Imperial College London

DrFrancescoMontomoli

Faculty of EngineeringDepartment of Aeronautics

Reader in Computational Aerodynamics
 
 
 
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Contact

 

+44 (0)20 7594 5151f.montomoli Website

 
 
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Location

 

215City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

89 results found

Pepper N, Montomoli F, Sharma S, 2019, Multiscale uncertainty quantification with arbitrary polynomial chaos, Computer Methods in Applied Mechanics and Engineering, Vol: 357, Pages: 1-20, ISSN: 0045-7825

This work presents a framework for upscaling uncertainty in multiscale models. The problem is relevant to aerospace applications where it is necessary to estimate the reliability of a complete part such as an aeroplane wing from experimental data on coupons. A particular aspect relevant to aerospace is the scarcity of data available.The framework needs two main aspects: an upscaling equivalence in a probabilistic sense and an efficient (sparse) Non-Intrusive Polynomial Chaos formulation able to deal with scarce data. The upscaling equivalence is defined by a Probability Density Function (PDF) matching approach. By representing the inputs of a coarse-scale model with a generalised Polynomial Chaos Expansion (gPCE) the stochastic upscaling problem can be recast as an optimisation problem. In order to define a data driven framework able to deal with scarce data a Sparse Approximation for Moment Based Arbitrary Polynomial Chaos is used. Sparsity allows the solution of this optimisation problem to be made less computationally intensive than upscaling methods relying on Monte Carlo sampling. Moreover this makes the PDF matching method more viable for industrial applications where individual simulation runs may be computationally expensive. Arbitrary Polynomial Chaos is used to allow the framework to use directly experimental data. Finally, the difference between the distributions is quantified using the Kolmogorov–Smirnov (KS) distance and the method of moments in the case of a multi-objective optimisation. It is shown that filtering of dynamical information contained in the fine-scale by the coarse model may be avoided through the construction of a low-fidelity, high-order model.

Journal article

Pepper N, Gerardo-Giorda L, Montomoli F, 2019, Meta-modeling on detailed geography for accurate prediction of invasive alien species dispersal, Scientific Reports, Vol: 9, ISSN: 2045-2322

Invasive species are recognized as a significant threat to biodiversity. The mathematical modeling of their spatio-temporaldynamics can provide significant help to environmental managers in devising suitable control strategies. Several mathematicalapproaches have been proposed in recent decades to efficiently model the dispersal of invasive species. Relying on theassumption that the dispersal of an individual is random, but the density of individuals at the scale of the population can beconsidered smooth, reaction-diffusion models are a good trade-off between model complexity and flexibility for use in differentsituations. In this paper we present a continuous reaction-diffusion model coupled with arbitrary Polynomial Chaos (aPC)to assess the impact of uncertainties in the model parameters. We show how the finite elements framework is well-suitedto handle important landscape heterogeneities as elevation and the complex geometries associated with the boundariesof an actual geographical region. We demonstrate the main capabilities of the proposed coupled model by assessing theuncertainties in the invasion of an alien species invading the Basque Country region in Northern Spain.

Journal article

Gaymann A, Montomoli F, 2019, Deep neural network and Monte Carlo tree search applied to fluid-structure topology optimization, Scientific Reports, Vol: 9, ISSN: 2045-2322

This paper shows the application of Deep Neural Network algorithms for Fluid-Structure Topology Optimization. The strategy offered is a new concept which can be added to the current process used to study Topology Optimization with Cellular Automata, Adjoint and Level-Set methods. The design space is described by a computational grid where every cell can be in two states: fluid or solid. The system does not require human intervention and learns through an algorithm based on Deep Neural Network and Monte Carlo Tree Search. In this work the objective function for the optimization is an incompressible fluid solver but the overall optimization process is independent from the solver. The test case used is a standard duct with back facing step where the optimizer aims at minimizing the pressure losses between inlet and outlet. The results obtained with the proposed approach are compared to the solution via a classical adjoint topology optimization code.

Journal article

Gaymann A, Montomoli F, Pietropaoli M, 2019, Fluid topology optimization: Bio-inspired valves for aircraft engines, International Journal of Heat and Fluid Flow, Vol: 79, Pages: 1-7, ISSN: 0142-727X

This work shows the introduction of three dimensional fluid topology optimization for the design of valves without moving parts that can be manufactured through 3D printing and used in aircraft engines. The design obtained is inherently safer than standard design with moving parts and it resembles closely to biological structures.A new three dimensional fluid topology optimization formulation to achieve such geometries is shown. The novelty of the method is linked to the applicability to realistic high Reynolds numbers, as in aircraft engines. This is the first time that such geometries are obtained for realistic operating conditions that are applicable to gas turbines.The optimal geometries are validated by using Computational Fluid Dynamics

Journal article

Tagarielli V, Gauch H, Montomoli F, Bisio V, rossin Set al., 2019, Predictions of the transient loading on box-like objects by arbitrary pressure waves in air, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol: 475, ISSN: 1364-5021

This study investigates the transient loading on rigid, isolated, box-like objects by impinging pressure waves of variable intensity and time duration. A numerical solver is used to predict the transient flow around the object and theconsequent pressure on the object’s surface. An analytical model is developed which is capable of predicting the transient loading history on the faces of a box-like object; it was found in good agreement with the numerical predictions.The numerical and analytical models are then used to construct non-dimensional design maps. Different regimes ofloading are identified and explored.

