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2025INF013
Doctoral thesis

Lattice Boltzmann methods for hydraulic turbomachines

  • 2025

PhD: Università della Svizzera italiana

Lattice Boltzmann method
Entropic multi-relaxation time method
Large eddy simulation
Turbomachinery
Hydraulic machines
GPU
Multi-GPU
Rotating stall
English In the field of energy production, hydraulic turbomachines such as turbines and pump-turbines often operate under part-load conditions, where the flow exhibits highly complex and unsteady behaviour. Simulating such flows numerically is challenging, as many transient phenomena are dominated by turbulent effects. Traditional Reynolds-averaged Navier-Stokes (RANS) methods often fail to capture these dynamics accurately due to their dependence on turbulence models, while large eddy simulations (LES) are typically too computationally demanding for industrial use. The lattice Boltzmann method (LBM) offers a promising alternative, with its high parallelizability and potential for accurate simulations of turbulent flows, but is largely absent in the context of unsteady flows in hydraulic turbomachines. This work aims to investigate whether the LBM approach can provide accurate and computationally feasible simulations of turbulent, unsteady flows in rotating hydraulic machinery. Specifically, the objectives are: (1) to develop a dedicated, GPU-accelerated fluid solver, TurboLaB, based on the entropic multi-relaxation time method, and tailored for internal flows in rotating geometries and complex boundary conditions; (2) to investigate the requirements and provide solutions for the treatment of the high Reynolds numbers encountered in such applications; (3) to validate the solver against academic and industrial benchmarks; and (4) to assess its performance in capturing key unsteady phenomena in complex hydraulic machines. The thesis presents the underlying governing equations and numerical methods, solver architecture, and a series of verification and validation studies. Results demonstrate that the proposed LBM approach can deliver both accuracy and efficiency, supporting its potential for broader application in turbomachinery simulation.
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  • English
Classification
Computer science and technology
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License undefined
Open access status
green
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https://n2t.net/ark:/12658/srd1332900
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  • 2025INF013: 15