Experiment and computation multi-fidelity multi-objective airfoil design optimization of fixed-wing UAV

Owning to the requirements for longer endurance, larger payloads as well as simpler mechanical structure of tactical UAV, the two-element airfoil technology is introduced. Firstly, the aerodynamic characteristics of five common low-speed airfoils are calculated and compared. The airfoil with good aerodynamic performance after segmentation is selected as initial airfoil. Then, the influence of different segmentation types on the aerodynamic characteristics of airfoils is analyzed, and the segmentation type which brings the best aerodynamic benefits is selected. The slot parameters are optimized by NSGA-II optimization algorithm under cruising, take-off, and a large angle of attack states to obtain a high-performance self-adaption tactical UAV airfoil. Finally, the new airfoil is applied to Sparrow-hawk tactical UAV, the CL1.5 /CD of acceleration state increases 15.5%, the CL of large angle of attack increases 20.4% and the CL1.5 /CD of cruising state after enlarge the aspect ratio increase12.3%.

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“…So it is often used in aerodynamic optimization. [19][20][21] The parameter settings of NSGA-II algorithm in this paper are shown in Table 1:…”

Section: Optimization Of S-type Segmentation and Results Analysismentioning

confidence: 99%

Design, optimization and application of two-element airfoils for tactical UAV

Shunshun

Guo

2022

Advances in Mechanical Engineering

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“…As stated in Section 1, the overall computational efficiency of BO can be increased by leveraging inexpensive LF datasets. MF BO has been successfully used in many applications such as hyperparameter tuning [41][42][43][44], finding Pareto fronts in multi-objective optimizations [45][46][47], and solving non-linear state-space models [48,49]. For MFBO, both the emulator and the AF must accommodate the multi-source and unbalanced 5 nature of the data.…”

Section: Existing Multi-fidelity Bo Techniquesmentioning

confidence: 99%

Multi-Fidelity Cost-Aware Bayesian Optimization

Foumani¹,

Shishehbor²,

Yousefpour³

et al. 2022

Preprint

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Bayesian optimization (BO) is increasingly employed in critical applications such as materials design and drug discovery. An increasingly popular strategy in BO is to forgo the sole reliance on high-fidelity data and instead use an ensemble of information sources which provide inexpensive low-fidelity data. The overall premise of this strategy is to reduce the overall sampling costs by querying inexpensive low-fidelity sources whose data are correlated with high-fidelity samples. Here, we propose a multi-fidelity cost-aware BO framework that dramatically outperforms the state-of-the-art technologies in terms of efficiency, consistency, and robustness. We demonstrate the advantages of our framework on analytic and engineering problems and argue that these benefits stem from our two main contributions: (1) we develop a novel acquisition function for multi-fidelity cost-aware BO that safeguards the convergence against the biases of low-fidelity data, and(2) we tailor a newly developed emulator for multi-fidelity BO which enables us to not only simultaneously learn from an ensemble of multi-fidelity datasets, but also identify the severely biased low-fidelity sources that should be excluded from BO.

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“…AtthaphonAriyarit used an evolutionary algorithm and a gradient-based method for multiobjective problems [21]. PhiboonTharathep proposed a multifidelity surrogate model and used nondominated sorting genetic algorithm II to solve multifidelity multiobjective airfoil design problem of UAV [22,23]. An evolutionary algorithm is a type of heuristic search algorithm created by combining computer science and biology almost by corresponding laws [24].…”

Section: Introductionmentioning

confidence: 99%

Optimization of UAV Airfoil Based on Improved Particle Swarm Optimization Algorithm

Jiang

2022

International Journal of Aerospace Engineering

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Airfoil optimization is an essential task in the aerodynamic layout design of the unmanned aerial vehicle (UAV). An objective optimization function was constructed based on the airfoil power factor and handling stability at various attack angles. The parametric mathematical model of the airfoil and aerodynamic parameter proxy model of airfoil were constructed using the Hicks-Henne improved function and CFD solution sample, focusing on the issues with particle swarm optimization algorithms such as slow convergence, a tendency to fall into local optimal solutions, and oscillation at a late stage; an optimization method for the low-speed airfoil of a small UAV based on improved particle swarm optimization was developed. When compared to standard particle swarm optimization, selective regenerative particle swarm optimization, and improved particle swarm optimization, the results indicate that the maximum thickness of the optimized rear airfoil decreases from 19.77% to 18.76%, the number of iterations decreases from 112 to 31, and the search speed of the improved particle swarm optimization significantly improves; the CFD results indicate that the optimized rear airfoil exhibits superior aerodynamic performance. On average, the airfoil’s maximum lift-to-drag ratio is increased by 11.9%, its maximum power factor is increased by 12.5%, and its pitching moment is reduced by 8.4%. Within the UAV’s speed range, the aerodynamic performance is stable.

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Experiment and computation multi-fidelity multi-objective airfoil design optimization of fixed-wing UAV

Cited by 25 publications

References 35 publications

Design, optimization and application of two-element airfoils for tactical UAV

Design, optimization and application of two-element airfoils for tactical UAV

Multi-Fidelity Cost-Aware Bayesian Optimization

Optimization of UAV Airfoil Based on Improved Particle Swarm Optimization Algorithm

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