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

Informazioni relative al progetto

MAGISTER

ID dell’accordo di sovvenzione: 766264

DOI

10.3030/766264

Progetto chiuso

Data della firma CE 3 Agosto 2017

Data di avvio 1 Settembre 2017

Data di completamento 30 Novembre 2021

Finanziato da

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Costo totale

€ 3 873 159,00

Contributo UE

€ 3 873 159,00

Coordinato da

UNIVERSITEIT TWENTE
Netherlands

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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

Pubblicazioni

Book chapters (1)

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

Autori: Maximilian L. Croci, Ushnish Sengupta, Matthew P. Juniper
Pubblicato in: Computational Science – ICCS 2021 - 21st International Conference, Krakow, Poland, June 16–18, 2021, Proceedings, Part V, Numero 12746, 2021, Pagina/e 408-419, ISBN 978-3-030-77976-4
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-77977-1_33

Conference proceedings (27)

Reduced order models applied to laminar diffusion flames

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

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

Autori: Sengupta, Ushnish; Croci, Maximilian L.; Juniper, Matthew P.
Pubblicato in: Numero 1, 2021
Editore: Cornell University

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

Autori: Ushnish Sengupta; Carl Edward Rasmussen; Matthew P. Juniper
Pubblicato in: Numero 2, 2021
Editore: Proceedings of the ASME Turbo Expo
DOI: 10.1115/1.4049762

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

Autori: Kulkarni S, Guo S, Silva CF, Polifke W.
Pubblicato in: 2021
Editore: ASME Turbo Expo 2021
DOI: 10.13140/rg.2.2.25121.12642

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

Autori: Ushnish Sengupta1 and Matthew P. Juniper1
Pubblicato in: 2021
Editore: Symposium on Thermoacoustics in Combustion: Industry meets Academia (SoTiC 2021)

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

Autori: Sengupta, Ushnish ; Waxenegger-Wilfing, Guenther ; Martin, Jan ; Hardi, Justin ; Juniper, Matthew
Pubblicato in: Avoiding High-frequency Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Bayesian Deep Learning, 2020
Editore: 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

Autori: P. W. Agostinelli; B. Rochette; D. Laera; J. Dombard; B. Cuenot; L. Gicquel
Pubblicato in: Crossref, Numero 5, 2021, ISSN 1527-2435
Editore: Physics of Fluids
DOI: 10.1063/5.0040719

Fusing model ensembles and observations together with Bayesian neural networks

Autori: Amos, Matt ; Sengupta, Ushnish ; Hosking, Scott ; Young, Paul
Pubblicato in: 2021
Editore: EGU General Assembly Conference Abstracts

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

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

Autori: Michael McCartney, Wolfgang Polifke
Pubblicato in: Proceedings of the ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, 2020
Editore: ASME Turbo Expo 2020
DOI: 10.1115/gt2020-14834

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

Autori: Sara Navarro Arredondo, Jim Kok
Pubblicato in: Proceedings of the 26th International Congress on Sound and Vibration, Numero 26, 2019, ISBN 978-1-9991810-0-0
Editore: Canadian Acoustical Association

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

Autori: Alireza Ghasemi, J.B.W. Kok
Pubblicato in: Proceedings of the 26th International Congress on Sound and Vibration, Numero 26, 2019, ISBN 978-1-9991810-0-0
Editore: Canadian Acoustical Association

Bayesian machine learning for the prognosis of combustion instabilities from noise

Autori: Ushnish Sengupta Carl Rasmussen Matthew Juniper
Pubblicato in: Proceedings of the ASME 2020 Turbomachinery Technical Conference Exposition, 2020
Editore: Proceedings of the ASME 2020 Turbomachinery Technical Conference Exposition
DOI: 10.31224/osf.io/ysgp4

Ensembling geophysical models with Bayesian Neural Networks

Autori: Sengupta, Ushnish; Amos, Matt; Hosking, J. Scott; Rasmussen, Carl Edward; Juniper, Matthew; Young, Paul J.
Pubblicato in: Numero 2, 2020
Editore: Cornell University
DOI: 10.17863/cam.60032

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

Autori: Ushnish Sengupta, Maximilian L. Croci, Matthew P. Juniper
Pubblicato in: 2020
Editore: Cornell University

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

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

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

Autori: Agostinelli, P. W., Kwah, Y. H., Richard, S., Exilard, G., Dawson, J. R., Gicquel, L., & Poinsot, T.
Pubblicato in: 2020
Editore: 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

Autori: Maximilian L. Croci1 2, Ushnish Sengupta1 and Matthew P. Juniper1
Pubblicato in: Symposium on Thermoacoustics in Combustion: Industry meets Academia (SoTiC 2021), 2021
Editore: SoTiC 2021 - Symposium on Thermoacoustics in Combustion: Industry meets Academia, 2021

Numerical design of Luenberger observers for nonlinear systems

Autori: Louise da C. Ramos, Florent Di Meglio, Valery Morgenthaler, Luis F. Figueira da Silva, Pauline Bernard
Pubblicato in: 2020 59th IEEE Conference on Decision and Control (CDC), 2020, Pagina/e 5435-5442, ISBN 978-1-7281-7447-1
Editore: IEEE
DOI: 10.1109/cdc42340.2020.9304163

Reduced Order Models Applied to Laminar Diffusion Flames

Autori: N. L. M. B. Junqueira, L. F. Figueira da Silva, L. C. Ramos
Pubblicato in: 2020 Brazilian Congress of Thermal Sciences and Engineering, Online., 2020
Editore: 2020 Brazilian Congress of Thermal Sciences and Engineering, Online.

