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Benjamin Sanderse
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Review of computational fluid dynamics for wind turbine wake aerodynamics
B Sanderse, SP Pijl, B Koren
Wind Energy 14 (7), 799-819, 2011
8082011
Aerodynamics of wind turbine wakes
B Sanderse
Energy research Centre of the Netherlands, ECN-E-09-016, 2009
433*2009
Accuracy analysis of explicit Runge–Kutta methods applied to the incompressible Navier–Stokes equations
B Sanderse, B Koren
Journal of Computational Physics 231 (8), 3041-3063, 2012
1252012
Energy-conserving Runge-Kutta methods for the incompressible Navier-Stokes equations
B Sanderse
Journal of Computational Physics 233, 100-131, 2013
1022013
Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods
B Sanderse
Journal of Computational Physics 421, 109736, 2020
302020
Scientific machine learning for closure models in multiscale problems: a review
B Sanderse, P Stinis, R Maulik, SE Ahmed
arXiv preprint arXiv:2403.02913, 2024
262024
Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model
F Gugole, LE Coffeng, W Edeling, B Sanderse, SJ de Vlas, D Crommelin
PLoS computational biology 17 (9), e1009355, 2021
172021
Comparison of neural closure models for discretised PDEs
H Melchers, D Crommelin, B Koren, V Menkovski, B Sanderse
Computers & Mathematics with Applications 143, 94-107, 2023
162023
Constraint-consistent Runge–Kutta methods for one-dimensional incompressible multiphase flow
B Sanderse, AEP Veldman
Journal of Computational Physics 384, 170-199, 2019
162019
An Adaptive Minimum Spanning Tree Multielement Method for Uncertainty Quantification of Smooth and Discontinuous Responses
YV Halder, B Sanderse, B Koren
SIAM Journal on Scientific Computing 41 (6), A3624-A3648, 2019
152019
Bayesian model calibration with interpolating polynomials based on adaptively weighted Leja nodes
LMM van den Bos, B Sanderse, W Bierbooms, GJW van Bussel
arXiv preprint arXiv:1802.02035, 2018
142018
Analysis of time integration methods for the compressible two-fluid model for pipe flow simulations
B Sanderse, IE Smith, MHW Hendrix
International Journal of Multiphase Flow 95, 155-174, 2017
142017
Energy-conserving discretization methods for the incompressible Navier-Stokes equations: application to the simulation of wind-turbine wakes
B Sanderse
Technische Universiteit Eindhoven, 2013
142013
Energy-conserving neural network for turbulence closure modeling
T van Gastelen, W Edeling, B Sanderse
Journal of Computational Physics 508, 113003, 2024
132024
Adjoint Optimisation for Wind Farm Flow Control with a Free-Vortex Wake Model
MJ Broek, D De Tavernier, B Sanderse, JW van Wingerden
Renewable Energy 201, 752-765, 2022
132022
Adaptive sampling-based quadrature rules for efficient Bayesian prediction
LMM van den Bos, B Sanderse, W Bierbooms
Journal of Computational Physics 417, 109537, 2020
132020
Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling
B Sanderse, VV Dighe, K Boorsma, G Schepers
Wind Energy Science 7 (2), 759-781, 2022
122022
Reduced order models for the incompressible Navier‐Stokes equations on collocated grids using a ‘discretize‐then‐project’approach
SK Star, B Sanderse, G Stabile, G Rozza, J Degroote
International Journal for Numerical Methods in Fluids 93 (8), 2694-2722, 2021
122021
Boundary treatment for fourth-order staggered mesh discretizations of the incompressible Navier–Stokes equations
B Sanderse, R Verstappen, B Koren
Journal of Computational Physics 257, 1472-1505, 2014
122014
Global sensitivity analysis of model uncertainty in aeroelastic wind turbine models
P Kumar, B Sanderse, K Boorsma, M Caboni
Journal of Physics: Conference Series 1618 (4), 042034, 2020
112020
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