IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i13p3400-d379423.html
   My bibliography  Save this article

An Inverse Design Method for Airfoils Based on Pressure Gradient Distribution

Author

Listed:
  • Yufei Zhang

    (School of Aerospace Engineering, Tsinghua University, Beijing 100084, China)

  • Chongyang Yan

    (School of Aerospace Engineering, Tsinghua University, Beijing 100084, China)

  • Haixin Chen

    (School of Aerospace Engineering, Tsinghua University, Beijing 100084, China)

Abstract

An airfoil inverse design method is proposed by using the pressure gradient distribution as the design target. The adjoint method is used to compute the derivatives of the design target. A combination of the weighted drag coefficient and the target dimensionless pressure gradient is applied as the optimization objective, while the lift coefficient is considered as a constraint. The advantage of this method is that the designer can sketch a rough expectation of the pressure distribution pattern rather than a precise pressure coefficient under a certain lift coefficient and Mach number, which can greatly reduce the design iteration in the initial stage of the design process. Multiple solutions can be obtained under different objective weights. The feasibility of the method is validated by a supercritical airfoil and a supercritical natural laminar flow airfoil, which are designed based on the target pressure gradients on the airfoils. Eight supercritical airfoils are designed under different upper surface pressure gradients. The drag creep and drag divergence characteristics of the airfoils are numerically tested. The shockfree airfoil demonstrates poor performance because of a high suction peak and the double-shock phenomenon. The adverse pressure gradient on the upper surface before the shockwave needs to be less than 0.2 to maintain both good drag creep and drag divergence characteristics.

Suggested Citation

  • Yufei Zhang & Chongyang Yan & Haixin Chen, 2020. "An Inverse Design Method for Airfoils Based on Pressure Gradient Distribution," Energies, MDPI, vol. 13(13), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3400-:d:379423
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/13/3400/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/13/3400/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohammed Abdul Raheem & Prasetyo Edi & Amjad A. Pasha & Mustafa M. Rahman & Khalid A. Juhany, 2019. "Numerical Study of Variable Camber Continuous Trailing Edge Flap at Off-Design Conditions," Energies, MDPI, vol. 12(16), pages 1-22, August.
    2. Hui Tang & Yulong Lei & Xingzhong Li & Ke Gao & Yanli Li, 2020. "Aerodynamic Shape Optimization of a Wavy Airfoil for Ultra-Low Reynolds Number Regime in Gliding Flight," Energies, MDPI, vol. 13(2), pages 1-27, January.
    3. Yufei Zhang & Yuhui Yin, 2019. "Study on Riblet Drag Reduction Considering the Effect of Sweep Angle," Energies, MDPI, vol. 12(17), pages 1-20, September.
    4. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ion Malael & Ioana Octavia Bucur, 2021. "Numerical Evaluation of the Flow around a New Vertical Axis Wind Turbine Concept," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    2. Liyue Wang & Haochen Zhang & Cong Wang & Jun Tao & Xinyue Lan & Gang Sun & Jinzhang Feng, 2024. "A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models," Mathematics, MDPI, vol. 12(10), pages 1-21, May.

    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. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Yang, Lin & Pang, Shujiang & Wang, Xiaoyan & Du, Yi & Huang, Jieyu & Melching, Charles S., 2021. "Optimal allocation of best management practices based on receiving water capacity constraints," Agricultural Water Management, Elsevier, vol. 258(C).
    3. Wu, Jiansong & Zhang, Linlin & Bai, Yiping & Reniers, Genserik, 2022. "A safety investment optimization model for power grid enterprises based on System Dynamics and Bayesian network theory," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Xu, Xiangdong & Qu, Kai & Chen, Anthony & Yang, Chao, 2021. "A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    5. Wang, Yongli & Li, Jiapu & Wang, Shuo & Yang, Jiale & Qi, Chengyuan & Guo, Hongzhen & Liu, Ximei & Zhang, Hongqing, 2020. "Operational optimization of wastewater reuse integrated energy system," Energy, Elsevier, vol. 200(C).
    6. Changyu Zhou & Guohe Huang & Jiapei Chen, 2019. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks," Energies, MDPI, vol. 12(13), pages 1-21, June.
    7. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    8. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    9. Yuhong Shuai & Liming Yao, 2021. "Adjustable Robust Optimization for Multi-Period Water Allocation in Droughts Under Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4043-4065, September.
    10. S. Arunvinthan & V.S. Raatan & S. Nadaraja Pillai & Amjad A. Pasha & M. M. Rahman & Khalid A. Juhany, 2021. "Aerodynamic Characteristics of Shark Scale-Based Vortex Generators upon Symmetrical Airfoil," Energies, MDPI, vol. 14(7), pages 1-22, March.
    11. Yinhe Bu & Xingping Zhang, 2021. "On the Way to Integrate Increasing Shares of Variable Renewables in China: Experience from Flexibility Modification and Deep Peak Regulation Ancillary Service Market Based on MILP-UC Programming," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    12. Donovin D. Lewis & Aron Patrick & Evan S. Jones & Rosemary E. Alden & Abdullah Al Hadi & Malcolm D. McCulloch & Dan M. Ionel, 2023. "Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study," Energies, MDPI, vol. 16(4), pages 1-23, February.
    13. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    14. Xu, Jie & Lv, Tao & Hou, Xiaoran & Deng, Xu & Liu, Feng, 2021. "Provincial allocation of renewable portfolio standard in China based on efficiency and fairness principles," Renewable Energy, Elsevier, vol. 179(C), pages 1233-1245.
    15. Qiaohua Fang & Xuezhe Wei & Haifeng Dai, 2019. "A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency," Energies, MDPI, vol. 12(6), pages 1-24, March.
    16. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    17. Ming Zhang & Qianwen Huang & Sihan Liu & Huiying Li, 2019. "Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment," Sustainability, MDPI, vol. 11(23), pages 1-27, November.
    18. Ruidi Chen & Ioannis Ch. Paschalidis, 2022. "Robust Grouped Variable Selection Using Distributionally Robust Optimization," Journal of Optimization Theory and Applications, Springer, vol. 194(3), pages 1042-1071, September.
    19. Darya Pyatkina & Tamara Shcherbina & Vadim Samusenkov & Irina Razinkina & Mariusz Sroka, 2021. "Modeling and Management of Power Supply Enterprises’ Cash Flows," Energies, MDPI, vol. 14(4), pages 1-17, February.
    20. Liu, Dunnan & Xiao, Bowen, 2018. "Exploring the development of electric vehicles under policy incentives: A scenario-based system dynamics model," Energy Policy, Elsevier, vol. 120(C), pages 8-23.

    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:gam:jeners:v:13:y:2020:i:13:p:3400-:d:379423. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.