Robust Design of Compressor Fan Blades Against Erosion

Kumar, Ameeta; Keane, Andy J.; Nair, Prasanth B.; Shahpar, Shahrokh

doi:10.1115/1.2202886

Cited by 35 publications

(15 citation statements)

References 36 publications

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“…A low-crowding algorithm, which maximizes the Euclidean distance between the Pareto points, is used to select points, which are then verified by running full-scale CFD simulations [17]. These points are updated to the existing data set and the Gaussian process emulator is re-trained on the new data set.…”

Section: Numerical Studies and Resultsmentioning

confidence: 99%

“…In the multiobjective formulation, the integrals in Equations (5) and (6) can be approximated using a cross-product ( p × q) of elements [17] or a combined array approach ( p + q) to elements [34]. This can be achieved by using any standard DOE techniques.…”

Section: Multiobjective Robust Designmentioning

confidence: 99%

“…Moreover, since robust design studies typically involve repeated evaluation of multidimensional integrals to calculate the performance robustness of many hundreds or possibly thousands of candidate designs, direct simulation techniques are computationally not viable. This has motivated the development of function approximation-based and other advanced techniques to reduce the computational cost; see, for example, References [11][12][13][17][18][19][20]. Another point worth noting is that in much of the earlier work on robust design, the focus has primarily been on minimizing a single objective indicating the performance robustness subject to a set of constraints.…”

Section: Introductionmentioning

confidence: 99%

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Robust design using Bayesian Monte Carlo

Kumar

Nair

Keane

et al. 2007

Numerical Meth Engineering

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SUMMARYIn this paper, we propose an efficient strategy for robust design based on Bayesian Monte Carlo simulation. Robust design is formulated as a multiobjective problem to allow explicit trade-off between the mean performance and variability. The proposed method is applied to a compressor blade design in the presence of manufacturing uncertainty. Process capability data are utilized in conjunction with a parametric geometry model for manufacturing uncertainty quantification. High-fidelity computational fluid dynamics simulations are used to evaluate the aerodynamic performance of the compressor blade. A probabilistic analysis for estimating the effect of manufacturing variations on the aerodynamic performance of the blade is performed and a case for the application of robust design is established. The proposed approach is applied to robust design of compressor blades and a selected design from the final Pareto set is compared with an optimal design obtained by minimizing the nominal performance. The selected robust blade has substantial improvement in robustness against manufacturing variations in comparison with the deterministic optimal blade. Significant savings in computational effort using the proposed method are also illustrated.

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Section: Numerical Studies and Resultsmentioning

confidence: 99%

Section: Multiobjective Robust Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust design using Bayesian Monte Carlo

Kumar

Nair

Keane

et al. 2007

Numerical Meth Engineering

Self Cite

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“…In the meantime, independent researchers have developed different concepts of robustness, measurements for robustness, and robust optimisation approaches in different research disciplines. The applications and studies of robust optimisation can be widely found in other non software engineering research literature [55], [56], [57], [58], [59] but are seldom found in the requirements engineering literature. In requirements engineering, to the best of our knowledge, there are only four studies applying robust optimisation on requirements engineering area.…”

Section: Uncertainty and Risk Handling In Requirements Engineeringmentioning

confidence: 99%

The Value of Exact Analysis in Requirements Selection

Harman

et al. 2017

IIEEE Trans. Software Eng.

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Uncertainty is characterised by incomplete understanding. It is inevitable in the early phase of requirements engineering, and can lead to unsound requirement decisions. Inappropriate requirement choices may result in products that fail to satisfy stakeholders' needs, and might cause loss of revenue. To overcome uncertainty, requirements engineering decision support needs uncertainty management. In this research, we develop a decision support framework METRO for the Next Release Problem (NRP) to manage algorithmic uncertainty and requirements uncertainty. An exact NRP solver (NSGDP) lies at the heart of METRO. NSGDP's exactness eliminates interference caused by approximate existing NRP solvers. We apply NSGDP to three NRP instances, derived from a real world NRP instance, RALIC, and compare with NSGA-II, a widely-used approximate (inexact) technique. We find the randomness of NSGA-II results in decision makers missing up to 99.95 percent of the optimal solutions and obtaining up to 36.48 percent inexact requirement selection decisions. The chance of getting an inexact decision using existing approximate approaches is negatively correlated with the implementation cost of a requirement (Spearman r up to À0:72). Compared to the inexact existing approach, NSGDP saves 15.21 percent lost revenue, on average, for the RALIC dataset.

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“…The computational cost of performing the large number of calculations required for a typical optimization is high and so a surrogate model approach [8] is often used. As an example, the work in [9] applies a surrogate model approach to aid in the design of compressor blades that maintain their performance even with erosion of the blade profile.…”

Section: The Present Daymentioning

confidence: 99%

Delivering better power: the role of simulation in reducing the environmental impact of aircraft engines

Menzies

2014

Phil. Trans. R. Soc. A.

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The growth in simulation capability over the past 20 years has led to remarkable changes in the design process for gas turbines. The availability of relatively cheap computational power coupled to improvements in numerical methods and physical modelling in simulation codes have enabled the development of aircraft propulsion systems that are more powerful and yet more efficient than ever before. However, the design challenges are correspondingly greater, especially to reduce environmental impact. The simulation requirements to achieve a reduced environmental impact are described along with the implications of continued growth in available computational power. It is concluded that achieving the environmental goals will demand large-scale multi-disciplinary simulations requiring significantly increased computational power, to enable optimization of the airframe and propulsion system over the entire operational envelope. However even with massive parallelization, the limits imposed by communications latency will constrain the time required to achieve a solution, and therefore the position of such large-scale calculations in the industrial design process.

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Robust Design of Compressor Fan Blades Against Erosion

Cited by 35 publications

References 36 publications

Robust design using Bayesian Monte Carlo

Robust design using Bayesian Monte Carlo

The Value of Exact Analysis in Requirements Selection

Delivering better power: the role of simulation in reducing the environmental impact of aircraft engines

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