Particulate-reinforced aluminum matrix composites produced by stirring casting method exhibit many advantages and are usually used in practical industries. The Particulate flow and distribution during the stirring have significant effects on composite casting properties and performances. In this investigation, to study the effect of stirring parameters on Particulate distribution, an experimental quenching apparatus was designed, and then A356/50μ mSiCp prepared with different stirring speeds and positions were carried out. The Particulate fractions at different locations of the prepared composites were quantitatively measured with micro-image analysis, and the charts of Particulate distribution along axial directions were summarized and analyzed. Based on liquid-solid multiphase flow theory and multiple rotating reference frame models, a mathematical model of Particulate-reinforced aluminum matrix composites stirring process established with consideration of relative flow between liquid and solid particle phases was applied to the experimental composite preparation. By comparing the simulation and experimental results, the effect of stirring condition on the composite slurry and Particulate flow as well as the final Particulate distribution were analyzed. The comparison shows that the simulated particle distribution exhibits well agreement with the experiment, indicating the validity and exactitude of the established model and method for actual composite stirring preparation. The study shows that low position of impeller would force more particles at the bottom region to flow with composite slurry, improving the particle distribution, and that high stirring speed can cause strong centrifugal force and radial flow of both composite slurry and particles, decreasing the particle uniformity in the composites.

One of the most common systems to collect the suspended particles from the air is fibrous filters. An important parameter to evaluate the filters performance is pressure drop. In this work the influence of the arrangement of the fibers at the fibrous media on the pressure drop is studied. Three different parallel, staggered and random arrangements are considered to model the fibrous media. The effects of these arrangements are investigated at different solid volume fractions. The results are compared and discussed with existing empirical and semi analytical relations. It is observed that using regular arrangement results in larger pressure drop than random one or which is predicted by empirical tests.

In recent decades, increasing population density and economic and industrial activities in metropolitan cities has increased traffic volumes and, consequently, increased levels of air pollution. The major source of air pollution in major developing cities is the massive transport of vehicles that use more than standard fuel and energy, and heavy traffic in the streets of these cities is often rooted in problems such as there is a lack of traffic management and traffic culture. One of the important issues in cities and metropolises that face pollution problems and harmful effects is the issue of informing about the future status of air quality and the amount of urban air pollution to the people. This can be achieved through daily or even hourly forecasts of air pollution and preventing people from being exposed to contaminated areas and their irreversible consequences. Therefore, the need to predict the quality of the air and the quantitative estimates of the concentration of pollutants in the aftermath of the equipment makes it felt that in this study, the problem of the prediction hourly concentration of Particulate matter (PM2. 5) in the district 11 municipalities of Tehran have exceeded 80% of the contaminated days under the influence of this pollutant. The difficulty and uncertainty associated with estimating and predicting the share of road traffic volume at the general level of air quality is the most important factor that can, if properly diagnosed, be very helpful. In order to take into account the effects of varying the volume of different traffic fleets in the process of changes in the concentration of pollutants and air pollution, it is necessary to pay attention to the effects of other influential variables including hydrological variables, geographical variables, etc. To achieve this, The methods of analytic analysis seem to be able to examine all of these effects together and in an omnipresent manner. The method used to predict this study is one of the methods for analyzing neural networks called Support Vector Machine (SVM). Artificial neural networks are important tools in the field of computational intelligence. Different types of artificial neural networks have been introduced, mainly in applications such as classification, clustering, pattern recognition, modeling and approximation of functions (or regression), control, estimation and optimization of the case Are used. Support Vector Machines (SVM) are a special type of neural network that, unlike other types of neural networks (such as multi-layer perceptron MLP and radial base functions of the RBF), instead of minimizing the error, minimize the operational risk of classification or modeling. Slowly This tool is very powerful and can be used in various fields such as classification, clustering and regression. The results of this study showed that SVM models work well in predicting the contribution and time share of urban traffic in propagation of Particulate matter, and predictions are well-coordinated with observations. It provides the opportunity to be used as an air quality management tool. Variable significance analysis results for SVM models provide this opportunity to be used as a tool for air quality management, in which the sensitivity of models to variations in emissions can be used to evaluate the effectiveness of a The air quality management scenario will test traffic fleet technology, combine the traffic fleet or its volume.

Air pollution negatively affects people's health and the economies of countries. Air pollutants can be categorized into three groups: Particulate Matter (PM), Volatile Organic Compounds, and microorganisms. Particulate Matter is the most important air pollutant, especially in major cities worldwide. Particulate Matter is classified into three groups according to their size: large particles (with a diameter between 2.5-10 microns), fine particles (with a diameter between 0.1-2.5 microns), and ultrafine particles (with a diameter between 0.1 microns and less). The wind flow behavior is an important factor in determining the particle concentrations in urban spaces. On the other hand, the urban geometry affects the wind velocity and consequently the outdoor air quality. Therefore, the proper design of urban context can be beneficial in reducing the impact of air pollution. However, this influence is often overlooked in the guidelines. Outdoor air quality is monitored by measuring particle concentrations and atmospheric parameters and the effect of the context on the dispersion of pollutants is not taken into account. Additionally, there are only a few urban air quality monitoring locations. The concentration of some pollutants (including PM) is determined in these monitoring stations. Therefore, they do not provide sufficient and comprehensive data to control urban air pollution. The Computational fluid dynamics analysis can be used to evaluate the wind flow behavior and pollutant dispersion and as a result, predict the urban air quality. This research aimed to study the behavior of wind flow and PM around buildings at different heights, street canyons, and building sides in a part of the residential context of Asalouyeh City. Accordingly, it investigated the effect of wind flow behavior on particle dispersion as well as the effect of height, and street and building orientation on wind flow behavior and PM dispersion. A section of the residential context in a high-density area was selected as the case study. The wind velocity, pressure, turbulence, flow pathlines, PM velocity and concentration, and particle tracks were defined at ground level, at different heights, in streets aligned with the wind direction and perpendicular to it, and on the windward and leeward side faces using CFD simulation in Ansys-Fluent 2021. An area of 129276 square meters with a length of 378 meters was simulated as a wind tunnel. Findings showed that the buildings act as a windbreak and cause wind velocity to decrease significantly in the leeward side face. As the height increased, resulting in higher wind velocity and turbulence, the length of the wind shadow decreased in the leeward side face. At higher altitudes, as the wind joined the free stream, flow turbulence was reduced. Accumulation of massless particles was observed in the leeward side face in the wind shadow area. The highest and lowest PM concentrations were observed in the windwards and the wind shadow, respectively. Therefore, the PM concentrations follow the wind velocity. Findings can be useful in studying outdoor air quality, as well as evaluating thermal comfort and choosing materials according to wind loads in urban spaces.