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Kingshuk Dasadhikari
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Proceedings Papers

Aerodynamic Optimization of an Axial Turbine Blade and Diffuser

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Kingshuk Dasadhikari, Yasunori Kimura, Yoshihiro Kuwamura, Hokuto Isoda, Fumito Hiratani
Proc. ASME. GT2024, Volume 12C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions, V12CT32A019, June 24–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: GT2024-124695
https://doi.org/10.1115/GT2024-124695
View Paper titled, Aerodynamic Optimization of an Axial Turbine Blade and Diffuser
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Topics: Blades, Diffusers, Optimization, Turbine blades, Turbines
Journal Articles

Rapid Algorithmic Blade Design Applying Machine Learning From Shape Optimization to Satisfy Multidisciplinary Constraints

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Kingshuk Dasadhikari, Yoshihiro Kuwamura
Journal: Journal of Turbomachinery
Publisher: ASME
Article Type: Research Papers
J. Turbomach. April 2024, 146(4): 041010.
Paper No: TURBO-23-1273
https://doi.org/10.1115/1.4064227
Published Online: December 21, 2023
View Articletitled, Rapid Algorithmic Blade Design Applying Machine Learning From Shape Optimization to Satisfy Multidisciplinary Constraints
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Topics: Blades, Design, Shape optimization, Optimization, Mach number
Proceedings Papers

Rapid Algorithmic Blade Design Applying Machine Learning From Shape Optimization to Satisfy Multidisciplinary Constraints

Available to Purchase

Kingshuk Dasadhikari, Yoshihiro Kuwamura
Proc. ASME. GT2023, Volume 13D: Turbomachinery — Multidisciplinary Design Approaches, Optimization, and Uncertainty Quantification; Radial Turbomachinery Aerodynamics; Unsteady Flows in Turbomachinery, V13DT34A016, June 26–30, 2023
Publisher: American Society of Mechanical Engineers
Paper No: GT2023-102700
https://doi.org/10.1115/GT2023-102700
View Paper titled, Rapid Algorithmic Blade Design Applying Machine Learning From Shape Optimization to Satisfy Multidisciplinary Constraints
Open the PDF for Rapid Algorithmic Blade Design Applying Machine Learning From Shape Optimization to Satisfy Multidisciplinary Constraints in another window
Topics: Blades, Design, Machine learning, Shape optimization