CFD Prediction of Airfoil Drag in Viscous Flow Using the Entropy Generation Method

Wang, Wei; Wang, Jun; Liu, Hui; Jiang, Boyan

doi:10.1155/2018/4347650

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…The second is due to inevitable errors in both the numerical simulation and the experimental test. The deviation between the calculated and experimental data is lower than 5%, which is acceptable according to previous studies [18][19][20][21]. The total grid quantity selected eventually reaches approximately 11.02 × 10 6 .…”

Section: Grid Independencesupporting

confidence: 82%

Multi-Objective Optimization for the Radial Bending and Twisting Law of Axial Fan Blades

et al. 2022

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The performance of low-pressure axial flow fans is directly affected by the three-dimensional bending and twisting of the blades. A new blade design method is adopted in this work, where the radial distribution of blade angle and blade bending angle is composed of standard-form rational quadratic Bézier curves. Dendrite Net is then trained to predict the pneumatic performance of the fan. A non dominated sorting genetic algorithm is employed to solve the global optimization problem of the total pressure coefficient and efficiency. The simulation results show that the optimal blade load distribution along the radial direction becomes uniform, and the suction surface separation vortex and passage vortex are restrained. On the other hand, the tip leakage vortex is enhanced and moves toward the blade leading edge. According to the experimental results, the maximum efficiency increases by 5.44%, and the maximum total pressure coefficient increases by 2.47% after optimization.

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Section: Grid Independencesupporting

confidence: 82%

Multi-Objective Optimization for the Radial Bending and Twisting Law of Axial Fan Blades

et al. 2022

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“…However, in a real flow, irreversible processes such as viscous effects cause a loss in p 0 , which it is important to control. Take the case of an aircraft; high losses over the wing are synonymous with high drag [55], while in the engines, high losses lead to decreased component efficiencies, both of which lead to a higher fuel consumption. The pressure loss at a given location s can be quantified through the loss coefficient…”

Section: Physical Insightmentioning

confidence: 99%

Polynomial Ridge Flowfield Estimation

Scillitoe,

Seshadri,

Wong

et al. 2021

Preprint

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Computational fluid dynamics plays a key role in the design process across many industries. Recently, there has been increasing interest in data-driven methods, in order to exploit the large volume of data generated by such computations. This paper introduces the idea of using spatially correlated polynomial ridge functions for rapid flowfield estimation. Dimension reducing ridge functions are obtained for numerous points within training flowfields. The functions can then be used to predict flow variables for new, previously unseen, flowfields. Their dimension reducing nature alleviates the problems associated with visualising high dimensional datasets, enabling improved understanding of design spaces and potentially providing valuable physical insights. The proposed framework is computationally efficient; consisting of either readily parallelisable tasks, or linear algebra operations. To further reduce the computational cost, ridge functions need only be computed at only a small number of subsampled locations. The flow physics encoded within covariance matrices obtained from the training flowfields can then be used to predict flow quantities, conditional upon those predicted by the ridge functions at the sampled points. To demonstrate the efficacy of the framework, the incompressible flow around an ensemble of aerofoils is used as a test case. On unseen aerofoils the ridge functions' predictive accuracy is found to be reasonably competitive with a state-of-the-art convolutional neural network (CNN). The local ridge functions can also be reused to obtain surrogate models for integral quantities such a loss coefficient, which is advantageous in situations where long-term storage of the CFD data is problematic. Finally, use of the ridge framework with varying boundary conditions is demonstrated on a three dimensional transonic wing flow.

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“…Saqr et al [ 8 ] investigated the entropy generation in a swirl pipe flow, using the realizable k – ε model. Wang et al [ 9 ] modeled the flow around the NACA0012 airfoil and discussed the effect of turbulence models on entropy generation, using five turbulence models, namely the standard k – ε model, renormalization group (RNG) k – ε model, standard k – ω model, shear stress transport (SST) k – ω model, and Spalart–Allmaras (S–A) model. Ghorani et al [ 10 ] used the entropy-generation-rate method with the SST k – ω model to investigate the flow within a centrifugal pump in a reverse mode.…”

Section: Introductionmentioning

confidence: 99%

Numerical Study of Entropy Generation in Fully Developed Turbulent Circular Tube Flow Using an Elliptic Blending Turbulence Model

Yang

2022

Entropy

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As computational fluid dynamics (CFD) advances, entropy generation minimization based on CFD becomes attractive for optimizing complex heat-transfer systems. This optimization depends on the accuracy of CFD results, such that accurate turbulence models, such as elliptic relaxation or elliptic blending turbulence models, become important. The performance of a previously developed elliptic blending turbulence model (the model) to predict the rate of entropy generation in the fully developed turbulent circular tube flow with constant heat flux was studied to provide some guidelines for using this class of turbulence model to calculate entropy generation in complex systems. The flow and temperature fields were simulated by using a CFD package, and then the rate of entropy generation was calculated in post-processing. The analytical correlations and results of two popular turbulence models (the realizable k–ε and the shear stress transport (SST) k–ω models) were used as references to demonstrate the accuracy of the model. The findings indicate that the turbulent Prandtl number (Prt) influences the entropy generation rate due to heat-transfer irreversibility. Prt = 0.85 produces the best results for the model. For the realizable k–ε and SST k–ω models, Prt = 0.85 and Prt = 0.92 produce the best results, respectively. For the realizable k–ε and the SST k–ω models, the two methods used to predict the rate of entropy generation due to friction irreversibility produce the same results. However, for the model, the rates of entropy generation due to friction irreversibility predicted by the two methods are different. The difference at a Reynolds number of 100,000 is about 14%. The method that incorporates the effective turbulent viscosity should be used to predict the rate of entropy generation due to friction irreversibility for the model. Furthermore, when the temperature in the flow field changes dramatically, the temperature-dependent fluid properties must be considered.

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CFD Prediction of Airfoil Drag in Viscous Flow Using the Entropy Generation Method

Cited by 4 publications

References 19 publications

Multi-Objective Optimization for the Radial Bending and Twisting Law of Axial Fan Blades

Multi-Objective Optimization for the Radial Bending and Twisting Law of Axial Fan Blades

Polynomial Ridge Flowfield Estimation

Numerical Study of Entropy Generation in Fully Developed Turbulent Circular Tube Flow Using an Elliptic Blending Turbulence Model

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