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http://hdl.handle.net/10985/6774
Airfoil Shape Optimization for Transonic Flows of Bethe–Zel’dovich–Thompson Fluids
CONGEDO, Pietro; CORRE, Christophe; CINNELLA, Paola
High-performance airfoils for transonic flows of Bethe–Zel’dovich–Thompson fluids are constructed using a robust and efficient Euler flow solver coupled with a multi-objective genetic algorithm. Bethe–Zel’dovich– Thompson fluids are characterized by negative values of the fundamental derivative of gasdynamics for a range of temperatures and pressures in the vapor phase, which leads to nonclassical gasdynamic behaviors such as the disintegration of compression shocks. Using Bethe–Zel’dovich–Thompson gases as working fluids may result in low drag exerted on airfoils operating at high transonic speeds, due to a substantial increase in the airfoil critical Mach number. This advantage can be further improved by a proper design of the airfoil shape, also leading to the enlargement of the airfoil operation range within which Bethe–Zel’dovich–Thompson effects are significant. Such a result is of particular interest in view of the exploitation of Bethe–Zel’dovich–Thompson fluids for the development of high-efficiency turbomachinery.
Publication précédent le recrutement de l'auteur à l'ENSAM
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10985/67742007-01-01T00:00:00ZCONGEDO, PietroCORRE, ChristopheCINNELLA, PaolaHigh-performance airfoils for transonic flows of Bethe–Zel’dovich–Thompson fluids are constructed using a robust and efficient Euler flow solver coupled with a multi-objective genetic algorithm. Bethe–Zel’dovich– Thompson fluids are characterized by negative values of the fundamental derivative of gasdynamics for a range of temperatures and pressures in the vapor phase, which leads to nonclassical gasdynamic behaviors such as the disintegration of compression shocks. Using Bethe–Zel’dovich–Thompson gases as working fluids may result in low drag exerted on airfoils operating at high transonic speeds, due to a substantial increase in the airfoil critical Mach number. This advantage can be further improved by a proper design of the airfoil shape, also leading to the enlargement of the airfoil operation range within which Bethe–Zel’dovich–Thompson effects are significant. Such a result is of particular interest in view of the exploitation of Bethe–Zel’dovich–Thompson fluids for the development of high-efficiency turbomachinery.Toward improved simulation tools for compressible turbomachinery: assessment of RBC schemes for the transonic NASA Rotor 37 benchmark case
http://hdl.handle.net/10985/7658
Toward improved simulation tools for compressible turbomachinery: assessment of RBC schemes for the transonic NASA Rotor 37 benchmark case
CINNELLA, Paola; MICHEL, Bruno
Residual-based-compact schemes (RBC) of 2nd and 3rd-order accuracy are applied to a challenging 3D ow through a transonic compressor. The proposed schemes provide almost mesh-converged solutions in good agreement with experimental data on relatively coarse grids, which allows computational cost reductions by a factor between 2 and 4 with respect to standard solvers for a given accuracy level.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/76582013-01-01T00:00:00ZCINNELLA, PaolaMICHEL, BrunoResidual-based-compact schemes (RBC) of 2nd and 3rd-order accuracy are applied to a challenging 3D ow through a transonic compressor. The proposed schemes provide almost mesh-converged solutions in good agreement with experimental data on relatively coarse grids, which allows computational cost reductions by a factor between 2 and 4 with respect to standard solvers for a given accuracy level.Convergence of Fourier-based time methods for turbomachinery wake passing problems
http://hdl.handle.net/10985/10074
Convergence of Fourier-based time methods for turbomachinery wake passing problems
GOMAR, Adrien; BOUVY, Quentin; SICOT, Frédéric; DUFOUR, Guillaume; CINNELLA, Paola; FRANCOIS, Benjamin
The convergence of Fourier-based time methods applied to turbomachinery flows is assessed. The focus is on the harmonic balance method, which is a timedomain Fourier-based approach standing as an efficient alternative to classical time marching schemes for periodic flows. In the literature, no consensus exists concerning the number of harmonics needed to achieve convergence for turbomachinery stage configurations. In this paper it is shown that the convergence of Fourier-based methods is closely related to the impulsive nature of the flow solution, which in turbomachines is essentially governed by the characteristics of the passing wakes between adjacent rows. As a result of the proposed analysis, a priori estimates are provided for the minimum number of harmonics required to accurately compute a given turbomachinery configuration. Their application to several contra-rotating open-rotor configurations is assessed, demonstrating the practical interest of the proposed methodology.