Probabilistic CFD computations of gas turbine vane under uncertain operational conditions

Karimi, Mohamad Sadeq; Salehi, Saeed; Raisee, Mehrdad; Hendrick, Patrick; Nourbakhsh, Ahmad

doi:10.1016/j.applthermaleng.2018.11.072

Cited by 28 publications

(3 citation statements)

References 25 publications

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“…During the above investigations, the disperse characteristics of fatigue life were sufficiently studied, laying the groundwork for fatigue reliability degree assessment. However, few works simultaneously consider the multiple uncertainties like load fluctuations [28][29][30][31], material variabilities [32][33][34][35] and model uncertainties [36][37][38][39], which disrupts the correlations between multisource uncertainties and therefore causes significant computational discrepancies in fatigue reliability assessment [40][41][42]. To consider multisource uncertainties in anti-fatigue design, various probabilistic analysis techniques are widely applied.…”

Section: Mcsmentioning

confidence: 99%

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Li,

Song

2024

CMES

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Fatigue reliability-based design optimization of aeroengine structures involves multiple repeated calculations of reliability degree and large-scale calls of implicit high-nonlinearity limit state function, leading to the traditional direct Monte Claro and surrogate methods prone to unacceptable computing efficiency and accuracy. In this case, by fusing the random subspace strategy and weight allocation technology into bagging ensemble theory, a random forest (RF) model is presented to enhance the computing efficiency of reliability degree; moreover, by embedding the RF model into multilevel optimization model, an efficient RF-assisted fatigue reliability-based design optimization framework is developed. Regarding the low-cycle fatigue reliability-based design optimization of aeroengine turbine disc as a case, the effectiveness of the presented framework is validated. The reliabilitybased design optimization results exhibit that the proposed framework holds high computing accuracy and computing efficiency. The current efforts shed a light on the theory/method development of reliability-based design optimization of complex engineering structures.

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Section: Mcsmentioning

confidence: 99%

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Li,

Song

2024

CMES

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“…The Ishigami function, originally formulated in [59], is often employed as a benchmark for UQ codes [21,[60][61][62][63]. The function has expression sin(x 1 ) + asin 2 (x 2 ) + bx 4 3 sin(x 1 ),…”

Section: Ishigamimentioning

confidence: 99%

Least Squares Approximation-based Polynomial Chaos Expansion for Uncertainty Quantification and Robust Optimization in Aeronautics

Mura

Ghisu

Shahpar

2020

Aiaa Aviation 2020 Forum

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For many engineering problems, reliability and robustness are far more important than the nominal performance when it comes to the choice of a design over another. Availability of state-of-the-art simulation codes and trusted modeling approaches help in reducing the epistemic uncertainties, but what is needed for decision-making is ultimately the knowledge of the aleatory uncertainty for the system of interest, and methods capable of minimizing such uncertainty while limiting the impact on the expected performance. A reliable measure of performance is only achieved if all uncertainties affecting the system's geometry, physical properties and operational conditions can be propagated through the model equations to the quantities of interest. Optimization in a deterministic sense, providing the best possible performance for nominal values of the design variables, is then replaced by Robust Optimization, which aims at maximizing the expected performance while minimizing the probability of its degradation. Today, multi-physics problems and demanding high-fidelity simulations are common denominators in Aeronautics as well as in many other industrial fields. It is then necessary to employ techniques that are able to perform uncertainty quantification and propagation at reasonable costs, without requiring the development of dedicated solvers: Non-Intrusive Polynomial Chaos is a prime candidate, but not all of its variants are equally accurate and cost-effective. Efficiency and flexibility of Least Squares surrogates and gradient-enhanced Polynomial Chaos expansions for Uncertainty Quantification and Robust Optimization are discussed in this paper employing an in-house framework. Advantages of the proposed methods over Collocation approaches are demonstrated by comparing the results for a selection of algebraic test cases and for the shape optimization of the NACA 0012 airfoil under transonic conditions.

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“…Parsi et al 14 developed a probabilistic model for predicting the dynamic contact force of honeycomb structures subjected to impacts at low velocities. Probabilistic methods have also been used in analysis and design in many engineering fields, such as vibrations, [15][16][17][18] fracture mechanics, [19][20][21][22] computational fluid mechanics, 23,24 and structural stability. 25,26 A stochastic/probabilistic analysis represents each imprecise parameter as a random variable that follows a specific probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Non-probabilistic models for in-plane elastic properties of cellular hexagonal honeycomb cores involving imprecise parameters

Alazwari

2021

Advances in Mechanical Engineering

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Inherent imprecisions present in the basic parameters of cellular honeycomb cores, such as the cell angle, the material properties, and the geometric parameters, need to be considered in the analysis and design to meet the high-performance requirements. In this paper, imprecisions associated with the basic parameters of honeycomb cores are considered. Non-probabilistic models for the in-plane elastic properties of hexagonal honeycomb cores are developed in which the imprecisely defined input and response parameters are represented by only their mean values and variations without the requirement of knowing the probability density distributions of the imprecise parameters as is required for probabilistic methods. Thus, the proposed models predict not only the nominal values of the in-plane elastic properties but also their variations from the respective mean values. The applicability of the proposed models is demonstrated by considering the analysis of the in-plane elastic properties of a honeycomb core made of aluminum 5052-H-32 in which the core material properties are defined by their mean values and variations. The results show that realistic variations of the in-plane elastic properties are obtained using the proposed non-probabilistic models. The sensitivity of the in-plane elastic properties to the imprecisions present in each basic parameter is also investigated.

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Probabilistic CFD computations of gas turbine vane under uncertain operational conditions

Cited by 28 publications

References 25 publications

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

Least Squares Approximation-based Polynomial Chaos Expansion for Uncertainty Quantification and Robust Optimization in Aeronautics

Non-probabilistic models for in-plane elastic properties of cellular hexagonal honeycomb cores involving imprecise parameters

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