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CORDIS

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

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Control of a thermoacoustic system using machine learning (opens in new window)

Control of thermoacoustic system using machine learning

Compressible LES of liquid fuel injection using AVBP. (opens in new window)

Compressible LES of liquid fuel injection using AVBP

Modelling of acoustically absorbing liners. (opens in new window)
Demo of of combustion instability surrogate model. (opens in new window)

Demo of LES of unstable spray flames

Uncertainty handling in engine operation. (opens in new window)

Uncertainty handling in engine operation

Droplet measurements data in an atmospheric test rig. (opens in new window)

Droplet measurements data in an atmospheric test rig

Development of the Discontinuous Galerkin discretization in SU2: application demo LES of spray flames. (opens in new window)

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

Compressible LES applied to combustion liners and dilution holes. (opens in new window)

Compressible LES applied to combustion liners and dilution holes

Simulation data of the effect of pressure variation on spray combustion. (opens in new window)

Simulation data of the effect of pressure variation on spray combustion

Machine learning in thermoacoustic measurements. (opens in new window)

Machine learning in thermoacoustic measurements

Comparison of different machine learning algorithms. (opens in new window)
Application of machine learning in CFD. (opens in new window)
UQ of spray combustion. (opens in new window)

Uncertainty Quantification of spray combustion

LES demo thermoacoustic instability in helicopter engine (opens in new window)
Measurement data of the acoustic response of kerosene spray flames. (opens in new window)

Measurement data of the acoustic response of kerosene spray flames

Summer school: Thermo-acoustics and combustion dynamics in aero gas turbine engines (opens in new window)

Thermo-acoustics and combustion dynamics in aero gas turbine engines

Workshop C (opens in new window)

Entrepreneurship, ethics, intellectual property rights and management

Workshop B (opens in new window)

CFD for spray flame simulations

Workshop D (opens in new window)

Measurements of spray flames in aircraft type combustors

Overview of Outreach activities. Final press release (opens in new window)

Overview of Outreach activities Final press release

Symposium: Future Aero gas turbine engines Com-bustion Dynamics+Acoustics: Prediction and Remedy (opens in new window)

Aero gas turbine engine Combustion Dynamics and Acoustics Prediction and Remedy

Workshop A (opens in new window)

Machine Learning, Combustion and Acoustics in aero engine combustors

Data Management Plan (DMP) (opens in new window)

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

Publications

Data Assimilation Using Heteroscedastic Bayesian Neural Network Ensembles for Reduced-Order Flame Models (opens in new window)

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

Reduced order models applied to laminar diffusion flames (opens in new window)

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

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

Author(s): Sengupta, Ushnish; Croci, Maximilian L.; Juniper, Matthew P.
Published in: Issue 1, 2021
Publisher: Cornell University

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise (opens in new window)

Author(s): Ushnish Sengupta; Carl Edward Rasmussen; Matthew P. Juniper
Published in: Issue 2, 2021
Publisher: Proceedings of the ASME Turbo Expo
DOI: 10.1115/1.4049762

Confidence in Flame Impulse Response Estimation by LES with Uncertain Thermal Boundary Condition (opens in new window)

Author(s): Kulkarni S, Guo S, Silva CF, Polifke W.
Published in: 2021
Publisher: ASME Turbo Expo 2021
DOI: 10.13140/rg.2.2.25121.12642

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

Author(s): Ushnish Sengupta1 and Matthew P. Juniper1
Published in: 2021
Publisher: Symposium on Thermoacoustics in Combustion: Industry meets Academia (SoTiC 2021)

Avoiding High-frequency Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Bayesian Deep Learning (opens in new window)

Author(s): Sengupta, Ushnish ; Waxenegger-Wilfing, Guenther ; Martin, Jan ; Hardi, Justin ; Juniper, Matthew
Published in: Avoiding High-frequency Thermoacoustic Instabilities in Liquid Propellant Rocket Engines Using Bayesian Deep Learning, 2020
Publisher: 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 (opens in new window)

Author(s): P. W. Agostinelli; B. Rochette; D. Laera; J. Dombard; B. Cuenot; L. Gicquel
Published in: Crossref, Issue 5, 2021, ISSN 1527-2435
Publisher: Physics of Fluids
DOI: 10.1063/5.0040719

Fusing model ensembles and observations together with Bayesian neural networks

Author(s): Amos, Matt ; Sengupta, Ushnish ; Hosking, Scott ; Young, Paul
Published in: 2021
Publisher: EGU General Assembly Conference Abstracts

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

Author(s): Ushnish Senguptaa, G ̈unther Waxenegger-Wilfingb, Jan Martinb,Justin Hardib, Matthew P. Junipera,∗
Published in: Fluid Dynamics (physics.flu-dyn); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG), 2021
Publisher: 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 (opens in new window)

