Modelling bacterial twitching in fluid flows: a CFD-DEM approach

Numerical investigation of the effect of the opening mode on the pressure relief process of engine nacelle

The pressure relief door (PRD) is a vital structure to ensure the safety and reliability of the engine. This paper established a zero-dimensional transient simulation mathematical model to study the plenum compartment pressure threshold and maximum opening angle effects on the nacelle pressure relief process under different opening modes. Then, a computational fluid dynamics model verified by experimental literature data was used to simulate the nacelle pressure relief process and to determine the influence of two different opening modes on the force and discharge characteristics of the PRD. The results of this study show that different opening modes strongly impact the nacelle pressure relief process. Reducing the nacelle compartment pressure threshold of the PRD opening can reduce the time required for the pressure relief process to reach the equilibrium stage. Reducing the maximum opening angle may increase the nacelle compartment pressure during the equilibrium stage. In addition, under the same nacelle compartment pressure thresholds and maximum opening angles, the pressure relief process under the vertical opening mode can reach a lower nacelle compartment pressure during the pressure relief equilibrium stage compared to that under the horizontal opening mode. Therefore, the vertical opening mode is better than the horizontal opening mode. This paper provides two lower calculation costs and high accuracy research models for studying the nacelle pressure relief process.

Employing computational fluid dynamics technique for analyzing the PACK-1300XY with methanol and isopropanol mixture

In this study, an innovative wire gauze structured packing, namely PACK-1300XY with a specific surface area of 1300 m²/m³ has been characterized by performing computational fluid dynamics (CFD) approach. Indeed, different features of this packing (height equivalent to a theoretical plate, wet/dry pressure drop, and mass transfer efficiency) were analyzed by analyzing the flow regime using the three-dimensional CFD approach with the Eulerian-Eulerian multiphase scenario. The results showed the mean relative deviation of 16% (for wet pressure drop), 14% (for dry pressure drop), and 17% (for mass transfer efficiency) between the CFD predictions and experimental measurements. These excellent levels of consistency between the numerical findings and experimental observations approve the usefulness of the CFD-based approach for reliable simulation of separation processes.

Hydrodynamics and direction change of tumbling bacteria

The bacterium Escherichia coli (E. coli) swims in viscous fluids by rotating several helical flagellar filaments, which are gathered in a bundle behind the cell during ‘runs’ wherein the cell moves steadily forward. In between runs, the cell undergoes quick ‘tumble’ events, during which at least one flagellum reverses its rotation direction and separates from the bundle, resulting in erratic motion in place and a random reorientation of the cell. Alternating between runs and tumbles allows cells to sample space by stochastically changing their propulsion direction after each tumble. The change of direction during a tumble is not uniformly distributed but is skewed towards smaller angles with an average of about 62°–68°, as first measured by Berg and Brown (1972). Here we develop a theoretical approach to model the angular distribution of swimming E. coli cells during tumbles. We first use past experimental imaging results to construct a kinematic description of the dynamics of the flagellar filaments during a tumble. We then employ low-Reynolds number hydrodynamics to compute the consequences of the kinematic model on the force and torque balance of the cell and to deduce the overall change in orientation. The results of our model are in good agreement with experimental observations. We find that the main change of direction occurs during the ‘bundling’ part of the process wherein, at the end of a tumble, the dispersed flagellar filaments are brought back together in the helical bundle, which we confirm using a simplified forced-sphere model.

Single-Cell Tracking on Polymer Microarrays Reveals the Impact of Surface Chemistry on Pseudomonas aeruginosa Twitching Speed and Biofilm Development

