Hakan Başağaoğlu

Lattice-Boltzmann simulations of repulsive particle-particle and particle-wall interactions: Coughing and choking

We propose and numerically investigate a new particle retention mechanism for particle entrapment in creeping flows in a constricted section of a saturated rough-walled narrow flow channel. We hypothesize that particles, whose size is smaller than channel width, can be temporarily or permanently immobilized in a flow channel away from channel walls due to particle-particle and particle-wall repulsive potentials, and, consequently, the flow field is clogged temporarily (coughing) or permanently (choking). Two mathematically simplified repulsive particle-particle and particle-wall interaction potentials are incorporated into a two-dimensional colloidal lattice-Boltzmann model. These potentials are two-body Lennard-Jones 12 and screened electrostatic repulsive potentials. Numerical simulations reveal that unlike in smooth-walled flow channels, particles are entrapped away from rough-walled channel walls and subsequently clog the flow field if fluid-drag and repulsive forces on particles are in balance. Off-balance forces, however, could result in temporary clogging if repulsive forces are stronger on the advancing edge of a particle than on its trailing edge. The new conceptualization and two-particle numerical simulations successfully captured (i) temporary entrapment of two particles (coughing), (ii) temporary entrapment of one of the particles with permanent entrapment of the other particle (coughing-choking), and (iii) permanent entrapment of both particles (choking) as a function of repulsive interaction strength.

Enhanced computational performance of the lattice Boltzmann model for simulating micron- and submicron-size particle flows and non-Newtonian fluid flows

Abstract Significant improvements in the computational performance of the lattice-Boltzmann (LB) model, coded in FORTRAN90, were achieved through application of enhancement techniques. Applied techniques include optimization of array memory layouts, data structure simplification, random number generation outside the simulation thread(s), code parallelization via OpenMP, and intra- and inter-timestep task pipelining. Effectiveness of these optimization techniques was measured on three benchmark problems: (i) transient flow of multiple particles in a Newtonian fluid in a heterogeneous fractured porous domain, (ii) thermal fluctuation of the fluid at the sub-micron scale and the resultant Brownian motion of a particle, and (iii) non-Newtonian fluid flow in a smooth-walled channel. Application of the aforementioned optimization techniques resulted in an average 21 × performance improvement, which could significantly enhance practical uses of the LB models in diverse applications, focusing on the fate and transport of nano-size or micron-size particles in non-Newtonian fluids.

Particle Shape Influences Settling and Sorting Behavior in Microfluidic Domains

We present a new numerical model to simulate settling trajectories of discretized individual or a mixture of particles of different geometrical shapes in a quiescent fluid and their flow trajectories in a flowing fluid. Simulations unveiled diverse particle settling trajectories as a function of their geometrical shape and density. The effects of the surface concavity of a boomerang particle and aspect ratio of a rectangular particle on the periodicity and amplitude of oscillations in their settling trajectories were numerically captured. Use of surrogate circular particles for settling or flowing of a mixture of non-circular particles were shown to miscalculate particle velocities by a factor of 0.9–2.2 and inaccurately determine the particles’ trajectories. In a microfluidic chamber with particles of different shapes and sizes, simulations showed that steady vortices do not necessarily always control particle entrapments, nor do larger particles get selectively and consistently entrapped in steady vortices. Strikingly, a change in the shape of large particles from circular to elliptical resulted in stronger entrapments of smaller circular particles, but enhanced outflows of larger particles, which could be an alternative microfluidics-based method for sorting and separation of particles of different sizes and shapes.

Computational performance of SequenceL coding of the lattice Boltzmann method for multi-particle flow simulations

Abstract This paper reports, for the first time, the computational performance of SequenceL for mesoscale simulations of large numbers of particles in a microfluidic device via the lattice-Boltzmann method. The performance of SequenceL simulations was assessed against the optimized serial and parallelized (via OpenMP directives) FORTRAN90 simulations. At present, OpenMP directives were not included in inter-particle and particle–wall repulsive (steric) interaction calculations due to difficulties that arose from inter-iteration dependencies between consecutive iterations of the do-loops. SequenceL simulations, on the other hand, relied on built-in automatic parallelism. Under these conditions, numerical simulations revealed that the parallelized FORTRAN90 outran the performance of SequenceL by a factor of 2.5 or more when the number of particles was 100 or less. SequenceL, however, outran the performance of the parallelized FORTRAN90 by a factor of 1.3 when the number of particles was 300. Our results show that when the number of particles increased by 30-fold, the computational time of SequenceL simulations increased linearly by a factor of 1.5, as compared to a 3.2-fold increase in serial and a 7.7-fold increase in parallelized FORTRAN90 simulations. Considering SequenceL’s efficient built-in parallelism that led to a relatively small increase in computational time with increased number of particles, it could be a promising programming language for computationally-efficient mesoscale simulations of large numbers of particles in microfluidic experiments.

Localization of chemical sources using e. coli chemotaxis

This paper furthers the application of chemotaxis to small-scale robots by simulating a system that localizes a chemical source in a dynamic fluid environment. This type of system responds to a chemical stimulus by mimicking, for example, the way that E. Coli bacteria move toward attractants (nutrients) and away from repellents. E. Coli use the intracellular signaling pathway to process the temporal change in the chemical concentration to determine if the cells should run or tumble. Previous work has shown that this process can be simulated with robots and used to localize chemical sources based upon a fixed nutrient gradient. Our work furthers this study by simulating the injection of an effluent of chemical at a specified location in an environment and uses computational fluid dynamics to model the interactions of the robot with the fluid while performing chemotaxis. The interactions between the chemical and fluid are also modelled with the advection diffusion equation to determine the concentration gradient. This method allows us to compute, over a lattice, the chemical concentration at all points and feed these results into an existing E. Coli controller for the robot, which results in the robot executing a tumble or a run according to a probabilistic formula. By simulating the robot in this complex environment, our work facilitates refinement of the chemotaxis controller while proving the ability of chemotactic robots to localize specific chemicals in environments that more closely resemble those encountered in the wide-ranging types of locations in which this robotic system might be deployed.