Particle Population Research Articles

Investigating the role of extracellular vesicles in peyronie's disease

A key effector cell in the pathophysiology of Peyronie's disease (PD) is the myofibroblast. Whilst the role of extracellular vesicles (EVs) and the secretome has been elucidated for other diseases, little is known about this in PD. This study aims to elucidate the role of EVs in myofibroblast transformation. Human primary fibroblasts were isolated from tunica albuginea (TA) of PD patients. Conditioned media was generated from cells treated with and without Transforming Growth Factor-ß1 (TGF-ß1). 10% Knockout Serum Replacement (KOSR) was used to avoid EV contamination from foetal calf serum (FCS). Conditioned media was concentrated after 72h and subjected to size-exclusion chromatography to isolate EVs and protein fractions. EVs were analysed via Tunable Resistive Pulse Sensing (TRPS) using the qNano Gold (Izon) to determine size, concentration, shape, and charge of the particles. Cells were treated with EVs in presence/absence of TGF-ß1 and myofibroblast transformation was quantified using In-Cell ELISA (ICE). TRPS analysis revealed that the TGF-ß1-treated cells produced more EVs (39.4 × 108 particles/mL) than untreated cells (7.24 × 108 particles/mL). Untreated cells showed a larger percentage population of particles >80nm compared to TGF-ß1-treated cells, which showed the highest concentration of particles at 70nm. Mean diameter was 92nm and 75nm for untreated and TGF-ß1-treated cells, respectively. There was no difference in charge of particles (zeta potential) between the groups. Slight differences in shape of particles could be detected, with the TGF-ß1-treated group showing more small but potentially elongated particles. No particles above 240nm were detected in either group. Preliminary ICE experiments revealed that isolated fractions impacted myofibroblast transformation. This is the first study to characterize the EV populations of fibroblasts and myofibroblasts in PD. Further work needs to be done to investigate the secretome and to identify the contents of these vesicles and their effect on fibroblast/myofibroblast biology in PD. Funded by ESSM (RG19-04)

Physical Correlations Lead to Kappa Distributions

The recently developed concept of “entropic defect” is important for understanding the foundations of thermodynamics in space plasma physics, and more generally for systems with physical correlations among their particles. Using this concept, this paper derives the basic formulation of the distribution function of velocities (or kinetic energies) in space plasma particle populations. Earlier analyses have shown how the formulation of kappa distributions is interwoven with the presence of correlations among the particles’ velocities. This paper shows, for the first time, that the reverse is true: the thermodynamics of particles’ physical correlations are consistent only with the existence of kappa distributions.

A highly robust numerical approach for handling the bi-variate soot population balance models in internal combustion engines

ABSTRACT It is widely acknowledged that the soot particulate pollutants pose a serious threat to both the environment and human health. Reducing the emission of these soot particulate pollutants from engines is very urgent, to which a highly efficient and robust modeling tool for soot emission prediction could contribute significantly. In this work, a new and robust numerical approach to handle the detailed soot models in engine combustion simulations is presented. The new approach adopts an advanced bi-variate moment projection method to treat the complex soot particle dynamics. The bi-variate moment projection method is superior to other methods of moments in terms that it is able to solve the soot moment closure problem due to oxidation, which governs the evolution of soot particle populations inside the engine cylinder. The proposed approach has been validated by simulating the detailed evolution processes of the soot particle population inside a dual-fueled, four-cylinder compression ignition engine. It is shown to be highly efficient and robust. It is able to provide a comprehensive investigation into the soot particle dynamics inside the engine cylinder at low computational cost. The emissions of soot particles under different engine operating conditions could be predicted with high accuracy. These findings suggest that the new approach proposed in this work is a promising framework for prediction and analysis of the emission of soot particulate pollutants from internal combustion engines. It is a useful tool to explore clean combustion technology.

