Imperial College London

Prof Francesco Montomoli

Faculty of EngineeringDepartment of Aeronautics

Professor 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

119 results found

Pepper N, Crespo L, Montomoli F, 2022, Adaptive learning for reliability analysis using Support Vector Machines, RELIABILITY ENGINEERING & SYSTEM SAFETY, Vol: 226, ISSN: 0951-8320

Journal article

Pepper N, Montomoli F, Sharma S, 2022, A Non-Parametric Histogram Interpolation Method for Design Space Exploration, JOURNAL OF MECHANICAL DESIGN, Vol: 144, ISSN: 1050-0472

Journal article

Hammond J, Montomoli F, Pietropaoli M, Sandberg RD, Michelassi Vet al., 2022, Machine Learning for the Development of Data-Driven Turbulence Closures in Coolant Systems, JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME, Vol: 144, ISSN: 0889-504X

Journal article

Hammond J, Pepper N, Montomoli F, Michelassi Vet al., 2022, Machine Learning Methods in CFD for Turbomachinery: A Review, INTERNATIONAL JOURNAL OF TURBOMACHINERY PROPULSION AND POWER, Vol: 7

Journal article

Hammond J, Marioni YF, Montomoli F, 2022, Error Quantification for the Assessment of Data-Driven Turbulence Models, FLOW TURBULENCE AND COMBUSTION, Vol: 109, Pages: 1-26, ISSN: 1386-6184

Journal article

Cassinelli A, Mateo Gabín A, Montomoli F, Adami P, Vázquez Díaz R, Sherwin SJet al., 2022, Reynolds sensitivity of the wake passing effect on a LPT cascade using spectral/hp element methods, International Journal of Turbomachinery, Propulsion and Power, Vol: 7, Pages: 8-8, ISSN: 2504-186X

Reynolds-Averaged Navier–Stokes (RANS) methods continue to be the backbone of CFD-based design; however, the recent development of high-order unstructured solvers and meshing algorithms, combined with the lowering cost of HPC infrastructures, has the potential to allow for the introduction of high-fidelity simulations in the design loop, taking the role of a virtual wind tunnel. Extensive validation and verification is required over a broad design space. This is challenging for a number of reasons, including the range of operating conditions, the complexity of industrial geometries and their relative motion. A representative industrial low pressure turbine (LPT) cascade subject to wake passing interactions is analysed, adopting the incompressible Navier–Stokes solver implemented in the spectral/hp element framework Nektar++. The bar passing effect is modelled by leveraging a spectral-element/Fourier Smoothed Profile Method. The Reynolds sensitivity is analysed, focusing in detail on the dynamics of the separation bubble on the suction surface as well as the mean flow properties, wake profiles and loss estimations. The main findings are compared with experimental data, showing agreement in the prediction of wake traverses and losses across the entire range of flow regimes, the latter within 5% of the experimental measurements.

Journal article

Hammond J, Pietropaoli M, Montomoli F, 2022, Topology optimisation of turbulent flow using data-driven modelling, STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, Vol: 65, ISSN: 1615-147X

Journal article

Bisio V, Montomoli F, Rossin S, Ruggiero M, Tagarielli VLet al., 2022, Predictions and uncertainty quantification of the loading induced by deflagration events on surrounding structures, Process Safety and Environmental Protection, Vol: 158, Pages: 445-460, ISSN: 0957-5820

The threat of accidental hydrocarbon explosions is of major concern to industrial operations; in particular, there is a need for design tools to assess and quantify the effects of potential deflagration events. Here we present a design methodology based on analytical models that allow assessing the loading and structural response of objects exposed to pressure waves generated by deflagration events. The models allow determining: i) the importance of Fluid-Structure Interaction (FSI) effects; ii) the transient pressure histories on box-like or circular cylindrical objects, including the effects of pressure clearing; iii) the dynamic response of structural components that can be idealised as fully clamped beams. We illustrate by three case studies the complete design methodology and validate the analytical models by comparing their predictions to those of detailed CFD and FE simulations. We employ the validated analytical models to perform Monte Carlo analyses to quantify, for box-like structures, how the uncertainty in input design variables propagates through to the expected maximum force and impulse. We present this information in the form of non-dimensional uncertainty maps.

