Number Of Primary Particles Research Articles

Radiative Pressure on Fractal Dust Grains in Oxygen-rich AGB Stars

There is still considerable debate on the exact composition of grains formed in the outflows of O-rich, asymptotic giant branch stars. Estimates of the expected condensation distances based on radiative transfer calculations show that iron-free silicates can condense close to the star but typically lack the opacity to drive an outflow unless they are large enough that radiation pressure due to scattering on the grains becomes significant. Iron-containing silicates have a much higher absorption opacity, but due to this stronger absorption, their expected condensation location is well beyond the expected dust formation zone. Recent measurements of the efficiency of SiO condensational growth have shown that this rate is low. The result of this low growth efficiency is that nucleation may persist for longer, giving a larger number of smaller primary particles, leading to an increased likelihood of particle aggregation in these outflows. In this work, we examine how the radiation pressure changes with the possible aggregation of these primary particles into fractal aggregates. Opacity calculations are made using optical properties of both forsterite and astronomical silicate for aggregates containing up to 256 primary particles and for fractal dimensions of 1.8 and 2.8. For primary particles of radius less than ∼0.1 μm, aggregation leads to an enhancement of the radiation pressure over an equivalent cloud of isolated primary particles.

Insights of mechanism and kinetics of acrylonitrile aqueous precipitation polymerization

Aqueous precipitation polymerization is commonly used in the industry due to its efficient heat dissipation, reaction stability and production flexibility. However, its mechanisms and kinetics are still partially unclear. The ammonium persulfate-ammonium sulfite monohydrate (APS-AS) initiated aqueous phase precipitation polymerization of acrylonitrile (AN) at low concentration was deeply investigated, with the APS initiation system as a control. The apparent kinetic equations for the APS and APS-AS initiation systems were determined to be Rp= K[AN]1.706[(NH4)2S2O8]0.704 and Rp= K[AN]3[(NH4)2S2O8]0.704[(NH4)2S2O3]−1.055, respectively. Monitoring AN distribution in the aqueous and polyacrylonitrile (PAN) phases revealed a rapid transition of the reaction site from the aqueous phase to the PAN phase following polymer precipitation. Zeta potential and primary particle number exposed that the nucleation site disparities between initiation systems led to different predominant factors affecting the polymerization rate. The change in molecular weight during polymerization had a maximum due to its dependence on the monomer concentration. The crystallinity of the product initially increased during polymerization, then decreased and eventually stabilized. Scanning electron microscopy (SEM) revealed that the diameter of the internal particles was larger, while the surface particles were smaller after coagulation. These findings offer crucial insights for optimizing aqueous precipitation polymerization and PAN particle property control.

Improving the predictions of black carbon (BC) optical properties at various aging stages using a machine-learning-based approach

Abstract. It is necessary to accurately determine the optical properties of highly absorbing black carbon (BC) aerosols to estimate their climate impact. In the past, there has been hesitation about using realistic fractal morphologies when simulating BC optical properties due to the complexity involved in the simulations and the cost of the computations. In this work, we demonstrate that, by using a benchmark machine learning (ML) algorithm, it is possible to make fast and highly accurate predictions of the optical properties for BC fractal aggregates. The mean absolute errors (MAEs) for the optical efficiencies ranged between 0.002 and 0.004, whereas they ranged between 0.003 and 0.004 for the asymmetry parameter. Unlike the computationally intensive simulations of complex scattering models, the ML-based approach accurately predicts optical properties in a fraction of a second. Physiochemical properties of BC, such as total particle size (number of primary particles (Npp), outer volume equivalent radius (ro), mobility diameter (Dm), outer primary particle size (ao), fractal dimension (Df), wavelength (λ), and fraction of coating (fcoating), were used as input parameters for the developed ML algorithm. An extensive evaluation procedure was carried out in this study while training the ML algorithms. The ML-based algorithm compared well with observations from laboratory-generated soot, demonstrating how realistic morphologies of BC can improve their optical properties. Predictions of optical properties like single-scattering albedo (ω) and mass absorption cross-section (MAC) were improved compared to the conventional Mie-based predictions. The results indicate that it is possible to generate optical properties in the visible spectrum using BC fractal aggregates with any desired physicochemical properties within the range of the training dataset, such as size, morphology, or organic coating. Based on these findings, climate models can improve their radiative forcing estimates using such comprehensive parameterizations for the optical properties of BC based on their aging stages.

