2018
DOI: 10.3390/aerospace5010032
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Buoyancy-Induced Heat Transfer inside Compressor Rotors: Overview of Theoretical Models

Abstract: Increasing pressures in gas-turbine compressors, particularly in aeroengines where the pressure ratios can be above 50:1, require smaller compressor blades and an increasing focus on blade-clearance control. The blade clearance depends on the radial growth of the compressor discs, which in turn depends on the temperature and stress in the discs. As the flow inside the disc cavities is buoyancy-driven, calculation of the disc temperature is a conjugate problem: the heat transfer from the disc is coupled with th… Show more

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Cited by 17 publications
(4 citation statements)
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“…Some engine designers use empirical formulae which are not always based on appropriate physical models. The authors have recently produced a series of publications on physicallybased theoretical modelling; an overview is presented by Owen et al [9].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Some engine designers use empirical formulae which are not always based on appropriate physical models. The authors have recently produced a series of publications on physicallybased theoretical modelling; an overview is presented by Owen et al [9].…”
Section: Literature Reviewmentioning
confidence: 99%
“…The unstable phenomena in the fluid present a challenge for Computational Fluid Dynamics (CFD). Successful application of CFD as a design tool requires experimental validation and can be supplemented by physically-based theoretical modelling as documented in Owen et al [6]. The practical application of this research is to improve the predictive capability of industrial conjugate models, including fatigue and component-life applications.…”
Section: Computational Modellingmentioning
confidence: 99%
“…This state suffers transitions as the value of R is further increased. Some industrial setups concerning the use of Rayleigh-Bénard convection equations can be found in [33][34][35][36][37][38][39][40]. Reduced-order models based on POD usually operate in two phases: (1) an off-line phase, where proper bases for the problem unknowns are computed from snapshots, and (2) an on-line phase, where the original partial differential equations are projected over the aforementioned bases.…”
Section: Introductionmentioning
confidence: 99%