Journal article

Sakuma M, Pepper N, Warnakulasuriya S, Montomoli F, Wuech-ner R, Bletzinger K-Uet al., 2022, Multi-fidelity uncertainty quantification of high Reynolds number turbulent flow around a rectangular 5:1 cylinder, 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, Publisher: Techno Press, Pages: 127-136, ISSN: 1226-6116

This work shows the application of Multi-fidelity Uncertainty Quantification to WindEngineering problems. As test case a rectangular shape is used, with a fillet radius, in orderto represent the geometrical variations that can affect buildings or other bluff bodies. Therectangular cylinder used has a chord-to-thickness ratio 5:1. This rectangular shape is animportant basic shape for wind engineering tasks, e.g. in case of buildings or other bluff bodiesexposed to the flow. Moreover it is well investigated and documented.Coarse and fine meshes are used as low and high fidelity models respectively. To performCFD simulations, the stabilized finite element methods are used in both the high and low fidelitymodel with a CFD code developed by TUM and the International Center for Numerical Methodsin Engineering. The underlying UQ framework is based on a Sparse Arbitrary Moment BasedAlgorithm (SAMBA) developed at ICL. In the formulation the number of simulations is reducedusing a Smolyak sparsity model.The multi-fidelity extension, with application to wind engineering problems is discussed andpresented in this work. The overall goal of such formulation is to gain an accuracy of mixed lowhigh fidelity simulations comparable to the ones obtained with only high fidelity simulations, ata fraction of the computational cost.

Conference paper

Raske N, Gonzalez OA, Furino S, Pietropaoli M, Shahpar S, Montomoli Fet al., 2022, THERMAL MANAGEMENT FOR ELECTRIFICATION IN AIRCRAFT ENGINES: OPTIMIZATION OF COOLANT SYSTEM

This work shows the application of fluid Topology Optimization for the thermal management of electric parts in aircraft engines. There is a growing trend in electrification of current engines, but this requires higher power to be dissipated. Standard coolant systems based on serpentines are not effective enough to remove the heat produced by these systems. In this work a new solution is shown which has a higher heat dissipation, lower pressure loss and a reduction in the mass of the heat exchanger. The optimization is carried out with the commercial solver TOffee. Manufacturing constraints allow the part to be manufacturable using two different methods: additive manufacturing and milling with diffusion bonding. Overall, the optimized geometry, a nearly 2D coldplate with extruded channels, shows higher heat dissipation and a reduction of weight of 38.5% if compared with the baseline heat exchanger. The overall pressure drop has been also reduced by 65%.

Conference paper

Frey Marioni Y, de Toledo Ortiz EA, Cassinelli A, Montomoli F, Adami P, Vazquez Ret al., 2021, A Machine Learning Approach to Improve Turbulence Modelling from DNS Data Using Neural Networks, INTERNATIONAL JOURNAL OF TURBOMACHINERY PROPULSION AND POWER, Vol: 6

Journal article

Pepper N, Gaymann A, Montomoli F, Sharma Set al., 2021, Local bi-fidelity field approximation with knowledge based neural networks for computational fluid dynamics, Scientific Reports, Vol: 11, Pages: 1-11, ISSN: 2045-2322

This work presents a machine learning based method for bi-fidelity modelling. The method, a Knowledge Based Neural Network (KBaNN), performs a local, additive correction to the outputs of a coarse computational model and can be used to emulate either experimental data or the output of a more accurate, but expensive, computational model. An advantage of the method is that it can scale easily with the number of input and output features. This allows bi-fidelity modelling approaches to be applied to a wide variety of problems, for instance in the bi-fidelity modelling of fields. We demonstrate this aspect in this work through an application to Computational Fluid Dynamics, in which local corrections to a velocity field are performed by the KBaNN to account for mesh effects. KBaNNs were trained to make corrections to the free-stream velocity field and the boundary layer. They were trained on a limited data-set consisting of simple two-dimensional flows. The KBaNNs were then tested on a flow over a more complex geometry, a NACA 2412 airfoil. It was demonstrated that the KBaNNs were still able to provide a local correction to the velocity field which improved its accuracy. The ability of the KBaNNs to generalise to flows around new geometries that share similar physics is encouraging. Through knowledge based neural networks it may be possible to develop a system for bi-fidelity, computer based design which uses data from past simulations to inform its predictions.

