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Robust optimization design of a flying wing using adjoint and uncertainty-based aerodynamic optimization approach

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Abstract

Robust optimization design is significant and urgently required for the fly wings, owing to its unique characteristics. However, there is a lack of efficient tools for performing shape optimization which considers multiple uncertainties. This is in part because implementing robust design in the widely used and very efficient adjoint-based optimization method is challenging. This paper addresses this need by developing an uncertainty-based optimization design framework where the gradient-enhanced polynomial chaos expansion and discrete, adjoint-based optimization framework are coupled to perform shape optimization under multiple uncertainties. The gradient information from adjoint equation is applied to improve the computation efficiency. The objective function is the statistic moment, consisting of mean and standard deviation. The gradients of the statistic moment are computed using the adjoint-based system and reconstructing a regression algorithm. A flying wing configuration with deterministic and two uncertainty-based optimizations is performed. The first uncertainty-based optimization considers flight conditions, Mach and angle of attack, and the second one added the planform uncertainty parameters, i.e., inner and outer wing sweep angle. The uncertainty-based optimizations gain reductions of statistic moments by 8.58% and 5.3%, respectively. Compared with the deterministic optimization, the uncertainty-based optimizations behave much better in robustness but sacrifice a small aerodynamic performance. The successful uncertainty-based optimization enables acceptable risks of fly wing design in the development process and indicates that our established framework can be applied for future aircraft robust optimization design.

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Notes

  1. https://github.com/mdolab/adflow.

  2. https://github.com/mdolab/pygeo.

  3. https://github.com/mdolab/idwarp.

  4. https://github.com/mdolab/pyoptsparse.

  5. https://github.com/mdolab/adflow.

  6. https://github.com/mdolab/pyhyp.

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Acknowledgements

This work was supported by Postdoctoral Research Foundation of China under grant number 2021M692569 and the National Natural Science Foundation of China under grant number 12002284 and 11902320.

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Authors and Affiliations

  1. School of Aerospace Engineering, Xi’an JiaoTong University, Xi’an, 710049, China

    Yayun Shi

  2. Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000, China

    Qinsheng Lan

  3. School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, China

    Xiayu Lan & Tihao Yang

  4. Research Development Department, China Academy of Launch Vehicle Technology, Beijing, 100076, China

    Jianhui Wu

  5. Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing, 100190, China

    Bo Wang

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  1. Yayun Shi
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  2. Qinsheng Lan
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  3. Xiayu Lan
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  4. Jianhui Wu
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  5. Tihao Yang
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  6. Bo Wang
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Correspondence to Bo Wang.

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Shi, Y., Lan, Q., Lan, X. et al. Robust optimization design of a flying wing using adjoint and uncertainty-based aerodynamic optimization approach. Struct Multidisc Optim 66, 110 (2023). https://doi.org/10.1007/s00158-023-03559-z

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