Magnetic Field-Optimized Paramagnetic Nanoprobe for T2/T1 Switchable Histopathological-Level MRI.

Traditional magnetic resonance imaging (MRI) contrast agents (CAs) are a type of "always on" system that accelerates proton relaxation regardless of their enrichment region. This "always on" feature leads to a decrease in signal differences between lesions and normal tissues, hampering their applications in accurate and early diagnosis. Herein, we report a strategy to fabricate glutathione (GSH)-responsive one-dimensional (1-D) manganese oxide nanoparticles (MONPs) with improved T2 relaxivities and achieve effective T2/T1 switchable MRI imaging of tumors. Compared to traditional contrast agents with high saturation magnetization to enhance T2 relaxivities, 1-D MONPs with weak Ms effectively increase the inhomogeneity of the local magnetic field and exhibit obvious T2 contrast. The inhomogeneity of the local magnetic field of 1-D MONPs is highly dependent on their number of primary particles and surface roughness according to Landau-Lifshitz-Gilbert simulations and thus eventually determines their T2 relaxivities. Furthermore, the GSH responsiveness ensures 1-D MONPs with sensitive switching from the T2 to T1 mode in vitro and subcutaneous tumors to clearly delineate the boundary of glioma and metastasis margins, achieving precise histopathological-level MRI. This study provides a strategy to improve T2 relaxivity of magnetic nanoparticles and construct switchable MRI CAs, offering high tumor-to-normal tissue contrast signal for early and accurate diagnosis.

Effect of co-combustion of coal with biomass on the morphology of soot

This study performed an experimental investigation to explore the effect of co-combustion of coal with biomass on the morphological parameters and nanostructure of soot. Three types of soot, namely Shenghua coal (SH) soot, Pinewood biomass (PW) soot, and Co-combustion of SH and PW (CC) soot, were collected from the combustion flames of Shenhua coal, Pinewood biomass, and a mixture of coal and biomass. Thereafter, the soot samples were observed and analyzed by means of transmission electron microscope (TEM) and scanning electron microscope (SEM) to obtain their morphological parameters and nanostructure characteristics. The TEM results showed that the size of nascent soot particles generated in co-combustion flame is the largest, rather than between the sizes of PW and SH soot. The nanostructure of the three types of soot primary particles is mainly composed of straight parallel crystal stripes. The separation distance of crystal stripes in SH soot was measured to be larger than that in PW soot, indicating that the crystal structure of soot generated in Pine biomass combustion flame is more compact. It was also observed that both coal-derived primary soot particles and biomass-derived primary soot particles were formed in the co-combustion flame. SEM results indicated that PW soot agglomerates have stronger aggregation performance and more compact structure then SH soot and CC soot. The primary particle size of SH and CC soot agglomerates was mainly concentrated between 50 and 70 nm, while that of PW soot agglomerates was relatively uniform. Furthermore, the number of primary particles in SH soot and CC soot was distributed over a wide range compared to PW soot.

Seasonal variations in composition and sources of atmospheric ultrafine particles in urban Beijing based on near-continuous measurements

Abstract. Understanding the composition and sources of atmospheric ultrafine particles (UFPs) is essential in evaluating their exposure risks. It requires long-term measurements with high time resolution, which are scarce to date. We performed near-continuous measurements of UFP composition during four seasons in urban Beijing using a thermal desorption chemical ionization mass spectrometer, accompanied by real-time size distribution measurements. We found that UFPs in urban Beijing are dominated by organic components, varying seasonally from 68 % to 81 %. CHO organics (i.e., molecules containing carbon, hydrogen, and oxygen) are the most abundant in summer, while sulfur-containing organics, some nitrogen-containing organics, nitrate, and chloride are the most abundant in winter. With the increase of particle diameter, the contribution of CHO organics decreases, while that of sulfur-containing and nitrogen-containing organics, nitrate, and chloride increases. Source apportionment analysis of the UFP organics indicates contributions from cooking and vehicle sources, photooxidation sources enriched in CHO organics, and aqueous/heterogeneous sources enriched in nitrogen- and sulfur-containing organics. The increased contributions of cooking, vehicle, and photooxidation components are usually accompanied by simultaneous increases in UFP number concentrations related to cooking emission, vehicle emission, and new particle formation, respectively, while the increased contribution of the aqueous/heterogeneous composition is usually accompanied by the growth of UFP mode diameters. The highest UFP number concentrations in winter are due to the strongest new particle formation, the strongest local primary particle number emissions, and the slowest condensational growth of UFPs to larger sizes. This study provides a comprehensive understanding of urban UFP composition and sources and offers valuable datasets for the evaluation of UFP exposure risks.