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/100742014-01-01T00:00:00ZGOMAR, AdrienBOUVY, QuentinSICOT, FrédéricDUFOUR, GuillaumeCINNELLA, PaolaFRANCOIS, BenjaminThe convergence of Fourier-based time methods applied to turbomachinery flows is assessed. The focus is on the harmonic balance method, which is a timedomain Fourier-based approach standing as an efficient alternative to classical time marching schemes for periodic flows. In the literature, no consensus exists concerning the number of harmonics needed to achieve convergence for turbomachinery stage configurations. In this paper it is shown that the convergence of Fourier-based methods is closely related to the impulsive nature of the flow solution, which in turbomachines is essentially governed by the characteristics of the passing wakes between adjacent rows. As a result of the proposed analysis, a priori estimates are provided for the minimum number of harmonics required to accurately compute a given turbomachinery configuration. Their application to several contra-rotating open-rotor configurations is assessed, demonstrating the practical interest of the proposed methodology.Bayesian quantification of thermodynamic uncertainties in dense gas flows
http://hdl.handle.net/10985/8640
Bayesian quantification of thermodynamic uncertainties in dense gas flows
MERLE, Xavier; CINNELLA, Paola
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 thermodynamic behavior of so-called dense gas flows, – i.e. flows of gases characterized by high molecular weights and complex molecules, working in thermodynamic conditions close to the liquid-vapor saturation curve–, are calibrated by means of Bayesian inference from reference aerodynamic data for a dense gas flow over a wing section. Flow thermodynamic conditions are such that the gas thermodynamic behavior strongly deviates from that of a perfect gas. In the aim of assessing the proposed methodology, synthetic calibration data –specifically, wall pressure data– are generated by running the numerical solver with a more complex and accurate thermodynamic model. The statistical model used to build the likelihood function includes a model-form inadequacy term, accounting for the gap between the model output associated to the best-fit parameters, and the rue phenomenon. Results show that, for all of the relatively simple models under investigation, calibrations lead to informative posterior probability density distributions of the input parameters and improve the predictive distribution significantly. Nevertheless, calibrated parameters strongly differ from their expected physical values. The relationship between this behavior and model-form inadequacy is discussed.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/86402014-01-01T00:00:00ZMERLE, XavierCINNELLA, PaolaA 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 thermodynamic behavior of so-called dense gas flows, – i.e. flows of gases characterized by high molecular weights and complex molecules, working in thermodynamic conditions close to the liquid-vapor saturation curve–, are calibrated by means of Bayesian inference from reference aerodynamic data for a dense gas flow over a wing section. Flow thermodynamic conditions are such that the gas thermodynamic behavior strongly deviates from that of a perfect gas. In the aim of assessing the proposed methodology, synthetic calibration data –specifically, wall pressure data– are generated by running the numerical solver with a more complex and accurate thermodynamic model. The statistical model used to build the likelihood function includes a model-form inadequacy term, accounting for the gap between the model output associated to the best-fit parameters, and the rue phenomenon. Results show that, for all of the relatively simple models under investigation, calibrations lead to informative posterior probability density distributions of the input parameters and improve the predictive distribution significantly. Nevertheless, calibrated parameters strongly differ from their expected physical values. The relationship between this behavior and model-form inadequacy is discussed.Bayesian quantification of thermodynamic uncertainties in dense gas flows
http://hdl.handle.net/10985/10073
Bayesian quantification of thermodynamic uncertainties in dense gas flows
MERLE, Xavier; CINNELLA, Paola
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 thermodynamic behavior of so-called dense gas flows, – i.e. flows of gases characterized by high molecular weights and complex molecules, working in thermodynamic conditions close to the liquid-vapor saturation curve–, are calibrated by means of Bayesian inference from reference aerodynamic data for a dense gas flow over a wing section. Flow thermodynamic conditions are such that the gas thermodynamic behavior strongly deviates from that of a perfect gas. In the aim of assessing the proposed methodology, synthetic calibration data –specifically, wall pressure data– are generated by running the numerical solver with a more complex and accurate thermodynamic model. The statistical model used to build the likelihood func-tion includes a model-form inadequacy term, accounting for the gap between the model output associated to the best-fit parameters, and the true phenomenon. Results show that, for all of the relatively simple models under investigation, calibrations lead to infor-mative posterior probability density distributions of the input parameters and improve the predictive distribution significantly. Nevertheless, calibrated parameters strongly differ from their expected physical values. The relationship between this behavior and model-form inadequacy is discussed.