Publications
137 results found
Sakai E, Meng B, Ahlfeld R, et al., 2018, Bi-fidelity UQ with Combination of co-Kriging and Arbitrary Polynomial Chaos: Film Cooling with Back Facing Step using RANS and DES, International Journal of Heat and Mass Transfer, ISSN: 0017-9310
Griffini D, Salvadori S, Carnevale M, et al., 2018, Uncertainty Quantification in Hydrodynamic Bearings, Energy Procedia, ISSN: 1876-6102
Gaymann A, Pietropaoli M, Crespo L, et al., 2018, Random Variable Estimation and Model Calibration in the Presence of Epistemic and Aleatory Uncertainties, SAE International Journal of Materials and Manufacturing, ISSN: 1946-3987
Sakai E, Meng B, Ahlfeld R, et al., 2018, Uncertainty Quantification Analysis of Back Facing Steps Film Cooling Configurations, ASME IGTI 2018
Gaymann A, Montomoli F, Pietropaoli M, 2018, Robust Fluid Topology Optimization Using Polynomial Chaos Expansions: TOffee, ASME IGTI 2018
Suman A, Casari N, Fabbri E, et al., 2018, Gas Turbine Fouling Tests: Review, Critical Analysis and Particle Impact Behavior Map, ASME IGTI 2018
Cassinelli A, Adami P, Sherwin S, et al., 2018, High Fidelity Spectral/hp Element Methods for Turbomachinery, ASME IGTI 2018
Casari N, Pinelli M, Suman A, et al., 2018, On Deposit Sintering and Detachment From Gas Turbines, ASME IGTI 2018
Gauch H, Montomoli F, Tagarielli V, 2018, On the role of fluid-structure interaction on structural loading by pressure waves in air, Journal of Applied Mechanics, ISSN: 0021-8936
Montomoli F, 2018, Future developments, Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines: Second Edition, Pages: 195-196, ISBN: 9783319929422
This chapter suggests future development in Uncertainty Quantification for Aircraft Engines.
Montomoli F, Massini M, 2018, Uncertainty quantification applied to gas turbine components, Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines: Second Edition, Pages: 157-193, ISBN: 9783319929422
The previous chapters analyzed the level of uncertainty in different gas turbine components, how this affects the performance such as life and fuel con- sumption, and the numerical uncertainty introduced by the CFD modeling itself. This chapter shows how uncertainty quantification techniques are used nowadays in CFD to study the impact of such manufacturing errors, pointing out, for each component, what has been learned and/or discovered using UQ, and which methodology has been used.
Massini M, Montomoli F, 2018, Manufacturing/in-service uncertainty and impact on life and performance of gas turbines/aircraft engines, Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines: Second Edition, Pages: 1-32, ISBN: 9783319929422
This chapter highlights the impact of manufacturing errors on performances of aircraft engines and gas turbines in general. The reader should use this chapter to identify the regions where uncertainty quantification (UQ) should be used to improve the reliability of a gas turbine design and define where this matters.
Casari N, Pinelli M, Suman A, et al., 2018, EBFOG: Deposition, Erosion, and Detachment on High-Pressure Turbine Vanes, JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME, Vol: 140, ISSN: 0889-504X
Montomoli F, 2018, Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines, Publisher: Springer, ISBN: 978-3319146805
This book introduces novel design techniques developed to increase the safety of aircraft engines. The authors demonstrate how the application of uncertainty methods can overcome problems in the accurate prediction of engine lift, caused by manufacturing error. This in turn ameliorates the difficulty of achieving required safety margins imposed by limits in current design and manufacturing methods. This text shows that even state-of-the-art computational fluid dynamics (CFD) is not able to predict the same performance measured in experiments; CFD methods assume idealised geometries but ideal geometries do not exist, cannot be manufactured and their performance differs from real-world ones. By applying geometrical variations of a few microns, the agreement with experiments improves dramatically, but unfortunately the manufacturing errors in engines or in experiments are unknown. In order to overcome this limitation, uncertainty quantification considers the probability density functions of manufacturing errors. It is then possible to predict the overall variation of the jet engine performance using stochastic techniques. Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines demonstrates that some geometries are not affected by manufacturing errors, meaning that it is possible to design safer engines. Instead of trying to improve the manufacturing accuracy, uncertainty quantification when applied to CFD, is able to indicate an improved design direction.
