IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v352y2023ics0306261923013363.html
   My bibliography  Save this article

Robust optimization and uncertainty quantification of a micro axial compressor for unmanned aerial vehicles

Author

Listed:
  • Cheng, Hongzhi
  • Li, Ziliang
  • Duan, Penghao
  • Lu, Xingen
  • Zhao, Shengfeng
  • Zhang, Yanfeng

Abstract

Axial compressors are susceptible to uncertainties during their manufacturing and operation, resulting in reduced efficiency and performance dispersion. However, uncertainty quantification and robust design of compressors remains challenging due to the complexity of structure and internal flow. In this study, an automated framework for uncertainty quantification and robustness optimization of micro axial compressors is proposed. Ten geometrical uncertainties are propagated for the nominal design point and two off-design points, i.e., near stall and choke conditions, respectively. The main objective of this paper is to optimize the aerodynamic robustness performance at these operating points. The sparse grid-based probabilistic collocation method is used to propagate these uncertainties, and a multi-objective genetic algorithm is employed to perform robust optimization based on a novel constructed surrogate model.

Suggested Citation

  • Cheng, Hongzhi & Li, Ziliang & Duan, Penghao & Lu, Xingen & Zhao, Shengfeng & Zhang, Yanfeng, 2023. "Robust optimization and uncertainty quantification of a micro axial compressor for unmanned aerial vehicles," Applied Energy, Elsevier, vol. 352(C).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923013363
    DOI: 10.1016/j.apenergy.2023.121972
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923013363
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121972?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ekradi, Khalil & Madadi, Ali, 2020. "Performance improvement of a transonic centrifugal compressor impeller with splitter blade by three-dimensional optimization," Energy, Elsevier, vol. 201(C).
    2. Guan, Jin & Lv, Xiaojing & Spataru, Catalina & Weng, Yiwu, 2021. "Experimental and numerical study on self-sustaining performance of a 30-kW micro gas turbine generator system during startup process," Energy, Elsevier, vol. 236(C).
    3. Tang, Xinzi & Wang, Zhe & Xiao, Peng & Peng, Ruitao & Liu, Xiongwei, 2020. "Uncertainty quantification based optimization of centrifugal compressor impeller for aerodynamic robustness under stochastic operational conditions," Energy, Elsevier, vol. 195(C).
    4. Tola, Vittorio & Lonis, Francesco, 2021. "Low CO2 emissions chemically recuperated gas turbines fed by renewable methanol," Applied Energy, Elsevier, vol. 298(C).
    5. Zhao, Yongning & Xu, Xiandong & Qadrdan, Meysam & Wu, Jianzhong, 2021. "Optimal operation of compressor units in gas networks to provide flexibility to power systems," Applied Energy, Elsevier, vol. 290(C).
    6. Li, Jinxing & Liu, Tianyuan & Wang, Yuqi & Xie, Yonghui, 2022. "Integrated graph deep learning framework for flow field reconstruction and performance prediction of turbomachinery," Energy, Elsevier, vol. 254(PC).
    7. Wu, Xianguo & Feng, Zongbao & Chen, Hongyu & Qin, Yawei & Zheng, Shiyi & Wang, Lei & Liu, Yang & Skibniewski, Miroslaw J., 2022. "Intelligent optimization framework of near zero energy consumption building performance based on a hybrid machine learning algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    8. Thomazoni, André Luis Ribeiro & Ermel, Conrado & Schneider, Paulo Smith & Vieira, Lara Werncke & Hunt, Julian David & Ferreira, Sandro Barros & Rech, Charles & Gouvêa, Vinicius Santorum, 2022. "Influence of operational parameters on the performance of Tesla turbines: Experimental investigation of a small-scale turbine," Energy, Elsevier, vol. 261(PB).
    9. Taimoor Akhtar & Christine Shoemaker, 2016. "Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection," Journal of Global Optimization, Springer, vol. 64(1), pages 17-32, January.
    10. Cheng, Hongzhi & Zhou, Chuangxin & Lu, Xingen & Zhao, Shengfeng & Han, Ge & Yang, Chengwu, 2023. "Robust aerodynamic optimization and design exploration of a wide-chord transonic fan under geometric and operational uncertainties," Energy, Elsevier, vol. 278(PB).
    11. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    12. Kim, Min Jae & Kim, Jeong Ho & Kim, Tong Seop, 2018. "The effects of internal leakage on the performance of a micro gas turbine," Applied Energy, Elsevier, vol. 212(C), pages 175-184.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cheng, Hongzhi & Zhou, Chuangxin & Lu, Xingen & Zhao, Shengfeng & Han, Ge & Yang, Chengwu, 2023. "Robust aerodynamic optimization and design exploration of a wide-chord transonic fan under geometric and operational uncertainties," Energy, Elsevier, vol. 278(PB).
