 13 Issue 1, p78, 36p
Predmety: COMPUTATIONAL fluid dynamics, AEROFOILS, LONG short-term memory, REYNOLDS number, COMPUTATIONAL aerodynamics, MULTI-objective optimization, MICRO air vehicles, SURROGATE-based optimization
Abstrakt: The optimization of low-Reynolds-number airfoils for micro unmanned aerial vehicles (UAVs) is challenging due to strong geometric nonlinearities, tight endurance requirements, and the need to maintain performance across multiple operating conditions. Classical surrogate-assisted optimization (SAO) methods combined with genetic algorithms become increasingly expensive and less reliable when class–shape transformation (CST)-based geometries are coupled with several flight conditions. Although deep learning surrogates have higher expressive power, their use in this context is often limited by insufficient local feature extraction, weak adaptation to changes in operating conditions, and a lack of robustness analysis. In this study, we construct a task-specific convolutional neural network–long short-term memory (CNN–LSTM) surrogate that jointly predicts the power factor, lift, and drag coefficients at three representative operating conditions (cruise, forward flight, and maneuver) for the same CST-parameterized airfoil and integrate it into an Non-dominated Sorting Genetic Algorithm II (NSGA-II)-based three-objective optimization framework. The CNN encoder captures local geometric sensitivities, while the LSTM aggregates dependencies across operating conditions, forming a compact encoder–aggregator tailored to low-Re micro-UAV design. Trained on a computational fluid dynamics (CFD) dataset from a validated SD7032-based pipeline, the proposed surrogate achieves substantially lower prediction errors than several fully connected and single-condition baselines and maintains more favorable error distributions on CST-family parameter-range extrapolation samples (±40%, geometry-valid) under the same CFD setup, while being about three orders of magnitude faster than conventional CFD during inference. When embedded in NSGA-II under thickness and pitching-moment constraints, the surrogate enables efficient exploration of the design space and yields an optimized airfoil that simultaneously improves power factor, reduces drag, and increases lift compared with the baseline SD7032. This work therefore contributes a three-condition surrogate–optimizer workflow and physically interpretable low-Re micro-UAV design insights, rather than introducing a new generic learning or optimization algorithm. [ABSTRACT FROM AUTHOR]
Copyright of Aerospace (MDPI Publishing) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáza: Complementary Index
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebscohost.com/openurl?sid=EBSCO:edb&genre=article&issn=22264310&ISBN=&volume=13&issue=1&date=20260101&spage=78&pages=78-113&title=Aerospace (MDPI Publishing)&atitle=CNN-LSTM%20Assisted%20Multi-Objective%20Aerodynamic%20Optimization%20Method%20for%20Low-Reynolds-Number%20Micro-UAV%20Airfoils.&aulast=Peng%2C%20Jinzhao&id=DOI:10.3390/aerospace13010078
    Name: Full Text Finder
    Category: fullText
    Text: Full Text Finder
    Icon: https://imageserver.ebscohost.com/branding/images/FTF.gif
    MouseOverText: Full Text Finder
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Peng%20J
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edb
DbLabel: Complementary Index
An: 191215744
RelevancyScore: 1082
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1082.40466308594
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: CNN-LSTM Assisted Multi-Objective Aerodynamic Optimization Method for Low-Reynolds-Number Micro-UAV Airfoils.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Peng%2C+Jinzhao%22">Peng, Jinzhao</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Enying%22">Li, Enying</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Hu%22">Wang, Hu</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Aerospace (MDPI Publishing); Jan2026, Vol. 13 Issue 1, p78, 36p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22COMPUTATIONAL+fluid+dynamics%22">COMPUTATIONAL fluid dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22AEROFOILS%22">AEROFOILS</searchLink><br /><searchLink fieldCode="DE" term="%22LONG+short-term+memory%22">LONG short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22REYNOLDS+number%22">REYNOLDS number</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTATIONAL+aerodynamics%22">COMPUTATIONAL aerodynamics</searchLink><br /><searchLink fieldCode="DE" term="%22MULTI-objective+optimization%22">MULTI-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22MICRO+air+vehicles%22">MICRO air vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22SURROGATE-based+optimization%22">SURROGATE-based optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The optimization of low-Reynolds-number airfoils for micro unmanned aerial vehicles (UAVs) is challenging due to strong geometric nonlinearities, tight endurance requirements, and the need to maintain performance across multiple operating conditions. Classical surrogate-assisted optimization (SAO) methods combined with genetic algorithms become increasingly expensive and less reliable when class–shape transformation (CST)-based geometries are coupled with several flight conditions. Although deep learning surrogates have higher expressive power, their use in this context is often limited by insufficient local feature extraction, weak adaptation to changes in operating conditions, and a lack of robustness analysis. In this study, we construct a task-specific convolutional neural network–long short-term memory (CNN–LSTM) surrogate that jointly predicts the power factor, lift, and drag coefficients at three representative operating conditions (cruise, forward flight, and maneuver) for the same CST-parameterized airfoil and integrate it into an Non-dominated Sorting Genetic Algorithm II (NSGA-II)-based three-objective optimization framework. The CNN encoder captures local geometric sensitivities, while the LSTM aggregates dependencies across operating conditions, forming a compact encoder–aggregator tailored to low-Re micro-UAV design. Trained on a computational fluid dynamics (CFD) dataset from a validated SD7032-based pipeline, the proposed surrogate achieves substantially lower prediction errors than several fully connected and single-condition baselines and maintains more favorable error distributions on CST-family parameter-range extrapolation samples (±40%, geometry-valid) under the same CFD setup, while being about three orders of magnitude faster than conventional CFD during inference. When embedded in NSGA-II under thickness and pitching-moment constraints, the surrogate enables efficient exploration of the design space and yields an optimized airfoil that simultaneously improves power factor, reduces drag, and increases lift compared with the baseline SD7032. This work therefore contributes a three-condition surrogate–optimizer workflow and physically interpretable low-Re micro-UAV design insights, rather than introducing a new generic learning or optimization algorithm. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Aerospace (MDPI Publishing) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=191215744
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/aerospace13010078
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 36
        StartPage: 78
    Subjects:
      – SubjectFull: COMPUTATIONAL fluid dynamics
        Type: general
      – SubjectFull: AEROFOILS
        Type: general
      – SubjectFull: LONG short-term memory
        Type: general
      – SubjectFull: REYNOLDS number
        Type: general
      – SubjectFull: COMPUTATIONAL aerodynamics
        Type: general
      – SubjectFull: MULTI-objective optimization
        Type: general
      – SubjectFull: MICRO air vehicles
        Type: general
      – SubjectFull: SURROGATE-based optimization
        Type: general
    Titles:
      – TitleFull: CNN-LSTM Assisted Multi-Objective Aerodynamic Optimization Method for Low-Reynolds-Number Micro-UAV Airfoils.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Peng, Jinzhao
      – PersonEntity:
          Name:
            NameFull: Li, Enying
      – PersonEntity:
          Name:
            NameFull: Wang, Hu
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Text: Jan2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 22264310
          Numbering:
            – Type: volume
              Value: 13
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Aerospace (MDPI Publishing)
              Type: main
ResultId 1