Journal article

Pepper N, Montomoli F, Sharma S, 2021, Identification of missing input distributions with an inverse multi-modal polynomial chaos approach based on scarce data, Probabilistic Engineering Mechanics, ISSN: 0266-8920

Journal article

Pepper N, Montomoli F, sharma S, 2021, Data fusion for Uncertainty Quantification with Non-intrusive Polynomial Chaos, Computer Methods in Applied Mechanics and Engineering, Vol: 374, ISSN: 0045-7825

This work presents a framework for updating an estimate of a probability distribution, arising from an uncertainty propagation using Non-intrusive Polynomial Chaos (NIPC), with scarce experimental measurements of a Quantity of Interest (QoI). In recent years much work has been directed towards developing methods of combining models of different accuracies in order to propagate uncertainty, but the problem of improving uncertainty propagations by considering evidence from both computational models and experiments has received less attention.The framework described here uses the Maximum Entropy Principle (MEP) to find an updated, least biased estimate of a probability distribution by maximising the entropy between the original and updated estimates. A constrained optimisation is performed to find the coefficients of a Polynomial Chaos Expansion (PCE) that minimise the Kullback–Leibler (KL) divergence between estimates, while ensuring that the new estimate conforms to constraints imposed by the available experimental measurements of the QoI. In this work a novel constraint is used, based upon the Dvoretzky–Kiefer–Wolfowitz inequality and the Massart bound (DKWM), as opposed to the more commonly used moment-based constraints. Such a constraint allows scarce experimental data to be used in informing the updated estimate of the probability distribution.

Journal article

Pepper N, Crespo L, Montomoli F, 2021, Adaptive Learning for Reliability Analysis using Support Vector Machines, Pages: 242-249

A novel algorithm is presented for adaptive learning of an unknown function that separates two regions of a domain. In the context of reliability analysis these two regions represent the failure domain, where a set of constraints or requirements are violated, and a safe domain where they are satisfied. The Limit State Function (LSF) separates these two regions. Evaluating the constraints for a given parameter point requires the evaluation of a computational model that may well be expensive. For this reason we wish to construct a meta-model that can estimate the LSF as accurately as possible, using only a limited amount of training data. This work presents an adaptive strategy employing a Support Vector Machine (SVM) as a meta-model to provide a semi-algebraic approximation of the LSF. We describe an optimization process that is used to select informative parameter points to add to training data at each iteration to improve the accuracy of this approximation. A formulation is introduced for bounding the predictions of the meta-model; in this way we seek to incorporate this aspect of Gaussian Process Models (GPMs) within a SVM meta-model. Finally, we apply our algorithm to two benchmark test cases, demonstrating performance that is comparable with, if not superior, to a standard technique for reliability analysis that employs GPMs.

Conference paper

Friso R, Oliani S, Casari N, Pinelli M, Suman A, Montomoli Fet al., 2021, Towards a machine learning based design for fouling of an axial turbine vane

Computational fluid dynamics (CFD) is increasingly used during the design phase of turbomachinery. Reducing the cost of such computations is one of the major challenges in the industrial field. The multi-physics phenomena and the multidisciplinary interactions needed for the design of the engine components are difficult to be faced with the classical design methods. In addition, the interest of manufacturers and operators of turbomachines in the increase in performance when degradation processes occur is growing. In the aeronautical sector degradation is among the most critical issues, as it can lead to the in-flight shutdown of the engine. In a bid to tackle these big problems, new design methods based on approximation techniques have been developed. These techniques are called surrogate models and are currently the most used design methods for the aerodynamic design of aircraft engines. In this work, the assessment of a surrogate surface built for the purpose of optimizing an HPT vane in degrading conditions is performed. Using machine learning and statistical techniques, a sensitivity analysis is conducted in order to reduce the problem dimensions. The results of the sensitivity analysis are used for a study of the surrogate, with the purpose of obtaining design guidelines when deterioration effects are considered in the design phase. The main outcome of this study is a map, that outlines the best design zone defined by the combination of the most influential parameters.

