Richard D. Sandberg

Orcid: 0000-0001-5199-3944

According to our database1, Richard D. Sandberg authored at least 14 papers between 2012 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

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Bibliography

2022
Multi-objective CFD-driven development of coupled turbulence closure models.
J. Comput. Phys., 2022

2021
Two Dimensional Analysis of Hybrid Spectral/Finite Difference Schemes for Linearized Compressible Navier-Stokes Equations.
J. Sci. Comput., 2021

Application of Gene Expression Programming to a-posteriori LES modeling of a Taylor Green Vortex.
J. Comput. Phys., 2021

2020
RANS turbulence model development using CFD-driven machine learning.
J. Comput. Phys., 2020

2019
A framework to develop data-driven turbulence models for flows with organised unsteadiness.
J. Comput. Phys., 2019

2018
Application of an evolutionary algorithm to LES modelling of turbulent transport in premixed flames.
J. Comput. Phys., 2018

2017
The boundary data immersion method for compressible flows with application to aeroacoustics.
J. Comput. Phys., 2017

2016
A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship.
J. Comput. Phys., 2016

Iterative learning control applied to a non-linear vortex panel model for improved aerodynamic load performance of wind turbines with smart rotors.
Int. J. Control, 2016

2015
Iterative learning control for load control of smart turbine blades with variable rotation rates.
Proceedings of the American Control Conference, 2015

2014
Iterative Learning Control for Improved Aerodynamic Load Performance of Wind Turbines With Smart Rotors.
IEEE Trans. Control. Syst. Technol., 2014

Computational fluid dynamics based iterative learning control for smart rotor enabled fatigue load reduction in wind turbines.
Proceedings of the American Control Conference, 2014

2013
Iterative learning control of wind turbine smart rotors with pressure sensors.
Proceedings of the American Control Conference, 2013

2012
Computational fluid dynamics based iterative learning control of peak loads in wind turbines.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012


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