Cracking and associated volumetric expansion of NMC811 secondary particles

Secondary particles comprising a large number of nickel-rich single crystal primary particles are extensively used as storage particles in cathodes of lithium-ion batteries. It is well-established that crack formation in secondary particles is an important degradation mode that contributes to decline in battery performance. Recent X-ray tomographic observations suggest that, at very low C-rates, concentration gradients of lithium within an NMC811 secondary particle are negligible yet cracking still occurs. Additionally, during delithiation the primary particles shrink yet a volumetric expansion of the secondary particle occurs. These observations are explained by a numerical model of distributed cracking due to the extreme anisotropy of lithiation strain of primary particles. The incompatible deformation from grain to grain induces large self-stresses even in the absence of spatial gradients in the lithium concentration. The stress state is sufficient to drive a dynamic catastrophic fracture event, and the associated kinetic energy acquired by the primary particles moves them apart (akin to an explosive event) with the carbon and binder domain surrounding each secondary particle restricting the outward motion of the primary particles. It is predicted that a volume expansion of the secondary particles on the order of 20 % accompanies cracking, in agreement with recently reported observations.

Morphology and nanostructure of soot particles from diesel engine under transient and steady-state operating conditions with a microalgae fuel component, dioctyl phthalate biofuel

In order to adhere to strict emissions standards, it is necessary to reduce diesel particulate emissions under real driving conditions. This can be achieved by reducing the production of soot particles and optimising the combustion process within the combustion chamber. This investigation examines the impact of engine operating conditions (transient and steady-state) on the characterisation of the soot particles. Soot samples from a common rail direct injection (CRDI), turbocharged six-cylinder diesel engine without any aftertreatment devices were collected on TEM grids. This investigation utilised both pure diesel and diesel-dioctyl phthalate (DOP) blends (D90DOP10 and D80DOP20). To correlate the soot characterisation results, diesel particulate emissions were also measured using a fast particle analyser (DMS500). The results indicate that emissions from transient cycles are higher in comparison to steady-state engine operation. Smaller primary particle diameter with a compact structure were observed for transient cycles when compared to steady-state operating conditions. Parameters such as number of primary particles within a single aggregate, radius of gyration, and soot aggregate area were also studied. Nanostructure analysis reveals that the soot particles from transient cycles could have lower oxidation reactivity and longer fringes with less curvature and interplanar spacing.

Diesel soot combustion in air-NO environment: Evolution of soot physical properties and fragmentation characteristics

Since the oxidation activity of nitrogen oxides on soot is obviously higher than that of O2, it is one of the most effective means to improve soot combustion in diesel particulate filter (DPF) by fully utilizing the oxidation activity of nitrogen oxides in diesel exhaust. This paper investigated the physical properties (i.e. morphology, primary particle diameter, fractal dimension and nanostructure) and oxidation-induced fragmentation characteristics of diesel exhaust soot particles during oxidation degrees (0 %, 20 %, 50 % and 80 %) in different atmospheres (air, air-1000 ppm NO and air-2000 ppm NO). The results showed that during the oxidation process the variation trends of soot morphology in air and air-NO environments were similar, while the number and size of primary particles in an aggregate decreased and the fractal dimension of the aggregate increased with the presence of NO in air atmosphere. With the progress of oxidation, the nanostructure of soot particles became more ordered, while this variation trend was slowed down when NO was added to the air atmosphere. This is because soot particles oxidized in air-NO atmospheres showed less probability of internal oxidation but more external oxidation than those in air atmosphere. Over the oxidation process, the soot aggregate fragmentation rate presented a decreasing variation trend under each oxidizing atmosphere, with a higher aggregate fragmentation rate and more apparent variations in air-NO atmospheres. Moreover, in the air atmosphere, the probability of primary soot particle fragmentation showed a consistent upward trend, while the addition of NO slowed down this trend and showed an upward trend in stages 1(0 % ∼ 20 %) and 2(20 % ∼ 50 %), but a downward trend in stage 3(50 % ∼ 80 %). This suggests that the addition of NO reduces the probability of oxidants (especially O2) entering the particles, which would lead to a decrease in the probability of primary soot fragmentation caused by internal oxidation.

