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Sensitivity of Supersonic ORC Turbine Injector Designs to Fluctuating Operating Conditions

Communication avec acte
Auteur
BUFI, Elio Antonio
134975 Laboratoire de Dynamique des Fluides [DynFluid]
CINNELLA, Paola
134975 Laboratoire de Dynamique des Fluides [DynFluid]
MERLE, Xavier
134975 Laboratoire de Dynamique des Fluides [DynFluid]
CINNELLA, Paola
134975 Laboratoire de Dynamique des Fluides [DynFluid]

URI
http://hdl.handle.net/10985/15321
DOI
DOI: 10.1115/GT2015-42193
Date
2015

Résumé

The design of an efficient organic rankine cycle (ORC) expander needs to take properly into account strong real gas effects that may occur in given ranges of operating conditions, which can also be highly variable. In this work, we first design ORC turbine geometries by means of a fast 2-D design procedure based on the method of characteristics (MOC) for supersonic nozzles characterized by strong real gas effects. Thanks to a geometric post-processing procedure, the resulting nozzle shape is then adapted to generate an axial ORC blade vane geometry. Subsequently, the impact of uncertain operating conditions on turbine design is investigated by coupling the MOC algorithm with a Probabilistic Collocation Method (PCM) algorithm. Besides, the injector geometry generated at nominal operating conditions is simulated by means of an in-house CFD solver. The code is coupled to the PCM algorithm and a performance sensitivity analysis, in terms of adiabatic efficiency and power output, to variations of the operating conditions is carried out.

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  • Dynamique des Fluides (DynFluid)

Documents liés

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  • Sparse Bayesian Learning of Explicit Algebraic Reynolds-Stress models for turbulent separated flows 
    Article dans une revue avec comité de lecture
    CHERROUD, Soufiane; MERLE, Xavier; ccCINNELLA, Paola; ccGLOERFELT, Xavier (Elsevier BV, 2022-12)
    A novel Sparse Bayesian Learning (SBL) framework is introduced for generating parsimonious stochastic algebraic stress closures for the Reynolds-Averaged Navier–Stokes (RANS) equations from high-fidelity data. The models ...
  • Efficient Uncertainty Quantification of Turbulent Flows through Supersonic ORC Nozzle Blades 
    Article dans une revue avec comité de lecture
    BUFI, Elio Antonio; CINNELLA, Paola (Elsevier, 2015)
    This work aims at assessing different Uncertainty Quantification (UQ) methodologies for the stochastic analysis and robust design of Organic Rankine Cycle (ORC) turbines under multiple uncertainties. Precisely, we investigate ...
  • Bayesian quantification of thermodynamic uncertainties in dense gas flows 
    Article dans une revue avec comité de lecture
    MERLE, Xavier; CINNELLA, Paola (Elsevier, 2015)
    A Bayesian inference methodology is developed for calibrating complex equations of state used in numerical fluid flow solvers. Precisely, the input parameters of three equations of state commonly used for modeling the ...
  • Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models 
    Article dans une revue avec comité de lecture
    ccDE ZORDO-BANLIAT, Maximilien; MERLE, Xavier; ccDERGHAM, Grégory; ccCINNELLA, Paola (Elsevier BV, 2020-04)
    The Reynolds-Averaged Navier-Stokes (RANS) equations represent the computational workhorse for engineering design, despite their numerous flaws. Improving and quantifying the uncertainties associated with RANS models is ...
  • Bayesian quantification of thermodynamic uncertainties in dense gas flows 
    Article dans une revue avec comité de lecture
    MERLE, Xavier; CINNELLA, Paola (Elsevier, 2014)
    A Bayesian inference methodology is developed for calibrating complex equations of state used in numerical fluid flow solvers. Precisely, the input parameters of three equations of state commonly used for modeling the ...

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