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Karthik Reddy Lyathakula
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Journal Articles

Design, Modeling, and Testing of a Novel Elastohydrodynamic Seal for Supercritical CO 2 Power Cycles

Mohammad Fuad Hassan, Sevki Cesmeci, Karthik Reddy Lyathakula, Hanping Xu
Journal: Journal of Tribology
Publisher: ASME
Article Type: Research Papers
J. Tribol. November 2025, 147(11): 114102.
Paper No: TRIB-24-1311
https://doi.org/10.1115/1.4067553
Published Online: February 5, 2025
View Article titled, Design, Modeling, and Testing of a Novel Elastohydrodynamic Seal for Supercritical CO 2 Power Cycles
Open the PDF for Design, Modeling, and Testing of a Novel Elastohydrodynamic Seal for Supercritical CO 2 Power Cycles in another window
Topics: Clearances (Engineering), Deformation, Design, Fluids, Leakage, Pressure, Flow (Dynamics), Supercritical carbon dioxide
Journal Articles

Physics-Informed Deep Learning-Based Proof-of-Concept Study of a Novel Elastohydrodynamic Seal for Supercritical CO 2 Turbomachinery

Karthik Reddy Lyathakula, Sevki Cesmeci, Matthew DeMond, Mohammad Fuad Hassan, Hanping Xu, Jing Tang
Journal: Journal of Energy Resources Technology
Publisher: ASME
Article Type: Research Papers
J. Energy Resour. Technol. December 2023, 145(12): 121705.
Paper No: JERT-23-1075
https://doi.org/10.1115/1.4063326
Published Online: September 29, 2023
View Article titled, Physics-Informed Deep Learning-Based Proof-of-Concept Study of a Novel Elastohydrodynamic Seal for Supercritical CO 2 Turbomachinery
Open the PDF for Physics-Informed Deep Learning-Based Proof-of-Concept Study of a Novel Elastohydrodynamic Seal for Supercritical CO 2 Turbomachinery in another window
Topics: Clearances (Engineering), Design, Flow (Dynamics), Leakage, Physics, Pressure, Rotors, Supercritical carbon dioxide, Turbomachinery, Sealing (Process)
Proceedings Papers

A Design Study of an Elasto-Hydrodynamic Seal for sCO 2 Power Cycle by Using Physics Informed Neural Network

Mohammad Towhidul Islam Rimon, Mohammad Fuad Hassan, Karthik Reddy Lyathakula, Sevki Cesmeci, Hanping Xu, Jing Tang
Proc. ASME. POWER2023, ASME Power Applied R&D 2023, V001T04A005, August 6–8, 2023
Publisher: American Society of Mechanical Engineers
Paper No: POWER2023-108802
https://doi.org/10.1115/POWER2023-108802
View Paper titled, A Design Study of an Elasto-Hydrodynamic Seal for sCO 2 Power Cycle by Using Physics Informed Neural Network
Open the PDF for A Design Study of an Elasto-Hydrodynamic Seal for sCO 2 Power Cycle by Using Physics Informed Neural Network in another window
Topics: Artificial neural networks, Design, Physics, Supercritical carbon dioxide, Thermodynamic power cycles
Proceedings Papers

A Proof-of-Concept Study of a Novel Elasto-Hydrodynamic Seal for CO 2

Karthik Reddy Lyathakula, Sevki Cesmeci, Mohammad Fuad Hassan, Hanping Xu, Jing Tang
Proc. ASME. POWER2022, ASME 2022 Power Conference, V001T12A003, July 18–19, 2022
Publisher: American Society of Mechanical Engineers
Paper No: POWER2022-86607
https://doi.org/10.1115/POWER2022-86607
View Paper titled, A Proof-of-Concept Study of a Novel Elasto-Hydrodynamic Seal for CO 2
Open the PDF for A Proof-of-Concept Study of a Novel Elasto-Hydrodynamic Seal for CO 2 in another window
Topics: Carbon dioxide, Leakage, Flow (Dynamics), Solid mechanics, Turbomachinery, Wear, Clearances (Engineering), Deformation, Energy generation, High pressure (Physics)
Proceedings Papers

Physics-Informed Deep Learning-Based Modeling of a Novel Elastohydrodynamic Seal for Supercritical CO 2 Turbomachinery

Karthik Reddy Lyathakula, Sevki Cesmeci, Matthew DeMond, Hanping Xu, Jing Tang
Proc. ASME. POWER2022, ASME 2022 Power Conference, V001T15A008, July 18–19, 2022
Publisher: American Society of Mechanical Engineers
Paper No: POWER2022-86597
https://doi.org/10.1115/POWER2022-86597
View Paper titled, Physics-Informed Deep Learning-Based Modeling of a Novel Elastohydrodynamic Seal for Supercritical CO 2 Turbomachinery
Open the PDF for Physics-Informed Deep Learning-Based Modeling of a Novel Elastohydrodynamic Seal for Supercritical CO 2 Turbomachinery in another window
Topics: Artificial neural networks, Modeling, Physics, Supercritical carbon dioxide, Turbomachinery