Ahlfeld R, Montomoli F, Carnevale M, et al., 2018, Autonomous uncertainty quantification for discontinuous models using multivariate Padé approximations, Journal of Turbomachinery, Vol: 140, Pages: 041004-1-041004-10, ISSN: 0889-504X
Problems in turbomachinery computational fluid dynamics (CFD) are often characterized by nonlinear and discontinuous responses. Ensuring the reliability of uncertainty quantification (UQ) codes in such conditions, in an autonomous way, is challenging. In this work, we suggest a new approach that combines three state-of-the-art methods: multivariate Padé approximations, optimal quadrature subsampling (OQS), and statistical learning. Its main component is the generalized least-squares multivariate Padé- Legendre (PL) approximation. PL approximations are globally fitted rational functions that can accurately describe discontinuous nonlinear behavior. They need fewer model evaluations than local or adaptive methods and do not cause the Gibbs phenomenon like continuous polynomial chaos methods. A series of modifications of the Padé algorithm allows us to apply it to arbitrary input points instead of optimal quadrature locations. This property is particularly useful for industrial applications, where a database of CFD runs is already available, but not in optimal parameter locations. One drawback of the PL approximation is that it is nontrivial to ensure reliability. To improve stability, we suggest to couple it with OQS. Our reasoning is that least-squares errors, caused by an ill-conditioned design matrix, are the main source of error. Finally, we use statistical learning methods to check smoothness and convergence. The resulting method is shown to efficiently and correctly fit thousands of partly discontinuous response surfaces for an industrial film cooling and shock interaction problem using only nine CFD simulations.
Tagarielli V, gauch HL, montomoli F, 2018, The response of an elastic-plastic clamped beam to transverse pressure loading, International Journal of Impact Engineering, Vol: 112, Pages: 30-40, ISSN: 0734-743X
This study presents a new analytical model to predict the response of elastic-plastic, fully clamped beams to transverse pressure loading. The model accounts for travelling elastic flexural waves, stationary and travelling plastic hinges, elastic-plastic stretching and plastic shear deformation. The predictions of the model are validated by detailed Finite Element simulations. The model is used to construct deformation mechanism maps and design charts.
Gaymann A, Pietropaoli M, Crespo LG, et al., 2018, Random Variable Estimation and Model Calibration in the Presence of Epistemic and Aleatory Uncertainties, SAE
© 2018 SAE International. All Rights Reserved. This paper presents strategies for evaluating the mean, variance, and failure probability of a response variable given measurements subject to both epistemic and aleatory uncertainties. We focus on a case in which standard sensor calibration techniques cannot be used to eliminate measurement error since the uncertainties affecting the metrology system depend upon the measurement itself (e.g., the sensor bias is not constant and the measurement noise is colored). To this end, we first characterize all possible realizations of the true response that might have led to each of such measurements. This process yields a surrogate of the data for the unobservable true response taking the form of a random variable. Each of these variables, called a Random Datum Model (RDM), is manufactured according to a measurement and to the underlying structure of the uncertainty. Several random variable estimation and model calibration techniques are used within the RDM framework to approximate and bound the three metrics of interest. In contrast to all approximations, the bounding techniques account for the irreducible prediction error caused by the uncertainty. The convergence of the predictions as a function of the number of observations available is evaluated numerically for several datasets. The model of the metrology system, and the main goals of this article were taken from the Sandia uncertainty quantification challenge [1]. The framework proposed not only applies to the metrology system posed therein but to systems having uncertainties that depend arbitrarily on the measurement.
