Benjamin Sanderse
 Full Name
 Dr.ir. B. Sanderse
 Function(s)
 Scientific Staff Member
 B.Sanderse@cwi.nl
 Telephone
 +31 20 592 4085
 Room
 L123
 Department(s)
 Scientific Computing
 Homepage
 http://www.thinkingslow.nl/
Biography
Benjamin Sanderse works as a tenure track researcher in the Scientific Computing group, focusing on numerical methods for uncertainty quantification and for solving partial differential equations occurring in fluid flow problems. Prior to his tenure track position, he worked at Shell Technology Centre Amsterdam on research and development of multiphase flow simulators in oil and gas applications. His PhD research was on new numerical methods for simulating incompressible flows occurring in wind energy applications, a combined position at Energy research Centre of the Netherlands (ECN) and CWI. He obtained his PhD degree cum laude (with honours) in 2013 from Eindhoven University of Technology. Before starting his PhD degree, he received his MSc degree in Aerospace Engineering at Delft University of Technology in 2008.Research
My research interest is to develop efficient methods for making predictions under uncertainty of physical systems by developing new methodologies that combine physical modelling approaches and datadriven techniques, in particular to applications involving fluid dynamic problems.
Currently the following two topics have my main interest:
 Uncertainty quantification: surrogate modelling for parametric PDEs, efficient solvers for Bayesian inverse problems, statistical learning for closure models, novel quadrature rules (with Laurent van den Bos, Yous van Halder).
 Computational fluid dynamics (CFD): time integration methods for differentialalgebraic equations arising from fluid dynamic problems, such as incompressible singlephase and multiphase NavierStokes equations.
Applications:
 Wind energy and in particular turbulent wind turbine wakes (EUROS project).
 Multiphase flow in reservoirs, wells, and pipelines.
 Sloshing of liquids in tankers (SLING project).
Publications

van den Bos, L.M.M, Sanderse, B, Blonk, L, Bierbooms, W.A.A.M, & Van Bussel, G.J.W. (2018). Efficient ultimate load estimation for offshore wind turbines using interpolating surrogate models. In Modeling and Simulation Technology. doi:10.1088/17426596/1037/6/062017

van Zwieten, J, Sanderse, B, Hendrix, M.H.W, Vuik, C, & Henkes, R.A.W.M. (2017). Efficient simulation of onedimensional twophase flow with a highorder hadaptive spacetime Discontinuous Galerkin method. Computers & Fluids, 156, 34–47. doi:10.1016/j.compfluid.2017.06.010

Sanderse, B, Eskerud Smith, I, & Hendrix, M.H.W. (2017). Analysis of time integration methods for the compressible twofluid model for pipe flow simulations. International Journal of Multiphase Flow, 95, 155–174. doi:10.1016/j.ijmultiphaseflow.2017.05.002

Capuano, F, Sanderse, B, De Angelis, E.M, & Coppola, G. (2017). A minimumdissipation timeintegration strategy for largeeddy simulation of incompressible turbulent flows. In AIMETA 2017 Proceedings of the XXIII Conference of the Italian Association of Theoretical and Applied Mechanics (pp. 2311–2323). Retrieved from http://hdl.handle.net/11588/689710

Capuano, F. (Francesco), Sanderse, B, De Angelis, E.M. (Enrico M.), & Coppola, G. (Gennaro). (2017). A minimumdissipation timeintegration strategy for largeeddy simulation of incompressible turbulent flows. In AIMETA 2017  Proceedings of the 23rd Conference of the Italian Association of Theoretical and Applied Mechanics (pp. 2311–2323).

Hendrix, M.H.W, Eskerud Smith, I, van Zwieten, J, & Sanderse, B. (2016). Comparison of numerical methods for slug capturing with the twofluid model. In Proceedings of the 9th International Conference on Multiphase Flow.

Sanderse, B, Haspels, M, & Henkes, R.A.W.M. (2015). Simulation of elongated bubbles in a channel using the twofluid model. Journal of Dispersion Science and Technology, 36(10), 1407–1418. doi:10.1080/01932691.2014.989571

Sanderse, B, Verstappen, R.W.C.P, & Koren, B. (2014). Boundary treatment for fourthorder staggered mesh discretizations of the incompressible NavierStokes equations. Journal of Computational Physics, 257(Part B), 1472–1505. doi:10.1016/j.jcp.2013.10.002

Sanderse, B. (2013, March 19). Energyconserving discretization methods for the incompressible NavierStokes equations.

Sanderse, B, & Koren, B. (2013). RungeKutta methods for the incompressible NavierStokes equations. In AIAApapers.
Current projects with external funding

Sloshing of Liquefied Natural Gas: subproject Variability (1410project2) (SLING)
Awards
 Stieltjes prijs 2013 (2013)
 5th PhD seminar on wind energy in Europe, Durham  Best paper 30 September  1 October (2009)