Wave-particle interactions toolset: A python-based toolset to model wave-particle interactions in the magnetosphere

Wave particle interactions are known to be an efficient yet unquantified driver of the variability of particle populations in Earth’s magnetosphere, and their quantification and understanding through modelling has been a subject of ceaseless and extensive research during the last decades. Moreover, there is an increasing interest in techniques for radiation belt remediation, which refers to artificially controlling energetic particle populations in the near-Earth space environment via the scattering of particles from artificially generated electromagnetic waves. Whereas numerous modelling techniques are described in literature, there is a lack of a unified open-source toolset that incorporates the equations and parameterizations used by different wave-particle interaction models in a user-friendly environment. We present WPIT, the Wave-Particle Interactions Toolset, an open source, Python-based set of tools for modelling the interactions between energetic charged particles and VLF waves in the magnetosphere through test particle simulations. WPIT incorporates key routines related to wave-particle interactions in Python modules and also in Jupyter Notebook environment, enabling the traceability of all relevant equations in terms of their derivation and key assumptions, together with the programming environment and integrated graphics that enable users to conduct state-of-the-art wave-particle interaction simulations rapidly and efficiently. WPIT can be used either as a stand-alone simulation tool or as a library of routines that the user can extract and incorporate into an independent simulation. We present an analytic description of the code, the methodology used, and examples based on each of the WPIT modules. WPIT examples include the exact reproduction of simulation results that have been reported in literature, based on the same sets of parameters and assumptions, allowing the user to expand upon state-of-the-art. Finally, using the WPIT toolset, we perform a parametric analysis on the onset of nonlinear interactions between electrons with whistler-mode waves by varying the relevant parameters of the waves (amplitude, wave normal angle and frequency), the particles (pitch angle and energy) and the plasma environment (electron density and ion composition).

Multi‑strategy Equilibrium Optimizer: An improved meta-heuristic tested on numerical optimization and engineering problems.

The Equilibrium Optimizer (EO) is a recently proposed intelligent optimization algorithm based on mass balance equation. It has a novel principle to deal with global optimization. However, when solving complex numerical optimization problems and engineering problems, the algorithm will get stuck into local optima and degrade accuracy. To address the issue, an improved Equilibrium Optimizer (IEO) based on multi-strategy optimization is proposed. First, Tent mapping is used to generate the initial location of the particle population, which evenly distributes the particle population and lays the foundation for diversified global search process. Moreover, nonlinear time parameter is used to update the position equation, which dynamically balances the exploration and exploitation phases of improved algorithm. Finally, Lens Opposition‑based Learning (LOBL) is introduced, which avoids local optimization by improving the population diversity of the algorithm. Simulation experiments are carried out on 23 classical functions, IEEE CEC2017 problems and IEEE CEC2019 problems, and the stability of the algorithm is further analyzed by Friedman statistical test and box plots. Experimental results show that the algorithm has good solution accuracy and robustness. Additionally, six engineering design problems are solved, and the results show that improved algorithm has high optimization efficiency achieves cost minimization.

Grain Initiation and Grain Refinement: An Overview

Heterogeneous nucleation and grain initiation are two different processes in early-stage solidification (ESS), although both are deterministic. Heterogeneous nucleation refers to the formation of a 2-dimensional (2D) nucleus (a crystal plane of the solid) that can template further growth, while grain initiation is the formation of a hemispherical cap (3D) from which isothermal growth is possible. It is both theoretically and practically beneficial to separate heterogeneous nucleation from grain initiation. This paper provides an overview of our recent understanding of grain initiation behaviour under different conditions and its consequences on grain refinement. After a brief review of the processes involved in the ESS, we present the grain initiation behaviour on a single substrate. This is followed by grain initiation behaviour in systems with a population of nucleant particles with varying particle types (corresponding to varying nucleation undercoolings), where we give detailed descriptions of progressive grain initiation, explosive grain initiation, hybrid grain initiation, grain initiation maps and grain refinement maps. We then provide a brief introduction to the rules that govern competition for heterogeneous nucleation and grain initiation among multiple types of nucleant particles with varying particles’ nucleation undercoolings and sizes. Finally, we present the practical implications of grain refinement maps to grain refinement. A key finding from this work is that more significant grain refinement can be achieved by promoting explosive grain initiation using impotent nucleant particles, which is opposite to the traditional approach for grain refinement where potent particles are used to enhance heterogeneous nucleation.

