An Experimental Study on the Effect of Premixed and Equivalence Ratios on CO and HC Emissions of Dual Fuel HCCI Engine

In this study, effects of premixed and equivalence ratios on CO and HC emissions of a dual fuel HCCI engine are investigated. Tests were conducted on a single-cylinder engine with compression ratio of 17.5. Premixed gasoline is provided by a carburetor connected to intake manifold and equipped with a screw to adjust premixed air-fuel ratio, and diesel fuel is injected directly into the cylinder through an injector at pressure of 250 bars. A heater placed at inlet manifold is used to control the intake charge temperature. Optimal intake charge temperature results in better HCCI combustion due to formation of a homogeneous mixture, therefore, all tests were carried out over the optimum intake temperature of 110-115 ºC. Timing of diesel fuel injection has a great effect on stratification of in-cylinder charge and plays an important role in HCCI combustion phasing. Experiments indicated 35 BTDC as the optimum injection timing. Varying the coolant temperature in a range of 40 to 70 ºC, better HCCI combustion was achieved at 50 ºC. Therefore, coolant temperature was maintained 50 ºC during all tests. Simultaneous investigation of effective parameters on HCCI combustion was conducted to determine optimum parameters resulting in fast transition to HCCI combustion. One of the advantages of the method studied in this study is feasibility of easy and fast transition of typical diesel engine to a dual fuel HCCI engine. Results show that increasing premixed ratio, while keeping EGR rate constant, increases unburned hydrocarbon (UHC) emissions due to quenching phenomena and trapping of premixed fuel in crevices, but CO emission decreases due to increase in CO to CO2 reactions

Joint interpretation of magnetic and gravity data at the Golgohar mine in Iran

Geophysical modelling can take advantage of combining more geophysical data with the aim of decreasing the non-uniqueness of the resulting interpretation. The combination of gravity and magnetic data can yield useful information about the source properties by using the Poisson's analysis, which may help to infer the source properties, density and magnetic susceptibility, of the causative sources. Such estimates can be set as constraints for further interpretation. We consider first the synthetic data of a homogeneous block, for which correlation analysis allows the estimation of the Poisson ratio. The estimated source parameters are then used to invert gravity and magnetic data successfully. We applied this approach to the gravity and magnetic datasets in the Golgohar iron ore complex area, located in the Sanandaj-Sirjan zone (Iran). We performed a correlation analysis between the reduced to the pole magnetic data and the vertical gravity gradients of the 1st and 2nd order, respectively. We estimated strong magnetization contrasts between the mineral rocks and the surrounding host rocks. Further quantitative information about the source depth and geometry are obtained by 2D inverse modelling of both gravity and magnetic data, based on the damped weighted minimum-length solution. Even in this case, a-priori information from the correlation analysis leads to constrain the inverse process of both gravity and magnetic data. Inverse modelling confirms a high correlation between both susceptibility and density models, both horizontally and vertically, and show the presence of isolated sources with high density and magnetization contrasts, in good accordance with the average physical parameters of iron‑gold deposit formations

Investigation of the Performance of Artificial Intelligence Methods in Estimating the Crest Settlement of Rockfill Dam with a Central Core

Unpredictable settlement of earth dams has led researchers to develop new methods such as artificial neural networks, wavelet theory, fuzzy logic, and a combination of them. These methods do not require time-consuming analyses for estimation. In this research, the amount of settlement in rockfill dams with a central core has been estimated using artificial intelligence methods. The data of 35 rockfill dams with a central core were used to train and validate the models. The artificial neural network, wavelet transform model, and fuzzy-neural adaptive inference system are the proposed models which were used in the present study. According to the results, the best model for an artificial neural network had two hidden layers, the first layer of 18 neurons and the second layer of 7 neurons, with the Tansig-Tansig activation function, with a coefficient of determination R2=0.4969. The best model for the fuzzy-neural inference system had the ring function (Dsigmoid) as a membership function, with three membership functions and 142 repetitions with a coefficient of determination R2=0.2860. Also, combining wavelet-neural network conversion with the coif2 wavelet function due to the more adaptation this function has to the input variables, the better the performance, and this function, with a coefficient of determination R2=0.9447, had the highest accuracy compared to other models

How Iranian people perceived the COVID-19 crisis? Explored findings from a qualitative study: Current concerns, ethics and global solidarity

Objectives. This nation-wide project aimed to investigate the common perceptions and concerns regarding COVID-19 outbreak in Iran. Methods. This qualitative study was conducted in Iran from February to March 2020 via an online open-ended questionnaire. The participants were also selected using convenience and snowball sampling methods. As well, the data collection process continued until data saturation was achieved. Thematic content analysis was utilized to analysis the transcribed texts. Results. The statements retrieved also represented the most challenging psychological stress experienced by the participants. Four themes were accordingly recognized based on the content analysis including stressful conditions, health concerns, social and political concerns, and economic concerns. Throughout the study, a major proportion of the participants commented that psychological disorders such as fear, anxiety, stress, and ennui were their main challenges regarding this pandemic. Furthermore, lack of social responsibility, worries about high-risk and susceptible groups, decreased economic power of the public, financial hardships for low-income groups, shortage of healthcare facilities, and adverse effects of disinfectants were expressed as the main concerns. Conclusions. As a whole, it is evident that people have confronted with several challenges and need help together with effective policies and strategies during and after COVID-19 pandemic to reduce their current concerns. The study findings provided a favorable ground to develop and adopt the required policies in Iran and other countries. It was concluded that creating local, national, and global solidarity in such outbreaks is an inevitable necessity