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Senthil Krishnababu
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Proceedings Papers

Application of Machine Learning to Forced Response Predictions of an Industrial Axial Compressor Rotor Blade

Giuseppe Bruni, Senthil Krishnababu, Simon Jackson
Proc. ASME. GT2022, Volume 8A: Structures and Dynamics — Aerodynamics Excitation and Damping; Bearing and Seal Dynamics; Emerging Methods in Engineering Design, Analysis, and Additive Manufacturing; Fatigue, Fracture, and Life Prediction, V08AT21A019, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82427
https://doi.org/10.1115/GT2022-82427
View Paper titled, Application of Machine Learning to Forced Response Predictions of an Industrial Axial Compressor Rotor Blade
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Topics: Blades, Compressors, Machine learning, Rotors, Design, Artificial neural networks, Computational fluid dynamics, Algorithms, Cycles, Damping
Proceedings Papers

Probabilistic Assessment of Gas Turbine Compressor Blade HCF Life

Zhiqiang Meng, Senthil Krishnababu, Simon Jackson, Björn Sjödin
Proc. ASME. GT2022, Volume 8B: Structures and Dynamics — Probabilistic Methods; Rotordynamics; Structural Mechanics and Vibration, V08BT25A005, June 13–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: GT2022-82323
https://doi.org/10.1115/GT2022-82323
View Paper titled, Probabilistic Assessment of Gas Turbine Compressor Blade HCF Life
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Topics: Blades, Compressors, Design, Gas turbines, High cycle fatigue
Journal Articles

Application of Machine Learning to Forced Response Predictions of an Industrial Axial Compressor Rotor Blade

Giuseppe Bruni, Senthil Krishnababu, Simon Jackson
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. January 2023, 145(1): 011019.
Paper No: GTP-22-1341
https://doi.org/10.1115/1.4055634
Published Online: October 21, 2022
View Article titled, Application of Machine Learning to Forced Response Predictions of an Industrial Axial Compressor Rotor Blade
Open the PDF for Application of Machine Learning to Forced Response Predictions of an Industrial Axial Compressor Rotor Blade in another window
Topics: Artificial neural networks, Blades, Compressors, Computational fluid dynamics, Damping, Design, Machine learning, Mode shapes, Resonance, Modal analysis
Proceedings Papers

AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines

Senthil Krishnababu, Omar Valero, Roger Wells
Proc. ASME. GT2021, Volume 2D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V02DT36A007, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-58925
https://doi.org/10.1115/GT2021-58925
View Paper titled, AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines
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Topics: Industrial gases, Physics, Turbines, Manufacturing, Compressors, Gas turbines, Maintenance, Teams, Artificial neural networks, Blocks (Building materials)
Proceedings Papers

On the Forced Response Predictions and Life Improvements of an Industrial Axial Compressor Rotor Blade

Senthil Krishnababu, Giuseppe Bruni, Agnieszka Frach
Proc. ASME. GT2021, Volume 9A: Structures and Dynamics — Aerodynamics Excitation and Damping; Bearing and Seal Dynamics; Emerging Methods in Design and Engineering, V09AT23A007, June 7–11, 2021
Publisher: American Society of Mechanical Engineers
Paper No: GT2021-58923
https://doi.org/10.1115/GT2021-58923
View Paper titled, On the Forced Response Predictions and Life Improvements of an Industrial Axial Compressor Rotor Blade
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Topics: Blades, Compressors, Rotors, Design, Engines, Excitation, High cycle fatigue, Resonance, Computation, Cycles
Proceedings Papers

Squealer Tip Treatment Design for Axial Compressors

Giuseppe Bruni, James Taylor, Senthil Krishnababu, Robert Miller, Roger Wells
Proc. ASME. GT2020, Volume 2A: Turbomachinery, V02AT32A035, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-14906
https://doi.org/10.1115/GT2020-14906
View Paper titled, Squealer Tip Treatment Design for Axial Compressors
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Topics: Compressors, Computational fluid dynamics, Design, Leakage
Proceedings Papers

Quick Start of an Industrial Gas Turbine Engine Through the Development of “Silent Start” VGV Schedule

Senthil Krishnababu, Vili Panov, Simon Jackson, Andrew Dawson
Proc. ASME. GT2020, Volume 2A: Turbomachinery, V02AT32A063, September 21–25, 2020
Publisher: American Society of Mechanical Engineers
Paper No: GT2020-15983
https://doi.org/10.1115/GT2020-15983
View Paper titled, Quick Start of an Industrial Gas Turbine Engine Through the Development of “Silent Start” VGV Schedule
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Topics: Blades, Compressors, Engines, Gas turbines, Guide vanes, High pressure (Physics), Industrial gases, Optimization, Rotors, Stress
Journal Articles

Multiscale Parallelized Computational Fluid Dynamics Modeling Toward Resolving Manufacturable Roughness

Marios Kapsis, Li He, Yan Sheng Li, Omar Valero, Roger Wells, Senthil Krishnababu, Gaurav Gupta, Jayanta Kapat, Megan Schaenzer
Journal: Journal of Engineering for Gas Turbines and Power
Publisher: ASME
Article Type: Research-Article
J. Eng. Gas Turbines Power. February 2020, 142(2): 021001.
Paper No: GTP-19-1570
https://doi.org/10.1115/1.4045481
Published Online: December 12, 2019
View Article titled, Multiscale Parallelized Computational Fluid Dynamics Modeling Toward Resolving Manufacturable Roughness
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Topics: Flow (Dynamics), Surface roughness, Computational fluid dynamics, Modeling, Simulation
Proceedings Papers

Multi-Scale Parallelised CFD Modelling Towards Resolving Manufacturable Roughness

Marios Kapsis, Li He, Yan Sheng Li, Roger Wells, Omar Valero, Senthil Krishnababu, Gaurav Gupta, Jayanta Kapat, Megan Schaenzer
Proc. ASME. GT2019, Volume 2C: Turbomachinery, V02CT41A012, June 17–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: GT2019-90314
https://doi.org/10.1115/GT2019-90314
View Paper titled, Multi-Scale Parallelised CFD Modelling Towards Resolving Manufacturable Roughness
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Topics: Computational fluid dynamics, Modeling, Surface roughness, Flow (Dynamics), Resolution (Optics), Design, Additive manufacturing, Finishes, Simulation, Spacetime