Aircraft Design Optimization with Uncertainty Based on Fuzzy Clustering Analysis

Du, Shengchao; Wang, Lifeng

doi:10.1061/(asce)as.1943-5525.0000517

Cited by 11 publications

(4 citation statements)

References 24 publications

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“…In this study, we applied the Latin hypercube sampling strategy [44] combined with a local sensitivity analysis approach [19]. Latin hypercube sampling, firstly named after McKay et al [44], is an efficient multidimensional sampling similar to Monte Carlo sampling (MCS) but requiring much fewer runs to avoid the clumpy size of uniform random sampling [45]. It splits the range of each variable into different intervals of equal probability, where one value is randomly selected from each interval [46].…”

Section: Methodsmentioning

confidence: 99%

Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model

Demirel

Koch

Mendiguren

et al. 2018

Water

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Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represent an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity typically do not reflect other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). The Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definitions based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

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Section: Methodsmentioning

confidence: 99%

Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model

Demirel

Koch

Mendiguren

et al. 2018

Water

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“…Fuzzy clustering analysis can be used to decrease the amount of sample points for construction of the surrogates. 181 In order to enhance multi-fidelity optimization and uncertainty quantification, a multi-fidelity locally optimized surrogate that is more efficient than global single-fidelity ones is proposed. 182 To represent both epistemic and aleatory uncertainty a surrogate modeling approach based on non-deterministic Kriging is proposed.…”

Section: Uncertainty-based Multidisciplinary Design and Optimizationmentioning

confidence: 99%

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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Evaluation and assessment of novel technologies for aerospace applications is essential for business strategy and decision making regarding development efforts. Since technology is evaluated in the conceptual design phase and little is known about the technology, large uncertainty is present. This uncertainty needs to be accurately assessed and managed. To investigate the research efforts that have been performed to perform technology evaluation under uncertainty, a literature review was conducted, focusing on methods and modeling approaches to assign and quantify these uncertainties. It is found that probability theory is still the most popular theory for representing uncertainty. Polynomial Chaos Expansions and Stochastic Collocation methods are gaining popularity for propagating uncertainty through a modeling environment, but Monte Carlo Simulations are still widely used. Commonly, surrogate models are used to reduce computational effort. Other efforts focus on the use of multifidelity approaches to reduce computational effort when high-fidelity methods are required. Four issues that may need to be addressed in future research were identified.

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“…In the context of an UMDO framework, cluster analysis not only can help partition the design space for multidisciplinary optimization, but can also separate the parameter uncertainty space [13]. The former enables the construction of individual supervised regression models, while the latter improves the accuracy of uncertainty analysis [137]. Therefore, this section provides a brief overview of commonly used cluster analysis methods.…”

Section: Unsupervised Learning Methods For Data Processingmentioning

confidence: 99%

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

Wang,

Fan,

Qiang

2023

Symmetry

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The design of aerospace systems is recognized as a complex interdisciplinary process. Many studies have shown that the exchange of information among multiple disciplines often results in strong coupling and nonlinearity characteristics in system optimization. Meanwhile, inevitable multi-source uncertainty factors continuously accumulate during the optimization process, greatly compromising the system’s robustness and reliability. In this context, uncertainty-based multidisciplinary design optimization (UMDO) has emerged and has been preliminarily applied in aerospace practices. However, it still encounters major challenges, including the complexity of multidisciplinary analysis modeling, and organizational and computational complexities of uncertainty analysis and optimization. Extensive research has been conducted recently to address these issues, particularly uncertainty analysis and artificial intelligence strategies. The former further enriches the UMDO technique, while the latter makes outstanding contributions to addressing the computational complexity of UMDO. With the aim of providing an overview of currently available methods, this paper summarizes existing state-of-the art UMDO technologies, with a special focus on relevant intelligent optimization strategies.

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Aircraft Design Optimization with Uncertainty Based on Fuzzy Clustering Analysis

Cited by 11 publications

References 24 publications

Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model

Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

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