Assessment of a Machine-Learnt Adaptive Wall-Function in a Compressor Cascade With Sinusoidal Leading Edge

Tieghi, Lorenzo; Corsini, Alessandro; Delibra, Giovanni; Angelini, Gino

doi:10.1115/1.4048568

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…The present results suggest that the mixing length turbulence modelling implemented in MULTALL does not allow capturing all the loss mechanisms at the same detail level as state-of-the-art CFD codes. Accordingly, it is planned to try implementing in MULTALL a new wall function based on machine learning: the preliminary tests on the potentialities of such approach led to promising results in [30].…”

Section: Discussionmentioning

confidence: 99%

Assessment of MULTALL as Computational Fluid Dynamics Code for the Analysis of Tube-Axial Fans

Danieli

Masi

Delibra

et al. 2021

Journal of Turbomachinery

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This work deals with the application of the open source CFD code MULTALL to the analysis of tube-axial-fans. The code has been widely validated in the literature for high-speed turbomachine flows but not applied yet to low speed tutbomachines. The aim of this work is to assess the degree of reliability of MULTALL as a tool for simulating the internal flow in industrial axial-flow fan rotors. To this end, the predictions of the steady-state air flow field in the annular sector of a 315 mm tube-axial fan obtained by MULTALL 18.3 are compared with those obtained by two state-of-the-art CFD codes and experimental data of the global aerodynamic performance of the fan and the pitch-wise averaged velocity distribution downstream of the rotor. All the steady-state RANS calculations were performed on either fully structured hexahedron or hexa-dominant grids using classical formulations of algebraic turbulence models. The pressure curve and the trend of the aeraulic efficiency in the stable operation range of the fan predicted by MULTALL show very good agreement with both the experimental data and the other CFD results. Although the estimation of the fan efficiency predicted by MULTALL can be noticeably improved by the more sophisticated state-of-the-art CFD codes, the analysis of the velocity distribution at the rotor exit supports the use of MULTALL as a reliable CFD analysis tool for designers of low-speed axial fans.

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Section: Discussionmentioning

confidence: 99%

Assessment of MULTALL as Computational Fluid Dynamics Code for the Analysis of Tube-Axial Fans

Danieli

Masi

Delibra

et al. 2021

Journal of Turbomachinery

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“…The application of neural networks to construct datadriven wall surrogate models can achieve a compromise between accuracy and solution efficiency. Tieghi et al [96] constructed a data-driven wall-function to k-epsilon simulations of a 2D periodic hill and a modified compressor cascade National Advisory Committee for Aeronautics (NACA) airfoil with a sinusoidal leading edge. The wall-function was trained by the multilayer perceptron ANN to obtain turbulent production and dissipation values near the walls.…”

Section: Transition Modeling and Turbulence Modelingmentioning

confidence: 99%

A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models

Wang,

Zhang,

Wang

et al. 2024

Mathematics

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With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed.

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A Machine-Learnt Wall Function for Rotating Diffusers

Tieghi

Corsini

Delibra

et al. 2021

Journal of Turbomachinery

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Data-driven techniques have proved their effectiveness in many engineering applications. Machine-learning has gradually become a paradigm to explore innovative designs in turbomachinery. However, industrial Computational Fluid Dynamics (CFD) experts are still reluctant to embed similar approaches in standard practice and very few solutions have been proposed so far. The aim of the work is to prove that standard wall treatments can obtain serious benefits from machine-learning modelling. Turbomachinery flow modelling lives in a constant compromise between accuracy and the computational costs of numerical simulations. One of the key factors of process is defining a proper wall treatment. Many works point out how insufficient resolutions of boundary layers may lead to incorrect predictions of turbomachinery performances. Wall functions are universally exploited to replicate the physics of boundary layers where grid resolution does not suffice. Despite their popularity, these functions are frequently applied in flows where the ground assumptions cease to be true, such as rotating passages or swirled flows. In these flows, the mathematical formulations of wall functions do not account for the distortion on the boundary layer due to the combined action of centrifugal and Coriolis forces. Here we will derive a wall function for rotating passages, through means of machine-learning. The algorithm is directly implemented in the N-S equations solver. Cross-validation results show that the machine-learnt wall treatment is able to effectively correct the turbulent kinetic energy field near the solid walls, without impairing the accuracy of the k-epsilon model.

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Assessment of a Machine-Learnt Adaptive Wall-Function in a Compressor Cascade With Sinusoidal Leading Edge

Cited by 4 publications

References 6 publications

Assessment of MULTALL as Computational Fluid Dynamics Code for the Analysis of Tube-Axial Fans

Assessment of MULTALL as Computational Fluid Dynamics Code for the Analysis of Tube-Axial Fans

A Review of Intelligent Airfoil Aerodynamic Optimization Methods Based on Data-Driven Advanced Models

A Machine-Learnt Wall Function for Rotating Diffusers

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