H2/NH3 Addition Effects on Flame Characteristics and Soot Formation in Ethylene Inverse Diffusion Flame

Zero-carbon alternative fuels such as hydrogen and ammonia are gaining popularity. Blending these fuels with hydrocarbons is an intermediate approach to mitigate soot and carbon dioxide production. Recent studies have concentrated their attention to the effects of ammonia and hydrogen on the soot production of hydrocarbon fuels. It is still necessary to completely comprehend how this influence is dependent on the type of fuel and flame configuration. In this work, the effect of hydrogen and ammonia addition on soot production in ethylene laminar inverse diffusion flames (IDF) was numerically examined for the first time. The thermal, chemical, and combined effects of hydrogen and ammonia were assessed by fictitious species. The experimental results from the literature were used to validate the temperature and soot volume fraction profiles. Results indicated that the flame temperature and the production of radicals are promoted chemically by hydrogen addition but inhibited under the thermal effect of ammonia. The principal source of the reduction in OH-represented flame height, soot volume fraction, average diameter, and primary particle number density in the IDF is the thermal effect of additives, and hydrogen addition performs better than ammonia. The major and intermediate species, the aromatic hydrocarbons, and the soot formation or oxidation reaction rates are decreased mainly by the hydrogen and ammonia thermal effect while increased moderately by the chemical effect. Therein, the reduction in soot generation is mainly due to the polycyclic aromatic hydrocarbon condensation rate being slowed down by additives. The inhibition is attributed to the thermal effect of additions, and hydrogen behaves better than ammonia.

PAH laser diagnostics and soot particle dynamics in gasoline co-flow flames doped with n-butanol

As a carbon-neutral fuel, bio-butanol mixed with gasoline has become a potential decarbonization method in transportation field. In this work, the effects of n-butanol addition to gasoline on PAH and soot formation were investigated experimentally and numerically. As n-butanol ratio increases, flame lift-off height, flame height and OH/CH chemiluminescence intensity decrease gradually. The n-butanol addition has inhibitory effect on PAH with different sizes, and the inhibitory effect increases with the increasing aromatic-ring size. As ring size increases, the high concentration region gradually evolves from the center of upper flame to the two wings of downstream flame. The primary particle size and number density show a monotonically decreasing trend with n-butanol ratio, attributing to the significant decrease of nucleation and surface growth rate. For particle surface growth, the decreasing trend of PAH condensation is higher than that of HACA with the n-butanol ratio. The contribution of HACA to particle growth increases with n-butanol ratio, and replaces PAH condensation as the main mechanism when n-butanol ratio exceeds 60%. For particle oxidation, n-butanol addition leads to a slight increase in OH oxidation rate and a significant decrease in O2 oxidation rate, while O2 oxidation is always the dominant mechanism for particle oxidation.

Insight of soot nanostructure and oxidation behavior in ammonia / ethylene coaxial diffusion flame

In this work, the effects of ammonia addition on the morphology evolution, nanostructure and oxidation reactivity of soot particles in ethylene laminar diffusion flame were investigated. Soot sampling was performed using a thermophoresis probe and the morphological evolution of the particles was observed by transmission electron microscopy. In addition, high resolution transmission electron microscopy, Raman spectroscopy and thermogravimetric analysis were adopted to study the nanostructure and oxidation reactivity of soot particles sampled by quartz plate samples. The experimental results show that ammonia addition increases the flame height and compresses the soot formation region, delaying the evolution of each generation stage. For the primary soot particles, the surface growth is inhibited by ammonia doping, and the average particle size d¯p is reduced by about 33.13%. In addition, ammonia doping greatly inhibits the condensation between the primary particles of soot, and the parameters of the agglomerates such as the average area of the agglomerates A¯a, the number of primary particles N¯p and the fractal dimension Df are greatly reduced. Compared to the 100% argon doping ratio, ammonia doping reduced the average area of the agglomerates by approximately 76.27%, and the number of primary particles contained in the aggregate decreased by about 50%. The fringe length of soot particles decreases by about 15.38%, the fringe spacing increases by about 1.67% and the fringe tortuosity increases by about 4.23%, indicating that the degree of graphitization of soot decreases, the nanostructure becomes more disordered and the possibility of soot oxidation increases. The addition of ammonia results in the increase of disordered carbon in the soot of ethylene flame, and the activation energy of soot decreases significantly, so the soot has stronger oxidation reactivity.

