Machine learning for Advanced Gas turbine Injection SysTems to Enhance combustoR performance.

Información del proyecto

MAGISTER

Identificador del acuerdo de subvención: 766264

DOI

10.3030/766264

Proyecto cerrado

Fecha de la firma de la CE 3 Agosto 2017

Fecha de inicio 1 Septiembre 2017

Fecha de finalización 30 Noviembre 2021

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 3 873 159,00

Aportación de la UE

€ 3 873 159,00

Coordinado por

UNIVERSITEIT TWENTE
Netherlands

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

Documents, reports (15)

Control of a thermoacoustic system using machine learning

Control of thermoacoustic system using machine learning

Compressible LES of liquid fuel injection using AVBP.

Compressible LES of liquid fuel injection using AVBP

Modelling of acoustically absorbing liners.
Demo of of combustion instability surrogate model.

Demo of LES of unstable spray flames

Uncertainty handling in engine operation.

Uncertainty handling in engine operation

Droplet measurements data in an atmospheric test rig.

Droplet measurements data in an atmospheric test rig

Development of the Discontinuous Galerkin discretization in SU2: application demo LES of spray flames.

Development of the Discontinuous Galerkin discretization in SU2 application demo LES of spray flames

Compressible LES applied to combustion liners and dilution holes.

Compressible LES applied to combustion liners and dilution holes

Simulation data of the effect of pressure variation on spray combustion.

Simulation data of the effect of pressure variation on spray combustion

Machine learning in thermoacoustic measurements.

Machine learning in thermoacoustic measurements

Comparison of different machine learning algorithms.
Application of machine learning in CFD.
UQ of spray combustion.

Uncertainty Quantification of spray combustion

LES demo thermoacoustic instability in helicopter engine
Measurement data of the acoustic response of kerosene spray flames.

Measurement data of the acoustic response of kerosene spray flames

Other (7)

Summer school: Thermo-acoustics and combustion dynamics in aero gas turbine engines

Thermo-acoustics and combustion dynamics in aero gas turbine engines

Workshop C

Entrepreneurship, ethics, intellectual property rights and management

Workshop B

CFD for spray flame simulations

Workshop D

Measurements of spray flames in aircraft type combustors

Overview of Outreach activities. Final press release

Overview of Outreach activities Final press release

Symposium: Future Aero gas turbine engines Com-bustion Dynamics+Acoustics: Prediction and Remedy

Aero gas turbine engine Combustion Dynamics and Acoustics Prediction and Remedy

Workshop A

Machine Learning, Combustion and Acoustics in aero engine combustors

Open Research Data Pilot (1)

Data Management Plan (DMP)

Mandatory deliverable as consortium decided not to opt out of the pilot on open research.

Websites, patent fillings, videos etc. (1)

MAGISTER web site operational

Publicaciones

Book chapters (1)

Data Assimilation Using Heteroscedastic Bayesian Neural Network Ensembles for Reduced-Order Flame Models

Autores: Maximilian L. Croci, Ushnish Sengupta, Matthew P. Juniper
Publicado en: Computational Science – ICCS 2021 - 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part V, Edición 12746, 2021, Página(s) 408-419, ISBN 978-3-030-77976-4
Editor: Springer International Publishing
DOI: 10.1007/978-3-030-77977-1_33

Conference proceedings (27)

Reduced order models applied to laminar diffusion flames

Autores: Nicole Lopes M. B. Junqueira, Luís Fernando Figueira da Silva, Louise Da Costa Ramos
Publicado en: Procceedings of the 18th Brazilian Congress of Thermal Sciences and Engineering, 2020
Editor: ABCM
DOI: 10.26678/abcm.encit2020.cit20-0196

Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles

Autores: Sengupta, Ushnish; Croci, Maximilian L.; Juniper, Matthew P.
Publicado en: Edición 1, 2021
Editor: Cornell University

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Autores: Ushnish Sengupta; Carl Edward Rasmussen; Matthew P. Juniper
Publicado en: Edición 2, 2021
Editor: Proceedings of the ASME Turbo Expo
DOI: 10.1115/1.4049762

Confidence in Flame Impulse Response Estimation by LES with Uncertain Thermal Boundary Condition

Autores: Kulkarni S, Guo S, Silva CF, Polifke W.
Publicado en: 2021
Editor: ASME Turbo Expo 2021
DOI: 10.13140/rg.2.2.25121.12642

