A mixed design approach for probabilistic structural durability

Choi, Kyung K.; Yu, Xiaoming; Chang, Kuang-Hua

doi:10.2514/6.1996-4063

Cited by 14 publications

(2 citation statements)

References 13 publications

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“…RIA is a method for directly solving β [13] . In this method, the uncertain parameters under analysis need to be standardized using a certain transformation.…”

Section: Reliability Index Approachmentioning

confidence: 99%

Reliability Based Design Optimization of a novel double-layer staggered octocopter with improved surrogate model

Zhu,

Nie,

Wei

et al. 2023

Preprint

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Surrogate models are commonly used in the aircraft design process to save costs, but the predictions of simplified surrogate models often exist errors. Therefore, improving the accuracy of the surrogate model and conducting uncertainty analysis specifically for the surrogate model are necessary. In this study, a multi-objective optimization was performed on a novel configuration octocopter drone that demonstrated significantly higher aerodynamic efficiency compared to conventional configurations. The aim was to reduce the potential design failures resulting from the use of surrogate models by conducting uncertainty analysis. Under the guidance of experimental design, sample points were selected for computational fluid dynamics(CFD) simulation and a surrogate model was constructed. The errors of the surrogate model were fitted using a normal distribution, and uncertainty analysis and constraints were applied to the surrogate model using the Reliability Index Approach (RIA). The NSGA-III algorithm was employed for optimization. Based on the approximate Pareto front obtained from optimization, additional sample points were selected using orthogonal experimental design to supplement the dataset. The surrogate model was then improved, and the changes in accuracy were analyzed by comparing the probability distribution functions of the errors before and after adding the sample points. The final Pareto front was obtained. Through CFD simulation verification, the surrogate model achieved a prediction accuracy of 99.6%. The optimized unmanned aerial vehicle(UAV) exhibited a 113.341% improvement in thrust-to-weight ratio(y) and a 26.5% increase in aero ratio(a). The research demonstrates that the improved surrogate model has a high prediction accuracy and the methodology based on the improved surrogate model and RIA uncertainty analysis plays an important role in the optimization of novel rotorcraft.

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“…RIA is a method for directly solving β [13] . In this method, the uncertain parameters under analysis need to be standardized using a certain transformation.…”

Section: Reliability Index Approachmentioning

confidence: 99%

Reliability Based Design Optimization of a novel double-layer staggered octocopter with improved surrogate model

Zhu,

Nie,

Wei

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…The most probable point is generally located using approximate probability integration methods such as first‐order reliability method and second‐order reliability method. The RBDO problem can generally be formulated as follows:

Miminize f (μ_{x})

\begin{matrix} left \\ Subject to \\ R_{j} \leq prob (G_{j} (X) \geq 0), \\ j = 1, \dots, k \\ μ_{x}^{l} \leq μ \leq μ_{x}^{u} \end{matrix}

where f ( μ x ) and G j ( X ) are the objective and constraint functions, respectively, X is the random design vector with mean μ x , k is the number of probabilistic constraints, and R j represents the desired reliability. The probabilistic constraints are described by the limit state function G j ( X ) = 0, which is mainly used to divide design space into safe region G j ( X ) > 0 and failure region G j ( X ) < 0.…”

Section: Reliability‐based Design Optimizationmentioning

confidence: 99%

Reliability‐Based Design Optimization Considering Probabilistic Degradation Behavior

Rathod

Yadav

Rathore

et al. 2012

Quality & Reliability Eng

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This article proposes a reliability-based design optimization methodology by incorporating probabilistic degradation in the fatigue resistance of material. The probabilistic damage accumulation is treated as a measure of degradation in the fatigue resistance of material and modeled as nonstationary probabilistic process to capture the time-dependent distribution parameters of damage accumulation. The proposed probabilistic damage accumulation model is then incorporated into reliability-based design optimization model by building a dynamic reliability model inferred from the stress-strength interference model. The proposed approach facilitates to capture the dynamic degradation behavior while optimizing design variables at an early design stage to improve the overall reliability of product. The applicability of the proposed approach is demonstrated using suitable examples.

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