An Adaptive Sequential Sampling Strategy-Based Multi-Objective Optimization of Aerodynamic Configuration for a Tandem-Wing UAV via a Surrogate Model
Multi-objective optimization of aerodynamic configuration for a tandem-wing unmanned aerial vehicle (UAV) via a surrogate model is appropriate in the primary stages of aircraft design. This study presents an adaptive sequential sampling strategy, which takes into account the principle of entropy ran...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9635761/ |
| _version_ | 1819004834520498176 |
|---|---|
| author | Qingli Shi Hua Wang Hao Cheng Feng Cheng Menglong Wang |
| author_facet | Qingli Shi Hua Wang Hao Cheng Feng Cheng Menglong Wang |
| author_sort | Qingli Shi |
| collection | DOAJ |
| description | Multi-objective optimization of aerodynamic configuration for a tandem-wing unmanned aerial vehicle (UAV) via a surrogate model is appropriate in the primary stages of aircraft design. This study presents an adaptive sequential sampling strategy, which takes into account the principle of entropy rank and selection pooling based on a sigmoid function (ESP), in order to save time and construct a surrogate model database with considerable approximation accuracy. The entire procedure of optimization is divided into four parts, involving problem formulation for design variables and objectives, database construction for the surrogate model, multi-objective optimization with the surrogate models, and ESP adaptive sequential sampling to update the database. Firstly, a comparative study of the different surrogate models is carried out to assess their approximation performance. This verifies that the radial basis function (RBF) surrogate model outperforms the other models across the board. Then, we conduct two tests with typical mathematical problems to validate the effectiveness and applicability of the proposed method. We also develop a multi-objective optimization of the aerodynamic configuration for a tandem-wing UAV, aiming to maximize the lifting coefficient at the ascent (<inline-formula> <tex-math notation="LaTeX">$C_{Lascent}$ </tex-math></inline-formula>) and the lift-drag ratio (<inline-formula> <tex-math notation="LaTeX">$K_{cruise}$ </tex-math></inline-formula>) during the cruise. In this case, the RBF surrogate model is proven more suitable than the other common methods to replace the real values calculated by the non-planar vortex-lattice method (VLM) during the process of optimization. Furthermore, a comparison with large minimal distance (LMD) sequential sampling and disposable Latin hypercube sampling (LHS) is carried out alongside the optimization. These results show that the approximation precision achieved using ESP strategy is greater, highlighting the superiority of the ESP adaptive sequential sampling strategy in reducing the number of samples and raising the approximation accuracy. Finally, after the refinement of the database, an optimal Pareto front set is obtained to guide the primary design of the aerodynamic configuration for the tandem-wing UAV. Then, it is verified that the selected trade-off optimal design point has a better aerodynamic performance than the initial reference point, improving <inline-formula> <tex-math notation="LaTeX">$C_{Lascent}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$K_{cruise}$ </tex-math></inline-formula> by 6.44% and 10.85%, respectively. |
| first_indexed | 2024-12-20T23:43:12Z |
| format | Article |
| id | doaj.art-9ccb86e9ffad4905a1429f1d307e1957 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T23:43:12Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9ccb86e9ffad4905a1429f1d307e19572022-12-21T19:23:02ZengIEEEIEEE Access2169-35362021-01-01916413116414710.1109/ACCESS.2021.31327759635761An Adaptive Sequential Sampling Strategy-Based Multi-Objective Optimization of Aerodynamic Configuration for a Tandem-Wing UAV via a Surrogate ModelQingli Shi0https://orcid.org/0000-0002-7801-5670Hua Wang1Hao Cheng2https://orcid.org/0000-0003-2166-6867Feng Cheng3https://orcid.org/0000-0003-1513-512XMenglong Wang4https://orcid.org/0000-0001-6613-8094School of Astronautics, Beihang University, Beijing, ChinaSchool of Astronautics, Beihang University, Beijing, ChinaSchool of Astronautics, Beihang University, Beijing, ChinaChina Academy of Launch Vehicle Technology, Beijing, ChinaBeijing Electro-Mechanical Engineering Institute, Beijing, ChinaMulti-objective optimization of aerodynamic configuration for a tandem-wing unmanned aerial vehicle (UAV) via a surrogate model is appropriate in the primary stages of aircraft design. This study presents an adaptive sequential sampling strategy, which takes into account the principle of entropy rank and selection pooling based on a sigmoid function (ESP), in order to save time and construct a surrogate model database with considerable approximation accuracy. The entire procedure of optimization is divided into four parts, involving problem formulation for design variables and objectives, database construction for the surrogate model, multi-objective optimization with the surrogate models, and ESP