Eajf Frank Peters

Bio: Eajf Frank Peters is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Immersed boundary method & Direct numerical simulation. The author has an hindex of 27, co-authored 102 publications receiving 2188 citations. Previous affiliations of Eajf Frank Peters include Royal Dutch Shell & University of Amsterdam.

A new drag correlation from fully resolved simulations of flow past monodisperse static arrays of spheres

Abstract: Fully resolved simulations of flow past fixed assemblies of monodisperse spheres in face-centered-cubic array or random configurations, are performed using an iterative immersed boundary method. A methodology has been applied such that the computed gas–solid force is almost independent of the grid resolution. Simulations extend the previously similar studies to a wider range of solids volume fraction ( inline image[0.1, 0.6]) and Reynolds number (Re inline image[50, 1000]). A new drag correlation combining the existed drag correlations for low-Re flows and single-sphere flows is proposed, which fits the entire dataset with an average relative deviation of 4%. This correlation is so far the best possible expression for the drag force in monodisperse static arrays of spheres, and is the most accurate basis to introduce the particle mobility for dynamic gas–solid systems, such as in fluidized beds.

Rejection-free Monte Carlo sampling for general potentials.

Abstract: A Monte Carlo method to sample the classical configurational canonical ensemble is introduced. In contrast to the Metropolis algorithm, where trial moves can be rejected, in this approach collisions take place. The implementation is event-driven; i.e., at scheduled times the collisions occur. A unique feature of the new method is that smooth potentials (instead of only step-wise changing ones) can be used. In addition to an event-driven approach, where all particles move simultaneously, we introduce a straight event-chain implementation. As proof of principle, a system of Lennard-Jones particles is simulated.

Elimination of time step effects in DPD

Abstract: The equilibrium statistics of quantities computed by means of DPD (dissipative particle dynamics) are usually very sensitive to the time step used in the simulation. In this letter we show how to eliminate this sensitivity by considering the irreversible dynamics in DPD as the limiting case of a thermostat that leaves Maxwell-Boltzmann distribution invariant even for finite time steps. The remaining dependency on time step is solely due to the discretization of the conservative part of the dynamics and is independent of the thermostat.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Insights into phase transition kinetics from colloid science

Abstract: Colloids display intriguing transitions between gas, liquid, solid and liquid crystalline phases. Such phase transitions are ubiquitous in nature and have been studied for decades. However, the predictions of phase diagrams are not always realized; systems often become undercooled, supersaturated, or trapped in gel-like states. In many cases the end products strongly depend on the starting position in the phase diagram and discrepancies between predictions and actual observations are due to the intricacies of the dynamics of phase transitions. Colloid science aims to understand the underlying mechanisms of these transitions. Important advances have been made, for example, with new imaging techniques that allow direct observation of individual colloidal particles undergoing phase transitions, revealing some of the secrets of the complex pathways involved.

Hydrogen Bonding in the Electronic Excited State

Abstract: Because of its fundamental importance in many branches of science, hydrogen bonding is a subject of intense contemporary research interest. The physical and chemical properties of hydrogen bonds in the ground state have been widely studied both experimentally and theoretically by chemists, physicists, and biologists. However, hydrogen bonding in the electronic excited state, which plays an important role in many photophysical processes and photochemical reactions, has scarcely been investigated.Upon electronic excitation of hydrogen-bonded systems by light, the hydrogen donor and acceptor molecules must reorganize in the electronic excited state because of the significant charge distribution difference between the different electronic states. The electronic excited-state hydrogen-bonding dynamics, which are predominantly determined by the vibrational motions of the hydrogen donor and acceptor groups, generally occur on ultrafast time scales of hundreds of femtoseconds. As a result, state-of-the-art femtos...