Nozzle zone agglomeration in spray dryers: Determination of the agglomeration efficiency in the fines return by means of agglomerate properties and residence time distribution

In multi-stage spray dryers, agglomeration is forced by recirculating fine material into the nozzle zone. Drying droplets collide with dry particles and form agglomerates. The aim of this study was to achieve a more comprehensive understanding and control of agglomerate formation. Agglomerates with different properties were produced using a pilot spray drying plant. An increased air mass flow of the fluidized bed as well as a reduced solids content of the feed solution resulted in an increased number of primary particles per agglomerate. A residence time-based approach was then applied to determine the agglomeration efficiency, defined as the number of successfully adhered primary particles per recirculation. It ranged from 0.33 to 0.71 and was correlated to collision and sticking probabilities in the nozzle zone. This value is of importance both for process optimization and for the validation of simulation approaches.

Preparation of AlN with low agglomeration using polyethylene glycol and emulsifier to disperse the ultrafine raw powders

In this study, aluminium nitride (AlN) powder was prepared by the carbothermal reduction–nitridation method using ultrafine alumina and carbon black as the raw materials. A polyethylene glycol (PEG) and emulsifier (OP-10) disperse system was used to distribute the raw powders during ball milling. The mixing slurries and as-received AlN products were investigated using a viscometer, X-ray diffraction, field emission scanning electron microscopy, oxygen nitrogen hydrogen analyser, TG analysis and a laser particle size analyser. The results showed that the OP-10/PEG composite system reduced the viscosity of the slurry containing alumina and carbon during ball milling and significantly improved the wettability of water on the carbon black powder. The viscosity decrease improved the distribution of alumina and carbon under the same ball-milling conditions. At the same time, a dispersed structure was formed in which the OP-10 micelles separated alumina particles with carbon particles. Furthermore, the micelles with carbon were separated by the long PEG chains. This increased the number of primary particles below 2 μm and decreased the number of agglomerates above 5 μm in the final CRN AlN products. Based on the experimental results, herein the dispersion mechanism of the OP-10/PEG composite system on fine alumina and carbon black is proposed. Moreover, the rationality of the composite system is discussed.

TopasOpt: An open-source library for optimization with Topas Monte Carlo.

To describe and test TopasOpt: a free, open-source and extensible library for performing mathematical optimization of Monte Carlo simulations in Topas. TopasOpt enables any Topas model to be transformed into an optimization problem, and any parameter within the model to be treated as an optimization variable. Three case studies are presented. The starting model consists of a 10 MeV electron beam striking a tungsten target. The resulting bremsstrahlung X-ray spectrum is collimated by a primary and secondary collimator before being scored in a water tank. In the first case study (electron phase space optimization), five parameters describing the electron beam were treated as optimization variables and assigned a random starting value. An objective function was defined based on differences of depth-dose and profiles in water between the original (ground truth) model and a given model generated by TopasOpt. The problem was solved using Bayesian Optimization and the Nelder-Mead method. One hundred iterations were run in each case. In the second case study, (collimator geometry optimization), this process was repeated, but three geometric parameters defining the secondary collimator were treated as optimization variables and assigned random starting values, and forty iterations were run. In the third case study, the optimization was repeated with different number of primary particles to study the effect of noise on convergence. For case 1 (phase space optimization), both optimization algorithms successfully minimized the objective function, with absolute mean differences in profile dose of 0.4% (Bayesian) and 0.3% (Nelder-Mead) and 0.2% in depth-dose for both algorithms. The beam energy was recovered to within 1%, however some parameters had relative errors of up to 171% - a result consistent with the known X-ray dose is insensitivity to many electron beam parameters. For case 2 (geometry optimization), absolute mean differences in profile dose were 0.6% (Bayesian) and 0.9% (Nelder-Mead), and 0.5% and 0.9% in depth-dose. The maximum percentage error in any parameter was 9% with Bayesian Optimization and 28% with Nelder-Mead. Finally, the Bayesian Optimization algorithm was demonstrated to be robust to moderate levels of noise; when the standard deviation of the objective function was 16% of the mean, the maximum error in any parameter value was 16%, and the absolute mean difference in dose was 0.9% (profile) and 0.8% (depth-dose). An open-source library for optimization with Topas Monte Carlo has been developed, tested, and released. This tool will improve accuracy and efficiency in any situation in which the optimal value of a parameter in a Monte Carlo simulation is unknown. Applications for this tool include (1) The design of new components (2) Reverse engineering of models based on limited experimental or published data, and (3) Tuning of Monte Carlo "hyper parameters" such as variance reduction, physics settings, or scoring parameters.

