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Journal Articles

Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration

Soumalya Sarkar, Sudeepta Mondal, Michael Joly, Matthew E. Lynch, Shaunak D. Bopardikar, Ranadip Acharya, Paris Perdikaris

J. Mech. Des. December 2019, 141(12): 121001.

Paper No: MD-19-1167

DOI: https://doi.org/10.1115/1.4044598

Published Online: September 30, 2019

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Topics: Calibration, Optimization, Design

Journal Articles

Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Precompression

Michael Joly, Soumalya Sarkar, Dhagash Mehta

J. Turbomach. May 2019, 141(5): 051011.

Paper No: TURBO-18-1262

DOI: https://doi.org/10.1115/1.4041808

Published Online: January 25, 2019

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Topics: Design, Optimization, Machine learning, Computational fluid dynamics, Rotors, Compressors

Proceedings Papers

Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Pre-Compression

Michael Joly, Soumalya Sarkar, Dhagash Mehta

Proc. ASME. GT2018, Volume 2C: Turbomachinery, V02CT42A050, June 11–15, 2018

Paper No: GT2018-77098

DOI: https://doi.org/10.1115/GT2018-77098

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Topics: Compression, Compressors, Machine learning, Optimization, Rotors, Design, Computational fluid dynamics, Failure, Geometry, NASA

Proceedings Papers

Full Design of a Highly Loaded and Compact Contra-Rotating Fan Using Multidisciplinary Evolutionary Optimization

Michael Joly, Tom Verstraete, Guillermo Paniagua

Proc. ASME. GT2013, Volume 6B: Turbomachinery, V06BT43A009, June 3–7, 2013

Paper No: GT2013-94433

DOI: https://doi.org/10.1115/GT2013-94433

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Topics: Design, Optimization, Computational fluid dynamics, Flow (Dynamics), Rotors, Fans, Pareto optimization, Algorithms, Machinery, Pressure

Proceedings Papers

Full Design of a Highly Loaded Fan by Multi-Objective Optimization of Through-Flow and High-Fidelity Aero-Mechanical Performances

Michael Joly, Tom Verstraete, Guillermo Paniagua

Proc. ASME. GT2012, Volume 8: Turbomachinery, Parts A, B, and C, 2293-2301, June 11–15, 2012

Paper No: GT2012-69686

DOI: https://doi.org/10.1115/GT2012-69686

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Topics: Design, Flow (Dynamics), Pareto optimization, Optimization, Computational fluid dynamics, Algorithms, Axial flow, Compressors, Design methodology, Fluids

Journal citation

NARROW

Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration

Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Precompression

Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Pre-Compression

Full Design of a Highly Loaded and Compact Contra-Rotating Fan Using Multidisciplinary Evolutionary Optimization

Full Design of a Highly Loaded Fan by Multi-Objective Optimization of Through-Flow and High-Fidelity Aero-Mechanical Performances

Journals

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Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration

Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Precompression

Machine Learning Enabled Adaptive Optimization of a Transonic Compressor Rotor With Pre-Compression

Full Design of a Highly Loaded and Compact Contra-Rotating Fan Using Multidisciplinary Evolutionary Optimization

Full Design of a Highly Loaded Fan by Multi-Objective Optimization of Through-Flow and High-Fidelity Aero-Mechanical Performances

Journals

Conference Proceedings

eBooks

Resources

Opportunities

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