Author(s): Michael McCartney, Wolfgang Polifke
Published in: Proceedings of the ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition, 2020
Publisher: ASME Turbo Expo 2020
DOI: 10.1115/gt2020-14834

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

Author(s): Sara Navarro Arredondo, Jim Kok
Published in: Proceedings of the 26th International Congress on Sound and Vibration, Issue 26, 2019, ISBN 978-1-9991810-0-0
Publisher: Canadian Acoustical Association

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

Author(s): Alireza Ghasemi, J.B.W. Kok
Published in: Proceedings of the 26th International Congress on Sound and Vibration, Issue 26, 2019, ISBN 978-1-9991810-0-0
Publisher: Canadian Acoustical Association

Bayesian machine learning for the prognosis of combustion instabilities from noise (opens in new window)

Author(s): Ushnish Sengupta Carl Rasmussen Matthew Juniper
Published in: Proceedings of the ASME 2020 Turbomachinery Technical Conference Exposition, 2020
Publisher: Proceedings of the ASME 2020 Turbomachinery Technical Conference Exposition
DOI: 10.31224/osf.io/ysgp4

Ensembling geophysical models with Bayesian Neural Networks (opens in new window)

Author(s): Sengupta, Ushnish; Amos, Matt; Hosking, J. Scott; Rasmussen, Carl Edward; Juniper, Matthew; Young, Paul J.
Published in: Issue 2, 2020
Publisher: Cornell University
DOI: 10.17863/cam.60032

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

Author(s): Ushnish Sengupta, Maximilian L. Croci, Matthew P. Juniper
Published in: 2020
Publisher: Cornell University

Comparison of Machine Learning Algorithms in the Interpolation and Extrapolation of Flame Describing Functions (opens in new window)

Author(s): Michael McCartney, Matthias Haeringer, Wolfgang Polifke
Published in: Volume 4B: Combustion, Fuels, and Emissions, 2019, ISBN 978-0-7918-5862-2
Publisher: American Society of Mechanical Engineers
DOI: 10.1115/gt2019-91319

Numerical and Experimental Flame Stabilization Analysis in the New SpinningCombustion Technology Framework (opens in new window)

Author(s): Agostinelli, P. W., Kwah, Y. H., Richard, S., Exilard, G., Dawson, J. R., Gicquel, L., & Poinsot, T.
Published in: 2020
Publisher: 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

Author(s): Maximilian L. Croci1 2, Ushnish Sengupta1 and Matthew P. Juniper1
Published in: Symposium on Thermoacoustics in Combustion: Industry meets Academia (SoTiC 2021), 2021
Publisher: SoTiC 2021 - Symposium on Thermoacoustics in Combustion: Industry meets Academia, 2021

Numerical design of Luenberger observers for nonlinear systems (opens in new window)

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

Reduced Order Models Applied to Laminar Diffusion Flames

Author(s): N. L. M. B. Junqueira, L. F. Figueira da Silva, L. C. Ramos
Published in: 2020 Brazilian Congress of Thermal Sciences and Engineering, Online., 2020
Publisher: 2020 Brazilian Congress of Thermal Sciences and Engineering, Online.

Reduced Order Model of Laminar Premixed Inverted Conical Flames (opens in new window)

Author(s): Louise da Costa Ramos, Florent Di Meglio, Luis Fernando F. Da Silva, Valery Morgenthaler
Published in: AIAA Scitech 2020 Forum, 2020, ISBN 978-1-62410-595-1
Publisher: 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 (opens in new window)

Author(s): Nils Christian A. Wilhelmsen, Florent Di Meglio
Published in: 2020 59th IEEE Conference on Decision and Control (CDC), 2020, Page(s) 658-665, ISBN 978-1-7281-7447-1
Publisher: IEEE
DOI: 10.1109/cdc42340.2020.9304413

The influence of the learning data on the reduced order model of laminar non-premixed flames (opens in new window)

Author(s): Nicole Lopes Junqueira Luis Fernando Figueira da Silva , Louise da Costa Ramos , Igor Braga de Paula
Published in: 26 International Congress of Mechanical Engineering, 2021
Publisher: 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 (opens in new window)

Author(s): Francesco Garita; Hans Yu; Matthew P. Juniper
Published in: Issue 4, 2021, ISSN 1528-8919
Publisher: 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 (opens in new window)

Author(s): Edmond Shehadi, Edwin van der Weide
Published in: AIAA Scitech 2021 Forum, 2021, ISBN 978-1-62410-609-5
Publisher: 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