Simplifying population balance models to promote broader use in industry

The approach outlined in this paper takes advantage of the existence of stationary states and similarity solutions to reduce a one-dimensional population balance model with particle volume as the internal dimension from a partial integro-differential equation to a single ordinary differential equation for the evolution of the mean size. The mechanisms of accretional growth, collisional growth and breakage, either individually or in all possible combinations, are considered. Power-law rate kernels are employed and a self-similar fragment distribution is assumed for the breakage term. When expressed in terms of the evolution of the scaled number-mean particle volume, the model parameters consist of kernel orders and the characteristic times associated with each mechanism. Equations for the scaled moments of the stationary states and similarity solutions are exhibited for all possible mechanism combinations as are equations for the trajectories of scaled number-mean particle volume. Stability of the stationary states and similarity solutions is addressed in an appendix. It should be clear that when this level of simplification is possible, the subject of population balance modeling becomes considerably easier to teach while its use becomes much easier to promote in industry. The use of this simplified approach for extraction of model parameters from experiment is demonstrated in an example, followed by a brief discussion of using the results for scale up. To close out the paper, the same approach, but now in two internal dimensions (particle volume and surface area), is used to demonstrate the capability of the model to unify a set of operating conditions for carbon black manufacture across multiple grades and two different types of synthesis reactors. This is done by extracting the characteristic coalescence time at multiple temperatures and correlating the results in an Arrhenius plot. The model matches known values of primary particle size, and predicts a key indicator of the product’s fractal aggregate structure, namely the number of primary particles per aggregate. The model results are then pushed to yield insight into the nature of the possible collision and coalescence mechanisms.

An investigation into oxidation-induced fragmentation of soot aggregates by Langevin dynamics simulations

The present work proposes a novel oxidation-induced fragmentation model for soot aggregates which is implemented as a submodel in Langevin dynamics (LD) simulations. The surface growth and particle aggregation are first simulated in the LD framework to generate a large number of aggregates with sufficient geometric diversities resembling realistic soot aggregates. The fragmentation of these aggregates induced by surface oxidation through reaction with molecular oxygen at high temperatures is then modelled. The probability density function (PDF) of a newly proposed fragmentation pattern factor is obtained from the statistical analysis of the fragment sizes. The probability of forming a monomer fragment and a remaining aggregate fragment (monomer fragmentation) is found to be two orders of magnitude higher than that of forming two fragments of similar size (equal-sized fragmentation). The relative importance of various aggregate characteristic parameters affecting the fragmentation pattern, i.e., splitting size ratio, is quantified by a Random Forest regression. It is found that the standard deviation of the overlapping coefficient is the most important parameter, followed by the global average overlapping coefficient, number of primary particles, fractal prefactor, and the fractal dimension. An exponential function is fitted to the PDF of the fragmentation pattern factor. The functional form is independent of the aggregate parameters and the fitted function shows good agreements with the statistics of the fragmentation pattern factor.