Ahlfeld R, Montomoli F, 2017, A single formulation for uncertainty propagation in turbomachinery: SAMBA PC, Journal of Turbomachinery, Vol: 139, Pages: 1-10, ISSN: 0889-504X
This work newly proposes an uncertainty quantification (UQ) method named sparse approximation of moment-based arbitrary polynomial chaos (SAMBA PC) that offers a single solution to many current problems in turbomachinery applications. At the moment, every specific case is characterized by a variety of different input types such as histograms (from experimental data), normal probability density functions (PDFs) (design rules) or fat tailed PDFs (for rare events). Thus, the application of UQ requires the adaptation of ad hoc methods for each individual case. A second problem is that parametric PDFs have to be determined for all inputs. This is difficult if only few samples are available. In gas turbines, however, the collection of statistical information is difficult, expensive, and having scarce information is the norm. A third critical limitation is that if using nonintrusive polynomial chaos (NIPC) methods, the number of required simulations grows exponentially with increasing numbers of input uncertainties: the so-called “curse of dimensionality.” It is shown that the fitting of parametric PDFs to small data sets can lead to large bias and the direct use of the available data is more accurate. This is done by propagating uncertainty through several test functions and the computational fluid dynamics (CFD) simulation of a diffuser, highlighting the impact of different PDF fittings on the output. From the results, it is concluded that the direct propagation of the experimental data set is preferable to the fit of parametric distributions if data is scarce. Thus, the suggested method offers an alternative to the maximum entropy theorem to handle scarce data. SAMBA simplifies the mathematical procedure for many different input types by basing the polynomial expansion on moments. Its moment-based expansion automatically takes care of arbitrary combinations of different input data. It is also numerically efficient compared to other UQ implementations. The re
Ahlfeld R, Carnevale M, Salvadori S, et al., 2017, An Autonomous Uncertainty Quantification Method for the Digital Age: Transonic Flow Simulations Using Multivariate Padé Approximations, Journal of Turbomachinery, ISSN: 0889-504X
Ahlfeld R, Montomoli F, 2017, A SINGLE FORMULATION FOR UNCERTAINTY PROPAGATION INTURBOMACHINERY: SAMBA, ASME IGTI Turbo Expo 2016
Gaymann A, Montomoli F, Pietropaoli M, 2017, Design for Additive Manufacturing: Valves Without Moving Parts, ASME IGTI Turbo Expo 2017
Ahlfeld R, Carnevale M, Montomoli F, et al., 2017, An Autonomous Uncertainty Quantification Method for the Digital Age: Transonic Flow Simulations Using Multivariate Padé Approximations, ASME IGTI Turbo Expo 2017
Casari N, Pinelli M, Suman A, et al., 2017, EBFOG: DEPOSITION, EROSION AND DETACHMENT ON HIGH PRESSURE TURBINE VANES, ASME Turbo Expo: Turbine Technical Conference and Exposition, Publisher: AMER SOC MECHANICAL ENGINEERS
Montomoli F, Pietropaoli M, Ahlfeld R, et al., 2017, DESIGN FOR ADDITIVE MANUFACTURING: INTERNAL CHANNEL OPTIMIZATION, Journal of Engineering for Gas Turbines and Power - Transactions of the ASME, ISSN: 0742-4795
Ahlfeld R, Montomoli F, Carnevale M, et al., 2017, Stochastic variation of the aero-thermal flow field in a cooled high-pressure transonic vane configuration, European Turbomachinery Conference
Casari N, Pinelli M, Suman A, et al., 2017, Gas turbine blade geometry variation due to fouling, European Turbomachinery Conference
Casari N, Pinelli M, Suman A, et al., 2017, An energy based fouling model for gas turbines: EBFOG, Journal of Turbomachinery - Transactions of the ASME, Vol: 139, Pages: 021002-1-021002-8, ISSN: 0889-504X
Fouling is a major problem in gas turbines for aeropropulsionbecause the formation of aggregates on the wet surfacesof the machine affects aerodynamic and heat loads.The representation of fouling in CFD is based on the evaluationof the sticking probability, i.e. the probability a particletouching a solid surface has to stick to that surface. Two mainmodels are currently available in literature for the evaluation ofthe sticking coefficient: one is based on a critical threshold forthe viscosity, the other is based on the normal velocity to thesurface. However, both models are application specific and lackgenerality.This work presents an innovative model for the estimationof the sticking probability. This quantitiy is evaluated by comparingthe kinetic energy of the particle with an activation energywhich describes the state of the particle. The sticking criteriontakes the form of an Arrhenius-type equation. A general formulationfor the sticking coefficient is obtained. The method,named EBFOG (Energy Based FOulinG), is the first ”energy”based model presented in the open literature able to account anycommon deposition effect in gas turbines.The EBFOG model is implemented into a Lagrangian trackingprocedure, coupled to a fully three-dimensional CFD solver.Particles are tracked inside the domain and equations for the momentumand temperature of each particle are solved. The localgeometry of the blade is modified accordingly to the deposition rate. The mesh is modified and the CFD solver updates the flowfield.The application of this model to particle deposition in highpressure turbine vanes is investigated, showing the flexibility ofthe proposed methodology. The model is particularly importantin aircraft engines where the effect of fouling for the turbine, inparticular the reduction of the HP nozzle throat area, influencesheavily the performance by reducing the core capacity. The energybased approach is used to quantify the throat area reductionrate and estimate
Casari N, Pinelli M, Suman A, et al., 2017, Gas turbine blade geometry variation due to fouling, ISSN: 2313-0067
Solid particles ingestion is a severe problem for gas turbines. In the aero-propulsion field the main problems related to this phenomenon occur on the hot sections of the machinery. Impinging particles can stick or Erode the blade material. The deposition on the turbine blades is the main issue among the two and the clogging of cooling holes can even speed up this process rising the blade surface temperature. An higher temperature affects negatively the deposition problems, increasing particle stickiness. In this paper an innovative approach to account for fouling and erosion effects on turbine vanes is presented. An energetic model to predict the sticking probability is used (EBFOG, from Energy Based FOulinG) and the erosion is evaluated through the model proposed by Tabakoff. Geometry variation of blades subject to fouling are investigated by means of a moving mesh technique which accounts for the boundary displacement of the blade surface.