Simulation and Experiment of Sieving Process of Sieving Device for Tiger Nut Harvester

In order to realize mechanized and efficient harvesting of tiger nuts, study the efficient screening technology of beans and soil in a mechanized harvesting operation and improve the harvesting operation efficiency of crawler-type tiger nut harvesters, a theoretical analysis of the motion process of detritus particles on a sieve surface was conducted to determine the main factors affecting the motion of the particles on the sieve surface. A numerical simulation of the sieving process using the discrete element method was conducted to improve the screening efficiency of tiger nuts. The transport law of the debris particle population was analyzed from different perspectives, such as the average velocity of particle motion, particle distribution rate, screening efficiency and loss rate. The effects of factors such as screen amplitude (SA), vibration frequency (VF) and inclination angle (IA) on the sieving performance of the tiger nut threshing and screening device were investigated. The results show that sieving performance evaluation indexes, such as the average speed of particle movement, particle distribution rate, screening efficiency and loss rate, are non-linearly related to the factors of screen amplitude, vibration frequency and screen inclination angle; the effects of amplitude and frequency on the distribution particle size are consistent and show a gradual increase, with the distribution particle size reaching 3.32 mm at an amplitude of 14 mm and 3.46 mm at a frequency of 22 Hz. In the sieving process, the average velocity of the particle population decreases gradually along the direction of motion, and the influence of each factor on the average velocity of the particle population in the motion of the detritus is similar, all showing an increasing trend. This study can provide a reference for exploring the transport law of particles and the efficient screening technology of tiger nuts. Field harvesting tests showed that the screening efficiency and loss rate were 92.87% and 0.83%, respectively, at a screen amplitude of 14 mm, a vibration frequency of 10 Hz and an inclination angle of 2°, and the test results corresponded to the simulation results and met the design requirements of the tiger nut harvester. This study can provide reference for the investigation of the particle transport law and efficient screening technology for tiger nuts.

On the relation between apparent ion and total particle growth rates in the boreal forest and related chamber experiments

Abstract. The understanding of new particle formation and growth processes is critical for evaluating the role of aerosols in climate change. One of the knowledge gaps is the ion–particle interaction during the early growth process, especially in the sub-3 nm range, where direct observations are sparse. While molecular interactions would imply faster growth rates of ions compared to neutral particles, this phenomenon is not widely observed in the atmosphere. Here, we show field measurements in the boreal forest indicating a smaller apparent growth rate of the ion population compared to the total particles. We use aerosol dynamics simulations to demonstrate that this effect can be caused by the changing importance of ion-induced nucleation mechanisms during the day. We further compare these results with chamber experiments under similar conditions, where we demonstrate that this effect critically depends on the abundance of condensable vapors and the related strength of ion-induced nucleation. Our results imply that atmospheric ion growth rate measurements below 3 nm need to be evaluated very carefully as they do not represent condensational growth alone but are influenced by ion–particle population interactions.

Magnetic local time (MLT) dependence of auroral peak emission height and morphology

Abstract. We investigate the bulk behaviour of auroral structures and peak emission height as a function of magnetic local time (MLT). These data are collected from the Fennoscandian Lapland and Svalbard latitudes from seven identical auroral all-sky cameras (ASC) over about one solar cycle. The analysis focusses on green auroral emission, which is where the morphology is most clearly visible and the number of images is the highest. The typical peak emission height of the green and blue aurora varies from 110 km on the nightside to about 118 km in the morning MLT over the Lapland region. It stays systematically higher (at 118–120 km) at high latitudes (Svalbard) during the nighttime and reaches 140 km at around magnetic noon. During high solar wind speed (above 500 km s−1), nightside emission heights appear about 5 km lower than during slow solar wind speed (below 400 km s−1). The sign of the interplanetary magnetic field (IMF) has nearly no effect on the emission heights in the night sector, but the northward IMF causes lower emission heights at dawn over Lapland and during the noon hours over Svalbard. While the former is interpreted as a change in the particle population within the field-of-view (FoV), the latter is rather due to the movement of the cusp location due to the IMF orientation. The morning sector heights also show a pronounced difference when previously detected pulsating aurora (PsA) events have been excluded/included in the dataset, suggesting that this type of aurora is a dominant phenomenon in the morning and an important dissipation mechanism. An increase of complex auroral structures in the midnight hours agrees with the average substorm occurrence. This increase is amplified during stronger solar wind driving and during higher geomagnetic activity (as measured by auroral electrojet index, AL). During high solar wind speed, the high latitude auroral evolution shows particularly complex morphology, which is not limited to the nightside but rather only excludes the magnetic noon hours. An increase in the geomagnetic activity further enhances the structural complexity of the aurora in the morning sector.

Machine learning approaches to root cause analysis, characterization, and monitoring of subvisible particles in monoclonal antibody formulations.