    2. Li, Jinxing & Liu, Tianyuan & Zhu, Guangya & Li, Yunzhu & Xie, Yonghui, 2023. "Uncertainty quantification and aerodynamic robust optimization of turbomachinery based on graph learning methods," Energy, Elsevier, vol. 273(C).
    3. Juliane Müller, 2017. "SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems," INFORMS Journal on Computing, INFORMS, vol. 29(4), pages 581-596, November.
    4. Zhao, Yincheng & Zhang, Guozhou & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2023. "Meta-learning based voltage control strategy for emergency faults of active distribution networks," Applied Energy, Elsevier, vol. 349(C).
    5. Wenjiao Qi & Qinghua Deng & Yu Jiang & Qi Yuan & Zhenping Feng, 2018. "Disc Thickness and Spacing Distance Impacts on Flow Characteristics of Multichannel Tesla Turbines," Energies, MDPI, vol. 12(1), pages 1-25, December.
    6. Marco Bicchi & Michele Marconcini & Ernani Fulvio Bellobuono & Elisabetta Belardini & Lorenzo Toni & Andrea Arnone, 2023. "Multi-Point Surrogate-Based Approach for Assessing Impacts of Geometric Variations on Centrifugal Compressor Performance," Energies, MDPI, vol. 16(4), pages 1-21, February.
    7. Roberta De Robbio, 2023. "Micro Gas Turbine Role in Distributed Generation with Renewable Energy Sources," Energies, MDPI, vol. 16(2), pages 1-37, January.
    8. Rodriguez-Roman, Daniel & Ritchie, Stephen G., 2020. "Surrogate-based optimization for multi-objective toll design problems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 485-503.
    9. Włodarski, Wojciech, 2019. "A model development and experimental verification for a vapour microturbine with a permanent magnet synchronous generator," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    10. Wei Li & Jisheng Liu & Pengcheng Fang & Jinxin Cheng, 2021. "A Novel Surface Parameterization Method for Optimizing Radial Impeller Design in Fuel Cell System," Energies, MDPI, vol. 14(9), pages 1-25, May.
    11. Tilocca, Giuseppe & Sánchez, David & Torres-García, Miguel, 2023. "Application of the theory of constraints to unveil the root causes of the limited market penetration of micro gas turbine systems," Energy, Elsevier, vol. 278(C).
    12. Wang, Xusheng & Lv, Xiaojing & Mi, Xicong & Spataru, Catalina & Weng, Yiwu, 2022. "Coordinated control approach for load following operation of SOFC-GT hybrid system," Energy, Elsevier, vol. 248(C).
    13. Chen, Siliang & Ge, Wei & Liang, Xinbin & Jin, Xinqiao & Du, Zhimin, 2024. "Lifelong learning with deep conditional generative replay for dynamic and adaptive modeling towards net zero emissions target in building energy system," Applied Energy, Elsevier, vol. 353(PB).
    14. Wenxiang Zhou & Sangwei Lu & Wenjie Kai & Jichang Wu & Chenyang Zhang & Feng Lu, 2023. "A Novel Adaptive Generation Method for Initial Guess Values of Component-Level Aero-Engine Start-Up Models," Sustainability, MDPI, vol. 15(4), pages 1-25, February.
    15. Seong Won Moon & Tong Seop Kim, 2020. "Advanced Gas Turbine Control Logic Using Black Box Models for Enhancing Operational Flexibility and Stability," Energies, MDPI, vol. 13(21), pages 1-23, October.
    16. Rong Huang & Jimin Ni & Houchuan Fan & Xiuyong Shi & Qiwei Wang, 2023. "Investigating a New Method-Based Internal Joint Operation Law for Optimizing the Performance of a Turbocharger Compressor," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    17. Nicola Menga & Akhila Mothakani & Maria Grazia De Giorgi & Radoslaw Przysowa & Antonio Ficarella, 2022. "Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine," Energies, MDPI, vol. 15(19), pages 1-22, October.
    18. Kiaee, Mehrdad & Tousi, A.M., 2021. "Vector-based deterioration index for gas turbine gas-path prognostics modeling framework," Energy, Elsevier, vol. 216(C).
    19. Rodriguez-Pastor, D.A. & Garcia-Guzman, A. & Marqués-Valderrama, I. & Ortiz, C. & Carvajal, E. & Becerra, J.A. & Soltero, V.M. & Chacartegui, R., 2024. "A flexible methanol-to-methane thermochemical energy storage system (TCES) for gas turbine (GT) power production," Applied Energy, Elsevier, vol. 356(C).
    20. Janusz Kotowicz & Mateusz Brzęczek & Aleksandra Walewska & Kamila Szykowska, 2022. "Methanol Production in the Brayton Cycle," Energies, MDPI, vol. 15(4), pages 1-14, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923013363. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.