Bacterial biofilms exhibit up to 1000 times greater resistance to antibiotic or host immune clearance than planktonic cells. Pseudomonas aeruginosa produces retractable type IV pili (T4P) that facilitate twitching motility on surfaces. The deployment of pili is one of the first responses of bacteria to surface interactions and because of their ability to contribute to cell surface adhesion and biofilm formation, this has relevance to medical device-associated infections. While polymer chemistry is known to influence biofilm development, its impact on twitching motility is not understood. Here, we combine a polymer microarray format with time-lapse automated microscopy to simultaneously assess P. aeruginosa twitching motility on 30 different methacrylate/acrylate polymers over 60 min post inoculation using a high-throughput system. During this critical initial period where the decision to form a biofilm is thought to occur, similar numbers of bacterial cells accumulate on each polymer. Twitching motility is observed on all polymers irrespective of their chemistry and physical surface properties, in contrast to the differential biofilm formation noted after 24 h of incubation. However, on the microarray polymers, P. aeruginosa cells twitch at significantly different speeds, ranging from 5 to ∼13 nm/s, associated with crawling or walking and are distinguishable from the different cell surface tilt angles observed. Chemometric analysis using partial least-squares (PLS) regression identifies correlations between surface chemistry, as measured by time-of-flight secondary ion mass spectrometry (ToF-SIMS), and both biofilm formation and single-cell twitching speed. The relationships between surface chemistry and these two responses are different for each process. There is no correlation between polymer surface stiffness and roughness as determined by atomic force measurement (AFM), or water contact angle (WCA), and twitching speed or biofilm formation. This reinforces the dominant and distinct contributions of material surface chemistry to twitching speed and biofilm formation.

CFD-DEM modelling of biofilm streamer oscillations and their cohesive failure in fluid flow

Biofilm streamer motion under different flow conditions is important for a wide range of industries. The existing work has largely focused on experimental characterisations of these streamers, which is often time-consuming and expensive. To better understand the physics of biofilm streamer oscillation and their interactions in fluid flow, a computational fluid dynamics-discrete element method model has been developed. The model was used to study the flow-induced oscillations and cohesive failure of single and multiple biofilm streamers. We have studied the effect of streamer length on the oscillation at varied flow rates. The predicted single biofilm streamer oscillations in various flow rates agreed well with experimental measurements. We have also investigated the effect of the spatial arrangement of streamers on interactions between two oscillating streamers in parallel and tandem arrangements. Furthermore, cohesive failure of streamers was studied in an accelerating fluid flow, which is important for slowing down biofilm-induced clogging.

NUFEB: A massively parallel simulator for individual-based modelling of microbial communities

We present NUFEB (Newcastle University Frontiers in Engineering Biology), a flexible, efficient, and open source software for simulating the 3D dynamics of microbial communities. The tool is based on the Individual-based Modelling (IbM) approach, where microbes are represented as discrete units and their behaviour changes over time due to a variety of processes. This approach allows us to study population behaviours that emerge from the interaction between individuals and their environment. NUFEB is built on top of the classical molecular dynamics simulator LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator), which we extended with IbM features. A wide range of biological, physical and chemical processes are implemented to explicitly model microbial systems, with particular emphasis on biofilms. NUFEB is fully parallelised and allows for the simulation of large numbers of microbes (10⁷ individuals and beyond). The parallelisation is based on a domain decomposition scheme that divides the domain into multiple sub-domains which are distributed to different processors. NUFEB also offers a collection of post-processing routines for the visualisation and analysis of simulation output. In this article, we give an overview of NUFEB’s functionalities and implementation details. We provide examples that illustrate the type of microbial systems NUFEB can be used to model and simulate.

Individual-based Models (IbM) are one of the most promising frameworks to study microbial communities, as they can explicitly describe the behaviour of each cell. The development of a general-purpose IbM solver should focus on efficiency and flexibility due to the unique characteristics of microbial systems. However, available tools for these purposes present significant limitations. Most of them only facilitate serial computing for single simulation, or only focus on biological processes, but do not model mechanical and chemical processes in detail. In this work, we introduce the IbM solver NUFEB that addresses some of these shortcomings. The tool facilitates the modelling of much needed biological, chemical, physical and individual microbes in detail, and offers the flexibility of model extension and customisation. NUFEB is also fully parallelised and allows for the simulation of large complex microbial system. In this paper, we first give an overview of NUFEB’s functionalities and implementation details. Then, we use NUFEB to model and simulate a biofilm system with fluid dynamics, and a large and complex biofilm system with multiple microbial functional groups and multiple nutrients.