Thermoacoustic stabilization of combustors with gradient-augmented Bayesian optimization and adjoint models

Autores: Ushnish Sengupta1 and Matthew P. Juniper1
Publicado en: 2021
Editor: Symposium on Thermoacoustics in Combustion: Industry meets Academia (SoTiC 2021)

Avoiding High-frequency Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Bayesian Deep Learning

Autores: Sengupta, Ushnish ; Waxenegger-Wilfing, Guenther ; Martin, Jan ; Hardi, Justin ; Juniper, Matthew
Publicado en: Avoiding High-frequency Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Bayesian Deep Learning, 2020
Editor: Conference: American Physical Society, Division of Fluid Dynamics Meeting 2020 (APS DFD 2020)
DOI: 10.13140/rg.2.2.15452.00649

Static mesh adaptation for reliable large eddy simulation of turbulent reacting flows

Autores: P. W. Agostinelli; B. Rochette; D. Laera; J. Dombard; B. Cuenot; L. Gicquel
Publicado en: Crossref, Edición 5, 2021, ISSN 1527-2435
Editor: Physics of Fluids
DOI: 10.1063/5.0040719

Fusing model ensembles and observations together with Bayesian neural networks

Autores: Amos, Matt ; Sengupta, Ushnish ; Hosking, Scott ; Young, Paul
Publicado en: 2021
Editor: EGU General Assembly Conference Abstracts

Forecasting Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Multimodal Bayesian Deep Learning

Autores: Ushnish Senguptaa, G ̈unther Waxenegger-Wilfingb, Jan Martinb,Justin Hardib, Matthew P. Junipera,∗
Publicado en: Fluid Dynamics (physics.flu-dyn); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG), 2021
Editor: Fluid Dynamics (physics.flu-dyn); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)

Online Detection of Combustion Instabilities Using Supervised Machine Learning

Autores: Michael McCartney, Wolfgang Polifke
Publicado en: Proceedings of the ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, 2020
Editor: ASME Turbo Expo 2020
DOI: 10.1115/gt2020-14834

A model to study spontaneous oscillations in a lean premixed combustor using non-linear analysis

Autores: Sara Navarro Arredondo, Jim Kok
Publicado en: Proceedings of the 26th International Congress on Sound and Vibration, Edición 26, 2019, ISBN 978-1-9991810-0-0
Editor: Canadian Acoustical Association

Numerical Study Of A Swirl Atomized Spray Response To Acoustic Perturbations.

Autores: Alireza Ghasemi, J.B.W. Kok
Publicado en: Proceedings of the 26th International Congress on Sound and Vibration, Edición 26, 2019, ISBN 978-1-9991810-0-0
Editor: Canadian Acoustical Association

Bayesian machine learning for the prognosis of combustion instabilities from noise

Autores: Ushnish Sengupta Carl Rasmussen Matthew Juniper
Publicado en: Proceedings of the ASME 2020 Turbomachinery Technical Conference Exposition, 2020
Editor: Proceedings of the ASME 2020 Turbomachinery Technical Conference Exposition
DOI: 10.31224/osf.io/ysgp4

Ensembling geophysical models with Bayesian Neural Networks

Autores: Sengupta, Ushnish; Amos, Matt; Hosking, J. Scott; Rasmussen, Carl Edward; Juniper, Matthew; Young, Paul J.
Publicado en: Edición 2, 2020
Editor: Cornell University
DOI: 10.17863/cam.60032

Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles

Autores: Ushnish Sengupta, Maximilian L. Croci, Matthew P. Juniper
Publicado en: 2020
Editor: Cornell University

Comparison of Machine Learning Algorithms in the Interpolation and Extrapolation of Flame Describing Functions

Autores: Michael McCartney, Matthias Haeringer, Wolfgang Polifke
Publicado en: Volume 4B: Combustion, Fuels, and Emissions, 2019, ISBN 978-0-7918-5862-2
Editor: American Society of Mechanical Engineers
DOI: 10.1115/gt2019-91319

Numerical and Experimental Flame Stabilization Analysis in the New SpinningCombustion Technology Framework

Autores: Agostinelli, P. W., Kwah, Y. H., Richard, S., Exilard, G., Dawson, J. R., Gicquel, L., & Poinsot, T.
Publicado en: 2020
Editor: In Proceedings of the ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition
DOI: 10.1115/gt2020-15035