adaptive sequential sampling to update the database. Firstly, a comparative study of the different surrogate models is carried out to assess their approximation performance. This verifies that the radial basis function (RBF) surrogate model outperforms the other models across the board. Then, we conduct two tests with typical mathematical problems to validate the effectiveness and applicability of the proposed method. We also develop a multi-objective optimization of the aerodynamic configuration for a tandem-wing UAV, aiming to maximize the lifting coefficient at the ascent (<inline-formula> <tex-math notation="LaTeX">$C_{Lascent}$ </tex-math></inline-formula>) and the lift-drag ratio (<inline-formula> <tex-math notation="LaTeX">$K_{cruise}$ </tex-math></inline-formula>) during the cruise. In this case, the RBF surrogate model is proven more suitable than the other common methods to replace the real values calculated by the non-planar vortex-lattice method (VLM) during the process of optimization. Furthermore, a comparison with large minimal distance (LMD) sequential sampling and disposable Latin hypercube sampling (LHS) is carried out alongside the optimization. These results show that the approximation precision achieved using ESP strategy is greater, highlighting the superiority of the ESP adaptive sequential sampling strategy in reducing the number of samples and raising the approximation accuracy. Finally, after the refinement of the database, an optimal Pareto front set is obtained to guide the primary design of the aerodynamic configuration for the tandem-wing UAV. Then, it is verified that the selected trade-off optimal design point has a better aerodynamic performance than the initial reference point, improving <inline-formula> <tex-math notation="LaTeX">$C_{Lascent}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$K_{cruise}$ </tex-math></inline-formula> by 6.44% and 10.85%, respectively.https://ieeexplore.ieee.org/document/9635761/Aerodynamic configuration optimizationmulti-objectiveradial basis functionadaptive sequential samplingentropy rank and selection pooling |
| spellingShingle | Qingli Shi Hua Wang Hao Cheng Feng Cheng Menglong Wang An Adaptive Sequential Sampling Strategy-Based Multi-Objective Optimization of Aerodynamic Configuration for a Tandem-Wing UAV via a Surrogate Model IEEE Access Aerodynamic configuration optimization multi-objective radial basis function adaptive sequential sampling entropy rank and selection pooling |
| title | An Adaptive Sequential Sampling Strategy-Based Multi-Objective Optimization of Aerodynamic Configuration for a Tandem-Wing UAV via a Surrogate Model |
| title_full | An Adaptive Sequential Sampling Strategy-Based Multi-Objective Optimization of Aerodynamic Configuration for a Tandem-Wing UAV via a Surrogate Model |
| title_fullStr | An Adaptive Sequential Sampling Strategy-Based Multi-Objective Optimization of Aerodynamic Configuration for a Tandem-Wing UAV via a Surrogate Model |
| title_full_unstemmed | An Adaptive Sequential Sampling Strategy-Based Multi-Objective Optimization of Aerodynamic Configuration for a Tandem-Wing UAV via a Surrogate Model |
| title_short | An Adaptive Sequential Sampling Strategy-Based Multi-Objective Optimization of Aerodynamic Configuration for a Tandem-Wing UAV via a Surrogate Model |
| title_sort | adaptive sequential sampling strategy based multi objective optimization of aerodynamic configuration for a tandem wing uav via a surrogate model |
| topic | Aerodynamic configuration optimization multi-objective radial basis function adaptive sequential sampling entropy rank and selection pooling |
| url | https://ieeexplore.ieee.org/document/9635761/ |
| work_keys_str_mv | AT qinglishi anadaptivesequentialsamplingstrategybasedmultiobjectiveoptimizationofaerodynamicconfigurationforatandemwinguavviaasurrogatemodel AT huawang anadaptivesequentialsamplingstrategybasedmultiobjectiveoptimizationofaerodynamicconfigurationforatandemwinguavviaasurrogatemodel AT haocheng anadaptivesequentialsamplingstrategybasedmultiobjectiveoptimizationofaerodynamicconfigurationforatandemwinguavviaasurrogatemodel AT fengcheng anadaptivesequentialsamplingstrategybasedmultiobjectiveoptimizationofaerodynamicconfigurationforatandemwinguavviaasurrogatemodel AT menglongwang anadaptivesequentialsamplingstrategybasedmultiobjectiveoptimizationofaerodynamicconfigurationforatandemwinguavviaasurrogatemodel AT qinglishi adaptivesequentialsamplingstrategybasedmultiobjectiveoptimizationofaerodynamicconfigurationforatandemwinguavviaasurrogatemodel AT huawang adaptivesequentialsamplingstrategybasedmultiobjectiveoptimizationofaerodynamicconfigurationforatandemwinguavviaasurrogatemodel AT haocheng adaptivesequentialsamplingstrategybasedmultiobjectiveoptimizationofaerodynamicconfigurationforatandemwinguavviaasurrogatemodel AT fengcheng adaptivesequentialsamplingstrategybasedmultiobjectiveoptimizationofaerodynamicconfigurationforatandemwinguavviaasurrogatemodel AT menglongwang adaptivesequentialsamplingstrategybasedmultiobjectiveoptimizationofaerodynamicconfigurationforatandemwinguavviaasurrogatemodel |