Metamodel-based optimization of the bolted connection of a wing spar considering fatigue resistance

Xia, Tianxiang; Yao, Weixing; Xu, Liu; Zou, Jun

doi:10.1177/0954410015598792

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…It was found that under the same loads, the optimized structure had a 15.7% reduction in the stress peak and a 122% increase in fatigue life [12]. Xia T et al optimized the bolt connection of the wing spar considering life constraints, and the weight of the optimized structure was only 85% of the original design, with the results showing that the optimized structure was the most effective [13]. Munk D J et al applied topology optimization technology to the structural design of aircraft components and designed a light aircraft landing gear [14].…”

Section: Introductionmentioning

confidence: 99%

Structural Optimization of AerMet100 Steel Torsion Spring Based on Strain Fatigue

Wang,

Li,

Chen

et al. 2023

Aerospace

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The torsion spring of a carrier-based aircraft landing gear is a key component, which is normally manufactured out of AerMet100 ultra-high-strength steel. The takeoff and landing performance is greatly influenced by its bearing capacity and structural durability. To carry out the structure anti-fatigue design, it is necessary to investigate the influence of the spring structure features on its fatigue life, based on which the strain fatigue analysis and parameter optimization design of the torsion spring are executed. Through the finite element analysis conducted with ABAQUS, it was determined that there exists serious stress concentration in the relief groove. Based on the theory of strain fatigue, the fatigue life of the torsion spring was obtained, and the fracture position and lifecycle were consistent with the test results. A structure optimization platform based on a parametric method was established. Samples were selected through the DOE (design of experiment), and a surrogate model was established based on RBF (radial basis functions), followed by optimization using MIGA (multi-island genetic algorithms). With the parameter optimization of the relief groove, the structure was reconstituted and reanalyzed. From the simulation results, the peak strain was reduced by 30.7%, while the fatigue life was increased by 86.2% under the same loads and constraints. Moreover, laboratory tests were performed on the torsion spring after reconstruction, which showed that the fatigue life increases by 85.6% after optimization. The method presented in this paper can provide theoretical support and technical guidance for the application and structural optimization of ultra-high-strength steel structures.

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Section: Introductionmentioning

confidence: 99%

Structural Optimization of AerMet100 Steel Torsion Spring Based on Strain Fatigue

Wang,

Li,

Chen

et al. 2023

Aerospace

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“…Munk et al (5) applied topology optimisation to aerospace design problems and designed a light aircraft landing gear. Xia et al (6) applied DOE (design of experiments) and Kriging model in the optimisation to minimise the weight of the bolted connection of a wing bar, and specimen fatigue tests were carried out to evaluate the optimisation process.…”

Section: Introductionmentioning

confidence: 99%

“…the distance between the axis of the drawbar body and center of groove outline cutter D 2 the distance between the axis of drawbar head and center of groove outline cutter D 3 the distance between the axis of the drawbar head and groove outline D 4 the distance between the axis of the drawbar head and center of groove edge D 5 height of groove D 6 the distance between the axis of the drawbar head and gradient starting point D 7 the distance between the axis of the drawbar head and gradient ending point D 8 the initial thickness of web E elastic modulus K cyclic strength coefficient l i lower limit on the dimension σ f fatigue strength coefficient σ m mean stress ε f fatigue ductility coefficient ε ea elastic strain ε pa plastic strain ε a total strain L life repeats of the structure me elastic poisson '…”

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confidence: 99%

Parameter optimisation of a carrier-based UAV drawbar based on strain fatigue analysis

et al. 2021

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Carrier-based unmanned aerial aircraft (UAV) structure is subjected to severe tensile load during takeoff, especially the drawbar, which affects its fatigue performance and structural safety. However, the complex structural features pose great challenges for the engineering design. Considering this situation, a structural design, fatigue analysis, and parameters optimisation joint working platform are urgently needed to solve this problem. In this study, numerical analysis of strain fatigue is carried out based on the laboratory fatigue failure of the carrier-based aircraft drawbar. Taking the sensitivity of drawbar parameters to stress and life into account and optimum design of drawbar with fatigue life as a target using the parametric method, this study also includes cutting-edge parameters of milling cutters, structural details of the drawbar and so on. Then an experimental design is applied using the Latin hypercube sampling method, and a surrogate model based on RBF neural network is established. Lastly, a multi-island genetic algorithm is introduced for optimisation. The results show that the error between the obtained optimal solution and simulation is 0.26%, while the optimised stress level is reduced by 15.7%, and the life of the drawbar is increased by 122%.

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“…A possible solution to address this problem is to replace the computationally expensive simulations model by surrogate models (inexpensive metamodels) to reduce the computational cost. Numerous existing methods for building surrogate models in recent metamodeling literature, the three most prevalent methods have been frequently applied to build a simplified metamodel for engineering simulations code: response surface methodology (RSM), 16 kriging, 16,17 and artificial neural networks (ANN). 18 Each has its advantages and disadvantages, which it is important to know to select a model adapted to the problem to be treated.…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary design optimization of stiffened panels using collaborative optimization and artificial neural network

Chagraoui

Soula

2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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A new method for solving the multidisciplinary design optimization problems with a minimal computational effort is presented. The proposed methodology is based on the combination of artificial neural network model and Improved Multi-Objective Collaborative Optimization. In the artificial neural network–Improved Multi-Objective Collaborative Optimization scheme, the back-propagation algorithm is used for training the artificial neural network metamodel and the Non-dominated Sorting Genetic Algorithm-II is used to search a Pareto optimality set for the objective functions of stiffened panels. The artificial neural network–Improved Multi-Objective Collaborative Optimization algorithm aims firstly to decompose the global optimization problem hierarchically into optimization design problem at system level and several sub-problems at sub-system level and secondly to replace each optimization problem at the system and subsystem levels by artificial neural network model to limit the computational cost. To highlight the efficiency and effectiveness of the proposed artificial neural network–Improved Multi-Objective Collaborative Optimization method, mathematical and engineering examples are presented. Results obtained from the application of the artificial neural network–Improved Multi-Objective Collaborative Optimization approach to an optimization problem of a stiffened panel are compared with those obtained by traditional optimization without using prediction tools. The new method (artificial neural network–Improved Multi-Objective Collaborative Optimization) was proven to be superior to traditional optimization. These results have confirmed the efficiency and effectiveness of the artificial neural network–Improved Multi-Objective Collaborative Optimization method. In addition, it converges at faster rate than traditional optimization. The traditional optimization method converges within 7918 s, while artificial neural network–Improved Multi-Objective Collaborative Optimization requires only 42 s, clearly, the artificial neural network–Improved Multi-Objective Collaborative Optimization method is much more efficient.

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Metamodel-based optimization of the bolted connection of a wing spar considering fatigue resistance

Cited by 7 publications

References 17 publications

Structural Optimization of AerMet100 Steel Torsion Spring Based on Strain Fatigue

Structural Optimization of AerMet100 Steel Torsion Spring Based on Strain Fatigue

Parameter optimisation of a carrier-based UAV drawbar based on strain fatigue analysis

Multidisciplinary design optimization of stiffened panels using collaborative optimization and artificial neural network

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