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/100732015-01-01T00:00:00ZMERLE, XavierCINNELLA, PaolaA 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 thermodynamic behavior of so-called dense gas flows, – i.e. flows of gases characterized by high molecular weights and complex molecules, working in thermodynamic conditions close to the liquid-vapor saturation curve–, are calibrated by means of Bayesian inference from reference aerodynamic data for a dense gas flow over a wing section. Flow thermodynamic conditions are such that the gas thermodynamic behavior strongly deviates from that of a perfect gas. In the aim of assessing the proposed methodology, synthetic calibration data –specifically, wall pressure data– are generated by running the numerical solver with a more complex and accurate thermodynamic model. The statistical model used to build the likelihood func-tion includes a model-form inadequacy term, accounting for the gap between the model output associated to the best-fit parameters, and the true phenomenon. Results show that, for all of the relatively simple models under investigation, calibrations lead to infor-mative posterior probability density distributions of the input parameters and improve the predictive distribution significantly. Nevertheless, calibrated parameters strongly differ from their expected physical values. The relationship between this behavior and model-form inadequacy is discussed.Predictive RANS simulations via Bayesian Model-Scenario Averaging
http://hdl.handle.net/10985/10035
Predictive RANS simulations via Bayesian Model-Scenario Averaging
CINNELLA, Paola
The turbulence closure model is the dominant source of error in most Reynolds-Averaged Navier–Stokes simulations, yet no reliable estimators for this error component currently exist. Here we develop a stochastic, a posteriori error estimate, calibrated to specific classes of flow. It is based on variability in model closure coefficients across multiple flow scenarios, for multiple closure models. The variability is estimated using Bayesian calibration against experimental data for each scenario, and Bayesian Model-Scenario Averaging (BMSA) is used to collate the resulting posteriors, to obtain a stochastic estimate of a Quantity of Interest (QoI) in an unmeasured (prediction) scenario. The scenario probabilities in BMSA are chosen using a sensor which automatically weights those scenarios in the calibration set which are similar to the prediction scenario. The methodology is applied to the class of turbulent boundary-layers subject to various pressure gradients. For all considered prediction scenarios the standard-deviation of the stochastic estimate is consistent with the measurement ground truth. Furthermore, the mean of the estimate is more consistently accurate than the individual model predictions.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/100352014-01-01T00:00:00ZCINNELLA, PaolaThe turbulence closure model is the dominant source of error in most Reynolds-Averaged Navier–Stokes simulations, yet no reliable estimators for this error component currently exist. Here we develop a stochastic, a posteriori error estimate, calibrated to specific classes of flow. It is based on variability in model closure coefficients across multiple flow scenarios, for multiple closure models. The variability is estimated using Bayesian calibration against experimental data for each scenario, and Bayesian Model-Scenario Averaging (BMSA) is used to collate the resulting posteriors, to obtain a stochastic estimate of a Quantity of Interest (QoI) in an unmeasured (prediction) scenario. The scenario probabilities in BMSA are chosen using a sensor which automatically weights those scenarios in the calibration set which are similar to the prediction scenario. The methodology is applied to the class of turbulent boundary-layers subject to various pressure gradients. For all considered prediction scenarios the standard-deviation of the stochastic estimate is consistent with the measurement ground truth. Furthermore, the mean of the estimate is more consistently accurate than the individual model predictions.Multi-fidelity optimization strategy for the industrial aerodynamic design of helicopter rotor blades
http://hdl.handle.net/10985/10072
Multi-fidelity optimization strategy for the industrial aerodynamic design of helicopter rotor blades
LEUSINK, Debbie; ALFANO, David; CINNELLA, Paola