Effects of blending methane, propane, and propylene on soot evolution in ethylene diffusion flames based on optical diagnostics

Soot evolution of the ethylene-based laminar coflow diffusion flame doping with methane, propane, and propylene with the blending ratio ranging from 0 to 50% has been investigated experimentally. These fuels were selected for their different fuel structure. Spatially resolved distribution maps of the flame temperature, soot volume fraction, the fluorescence signals of polycyclic aromatic hydrocarbons (PAHs), and primary soot particle diameter and number density were determined utilizing laser-induced incandescence (LII), laser-induced fluorescence (LIF), and ratio pyrometry (RP). It was found that the flame height surged and the flame temperature dropped dramatically when the propylene blending ratio was 20% while they were almost constant with the increasing addition of methane or propane. The soot/PAHs loading decreased monotonically with the addition of methane and the opposite was true in the cases of blending propylene. The synergistic effect of mixing propane into ethylene was found on both the soot/PAHs loading. The same trends were observed in the percentage of carbon conversion to soot and the primary soot particle diameter. It can be seen from the soot evolution analysis that methane addition suppressed the soot nucleation and surface growth simultaneously. The enhanced soot surface growth for the flames doping with propane at a low addition level and for the whole flame sets doping with propylene were attributed to the earlier inception time and the longer mass growth time. The preferential diffusion and weakened oxidation of soot particles because of the decreased flame temperature perhaps accounted for the transition from a non-smoking flame to a smoking flame when the propylene blending ratio was greater than 40%.

Electron tracks simulation in water: Performance comparison between GPU CPU and the EUMED grid installation

PurposeWe explored different technologies to minimize simulation time of the Monte-Carlo method for track generation following the Geant4-DNA processes for electrons in water. MethodsA GPU software tool is developed for electron track simulations. A similar CPU version is also developed using the same collision models. CPU simulations were carried out on a single user desktop computer and on the computing grid France Grilles using 10 and 100 computing nodes. Computing time results for CPU, GPU, and grid simulations are compared with those using Geant4-DNA processes. ResultsThe CPU simulations better performs when the number of electrons is less than 104 with 100 eV initial energy, this number decreases as the energy increases. The GPU simulations gives better results when the number of electrons is more than 104 with initial energy of 100 eV, this number decreases to 103 for electrons with 10KeV and increases back with higher energy. The use of the grid introduces an additional queuing time which slows down the overall simulation performance. Thus, the Grid gives better performance when the number of electrons is over 105 with initial energy of 10KeV, and this number decreases as the energy increases. ConclusionsThe CPU is best suited for small numbers of primary incident electrons. The GPU is best suited when the number of primary incident particles occupies sufficient resources on GPU card in order to get an important computing power. The grid is best suited for simulations with high number of primary incident electrons with high initial energy.

Investigation the effect of fuel injection strategies on combustion and morphology characteristics of PM in modern diesel engine operated with oxygenate fuel blending

• Oxygenated fuel improved size distribution of PM compared to the diesel fuel. • The soot morphological parameters were reduced from combustion of oxygenated fuel compared to the diesel combustion. • The number of soot particles and size of soot agglomerates per TEM image was smaller with increasing injection pressure. • Combined effect of oxygenated fuel and fuel injection strategy has beneficial effect on PM reduction combustion characteristics. • Post-injection strategy modified the morphology characteristics, observing reduction in number of primary particles and radius of soot agglomerates. The soot emissions can be decreased in diesel engine through an improved understanding of the soot formation processes and impact strategies of fuel injection using oxygenated cleaner fuel. The combined effect of the oxygenated fuel (B20) and strategies of fuel injection (FI) timings and pressures on characteristics of combustion and particulate matter (PM) was studied in modern diesel engine. The influence of B20 on PM concentration and morphology parameters such as number of primary particles ( n po ), radius of gyration ( R g ) and fractal dimension ( D f ) were also investigated under different injection strategies. It is found that the average values of cylinder pressure and ROHR (rate of heat release) from B20 combustion are higher than diesel fuel by 3% and 11%, respectively. The results showed that the particulate size distribution (PSD) of PM reduced from the combustion of B20 compared to the diesel fuel combustion. The same trends were obtained for high injection pressure without fuel post-injection (PI), while PSD increased when fuel PI is presented. The soot morphology parameters ( n po and R g ) are reduced during B20 combustion compared with diesel fuel under the various injection strategies. The number and size of soot particles was smaller with high injection pressure for with PI and without PI. Furthermore, the morphology characteristics were slightly modified, observing reduction in n po and R g under engagement of PI for both fuels under different injection pressures. The fractal dimension ( D f ) decreased from the combustion of B20 under PI strategy by 1.41and without PI by 1. 43 when compared to the diesel fuel.