Real-time parameter inference of nonlinear bluff-body-stabilized flame models using Bayesian neural network ensembles

Autores: Maximilian L. Croci1 2, Ushnish Sengupta1 and Matthew P. Juniper1
Publicado en: Symposium on Thermoacoustics in Combustion: Industry meets Academia (SoTiC 2021), 2021
Editor: SoTiC 2021 - Symposium on Thermoacoustics in Combustion: Industry meets Academia, 2021

Numerical design of Luenberger observers for nonlinear systems

Autores: Louise da C. Ramos, Florent Di Meglio, Valery Morgenthaler, Luis F. Figueira da Silva, Pauline Bernard
Publicado en: 2020 59th IEEE Conference on Decision and Control (CDC), 2020, Página(s) 5435-5442, ISBN 978-1-7281-7447-1
Editor: IEEE
DOI: 10.1109/cdc42340.2020.9304163

Reduced Order Models Applied to Laminar Diffusion Flames

Autores: N. L. M. B. Junqueira, L. F. Figueira da Silva, L. C. Ramos
Publicado en: 2020 Brazilian Congress of Thermal Sciences and Engineering, Online., 2020
Editor: 2020 Brazilian Congress of Thermal Sciences and Engineering, Online.

Reduced Order Model of Laminar Premixed Inverted Conical Flames

Autores: Louise da Costa Ramos, Florent Di Meglio, Luis Fernando F. Da Silva, Valery Morgenthaler
Publicado en: AIAA Scitech 2020 Forum, 2020, ISBN 978-1-62410-595-1
Editor: American Institute of Aeronautics and Astronautics
DOI: 10.2514/6.2020-0416

Estimating Both Reflection Coefficients of 2×2 Linear Hyperbolic Systems with Single Boundary Measurement

Autores: Nils Christian A. Wilhelmsen, Florent Di Meglio
Publicado en: 2020 59th IEEE Conference on Decision and Control (CDC), 2020, Página(s) 658-665, ISBN 978-1-7281-7447-1
Editor: IEEE
DOI: 10.1109/cdc42340.2020.9304413

The influence of the learning data on the reduced order model of laminar non-premixed flames

Autores: Nicole Lopes Junqueira Luis Fernando Figueira da Silva , Louise da Costa Ramos , Igor Braga de Paula
Publicado en: 26 International Congress of Mechanical Engineering, 2021
Editor: 26 International Congress of Mechanical Engineering
DOI: 10.26678/abcm.cobem2021.cob2021-0110

Assimilation of Experimental Data to Create a Quantitatively Accurate Reduced-Order Thermoacoustic Model

Autores: Francesco Garita; Hans Yu; Matthew P. Juniper
Publicado en: Edición 4, 2021, ISSN 1528-8919
Editor: Journal of Engineering for Gas Turbines and Power
DOI: 10.31224/osf.io/8bmaz

Ongoing Development of Non-reflective Boundary Conditions for Euler and Navier-Stokes Equations via the Discontinuous Galerkin Framework

Autores: Edmond Shehadi, Edwin van der Weide
Publicado en: AIAA Scitech 2021 Forum, 2021, ISBN 978-1-62410-609-5
Editor: American Institute of Aeronautics and Astronautics
DOI: 10.2514/6.2021-1660

ASSIMILATION OF EXPERIMENTAL DATA TO CREATE A QUANTITATIVELY-ACCURATE REDUCED ORDER THERMOACOUSTIC MODEL

Autores: Garita, F., Yu, H., & Juniper, M.
Publicado en: Proceedings of the ASME Turbo Expo 2020: Turbine Technical Conference and Exposition, 2020
Editor: ASME Turbo Expo 2020

Influence of Hole-to-Hole Interaction on the Acoustic Behavior of Multi-Orifice Perforated Plates

Autores: Alireza Javareshkian, Alexis Dancelme, Hongyu Chen, Thomas Sattelmayer
Publicado en: 2021
Editor: Journal of Engineering for Gas Turbines and Power
DOI: 10.1115/gt2021-58535

Peer reviewed articles (11)

Improved color-gradient method for lattice Boltzmann modeling of two-phase flows

Autores: T. Lafarge; P. Boivin; N. Odier; B. Cuenot
Publicado en: EISSN: 1089-7666, Edición 1, 2021, ISSN 1527-2435
Editor: Physics of Fluids
DOI: 10.1063/5.0061638