The industrial aerodynamic design of helicopter rotor blades needs to consider the two typical flight conditions of hover and forward flight simultaneously. Here, this multi-objective design problem is tackled by using a genetic algorithm, coupled to rotor performance simulation tools. The turn-around time of an optimization loop is acceptable in an industrial design loop when using low-cost, low-fidelity tools such as the comprehensive rotorcraft code HOST, but becomes excessively high when employing high-fidelity models like CFD methods. To incorporate high-fidelity models into the optimization loop while maintaining a moderate computational cost, a Multi-Fidelity Optimization (MFO) strategy is proposed: as a preliminary step, a HOST-based genetic algorithm optimization is used to reduce the parameter space and select a set of blade geometries used for initializing the high-fidelity stage. Secondly, the selected blades are re-evaluated by CFD and used to construct a high-fidelity surrogate model. Finally, a Surrogate Based Optimization (SBO) is carried out and the Pareto optimal individuals according to the SBO are recomputed by CFD for final performance evaluation. The proposed strategy is validated step by step. It is shown that an industrially acceptable number of CFD-simulations is sufficient to obtain blade designs with a significantly higher performance than the baseline and then SBO results issued from a standard Latin-Hypercube-Sampling initialization. The proposed MFO strategy represents an efficient method for the simultaneous optimization of rotor blade geometries in hover and forward flight.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/100722015-01-01T00:00:00ZLEUSINK, DebbieALFANO, DavidCINNELLA, PaolaThe industrial aerodynamic design of helicopter rotor blades needs to consider the two typical flight conditions of hover and forward flight simultaneously. Here, this multi-objective design problem is tackled by using a genetic algorithm, coupled to rotor performance simulation tools. The turn-around time of an optimization loop is acceptable in an industrial design loop when using low-cost, low-fidelity tools such as the comprehensive rotorcraft code HOST, but becomes excessively high when employing high-fidelity models like CFD methods. To incorporate high-fidelity models into the optimization loop while maintaining a moderate computational cost, a Multi-Fidelity Optimization (MFO) strategy is proposed: as a preliminary step, a HOST-based genetic algorithm optimization is used to reduce the parameter space and select a set of blade geometries used for initializing the high-fidelity stage. Secondly, the selected blades are re-evaluated by CFD and used to construct a high-fidelity surrogate model. Finally, a Surrogate Based Optimization (SBO) is carried out and the Pareto optimal individuals according to the SBO are recomputed by CFD for final performance evaluation. The proposed strategy is validated step by step. It is shown that an industrially acceptable number of CFD-simulations is sufficient to obtain blade designs with a significantly higher performance than the baseline and then SBO results issued from a standard Latin-Hypercube-Sampling initialization. The proposed MFO strategy represents an efficient method for the simultaneous optimization of rotor blade geometries in hover and forward flight.Modélisation quasi-dimensionnelle multizone de la phase de combustion dans un moteur à essence.
http://hdl.handle.net/10985/7435
Modélisation quasi-dimensionnelle multizone de la phase de combustion dans un moteur à essence.
KAPRIELIAN, Leslie; DEMOULIN, Marc; CINNELLA, Paola; DARU, Virginie
Quasi-dimensional models are needed in early design stages to evaluate engines sizing. They are based on principles of Thermodynamics and experimental correlations. Here, we improve the accuracy of the classical quasi-dimensional two-zone model by means of two successive modi cations. First, a third zone is added near the walls : in this zone, the gases burn more slowly due to heat losses to the walls. It allows to correctly simulate the attenuation of the combustion when the ame comes near the walls.Secondly, a multi-zone model is built to take into account temperature and concentration gradients in the ame. The models are validated by comparing heat release rates distributions predicted by the two-zone, the three-zone and the multi-zone models against experimental data.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/74352013-01-01T00:00:00ZKAPRIELIAN, LeslieDEMOULIN, MarcCINNELLA, PaolaDARU, VirginieQuasi-dimensional models are needed in early design stages to evaluate engines sizing. They are based on principles of Thermodynamics and experimental correlations. Here, we improve the accuracy of the classical quasi-dimensional two-zone model by means of two successive modi cations. First, a third zone is added near the walls : in this zone, the gases burn more slowly due to heat losses to the walls. It allows to correctly simulate the attenuation of the combustion when the ame comes near the walls.Secondly, a multi-zone model is built to take into account temperature and concentration gradients in the ame. The models are validated by comparing heat release rates distributions predicted by the two-zone, the three-zone and the multi-zone models against experimental data.Recent Progress in High-Order Residual-Based Compact Schemes for Compressible Flow Simulations: Toward Scale-Resolving Simulations and Complex Geometries
http://hdl.handle.net/10985/15375
Recent Progress in High-Order Residual-Based Compact Schemes for Compressible Flow Simulations: Toward Scale-Resolving Simulations and Complex Geometries
CINNELLA, Paola; GRIMICH, Karim; LERAT, Alain; OUTTIER, P. Y.