Processing stresses on therapeutic proteins may cause formation of subvisible particles. Different stress mechanisms generate particle populations with characteristic morphological "fingerprints,"and machine learning techniques like convolutional neural networks (CNNs) allow classification of microscopy images of these particles according to known stresses at their root cause. Using CNNs to classify novel particle types not included during network training may lead to inaccurate classification, however, using CNNs to monitor the presence of particulate matter not explicitly used in training could serve as a useful process analytical technology. We used CNNs to classify and identify the root cause of particles generated by subjecting three monoclonal antibodies (mAbs) to various common manufacturing stresses. We probed the generality of particles generated by stressing different mAbs in different formulations and showed that CNN analyses were sensitive not only to the applied stress, but also the buffer conditions and the particular mAb that generated particle populations. Thus, models trained on images of particles created with one mAb and buffer system may not provide accurate root cause analysis when applied to particles generated by other mAb and buffer systems. A lever-rule analysis of CNN-derived fingerprints was used to characterize the composition of mixtures of particle types. Finally, we monitored the temporal evolution of CNN-derived fingerprints when novel populations of particles, which were not included during training, were generated by pumping mAb solutions through a peristaltic pump.

Spheroplexes: Hybrid PLGA-cationic lipid nanoparticles, for in vitro and oral delivery of siRNA.

Vectorized small interfering RNAs (siRNAs) are widely used to induce gene silencing. Among the delivery systems used, lipid-based particles are the most effective. Our objective was the development of novel lipid-polymer hybrid nanoparticles, from lipoplexes (complexes of cationic lipid and siRNAs), and poly (lactic-co-glycolic acid) (PLGA), using a simple modified nanoprecipitation method. Due to their morphology, we called these hybrid nanoparticles Spheroplexes. We elucidated their structure using several physico-chemical techniques and showed that they are composed of a hydrophobic PLGA matrix, surrounded by a lipid envelope adopting a lamellar structure, in which the siRNA is complexed, and they retain surface characteristics identical to the starting nanoparticles, i.e. lipoplexes siRNA. We analyzed the composition of the particle population and determined the final percentage of spheroplexes within this population, 80 to 85% depending on the preparation conditions, using fluorescent markers and the ability of flow cytometry to detect nanometric particles (approximately 200nm). Finally, we showed that spheroplexes are very stable particles and more efficient than siRNA lipoplexes for the delivery of siRNA to cultured cells. We administered spheroplexes contain siRNAs targeting TNF-α to mice with ulcerative colitis induced by dextran sulfate and our results indicate a disease regression effect with a response probably mediated by their uptake by macrophages / monocytes at the level of lamina propria of the colon. The efficacy of decreased level of TNF-α in vivo seemed to be an association of spheroplexes polymer-lipid composition and the specific siRNA. These results demonstrate that spheroplexes are a promising hybrid nanoparticle for the oral delivery of siRNA to the colon.

Particle Swarm Optimization with Long and Short Term Memory in Feature Selection

<p>Taking each iteration of Particle swarm optimization (PSO) algorithm as a time node, the change of population in PSO algorithm can be regarded as a time series model. Particle population learns and evolves in multiple time nodes, which can be regarded as a dependent behavior on leader particles. In the traditional particle swarm optimization algorithm, this dependence behavior is independent of time, and its consideration standard is only the fitness value of particles. We deeply study the leadership mechanism of PSO algorithm in order to find a more robust leadership mechanism and improve the ability of PSO algorithm to explore the solution space, by extending the dependence behavior in the time dimension, we propose an improved PSO algorithm with long-term and short-term memory ability. In order to verify its performance, in the experimental part, we select 32 public data sets in UCI data to find the optimal feature subset. In a large number of feature selection experiments. The experimental results proofed that the performance of proposed algorithms is better than some state of the art algorithms. </p> <p> </p>

Charged Particle Fluxes Associated With CALIPSO Low Laser Energy Shots

The cloud-aerosol lidar and infrared pathfinder satellite observation (CALIPSO) mission was launched on 28 April 2006 into a 705-km Sun-synchronous orbit. At launch, the lidar laser canisters were filled with dry air at 130 kPa. The redundant canister pressure has dropped during the mission, reaching 7.6 kPa by late December 2021. Starting on June 23, 2016, individual laser shots started to exhibit anomalously low energies in regions of enhanced energetic charged particle fluxes. Small at first (< 1 mJ), drops in laser energy up to 61 mJ have been observed. This study determines the energetic charged particle populations associated spatially with low laser energy shots and the degree to which time variations in the low laser energy shots can be explained by time variations in the energetic charged particle fluxes. Particle observations from the NOAA-19 medium energy proton and electron detector (MEPED) are traced down magnetic field lines to estimate the fluxes at CALIPSO altitudes that are associated with individual low-energy shots. Predictions of the stability of particle bounce and drift motions in the geomagnetic field are used to sort low-energy shots and fluxes into different radiation belt regions. While most of the low-energy shots occur in the inner belt, there are clear spatial associations with outer belt electrons and with galactic cosmic rays (GCRs). As canister pressure has decreased, GCRs and electrons have become more effective in causing larger laser energy drops.