Journal article

Salvadori S, Carnevale M, Fanciulli A, Montomoli Fet al., 2019, Uncertainty Quantification of Non-Dimensional Parameters for a Film Cooling Configuration in Supersonic Conditions, FLUIDS, Vol: 4

Journal article

Suman A, Casari N, Fabbri E, Di Mare L, Montomoli F, Pinelli Met al., 2019, Generalization of particle impact behavior in gas turbine via non-dimensional grouping, Progress in Energy and Combustion Science, Vol: 74, Pages: 103-151, ISSN: 1873-216X

Fouling in gas turbines is caused by airborne contaminants which, under certain conditions, adhere to aerodynamicsurfaces upon impact. The growth of solid deposits causes geometric modifications of the blades in terms of bothmean shape and roughness level. The consequences of particle deposition range from performance deterioration tolife reduction to complete loss of power. Due to the importance of the phenomenon, several methods to model particlesticking have been proposed in literature. Most models are based on the idea of a sticking probability, defined as thelikelihood a particle has to stick to a surface upon impact. Other models investigate the phenomenon from adeterministic point of view by calculating the energy available before and after the impact. The nature of the materialsencountered within this environment does not lend itself to a very precise characterization, consequently, it is difficultto establish the limits of validity of sticking models based on field data or even laboratory scale experiments. As aresult, predicting the growth of solid deposits in gas turbines is still a task fraught with difficulty. In this work, two nondimensionalparameters are defined to describe the interaction between incident particles and a substrate, withparticular reference to sticking behavior in a gas turbine. In the first part of the work, historical experimental data onparticle adhesion under gas turbine-like conditions are analyzed by means of relevant dimensional quantities (e.g.particle viscosity, surface tension, and kinetic energy). After a dimensional analysis, the data then are classified usingnon-dimensional groups and a universal threshold for the transition from erosion to deposition and from fragmentationto splashing based on particle properties and impact conditions is identified. The relation between particle kineticenergy/surface energy and the particle temperature normalized by the softening temperature represents the originalnon-dimensional groups

Journal article

Gauch HL, Bisio V, Rossin S, Montomoli F, Tagarielli VLet al., 2019, Transient Loading on Turbomachinery Packages due to Pressure Waves Caused by Accidental Deflagration Events, ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition, Publisher: American Society of Mechanical Engineers

<jats:title>Abstract</jats:title> <jats:p>In this study we present the application of numerical and analytical models to predict the transient loading of structures by impinging pressure and shock waves in air, which have been recently developed by the authors. Non-dimensional design maps are provided which yield predictions of the maximum loads on structures as a function of the problem parameters. Practical example applications, with reference to typical structures used in turbomachinery packages, are presented. These examples demonstrate the superiority of the new modelling techniques to current industrial design guidelines which are mostly extrapolated from simplified methods developed for shock waves. Finally, conclusions are drawn regarding the nature of the loading exerted on the structure in different regimes of problem parameters.</jats:p>

Conference paper

Friso R, Casari N, Suman A, Pinelli M, Montomoli Fet al., 2019, A Design for Fouling Oriented Optimization of an HPT Nozzle, ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition, Publisher: American Society of Mechanical Engineers

<jats:title>Abstract</jats:title> <jats:p>Fouling and erosion are two problems that severely affect gas turbines. The shape of the blade, its roughness, and its structural stability can vary as a consequence of these phenomena. The outcomes of this occurrence can span from the efficiency reduction to the engine shut down according to the nature of the material ingested, to the concentration of contaminants in the air, the cleanliness of fuel and to the particular design of the machine.</jats:p> <jats:p>In this work, an axial turbine airfoil is modified according to the requirement of less sensibility to the phenomena above mentioned, utilizing an automatic optimization algorithm. An artificial neural network surrogate approach is used for searching the optimal shape, minimizing the computational cost of the entire process. The optimum design of the blade is therefore achieved, in order to reduce the effects of deposition on the performance.</jats:p> <jats:p>The methodology here proposed is fully general and it is applied to an HPT nozzle in the present analysis.</jats:p>