Recent developments about the extension of high-order Residual-Based Compact schemes to unsteady flows and complex configurations are discussed, with application to scale-resolving simulations and complex turbomachinery flows.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/153752015-01-01T00:00:00ZCINNELLA, PaolaGRIMICH, KarimLERAT, AlainOUTTIER, P. Y.Recent developments about the extension of high-order Residual-Based Compact schemes to unsteady flows and complex configurations are discussed, with application to scale-resolving simulations and complex turbomachinery flows.Robust prediction of dense gas flows under uncertain thermodynamic models
http://hdl.handle.net/10985/15563
Robust prediction of dense gas flows under uncertain thermodynamic models
MERLE, Xavier; CINNELLA, Paola
A Bayesian approach is developed to quantify uncertainties associated with the thermodynamic models used for the simulation of dense gas flows, i.e. flows of gases characterized by complex molecules of moderate to high molecular weight, in thermodynamic conditions of the general order of magnitude of the liquid/vapor critical point. The thermodynamic behaviour of dense gases can be modelled through equations of state with various mathematical structures, all involving a set of material-dependent coefficients. For several organic fluids of industrial interest abundant and high-quality thermodynamic data required to specify such coefficients are hardly available, leading to undetermined levels of uncertainty of the equation output. Additionally, the best choice for the kind of equation of state (mathematical form) to be used is not always easy to determine and it is often based on expert opinion. In other terms, equations of state introduce both parametric and model-form uncertainties, which need to be quantified to make reliable predictions of the flow field. In this paper we propose a statistical inference methodology for estimating both kinds of uncertainties simultaneously. Our approach consists of a calibration step and a prediction step. The former allows to infer on the parameters to be input to the equation of state, based on the observation of aerodynamic quantities like pressure measurements at some locations in the dense gas flow. The subsequent prediction step allows to predict unobserved flow configurations based on the inferred posterior distributions of the coefficients. Model-form uncertainties are incorporated in the prediction step by using a Bayesian model averaging (BMA) approach. This consists in constructing an average of the predictions of various competing models weighted by the posterior model probabilities. Bayesian averaging also provides a useful tool for making robust predictions from a set of alternative calibration scenarios (Bayesian model-scenario averaging or BMSA). The proposed methodology is assessed for a class of dense gas flows, namely transonic flows around an isolated airfoil, at various free-stream thermodynamic conditions in the dense-gas region.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/155632019-01-01T00:00:00ZMERLE, XavierCINNELLA, PaolaA Bayesian approach is developed to quantify uncertainties associated with the thermodynamic models used for the simulation of dense gas flows, i.e. flows of gases characterized by complex molecules of moderate to high molecular weight, in thermodynamic conditions of the general order of magnitude of the liquid/vapor critical point. The thermodynamic behaviour of dense gases can be modelled through equations of state with various mathematical structures, all involving a set of material-dependent coefficients. For several organic fluids of industrial interest abundant and high-quality thermodynamic data required to specify such coefficients are hardly available, leading to undetermined levels of uncertainty of the equation output. Additionally, the best choice for the kind of equation of state (mathematical form) to be used is not always easy to determine and it is often based on expert opinion. In other terms, equations of state introduce both parametric and model-form uncertainties, which need to be quantified to make reliable predictions of the flow field. In this paper we propose a statistical inference methodology for estimating both kinds of uncertainties simultaneously. Our approach consists of a calibration step and a prediction step. The former allows to infer on the parameters to be input to the equation of state, based on the observation of aerodynamic quantities like pressure measurements at some locations in the dense gas flow. The subsequent prediction step allows to predict unobserved flow configurations based on the inferred posterior distributions of the coefficients. Model-form uncertainties are incorporated in the prediction step by using a Bayesian model averaging (BMA) approach. This consists in constructing an average of the predictions of various competing models weighted by the posterior model probabilities. Bayesian averaging also provides a useful tool for making robust predictions from a set of alternative calibration scenarios (Bayesian model-scenario averaging or BMSA). The proposed methodology is assessed for a class of dense gas flows, namely transonic flows around an isolated airfoil, at various free-stream thermodynamic conditions in the dense-gas region.