Hybrid Particle Filter–Particle Swarm Optimization Algorithm and Application to Fuzzy Controlled Servo Systems

This article presents a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms. The new PF–PSO algorithm consists of two steps: the first generates randomly the particle population;and the second zooms the search domain. An application of this algorithm to the optimal tuning of proportional-integral-fuzzy controllers for the position control of a family of integral-type servo systems is then presented as a second contribution. The reduction in PF–PSO algorithm's cost function allows for reduced energy consumption of the fuzzy control system. A comparison with other metaheuristic algorithms on canonical test functions and experimental results are presented at the end of this article.

Adaptive particle swarm optimization approach to simultaneous reconfiguration and shunt capacitor allocation in radial distribution network

Simultaneous radial distribution network reconfiguration (RDNR) and shunt capacitor allocation (SCA) is one of the compensation techniques that are used for getting an improved radial structure with reduced real power loss and enhanced voltage stability. This study presents a novel adaptive particle swarm optimisation (APSO) technique for the simultaneous RDNR and SCA, which is a complex and nonlinear optimisation problem. Unlike the conventional particle swarm optimization (PSO) technique in which an initial population of particles is randomly generated, the fundamental loop concept is used to populate the search space of APSO with the candidate branches for each tie switch (open branch) in the loop. The candidate branches are preselected with the graph theory. This is done to mitigate infeasible configurations in the optimization process and also to ensure that the conditions for radiality of the network are satisfied. The effectiveness of the proposed APSO technique for simultaneous RDNR and SCA is demonstrated on the standard IEEE 33-bus and Nigerian Ayepe 34-bus RDNs using six event cases. The efficacy of the proposed APSO technique is further validated with the comparison of the observed simulation results with the reported results of similar work implemented with established algorithms like improved binary particle swarm optimization (IBPSO), modified pollinated flower algorithm (MFPA) and mixed integer linear programming (MILP). The result of the comparative study reveals that the proposed APSO technique outperforms the selected algorithms in most of the considered event cases.

Sensor for Determining Single Droplet Acidities in the Venusian Atmosphere

The cloud layers of Venus are known to have pressures and temperatures comparable to those on Earth, but, at the same time, many details about the environment inside them are unknown. The early consensus was that Venusian clouds are composed of droplets of near concentrated sulfuric acid with very limited water availability; newer models, however, suggest a pH range between −1 and 1, and these imply some form of a neutralizing agent and potentially complex chemical cycles. It is also possible that different populations of cloud particles have different acidities. To answer these questions, we propose an in situ acidity sensor that can statistically determine the acidities of individual cloud droplets from concentrated sulfuric acid (18 M) to deionized water, based on the fluorescence of a pigment that is immobilized in a film and read out using a set of excited LEDs and a camera. Here, we present the preliminary research and prototyping results and suggest a possible design for this sensor.

Gamma-ray observations of MAXI J1820+070 during the 2018 outburst

ABSTRACT MAXI J1820+070 is a low-mass X-ray binary with a black hole (BH) as a compact object. This binary underwent an exceptionally bright X-ray outburst from 2018 March to October, showing evidence of a non-thermal particle population through its radio emission during this whole period. The combined results of 59.5 h of observations of the MAXI J1820+070 outburst with the H.E.S.S., MAGIC and VERITAS experiments at energies above 200 GeV are presented, together with Fermi-LAT data between 0.1 and 500 GeV, and multiwavelength observations from radio to X-rays. Gamma-ray emission is not detected from MAXI J1820+070, but the obtained upper limits and the multiwavelength data allow us to put meaningful constraints on the source properties under reasonable assumptions regarding the non-thermal particle population and the jet synchrotron spectrum. In particular, it is possible to show that, if a high-energy (HE) gamma-ray emitting region is present during the hard state of the source, its predicted flux should be at most a factor of 20 below the obtained Fermi-LAT upper limits, and closer to them for magnetic fields significantly below equipartition. During the state transitions, under the plausible assumption that electrons are accelerated up to ∼500 GeV, the multiwavelength data and the gamma-ray upper limits lead consistently to the conclusion that a potential HE and very-HE gamma-ray emitting region should be located at a distance from the BH ranging between 1011 and 1013 cm. Similar outbursts from low-mass X-ray binaries might be detectable in the near future with upcoming instruments such as CTA.