Conference paper

Montomoli F, Antorkas S, Pietropaoli M, Gaymann A, Hammond J, Frey Y, Isakssonn N, Massini M, Vazquez R, Adami Pet al., 2021, Towards digital design of gas turbines, Journal of the Global Power and Propulsion Society, Vol: 2021

This paper shows the current research to move towards the full digital design of a gas turbine. In the last few years, technologies such as additive manufacturing are becoming more common for gas turbine applications, allowing greater flexibility in the design space. There is now a drive to fully exploit this flexibility and to design and validate new parts in a digital envir-onment. This work shows how optimization methods, mainly based on topology optimization strategies, can be used to fulfil these needs. Full digitalisation of the process has the potential to generate completely new designs, not based on past solutions. However, this requires more accurate estimators for critical applications, such as the high temperature components of high-pressure stages, in order to be sure that the solution is feasible and reliable. Recent development of Machine Learning methodologies is enabling estimators with greater accuracy to be produced. In particular the introduction of data driven modelling is able to leverage the results from high fidelity simulations, such as DNS, to enrich the RANS estimator in fluid topology optimization. In this work, a comparison of recent results associated to Gene Expression Programming and Neural Networks in topology optimization are shown. Finally an innovative strategy for Topology Optimization based on Deep Neural Networks is presented, opening new possibilities for the design of next generation gas turbines.

Journal article

Cassinelli A, Mateo A, Montomoli F, Adami P, Díaz RV, Sherwin SJet al., 2021, REYNOLDS SENSITIVITY OF THE WAKE PASSING EFFECT ON A LPT CASCADE USING SPECTRAL/HP ELEMENT METHODS

RANS methods will continue to be the backbone of CFD-based design, but the recent development of high-order unstructured solvers and meshing algorithms, combined with the lowering cost of HPC infrastructures, has the potential to allow for the introduction of high-fidelity simulations in the design loop, taking the role role of a virtual wind tunnel. A representative industrial low pressure turbine (LPT) cascade subject to wake passing interactions is analysed, adopting the incompressible Navier-Stokes solver implemented in the spectral/hp element framework Nektar++. The bar passing effect is modelled by leveraging a spectral-element/Fourier Smoothed Profile Method. The Reynolds sensitivity is analysed, focusing in detail on the dynamics of the separation bubble on the suction surface as well as mean flow properties, wake profiles and loss estimations. The main findings are compared with experimental data, showing remarkable agreement in the prediction of wake traverses and losses across the entire range of flow regimes.

Conference paper

Fadiga E, Casari N, Suman A, Pinelli M, Montomoli Fet al., 2021, Design considerations and numerical simulations of variable thickness scroll geometries, Pages: 1626-1636

Among the suitable expanders for small scale Organic Rankine Cycles (ORCs), scrolls are one of the most frequent solutions. The main advantages related to such machines are cost-effectiveness, high efficiency, silent operation, small size and high reliability. The great majority of scroll machines available on the market is designed using a spiral profile generated by the involute of a circle, resulting in a constant wall thickness. The main consequence of this approach is that an increase in the volume ratio has to be obtained with an increase in the scroll length, resulting in a machine augmented size. Currently, the state of the art in scroll expanders for small size ORC is to revert a compressor in its operation. Variable thickness geometries offer an alternative way to modify the expansion ratio of the machine, with no modifications on the scroll length. In this work, the authors have investigated a scroll geometry derived from a curvature radius parametrized with involute angle, firstly introduced by Gravesen and Henriksen (2001). In the beginning, the influence of the parameterization on the volume ratio has been deeply analyzed. At a later stage, a noteworthy geometry has been simulated using an extension of the OpenFOAM C++ toolbox for Computational Fluid Dynamics.

