Uncertainty propagation in aerodynamic forces and heating analysis for hypersonic vehicles with uncertain-but-bounded geometric parameters

Yan, Zheng; Qiu, Zhiping

doi:10.1016/j.ast.2018.02.028

Cited by 24 publications

(7 citation statements)

References 33 publications

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“…Because of the increase in physical system complexity and performance requirements, it's essential to decrease the deteriorating effect of input uncertainties and improve the performance robustness of physical systems. Uncertainty design technologies are used widely in various fields, such as mechanical engineering [1], [2], vehicle design and routing [3], [4], energy equipment [5], and architectural engineering [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Random and Interval Uncertain Variables Based on Polynomial Chaos Expansion Method

et al. 2019

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The problem of characterizing the sensitivity indices of structural performance considering random and interval uncertainty variables simultaneously is analyzed. Data-driven polynomial chaos expansion (PCE) is established to treat random variables with arbitrary input uncertainty information, and subsequently extended to consider interval uncertainty variables simultaneously. An adaptive-sparse polynomial chaos expansion calculation algorithm is proposed to determine the appropriate polynomial coefficients based on the available uncertainty information of random and interval uncertainty variables. Subsequently, the local and total sensitivity indices of random and interval uncertainty variables are calculated directly using the constructed PCE model. Two numerical and engineering examples are presented to demonstrate the effectiveness of the proposed approach. Compared with the results of the Monte Carlo simulation method, the proposed approach yields excellent results with significantly reduced computational effort in the three examples. INDEX TERMS Sensitivity analysis, polynomial chaos expansion, random variables, interval variables.

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Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Random and Interval Uncertain Variables Based on Polynomial Chaos Expansion Method

et al. 2019

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“…92,104 For this case, interval analysis, as a non-probabilistic method, has been preferred and performed in the aeroelastic airfoil models with non-probabilistic interval uncertainties. 92,[104][105][106][107][108][109][110][111][112][113] For instance, Zheng and Qiu 105 employed an interval uncertain model to quantify the stochasticity of the airfoil system's parameters. Both aerodynamic and structural parameters were considered to be dependent on an n-dimensional interval uncertain parameter vector γ = [γ 1 , γ 2 , .…”

Section: A Stochasticity Quantificationmentioning

confidence: 99%

Complex nonlinear dynamics and vibration suppression of conceptual airfoil models: A state-of-the-art overview

Liu

Kurths

et al. 2022

Chaos: An Interdisciplinary Journal of Nonlinear Science

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During the past few decades, several significant progresses have been made in exploring complex nonlinear dynamics and vibration suppression of conceptual aeroelastic airfoil models. Additionally, some new challenges have arisen. To the best of the author’s knowledge, most studies are concerned with the deterministic case; however, the effects of stochasticity encountered in practical flight environments on the nonlinear dynamical behaviors of the airfoil systems are neglected. Crucially, coupling interaction of the structure nonlinearities and uncertainty fluctuations can lead to some difficulties on the airfoil models, including accurate modeling, response solving, and vibration suppression. At the same time, most of the existing studies depend mainly on a mathematical model established by physical mechanisms. Unfortunately, it is challenging and even impossible to obtain an accurate physical model of the complex wing structure in engineering practice. The emergence of data science and machine learning provides new opportunities for understanding the aeroelastic airfoil systems from the data-driven point of view, such as data-driven modeling, prediction, and control from the recorded data. Nevertheless, relevant data-driven problems of the aeroelastic airfoil systems are not addressed well up to now. This survey contributes to conducting a comprehensive overview of recent developments toward understanding complex dynamical behaviors and vibration suppression, especially for stochastic dynamics, early warning, and data-driven problems, of the conceptual two-dimensional airfoil models with different structural nonlinearities. The results on the airfoil models are summarized and discussed. Besides, several potential development directions that are worth further exploration are also highlighted.

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“…The aerodynamic heat flux at the two-dimension stagnation point of the hypersonic wing can then be approximated as [30,32]:…”

Section: Parametric Geometry Modeling Of the Aerodynamic Shapementioning

confidence: 99%

Uncertainty-Based Comprehensive Optimization Design for the Thermal Protection System of Hypersonic Wing Structure

Wang

Luo

2022

Applied Sciences

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Due to the inherent uncertainties in material properties, loads, geometric dimensions, et al., the uncertainty-based optimization design method has become increasingly important for the design of the thermal protection system (TPS) by carefully considering the influences of uncertainties. In this study, an uncertainty-based comprehensive optimization design method, which sequentially performs the robust design of aerodynamic shape and structure size for the TPS of a hypersonic wing is proposed, on the presence of uncertain-but-bounded parameters. The robust design of the TPS’s aerodynamic shape is firstly carried out. The results show that the proposed method decreases the fluctuation of the lift-to-drag ratio by 5.7%, with a small increase of heat flux on the stagnation point by only 0.13% when compared with the conventional deterministic optimization method. After that, based on the optimized aerodynamic shape and heating loads, the robust design of the multilayer TPS tile is conducted. The results show that the mass of the TPS tile efficiently deceased from 2.713 kg to 2.445 kg by 9.89%, and the robustness of the optimized design is better than the initial design. Finally, the effectiveness of the proposed optimization method is validated by the heat insulting experiment of the typical multilayer TPS tiles.

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Uncertainty propagation in aerodynamic forces and heating analysis for hypersonic vehicles with uncertain-but-bounded geometric parameters

Cited by 24 publications

References 33 publications

Sensitivity Analysis of Random and Interval Uncertain Variables Based on Polynomial Chaos Expansion Method

Sensitivity Analysis of Random and Interval Uncertain Variables Based on Polynomial Chaos Expansion Method

Complex nonlinear dynamics and vibration suppression of conceptual airfoil models: A state-of-the-art overview

Uncertainty-Based Comprehensive Optimization Design for the Thermal Protection System of Hypersonic Wing Structure

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