Impact of water uptake and mixing state on submicron particle deposition in the human respiratory tract (HRT) based on explicit hygroscopicity measurements at HRT-like conditions

Abstract. Particle hygroscopicity plays a key role in determining the particle deposition in the human respiratory tract (HRT). In this study, the effects of hygroscopicity and mixing state on regional and total deposition doses on the basis of the particle number concentration for children, adults, and the elderly were quantified using the Multiple-Path Particle Dosimetry model, based on the size-resolved particle hygroscopicity measurements at HRT-like conditions (relative humidity = 98 %) performed in the North China Plain. The measured particle population with an external mixing state was dominated by hygroscopic particles (number fraction = (91.5 ± 5.7) %, mean ± standard deviation (SD); the same below). Particle hygroscopic growth in the HRT led to a reduction by around 24 % in the total doses of submicron particles for all age groups. Such a reduction was mainly caused by the growth of hygroscopic particles and was more pronounced in the pulmonary and tracheobronchial regions. Regardless of hygroscopicity, the elderly group of people had the highest total dose among three age groups, while children received the maximum total deposition rate. With 270 nm in diameter as the boundary, the total deposition doses of particles smaller than this diameter were overestimated, and those of larger particles were underestimated, assuming no particle hygroscopic growth in the HRT. From the perspective of the daily variation, the deposition rates of hygroscopic particles with an average of (2.88 ± 0.81) × 109 particles h−1 during the daytime were larger than those at night ((2.32 ± 0.24) × 109 particles h−1). On the contrary, hydrophobic particles interpreted as freshly emitted soot and primary organic aerosols exhibited higher deposition rates at nighttime ((3.39 ± 1.34) × 108 particles h−1) than those in the day ((2.58 ± 0.76) × 108 particles h−1). The traffic emissions during the rush hours enhanced the deposition rate of hydrophobic particles. This work provides a more explicit assessment of the impact of hygroscopicity and mixing state on the deposition pattern of submicron particles in the HRT.

Non-Hermitian skin effect in quadratic Lindbladian systems: An adjoint fermion approach

The skin effect has been discovered in non-Hermitian Hamiltonian systems where all the eigenstates have their amplitudes concentrating to the open boundaries of the systems and decaying exponentially into the bulk. Later, certain open systems obeying the quadratic Lindblad equation has also been found to exhibit the skin effect, which is manifested in the ``chiral damping" phenomenon as the particle populations, decaying from their initial uniform unity values, show asymmetry with respect to the open boundaries. However, in those open systems, each cell couples to the environment in an identical way. It is natural to expect that the long time steady state of those open systems shall have spatially uniform particle populations. Furthermore, particle population variations due to the excitation of normal modes on top of the steady state shall also not show asymmetry with respect to the open boundaries. To reconcile the natural expectations with the skin effect, we employ an adjoint fermion formalism to study the quadratic Lindbladian systems. We work out the long time steady state and the normal modes on top of it, which exhibit no asymmetry as expected. We show that it is the interference between the normal modes that gives rise to the skin effect.

Analysis of Population Control Techniques for Time-Dependent and Eigenvalue Monte Carlo Neutron Transport Calculations

An extensive study of population control techniques (PCTs) for time-dependent and eigenvalue Monte Carlo (MC) neutron transport calculations is presented. We define PCT as a technique that takes a censused population and returns a controlled, unbiased population. A new perspective based on an abstraction of particle census and population control is explored, paving the way to improved understanding and application of the concepts. Five distinct PCTs identified from the literature are reviewed: simple sampling, splitting-roulette (SR), combing (CO), modified combing, and duplicate-discard (DD). A theoretical analysis of how much uncertainty is introduced to a population by each PCT is presented. Parallel algorithms for the PCTs, applicable for both time-dependent and eigenvalue MC simulations, are proposed. The relative performance of the PCTs based on run time and tally mean error or standard deviation is assessed by solving time-dependent and eigenvalue test problems. It is found that SR and CO are equally the most performing techniques, closely followed by DD.