Conference paper

Ahlfeld R, Ciampoli F, Pietropaoli M, Pepper N, Montomoli Fet al., 2019, Data-driven uncertainty quantification for Formula 1: Diffuser, wing tip and front wing variations, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, ISSN: 0954-4070

© IMechE 2019. This work introduces a new uncertainty quantification method to better deal with scarce data and long simulation run times in Formula 1 design. Race cars are produced in low quantities and for maximum performance. Thus, their designing process is characterised by manufacturing data shortage and complex Computational Fluid Dynamics simulations with long run times. Their car aerodynamics is subject to many random variables that introduce uncertainty into the down-force and drag performance, such as variations in ride height, front wing direction and pitch angle. To accurately predict the car performance during a race, it is important to study the effect of these random variables. This assessment cannot be performed with the standard deterministic Computational Fluid Dynamics approaches used in Formula 1. Even with regard to stochastic approaches, no efficient method has so far been suggested that addresses the problem of data scarcity. The reason for this is that most efficient uncertainty quantification methods fit probability distributions to the scarce data. It is shown in this work that probability distribution fitting can create a significant error using a simple two-dimensional diffuser example. Subsequently, the use of a new data-driven Polynomial Chaos method and its sparse multi-dimensional extension is suggested and demonstrated for Formula 1 to reduce such errors. This method allows to avoid distribution fitting because it is based on pure data. SAMBA’s general formulation also makes it easier to combine any possible inputs within a sparse description for problems with many variables. SAMBA is applied to two realistic car three-dimensional Computational Fluid Dynamics simulations: a NACA 0012 tip wing and the front part of a Formula 1 car. The probabilistic variations of the lift and drag of these two configurations are calculated using SAMBA and shown to be significant.

Journal article

Suman A, Casari N, Fabbri E, Pinelli M, Di Mare L, Montomoli Fet al., 2019, Gas Turbine Fouling Tests: Review, Critical Analysis, and Particle Impact Behavior Map, Journal of Engineering for Gas Turbines and Power, Vol: 141, ISSN: 0742-4795

© Copyright 2019 by ASME. Fouling affects gas turbine operation, and airborne or fuel contaminants, under certain conditions, become very likely to adhere to surfaces if impact takes place. Particle sticking implies the change in shape in terms of roughness of the impinged surface. The consequences of these deposits could be dramatic: these effects can shut an aircraft engine down or derate a land-based power unit. This occurrence may happen due to the reduction of the compressor flow rate and the turbine capacity, caused by a variation in the HPT nozzle throat area (geometric blockage due to the thickness of the deposited layer and the aerodynamic blockage due to the increased roughness, and in turn boundary layer). Several methods to quantify particle sticking have been proposed in literature so far, and the experimental data used for their validation vary in a wide range of materials and conditions. The experimental analyzes have been supported by (and have given inspiration to) increasingly realistic mathematical models. Experimental tests have been carried out on (i) a full scale gas turbine unit, (ii) wind tunnel testing or hot gas facilities using stationary cascades, able to reproduce the same conditions of gas turbine nozzle operation and finally, (iii) wind tunnel testing or hot gas facilities using a coupon as the target. In this review, the whole variety of experimental tests performed are gathered and classified according to composition, size, temperature, and particle impact velocity. Using particle viscosity and sticking prediction models, over seventy (70) tests are compared with each other and with the model previsions providing a useful starting point for a comprehensive critical analysis. Due to the variety of test conditions, the related results are difficult to be pieced together due to differences in particle material and properties. The historical data of particle deposition obtained over thirty (30) years are classified using particle ki

Journal article

Pietropaoli M, Montomoli F, Gaymann A, 2019, Three dimensional fluid topology optimization for heat transfer, Structural and Multidisciplinary Optimization, Vol: 59, Pages: 801-812, ISSN: 1615-147X

In this work, an in house topology optimization (TO) solver is developed to optimize a conjugate heat transfer problem: realizing more complex and efficient coolant systems by minimizing pressure losses and maximizing the heat transfer. The TO method consists in an idealized sedimentation process in which a design variable, in this case impermeability, is iteratively updated across the domain. The optimal solution is the solidified region uniquely defined by the final distribution of impermeability. Due to the geometrical complexity of the optimal solutions obtained, this design method is not always suitable for classic manufacturing methods (molding, stamping....) On the contrary, it can be thought as an approach to better and fully exploit the flexibility offered by additive manufacturing (AM), still often used on old and less efficient design techniques. In the present article, the proposed method is developed using a Lagrangian optimization approach to minimize stagnation pressure dissipation while maximizing heat transfer between fluid and solid region. An impermeability dependent thermal conductivity is included and a smoother operator is adopted to bound thermal diffusivity gradients across solid and fluid. Simulations are performed on a straight squared duct domain. The variability of the results is shown on the basis of different weights of the objective functions. The solver builds automatically three-dimensional structures enhancing the heat transfer level between the walls and the flow through the generation of pairs of counter rotating vortices. This is consistent to solution proposed in literature like v-shaped ribs, even if the geometry generated is more complex and more efficient. It is possible to define the desired level of heat transfer and losses and obtain the closest optimal solution. It is the first time that a conjugate heat transfer optimization problem, with these constraints, has been tackled with this approach for three-dimensional geomet

