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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sat, 18 Jan 2020 00:42:26 GMT2020-01-18T00:42:26ZAirfoil Shape Optimization for Transonic Flows of Bethe–Zel’dovich–Thompson Fluids
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.Numerical Study of Multistage Transcritical Organic Rankine Cycle Axial Turbines
http://hdl.handle.net/10985/10145
Numerical Study of Multistage Transcritical Organic Rankine Cycle Axial Turbines
SCIACOVELLI, L.; CINNELLA, Paola
Transonic flows through axial, multi-stage, transcritical ORC turbines, are investigated by using a numerical solver including advanced multiparameter equations of state and a high-order discretization scheme. The working fluids in use are the refrigerants R134a and R245fa, classified as dense gases due to their complex molecules and relatively high molecular weight. Both inviscid and viscous numerical simulations are carried out to quantify the impact of dense gas effects and viscous effects on turbine performance. Both supercritical and subcritical inlet conditions are studied for the considered working fluids. In the former case, flow across the turbine is transcritical, since turbine output pressure is subcritical. Numerical results show that, due to dense gas effects characterizing the flow at supercritical inlet conditions, supercritical ORC turbines enable, for a given pressure ratio, a higher isentropic efficiency than subcritical turbines using the same working fluid. Moreover, for the selected operating conditions, R134a provides a better performance than R245fa.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/101452014-01-01T00:00:00ZSCIACOVELLI, L.CINNELLA, PaolaTransonic flows through axial, multi-stage, transcritical ORC turbines, are investigated by using a numerical solver including advanced multiparameter equations of state and a high-order discretization scheme. The working fluids in use are the refrigerants R134a and R245fa, classified as dense gases due to their complex molecules and relatively high molecular weight. Both inviscid and viscous numerical simulations are carried out to quantify the impact of dense gas effects and viscous effects on turbine performance. Both supercritical and subcritical inlet conditions are studied for the considered working fluids. In the former case, flow across the turbine is transcritical, since turbine output pressure is subcritical. Numerical results show that, due to dense gas effects characterizing the flow at supercritical inlet conditions, supercritical ORC turbines enable, for a given pressure ratio, a higher isentropic efficiency than subcritical turbines using the same working fluid. Moreover, for the selected operating conditions, R134a provides a better performance than R245fa.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 estimates of parameter variability in the k − ε turbulence model
http://hdl.handle.net/10985/10077
Bayesian estimates of parameter variability in the k − ε turbulence model
EDELING, W.N.; CINNELLA, Paola; DWIGHT, R.P.; BIJL, H.
In this paper we are concerned with obtaining estimates for the error in Reynolds-Averaged Navier-Stokes (RANS) simulations based on the Launder-Sharma k−ε turbulence closure model, for a limited class of flows. In particular we search for estimates grounded in uncertainties in the space of model closure coeffi-cients, for wall-bounded flows at a variety of favourable and adverse pressure gradients. In order to estimate the spread of closure coefficients which repro-duces these flows accurately, we perform 13 separate Bayesian calibrations – each at a different pressure gradient – using measured boundary-layer velocity profiles, and a statistical model containing a multiplicative model inadequacy term in the solution space. The results are 13 joint posterior distributions over coefficients and hyper-parameters. To summarize this information we compute Highest Posterior-Density (HPD) intervals, and subsequently represent the to-tal solution uncertainty with a probability-box (p-box). This p-box represents both parameter variability across flows, and epistemic uncertainty within each calibration. A prediction of a new boundary-layer flow is made with uncer-tainty bars generated from this uncertainty information, and the resulting error estimate is shown to be consistent with measurement data.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/100772014-01-01T00:00:00ZEDELING, W.N.CINNELLA, PaolaDWIGHT, R.P.BIJL, H.In this paper we are concerned with obtaining estimates for the error in Reynolds-Averaged Navier-Stokes (RANS) simulations based on the Launder-Sharma k−ε turbulence closure model, for a limited class of flows. In particular we search for estimates grounded in uncertainties in the space of model closure coeffi-cients, for wall-bounded flows at a variety of favourable and adverse pressure gradients. In order to estimate the spread of closure coefficients which repro-duces these flows accurately, we perform 13 separate Bayesian calibrations – each at a different pressure gradient – using measured boundary-layer velocity profiles, and a statistical model containing a multiplicative model inadequacy term in the solution space. The results are 13 joint posterior distributions over coefficients and hyper-parameters. To summarize this information we compute Highest Posterior-Density (HPD) intervals, and subsequently represent the to-tal solution uncertainty with a probability-box (p-box). This p-box represents both parameter variability across flows, and epistemic uncertainty within each calibration. A prediction of a new boundary-layer flow is made with uncer-tainty bars generated from this uncertainty information, and the resulting error estimate is shown to be consistent with measurement data.Numerical investigation of dense gas flows through transcritical multistage axial Organic Rankine Cycle turbines
http://hdl.handle.net/10985/7655
Numerical investigation of dense gas flows through transcritical multistage axial Organic Rankine Cycle turbines
SCIACOVELLI, Lucas; CINNELLA, Paola
Many recent studies suggest that supercritical Organic Rankine Cycles have a great potential for lowtemperature heat recovery applications, since they allow better recovery efficiency for a simplified cycle architecture. In this work we investigate flows of dense gases through axial, multi-stage, supercritical ORC turbines, using a numerical code including advanced equations of state and a high-order discretization scheme. Several working fluids are considered, and performances of supercritical turbines are compared to those of subcritical ones using the same fluids.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/76552013-01-01T00:00:00ZSCIACOVELLI, LucasCINNELLA, PaolaMany recent studies suggest that supercritical Organic Rankine Cycles have a great potential for lowtemperature heat recovery applications, since they allow better recovery efficiency for a simplified cycle architecture. In this work we investigate flows of dense gases through axial, multi-stage, supercritical ORC turbines, using a numerical code including advanced equations of state and a high-order discretization scheme. Several working fluids are considered, and performances of supercritical turbines are compared to those of subcritical ones using the same fluids.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.