Conference paper

Voet L, Ahlfeld R, Gaymann A, Laizet S, Montomoli Fet al., 2021, A hybrid approach combining DNS and RANS simulations to quantify uncertainties in turbulence modelling, Applied Mathematical Modelling: simulation and computation for engineering and environmental systems, Vol: 89, Pages: 885-906, ISSN: 0307-904X

Uncertainty quantification (UQ) has recently become an important part of the design process of countless engineering applications. However, up to now in computational fluid dynamics (CFD) the errors introduced by the turbulent viscosity models in Reynolds-Averaged Navier Stokes (RANS) models have often been neglected in UQ studies. Although Direct Numerical Simulations (DNS) are physically correct, obtaining a large enough set of DNS data for UQ studies is currently computationally intractable. UQ based only on RANS simulations or on DNS thus leads to physical and statistical inaccuracies in the output probability distribution functions (PDF). Therefore, three hybrid methods combining both RANS simulations and DNS to perform non-intrusive UQ are suggested in this work. Low-fidelity RANS simulations and high-fidelity DNS are combined to give an approximation of an output PDF using the advantages of both data sets: the physical accuracy via the DNS and the statistical accuracy via the RANS simulations. The hybrid methods are applied to the flow over 2D periodically arranged hills. It is shown that the Gaussian CoKriging (GCK) method is the best hybrid method and that a non-intrusive hybrid UQ approach combining both DNS and RANS simulations is possible, with both physically more accurate and statistically better PDF.

Journal article

Tagarielli V, Gauch H, Bisio V, Montomoli F, lines O, rossin Set al., 2020, Predictions of the transient loading exerted on circular cylinders by arbitrary pressure waves in air, Journal of Fluid Mechanics, Vol: 901, ISSN: 0022-1120

This study investigates the transient loading exerted on rigid circular cylinders by impinging pressure waves of arbitrary shape, amplitude, and time duration. Numerical calculations are used to predict the transient flow around the cylinder for wide ranges of geometric and loading parameters. An analytical model is developed to predict the transient loading history on the cylinder and this is found in good agreement with the results of the numerical calculations. Both models are used to identify and explore the different loading regimes, and to construct non16 dimensional maps to allow direct application of the findings of this study to the design of structures exposed to the threat of pressure wave loading.

Journal article

Cavazzini G, Giacomel F, Ardizzon G, Casari N, Fadiga E, Pinelli M, Suman A, Montomoli Fet al., 2020, CFD-based optimization of scroll compressor design and uncertainty quantification of the performance under geometrical variations, ENERGY, Vol: 209, ISSN: 0360-5442

Journal article

Hammond J, Montomoli F, Pietropaoli M, Sandberg R, Michelassi Vet al., 2020, MACHINE LEARNING FOR THE DEVELOPMENT OF DATA DRIVEN TURBULENCE CLOSURES IN COOLANT SYSTEMS, ASME Turbo Expo

Conference paper

Pepper N, Montomoli F, Giacomel F, Cavazzini G, Pinelli M, Casari N, Sharma Set al., 2020, Uncertainty Quantification and Missing Data for Turbomachinery With Probabilistic Equivalence and Arbitrary Polynomial Chaos, Applied to Scroll Compressors, ASME IGTI 2020

Conference paper

Friso R, Casari N, Pinelli M, Suman A, Montomoli Fet al., 2020, Uncertainty Analysis of Inflow Conditions on an HPT Gas Turbine Nozzle: Effect on Particle Deposition, ASME IGTI 2020

Conference paper

Antorkas S, Montomoli F, Massini M, 2020, Topology Optimization of Gas Turbine Blades for Additive Manufacturing, GPPS 2020

Conference paper

Pietropaoli M, Gaymann A, Montomoli F, 2020, Three-Dimensional Fluid Topology Optimization and Validation of a Heat Exchanger With Turbulent Flow, ASME IGTI 2020

Conference paper

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, Pages: 1-16, 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

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