Reduced Order Model of Laminar Premixed Inverted Conical Flames

Autori: Louise da Costa Ramos, Florent Di Meglio, Luis Fernando F. Da Silva, Valery Morgenthaler
Pubblicato in: AIAA Scitech 2020 Forum, 2020, ISBN 978-1-62410-595-1
Editore: 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

Autori: Nils Christian A. Wilhelmsen, Florent Di Meglio
Pubblicato in: 2020 59th IEEE Conference on Decision and Control (CDC), 2020, Pagina/e 658-665, ISBN 978-1-7281-7447-1
Editore: IEEE
DOI: 10.1109/cdc42340.2020.9304413

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

Autori: Nicole Lopes Junqueira Luis Fernando Figueira da Silva , Louise da Costa Ramos , Igor Braga de Paula
Pubblicato in: 26 International Congress of Mechanical Engineering, 2021
Editore: 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

Autori: Francesco Garita; Hans Yu; Matthew P. Juniper
Pubblicato in: Numero 4, 2021, ISSN 1528-8919
Editore: 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

Autori: Edmond Shehadi, Edwin van der Weide
Pubblicato in: AIAA Scitech 2021 Forum, 2021, ISBN 978-1-62410-609-5
Editore: 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

Autori: Garita, F., Yu, H., & Juniper, M.
Pubblicato in: Proceedings of the ASME Turbo Expo 2020: Turbine Technical Conference and Exposition, 2020
Editore: ASME Turbo Expo 2020

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

Autori: Alireza Javareshkian, Alexis Dancelme, Hongyu Chen, Thomas Sattelmayer
Pubblicato in: 2021
Editore: 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

Autori: T. Lafarge; P. Boivin; N. Odier; B. Cuenot
Pubblicato in: EISSN: 1089-7666, Numero 1, 2021, ISSN 1527-2435
Editore: Physics of Fluids
DOI: 10.1063/5.0061638

Modeling of Pulsating Inverted Conical Flames: a Numerical Instability Analysis

Autori: L. C. Ramos, L. F. Figueira da Silva, F. Di Meglio, V. Morgenthaler
Pubblicato in: Combustion Theory and Modeling, 2021, ISSN 1364-7830
Editore: Institute of Physics Publishing

Influence of an Oscillating Airflow on the PrefilmingAirblast Atomization Process

Autori: Thomas Christou Björn Stelzner Nikolaos Zarzalis
Pubblicato in: Atomization and Sprays, 2021, ISSN 1936-2684
Editore: Atomization and Sprays
DOI: 10.5445/ir/1000132623

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

Autori: Nilam Tathawadekar, Nguyen Anh Khoa Doan, Camilo F. Silva, Nils Thuerey
Pubblicato in: Proceedings of the Combustion Institute, 2020, ISSN 1540-7489
Editore: Combustion Institute
DOI: 10.1016/j.proci.2020.07.115

Numerical study of multicomponent spray flame propagation

Autori: Varun Shastry Quentin Cazeres Bastien Rochette Eleonore Riber Bénédicte Cuenot
Pubblicato in: Proceedings of the Combustion Institute, 2019, ISSN 1540-7489
Editore: Combustion Institute
DOI: 10.1016/j.proci.2020.07.090

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

Autori: Laera, D., Agostinelli, P. W., Selle, L., Caz res, Q., Oztarlik, G., Schuller, T., Gicquel, L., & Poinsot
Pubblicato in: Proceedings of the Combustion Institute, 2020, ISSN 1540-7489
Editore: Combustion Institute

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

Autori: P.W.Agostinelli D.Laera I.Boxx L.Gicquel T.Poinsotd
Pubblicato in: Combustion and Flame, 2021, ISSN 0010-2180
Editore: 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

Autori: Michael McCartney, Ushnish Sengupta, Matthew Juniper
Pubblicato in: Journal of Engineering for Gas Turbines and Power, 2021, ISSN 0742-4795
Editore: American Society of Mechanical Engineers
DOI: 10.1115/1.4052145

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

Autori: Michael McCartney, Matthias Haeringer, Wolfgang Polifke
Pubblicato in: Journal of Engineering for Gas Turbines and Power, Numero 142/6, 2020, ISSN 0742-4795
Editore: American Society of Mechanical Engineers
DOI: 10.1115/1.4045516

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

Autori: Nils Christian A. Wilhelmsen, Florent Di Meglio
Pubblicato in: IFAC-PapersOnLine, Numero 53/2, 2020, Pagina/e 4181-4188, ISSN 2405-8963
Editore: 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

Autori: Günther Waxenegger-Wilfing, Ushnish Sengupta, Jan Martin, Wolfgang Armbruster, Justin Hardi, Matthew Juniper, Michael Oschwald
Pubblicato in: Chaos: An Interdisciplinary Journal of Nonlinear Science, Numero 31/6, 2021, Pagina/e 063128, ISSN 1054-1500
Editore: American Institute of Physics
DOI: 10.1063/5.0038817

Thesis and dissertations (1)

Ensembling geophysical models with Bayesian Neural Networks

Autori: Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew Juniper, Paul J. Young
Pubblicato in: Advances in Neural Information Processing Systems (NeurIPS) 2020, 2020
Editore: Advances in Neural Information Processing Systems (NeurIPS) 2020