Salvadori S, Carnevale M, Ahlfeld R, et al., 2017, Stochastic variation of the aero-thermal flow field in a cooled high-pressure transonic vane configuration, ISSN: 2313-0067
In transonic high-pressure turbine stages, oblique shocks originated from vane trailing edges impact the rear suction side of each adjacent vane. High-pressure vanes are usually cooled to tolerate the combustor exit temperature levels, which would reduce dramatically the residual life of a solid vane. Then, it is highly probable that shock impingement will occur in proximity of one of the coolant rows. It has already been observed that the presence of an adverse pressure gradient generates non-negligible effects on heat load due to the increase in boundary layer thickness and turbulence level, with a detrimental impact on the local adiabatic effectiveness values. Furthermore, the generation of a tornado-like vortex has been recently observed that could further decrease the efficacy of the cooling system by moving cold flow far from the vane wall. It must be also underlined that manufacturing deviations and in-service degradation are responsible for the stochastic variation of geometrical parameters. This latter phenomenon greatly alters the unsteady location of the shock impingement and the time-dependent thermal load on the vane. Present work starts from what is shown in literature and provides a highly-detailed description of the aero-thermal field that occurs on a model that represents the flow conditions occurring on the rear suction side of a cooled vane. The numerical model is initially validated against the experimental data obtained by the University of Karlsruhe during TATEF2 EU project, and then an uncertainty quantification methodology based on the probabilistic collocation method and on Padè's polynomials is used to consider the probability distribution of the geometrical parameters. The choice of aleatory unknowns allows to consider the mutual effects between shock-waves, trailing edge thickness and hole diameter. Turbulence is modelled by using the Reynolds Stress Model already implemented in ANSYS® Fluent®. Special attention is paid to
Salvadori S, Carnevale M, Ahlfeld R, et al., 2017, Stochastic variation of the aero-thermal flow field in a cooled high-pressure transonic vane configuration, 12th European Conference on Turbomachinery Fluid Dynamics and Thermodynamics, ETC 2017, ISSN: 2410-4833
Copyright © by the Authors. In transonic high-pressure turbine stages, oblique shocks originated from vane trailing edges impact the rear suction side of each adjacent vane. High-pressure vanes are usually cooled to tolerate the combustor exit temperature levels, which would reduce dramatically the residual life of a solid vane. Then, it is highly probable that shock impingement will occur in proximity of one of the coolant rows. It has already been observed that the presence of an adverse pressure gradient generates non-negligible effects on heat load due to the increase in boundary layer thickness and turbulence level, with a detrimental impact on the local adiabatic effectiveness values. Furthermore, the generation of a tornado-like vortex has been recently observed that could further decrease the efficacy of the cooling system by moving cold flow far from the vane wall. It must be also underlined that manufacturing deviations and in-service degradation are responsible for the stochastic variation of geometrical parameters. This latter phenomenon greatly alters the unsteady location of the shock impingement and the time-dependent thermal load on the vane. Present work starts from what is shown in literature and provides a highly-detailed description of the aero-thermal field that occurs on a model that represents the flow conditions occurring on the rear suction side of a cooled vane. The numerical model is initially validated against the experimental data obtained by the University of Karlsruhe during TATEF2 EU project, and then an uncertainty quantification methodology based on the probabilistic collocation method and on Padè's polynomials is used to consider the probability distribution of the geometrical parameters. The choice of aleatory unknowns allows to consider the mutual effects between shock-waves, trailing edge thickness and hole diameter. Turbulence is modelled by using the Reynolds Stress Model already implemented in ANSYS® Fluent&re
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