Modeling of Pulsating Inverted Conical Flames: a Numerical Instability Analysis

Autores: L. C. Ramos, L. F. Figueira da Silva, F. Di Meglio, V. Morgenthaler
Publicado en: Combustion Theory and Modeling, 2021, ISSN 1364-7830
Editor: Institute of Physics Publishing

Influence of an Oscillating Airflow on the PrefilmingAirblast Atomization Process

Autores: Thomas Christou Björn Stelzner Nikolaos Zarzalis
Publicado en: Atomization and Sprays, 2021, ISSN 1936-2684
Editor: Atomization and Sprays
DOI: 10.5445/ir/1000132623

Modeling of the nonlinear flame response of a Bunsen-type flame via multi-layer perceptron

Autores: Nilam Tathawadekar, Nguyen Anh Khoa Doan, Camilo F. Silva, Nils Thuerey
Publicado en: Proceedings of the Combustion Institute, 2020, ISSN 1540-7489
Editor: Combustion Institute
DOI: 10.1016/j.proci.2020.07.115

Numerical study of multicomponent spray flame propagation

Autores: Varun Shastry Quentin Cazeres Bastien Rochette Eleonore Riber Bénédicte Cuenot
Publicado en: Proceedings of the Combustion Institute, 2019, ISSN 1540-7489
Editor: Combustion Institute
DOI: 10.1016/j.proci.2020.07.090

Stabilization mechanisms of CH4 premixed swirled flame enriched with a non-premixed hydrogen injection

Autores: Laera, D., Agostinelli, P. W., Selle, L., Caz res, Q., Oztarlik, G., Schuller, T., Gicquel, L., & Poinsot
Publicado en: Proceedings of the Combustion Institute, 2020, ISSN 1540-7489
Editor: Combustion Institute

Impact of wall heat transfer in Large Eddy Simulation of flame dynamics in a swirled combustion chamber

Autores: P.W.Agostinelli D.Laera I.Boxx L.Gicquel T.Poinsotd
Publicado en: Combustion and Flame, 2021, ISSN 0010-2180
Editor: Elsevier BV
DOI: 10.1016/j.combustflame.2021.111728

Reducing Uncertainty in the Onset of Combustion Instabilities Using Dynamic Pressure Information and Bayesian Neural Networks

Autores: Michael McCartney, Ushnish Sengupta, Matthew Juniper
Publicado en: Journal of Engineering for Gas Turbines and Power, 2021, ISSN 0742-4795
Editor: American Society of Mechanical Engineers
DOI: 10.1115/1.4052145

Comparison of Machine Learning Algorithms in the Interpolation and Extrapolation of Flame Describing Functions

Autores: Michael McCartney, Matthias Haeringer, Wolfgang Polifke
Publicado en: Journal of Engineering for Gas Turbines and Power, Edición 142/6, 2020, ISSN 0742-4795
Editor: American Society of Mechanical Engineers
DOI: 10.1115/1.4045516

An Observer for the Electrically Heated Vertical Rijke Tube with Nonlinear Heat Release

Autores: Nils Christian A. Wilhelmsen, Florent Di Meglio
Publicado en: IFAC-PapersOnLine, Edición 53/2, 2020, Página(s) 4181-4188, ISSN 2405-8963
Editor: IFAC-PapersOnLine
DOI: 10.1016/j.ifacol.2020.12.2461

Early detection of thermoacoustic instabilities in a cryogenic rocket thrust chamber using combustion noise features and machine learning

Autores: Günther Waxenegger-Wilfing, Ushnish Sengupta, Jan Martin, Wolfgang Armbruster, Justin Hardi, Matthew Juniper, Michael Oschwald
Publicado en: Chaos: An Interdisciplinary Journal of Nonlinear Science, Edición 31/6, 2021, Página(s) 063128, ISSN 1054-1500
Editor: American Institute of Physics
DOI: 10.1063/5.0038817

Thesis and dissertations (1)

Ensembling geophysical models with Bayesian Neural Networks

Autores: Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew Juniper, Paul J. Young
Publicado en: Advances in Neural Information Processing Systems (NeurIPS) 2020, 2020
Editor: Advances in Neural Information Processing Systems (NeurIPS) 2020