Publicaties
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Aeroelastic simulations of the DTU 10 MW turbine using tight coupling integration techniques KU Leuven
The present study aims to investigate a newly developed multirate time integration technique on aeroelastic simulations of the DTU 10 MW reference wind turbine inside a turbulent atmospheric boundary layer. The turbine is modelled using an actuator line model and a multibody structural solver is employed for the dynamic response. The turbine is studied under cases of laminar flow and a pressure driven boundary layer. Results show that the scheme ...
Pseudo-marginal approximation to the free energy in a micro-macro Markov chain Monte Carlo method KU Leuven
We introduce a generalized micro-macro Markov chain Monte Carlo (mM-MCMC) method with pseudo-marginal approximation to the free energy that is able to accelerate sampling of the microscopic Gibbs distributions when there is a time-scale separation between the macroscopic dynamics of a reaction coordinate and the remaining microscopic degrees of freedom. The mM-MCMC method attains this efficiency by iterating four steps: (i) propose a new value ...
Toward plasma drifts in EMC3: Implementation of gradient, divergence, and particle tracing schemes KU Leuven
Moment Models for Neutral Particles in High-Collisional Regimes for Plasma Edge Simulations KU Leuven
Convergence of the Micro-Macro Parareal Method for a Linear Scale-Separated Ornstein-Uhlenbeck SDE KU Leuven
Time-parallel methods can reduce the wall clock time required for the accurate numerical solution of differential equations by parallelizing across the time-dimension. In this paper, we present and test the convergence behavior of a multiscale, micro-macro version of a Parareal method for stochastic differential equations (SDEs). In our method, the fine propagator of the SDE is based on a high-dimensional slow-fast microscopic model; the coarse ...
A Micro-Macro Markov Chain Monte Carlo Method for Molecular Dynamics using Reaction Coordinate Proposals KU Leuven
We introduce a new micro-macro Markov chain Monte Carlo method to sample invariant distributions of molecular dynamics systems that exhibit a time-scale separation between the microscopic (fast) dynamics, and the macroscopic (slow) dynamics of some low-dimensional set of reaction coordinates. The algorithm enhances exploration of the state space in the presence of metastability by allowing larger proposed moves at the macroscopic level, on which ...