Journal article

Cassinelli A, Xu H, Montomoli F, Adami P, Diaz RV, Sherwin SJet al., 2019, ON THE EFFECT OF INFLOW DISTURBANCES ON THE FLOW PAST A LINEAR LPT VANE USING SPECTRAL/HP ELEMENT METHODS, ASME Turbo Expo: Turbomachinery Technical Conference and Exposition, Publisher: AMER SOC MECHANICAL ENGINEERS

Conference paper

Gaymann A, Schiaffini G, Massini M, Montomoli F, Corsini Aet al., 2019, Neural network topology for wind turbine analysis

Copyright © by the Authors. In this work Artificial Neural Networks (ANN) are used for a multi-target optimization of the aerodynamics of a wind turbine blade. The Artificial Neural Network is used to build a meta-model of the blade, which is then optimized according to the imposed criteria. The neural networks are trained with a data set built by a series of CFD simulations and their configuration (number of neurons and layers) selected to improve performances and avoid over-fitting. The basic configuration of the airfoil is the profile S809, which is commonly used in horizontal axis wind turbines (HAWT), equipped with a Coanda jet. The design position and momentum of the jet are optimized to maximize aerodynamic efficiency and minimize the power required to activate the Coanda Jet.

Conference paper

Suman A, Casari N, Fabbri E, Pinelli M, di Mare L, Montomoli Fet al., 2019, A Non-Dimensional Approach for Generalizing the Particle Impact Behavior of Gas Turbine Fouling, ASME Turbo Expo: Turbomachinery Technical Conference and Exposition, Publisher: AMER SOC MECHANICAL ENGINEERS

Conference paper

Sakai E, Meng B, Ahlfeld R, Klemmer K, Montomoli Fet 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

Journal article

Griffini D, Salvadori S, Carnevale M, Montomoli Fet al., 2018, Uncertainty Quantification in Hydrodynamic Bearings, Energy Procedia, ISSN: 1876-6102

Journal article

Gaymann A, Pietropaoli M, Crespo L, Kenny S, Montomoli Fet 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

Journal article

Sakai E, Meng B, Ahlfeld R, Montomoli Fet al., 2018, Uncertainty Quantification Analysis of Back Facing Steps Film Cooling Configurations, ASME IGTI 2018

Conference paper

Gaymann A, Montomoli F, Pietropaoli M, 2018, Robust Fluid Topology Optimization Using Polynomial Chaos Expansions: TOffee, ASME IGTI 2018

Conference paper

Suman A, Casari N, Fabbri E, Pinelli M, Di Mare L, Montomoli Fet al., 2018, Gas Turbine Fouling Tests: Review, Critical Analysis and Particle Impact Behavior Map, ASME IGTI 2018

Conference paper

Cassinelli A, Adami P, Sherwin S, Montomoli Fet al., 2018, High Fidelity Spectral/hp Element Methods for Turbomachinery, ASME IGTI 2018

Conference paper

Casari N, Pinelli M, Suman A, Di Mare L, Montomoli Fet al., 2018, On Deposit Sintering and Detachment From Gas Turbines, ASME IGTI 2018

Conference paper

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

Journal article

Montomoli F, 2018, Future developments, Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines: Second Edition, Pages: 195-196, ISBN: 9783319929422

© Springer Nature Singapore Pte Ltd. 2018. All rights reserved. This chapter suggests future development in Uncertainty Quantification for Aircraft Engines.

Book chapter

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

© Springer Nature Singapore Pte Ltd. 2018. All rights reserved. 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.

Book chapter

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

© Springer Nature Singapore Pte Ltd. 2018. All rights reserved. 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.

Book chapter

Casari N, Pinelli M, Suman A, di Mare L, Montomoli Fet al., 2018, EBFOG: Deposition, Erosion, and Detachment on High-Pressure Turbine Vanes, JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME, Vol: 140, ISSN: 0889-504X

Journal article

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.

Book

Ahlfeld R, Montomoli F, Carnevale M, Salvadori Set al., 2018, Autonomous uncertainty quantification for discontinuous models using multivariate Padé approximations, Journal of Turbomachinery, Vol: 140, ISSN: 0889-504X

© 2018 by ASME. 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.

Journal article

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