Author(s): Garita, F., Yu, H., & Juniper, M.
Published in: Proceedings of the ASME Turbo Expo 2020: Turbine Technical Conference and Exposition, 2020
Publisher: ASME Turbo Expo 2020

Influence of Hole-to-Hole Interaction on the Acoustic Behavior of Multi-Orifice Perforated Plates (opens in new window)

Author(s): Alireza Javareshkian, Alexis Dancelme, Hongyu Chen, Thomas Sattelmayer
Published in: 2021
Publisher: Journal of Engineering for Gas Turbines and Power
DOI: 10.1115/gt2021-58535

Improved color-gradient method for lattice Boltzmann modeling of two-phase flows (opens in new window)

Author(s): T. Lafarge; P. Boivin; N. Odier; B. Cuenot
Published in: EISSN: 1089-7666, Issue 1, 2021, ISSN 1527-2435
Publisher: Physics of Fluids
DOI: 10.1063/5.0061638

Modeling of Pulsating Inverted Conical Flames: a Numerical Instability Analysis

Author(s): L. C. Ramos, L. F. Figueira da Silva, F. Di Meglio, V. Morgenthaler
Published in: Combustion Theory and Modeling, 2021, ISSN 1364-7830
Publisher: Institute of Physics Publishing

Influence of an Oscillating Airflow on the PrefilmingAirblast Atomization Process (opens in new window)

Author(s): Thomas Christou Björn Stelzner Nikolaos Zarzalis
Published in: Atomization and Sprays, 2021, ISSN 1936-2684
Publisher: Atomization and Sprays
DOI: 10.1615/atomizspr.2021034553

Modeling of the nonlinear flame response of a Bunsen-type flame via multi-layer perceptron (opens in new window)

Author(s): Nilam Tathawadekar, Nguyen Anh Khoa Doan, Camilo F. Silva, Nils Thuerey
Published in: Proceedings of the Combustion Institute, 2020, ISSN 1540-7489
Publisher: Combustion Institute
DOI: 10.1016/j.proci.2020.07.115

Numerical study of multicomponent spray flame propagation (opens in new window)

Author(s): Varun Shastry Quentin Cazeres Bastien Rochette Eleonore Riber Bénédicte Cuenot
Published in: Proceedings of the Combustion Institute, 2019, ISSN 1540-7489
Publisher: Combustion Institute
DOI: 10.1016/j.proci.2020.07.090

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

Author(s): Laera, D., Agostinelli, P. W., Selle, L., Caz res, Q., Oztarlik, G., Schuller, T., Gicquel, L., & Poinsot
Published in: Proceedings of the Combustion Institute, 2020, ISSN 1540-7489
Publisher: Combustion Institute

Impact of wall heat transfer in Large Eddy Simulation of flame dynamics in a swirled combustion chamber (opens in new window)

Author(s): P.W.Agostinelli D.Laera I.Boxx L.Gicquel T.Poinsotd
Published in: Combustion and Flame, 2021, ISSN 0010-2180
Publisher: 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 (opens in new window)

Author(s): Michael McCartney, Ushnish Sengupta, Matthew Juniper
Published in: Journal of Engineering for Gas Turbines and Power, 2021, ISSN 0742-4795
Publisher: American Society of Mechanical Engineers
DOI: 10.1115/1.4052145

Comparison of Machine Learning Algorithms in the Interpolation and Extrapolation of Flame Describing Functions (opens in new window)

Author(s): Michael McCartney, Matthias Haeringer, Wolfgang Polifke
Published in: Journal of Engineering for Gas Turbines and Power, Issue 142/6, 2020, ISSN 0742-4795
Publisher: American Society of Mechanical Engineers
DOI: 10.1115/1.4045516

An Observer for the Electrically Heated Vertical Rijke Tube with Nonlinear Heat Release (opens in new window)

Author(s): Nils Christian A. Wilhelmsen, Florent Di Meglio
Published in: IFAC-PapersOnLine, Issue 53/2, 2020, Page(s) 4181-4188, ISSN 2405-8963
Publisher: 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 (opens in new window)

Author(s): Günther Waxenegger-Wilfing, Ushnish Sengupta, Jan Martin, Wolfgang Armbruster, Justin Hardi, Matthew Juniper, Michael Oschwald
Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science, Issue 31/6, 2021, Page(s) 063128, ISSN 1054-1500
Publisher: American Institute of Physics
DOI: 10.1063/5.0038817

Ensembling geophysical models with Bayesian Neural Networks

Author(s): Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew Juniper, Paul J. Young
Published in: Advances in Neural Information Processing Systems (NeurIPS) 2020, 2020
Publisher: Advances in Neural Information Processing Systems (NeurIPS) 2020

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