Benjamin Sanderse

Biography

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 data-driven 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 differential-algebraic equations arising from fluid dynamic problems, such as incompressible single-phase and multi-phase Navier-Stokes equations.

Applications:

• Wind energy and in particular turbulent wind turbine wakes (EUROS project).
• Multi-phase flow in reservoirs, wells, and pipelines.
• Sloshing of liquids in tankers (SLING project).

Publications

• van den Bos, L.M.M, & Sanderse, B. (2021). A geometrical interpretation of the addition of nodes to an interpolatory quadrature rule while preserving positive weights. Journal of Computational and Applied Mathematics, 391. doi:10.1016/j.cam.2021.113430
• Sanderse, B. (2020). Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods. Journal of Computational Physics, 421. doi:10.1016/j.jcp.2020.109736
• Caboni, M, Carrion, M, Rodriguez, C, Schepers, G, Boorsma, K, & Sanderse, B. (2020). Assessment of sensitivity and accuracy of BEM-based aeroelastic models on wind turbine load predictions. In Journal of Physics: Conference Series. doi:10.1088/1742-6596/1618/4/042015
• Kumar, P, Sanderse, B, Boorsma, K, & Caboni, M. (2020). Global sensitivity analysis of model uncertainty in aeroelastic wind turbine models. In Journal of Physics: Conference Series. doi:10.1088/1742-6596/1618/4/042034
• van den Bos, L.M.M, Sanderse, B, & Bierbooms, W.A.A.M. (2020). Adaptive sampling-based quadrature rules for efficient Bayesian prediction. Journal of Computational Physics, 417. doi:10.1016/j.jcp.2020.109537
• van den Bos, L.M.M, Bierbooms, W.A.A.M, Alexandre, A, Sanderse, B, & van Bussel, G.J.W. (2020). Fatigue design load calculations of the offshore NREL 5 MW benchmark turbine using quadrature rule techniques. Wind Energy, 23(5), 1181–1195. doi:10.1002/we.2470
• van den Bos, L.M.M, Sanderse, B, Bierbooms, W.A.A.M, & van Bussel, G.J.W. (2020). Generating nested quadrature rules with positive weights based on arbitrary sample sets. SIAM-ASA Journal on Uncertainty Quantification, 8(1), 139–169. doi:10.1137/18M1213373
• Hendrix, M.H.W, Sanderse, B, Breugem, W.-P, & Henkes, R.A.W.M. (2020). Simulation of slug propagation for by-pass pigging in two-phase stratified pipe flow. In Proceedings of the International Conference on Multiphase Production Technology (pp. 317–330).
• Sanderse, B, Buist, J.F.H, & Henkes, R.A.W.M. (2020). A novel pressure-free two-fluid model for one-dimensional incompressible multiphase flow. Journal of Computational Physics. doi:10.1016/j.jcp.2020.109919
• van Halder, Y, Sanderse, B, & Koren, B. (2019). An adaptive minimum spanning tree multielement method for uncertainty quantification of smooth and discontinuous responses. SIAM Journal on Scientific Computing, 41(6), A3624–A3648. doi:10.1137/18M1219643

Current projects with external funding

• Discretize first, reduce next: a new paradigm to closure for fluid flow simulation (None)
• Unravelling Neural Networks with Structure-Preserving Computing (Unravelling Neural Networks)
• WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)

Awards

• Stieltjes prijs 2013 (2013)
• 5th PhD seminar on wind energy in Europe, Durham - Best paper 30 September - 1 October (2009)