Uncertainty quantification and film cooling

D’Ammaro, Antonio; Montomoli, Francesco

doi:10.1016/j.compfluid.2012.10.021

Cited by 39 publications

(7 citation statements)

References 19 publications

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“…The optimization variables keep the same as in DO process (see Table 5). The fluctuation range of Re is given by referring to [3], while the range of r is set according to [2]. Both the Re and r varies around 20%  of its nominal value, and they follow uniform (abbreviated as U in Table 5) and normal distribution (abbreviated as N), respectively.…”

Section: Robust Optimization For Vgmentioning

confidence: 99%

“…The designs optimized purely for nominal performance may suffer from significantly degraded performance when uncertainty propagates, e.g., Bunker [2] showed that the geometric uncertainties can lead to a temperature increase of 40K at most for high-temperature blades, which should reduce the life of aero-engines by 30%. D'Ammaro and Montomoli [3] investigated the effects of fluctuations of operation condition on film-cooling effectiveness of a gas turbine blade; he showed that a 20% stochastic variation of the inlet total pressure gave a variation of the adiabatic effectiveness of about 80%. Therefore, extensive attentions [1,4] have been drawn to the robust design, which seeks to obtain solutions that keep high performance over a wide range.…”

Section: Introductionmentioning

confidence: 99%

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Robust Optimization for Vortex Generators in U-Bend Channel of High-Temperature Blades

Guo¹,

Tao²,

Song³

2019

Proceedings of Global Power &Amp; Propulsion Society

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The uncertainties such as manufacturing errors and environmental variations are inevitably encountered in engineering design, therefore in order to find robust solutions which keep high performance over a wide range, a meanvariance multi-objective robust optimization (RO) framework was proposed. Specifically, a kriging-based uncertainty quantification (UQ) formulation was proposed, which formulates uncertainty parameters and optimization design variables in product form, thereby the optimization search and UQ are conducted simultaneously in RO. Before RO, the sequential sampling strategies were employed to build high-accurate surrogate, which was shown effective by our five-variable engineering problem. Through aero-thermal design of vortex generators (VG) in U-bend channel of hightemperature blades, some issues such as constraints selection and knowledge mining in RO space were addressed. The RO of VG was carried out with one Gaussian-distributed and one uniformly-distributed uncertainty parameter, and three optimization design variables. One deterministic optimization (DO) process at the nominal condition and several RO process with different constraints were conducted. It was shown that the proposed RO was able to find robust solutions that have high-performance and are not sensitive to uncertainty fluctuations; which were validated by CFD. Meanwhile, the solution of DO was found helpful to exclude variance-dominated solutions which have much worse performance. But some constraints based on solution of DO would make the RO solution set to be null, therefore the RO can be an iterative process, in this regard, it is attractive to build high-accurate surrogates before RO. From another perspective, the several RO with different constraints are useful to group the solutions and get a insight into black-box design space like VG, which is valuable to select the final solution from many Pareto solutions.

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Section: Robust Optimization For Vgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Optimization for Vortex Generators in U-Bend Channel of High-Temperature Blades

Guo¹,

Tao²,

Song³

2019

Proceedings of Global Power &Amp; Propulsion Society

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“…For a CFD example, where the stochastic nature of flow around a turbine blade was studied with accelerated convergence speed, see D’Ammaro and Montomoli. 5 A study on the effect of various sampling schemes on convergence speed for UQ in CFD was performed by Hosder and Walters. 6…”

Section: Introductionmentioning

confidence: 99%

“…For a CFD example, where the stochastic nature of flow around a turbine blade was studied with accelerated convergence speed, see D'Ammaro and Montomoli. 5 A study on the effect of various sampling schemes on convergence speed for UQ in CFD was performed by Hosder and Walters. 6 Furthermore, computations can be achieved that are as much as three orders of magnitude cheaper, with a negligible error compared to the MCM approach, using the Probabilistic Collocation Method (PCM).…”

Section: Introductionmentioning

confidence: 99%

Data-driven uncertainty quantification for Formula 1: Diffuser, wing tip and front wing variations

Ahlfeld

Ciampoli

Pietropaoli

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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This work introduces a new uncertainty quantification method to better deal with scarce data and long simulation run times in Formula 1 design. Race cars are produced in low quantities and for maximum performance. Thus, their designing process is characterised by manufacturing data shortage and complex Computational Fluid Dynamics simulations with long run times. Their car aerodynamics is subject to many random variables that introduce uncertainty into the down-force and drag performance, such as variations in ride height, front wing direction and pitch angle. To accurately predict the car performance during a race, it is important to study the effect of these random variables. This assessment cannot be performed with the standard deterministic Computational Fluid Dynamics approaches used in Formula 1. Even with regard to stochastic approaches, no efficient method has so far been suggested that addresses the problem of data scarcity. The reason for this is that most efficient uncertainty quantification methods fit probability distributions to the scarce data. It is shown in this work that probability distribution fitting can create a significant error using a simple two-dimensional diffuser example. Subsequently, the use of a new data-driven Polynomial Chaos method and its sparse multi-dimensional extension is suggested and demonstrated for Formula 1 to reduce such errors. This method allows to avoid distribution fitting because it is based on pure data. SAMBA’s general formulation also makes it easier to combine any possible inputs within a sparse description for problems with many variables. SAMBA is applied to two realistic car three-dimensional Computational Fluid Dynamics simulations: a NACA 0012 tip wing and the front part of a Formula 1 car. The probabilistic variations of the lift and drag of these two configurations are calculated using SAMBA and shown to be significant.

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“…The baseline case has been studied with an Uncertainty Quantification study by D'Ammaro and Montomoli [17].…”

Section: Computational Detailsmentioning

confidence: 99%

Numerical and experimental investigation of a new film cooling geometry with high P/D ratio

Montomoli

D’Ammaro

Uchida

2013

International Journal of Heat and Mass Transfer

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In order to improve the coolant surface coverage, in the past years new geometries have been proposed with higher lateral fan-shaped angle and/or greater inter-hole pitch distance (P/D). Unfortunately it is not possible to increase the fan angle or the pitch distance even further without inducing a coolant separation and a drop in the overall effectiveness.This study proposes an innovative design which improves the lateral coverage and reduces the jet lift off. The results have been validated by a combination of numerical and experimental analyses: the experimental work has been assessed on a flat plate using thermo chromic liquid crystals and the results have been confirmed numerically by the CFD with the same conditions. The CFD simulations have been carried out considering a stochastic distribution for the free stream Mach number and the coolant blowing ratio.The experimental and computational results show an increase in lateral averaged adiabatic effectiveness of +30% than the baseline until a distance downstream of 20 times the coolant diameter.

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Uncertainty quantification and film cooling

Cited by 39 publications

References 19 publications

Robust Optimization for Vortex Generators in U-Bend Channel of High-Temperature Blades

Robust Optimization for Vortex Generators in U-Bend Channel of High-Temperature Blades

Data-driven uncertainty quantification for Formula 1: Diffuser, wing tip and front wing variations

Numerical and experimental investigation of a new film cooling geometry with high P/D ratio

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