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"Exhaust"
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A review on the engine performance and exhaust emission characteristics of diesel engines fueled with biodiesel blends
Biodiesels have gained much popularity because they are cleaner alternative fuels and they can be used directly in diesel engines without modifications. In this paper, a brief review of the key studies pertaining to the engine performance and exhaust emission characteristics of diesel engines fueled with biodiesel blends, exhaust aftertreatment systems, and low-temperature combustion technology is presented. In general, most biodiesel blends result in a significant decrease in carbon monoxide and total unburned hydrocarbon emissions. There is also a decrease in carbon monoxide, nitrogen oxide, and total unburned hydrocarbon emissions while the engine performance increases for diesel engines fueled with biodiesels blended with nano-additives. The development of automotive technologies, such as exhaust gas recirculation systems and low-temperature combustion technology, also improves the thermal efficiency of diesel engines and reduces nitrogen oxide and particulate matter emissions.
Journal Article
Engineering method for predicting rocket exhaust plumes at high altitude
The engineering method for the prediction of high-altitude rocket exhaust plume characteristics is proposed, which is based on Cai’s gas kinetic model and virtual collision method and the consideration of the influence on free flow. This engineering method can obtain the main characteristics such as plume range, species separation, flow characteristic distribution, and other main flow characteristics of high-altitude rockets. The single-phase gas of the axisymmetric circular nozzle of solid and liquid engines is taken as the research object, and the reliability of the prediction using the engineering method is verified by comparing the calculation results of the engineering method with the publication data and DSMC results. It provides a reference for the prediction of rocket exhaust plume characteristics at high altitudes.
Journal Article
Will sustainability fly? : aviation fuel options in a low-carbon world
\"While international negotiations to reduce greenhouse gas (GHG) emissions have been less than satisfactory, there is a presumption that a significant level of multi-lateral commitment will be realized at some point. International air and marine travel have been left to one side in past talks because the pursuit of agreement proceeds on the basis of commitment by sovereign nations and the effects of these specific commercial activities are, by their nature, difficult to corral and assign to specific national jurisdictions. However, air travel is increasing and, unless something is done, emissions from this segment of our world economy will form a progressively larger percentage of the total, especially as emissions fall in other activities.\" -- Provided by publisher.
Book
TSF-transformer: a time series forecasting model for exhaust gas emission using transformer
Monitoring and prediction of exhaust gas emissions for heavy trucks is a promising way to solve environmental problems. However, the emission data acquisition is time delayed and the pattern of emission is usually irregular, which makes it very difficult to accurately predict the emission state. To deal with these problems, in this paper, we interpret emission prediction as a time series prediction problem and explore a deep learning model, a time-series forecasting Transformer (TSF-Transformer) for exhaust gas emission prediction. The exhaust emission of the heavy truck is not directly predicted, but indirectly predicted by predicting the temperature and pressure changes of the exhaust pipe under the working state of the truck. The basis of our research is based on real-time data feeds from temperature and pressure sensors installed on the exhaust pipe of approximately 12,000 heavy trucks. Therefore, the task of time series forecasting consists of two key stages: monitoring and prediction. The former utilizes the server to receive the data sent by the sensors in real-time, and the latter uses these data as samples for network training and testing. The training of the network throughout the prediction process is done in an unsupervised manner. Also, to visualize the forecast results, we weight the forecast data with the truck trajectories and present them as heatmaps. To the best of our knowledge, this is the first case of using the Transformer as the core component of the prediction model to complete the task of exhaust emissions prediction from heavy trucks. Experiments show that the prediction model outperforms other state-of-the-art methods in prediction accuracy.
Journal Article
Green transportation
\"This STEM-based set of True Books introduces students to the engineering innovations that can help us reach more environmentally friendly goals\"-- Provided by publisher.
BOOK
Real-world tailpipe emissions from autorickshaws (3-wheelers) under heterogeneous traffic conditions
The current study aimed to measure real-world emissions of three-wheeled autorickshaws powered by CNG and parameters (such as speed, acceleration, air-fuel (A/F) ratio, and rpm) influencing 3-wheeler emission rates. Test vehicles manufactured under Bharat Standards BS-III and BS-IV were monitored for exhaust emissions in Delhi city using a portable exhaust emission measurement system (AVL Ditest Gas 1000). The average emission rates of CO, HC, and NO gases for on-road autorickshaws were found to be 0.015 ± 0.017, 0.003 ± 0.0017, and 0.007 ± 0.005 g/s, respectively. Further, the highest emission factor values of 3.98 g/km and 3.93 g/km were estimated for CO and HC+NO gases, respectively. These values were found to be 1.4–3.2 times higher than the respective BS emission norms (BS III-CO =1.25 g/km, HC+NO = 1.25 g/km; BS-IV-CO = 0.94 g/km and HC+NO = 0.94 g/km). In this study, it was observed that the driving pattern and emissions were affected by traffic characteristics, driver behavior (constant acceleration and deceleration), and vehicle characteristics. The air-fuel ratio (A/F) was found to correlate highly with emission rates, followed by acceleration/deceleration and speed. Further analysis found that more than 70% of the aggregated emissions were due to acceleration and deceleration, which contributed to nearly 70% of the travel time. This was followed by the breakdown of speed and emissions into different bins, which found that 20–30 kmph has a higher emission rate and 40–50 kmph bin has a lower emission rate.
Journal Article
Prediction of dispersion behavior of typical exhaust pollutants from hydraulic support transporters based on numerical simulation
We investigated the impact of exhaust emissions from hydraulic support transporters on the air quality in roadways in mines. The dispersion distribution of diesel exhaust pollutants emitted by hydraulic support transporters was simulated with a dynamic mesh and computational fluid dynamics (CFD) simulations. More specifically, the dispersion and distribution of the main exhaust pollutants CO, HC, and NOx emitted by vehicles under the influence of the roadway wind flow were simulated with CFD simulations; in addition, the dispersion characteristics of exhaust pollutants from hydraulic support transporters during multiple driving phases in an alleyway (from transporting material, being unloaded at idle speed, to driving off without load) were predicted. The simulation results show that exhaust pollutants emitted by moving hydraulic support transporters can pollute the air in roadways and negatively affect the performance of gas monitoring devices in the roadway. Therefore, coal mining companies should optimize the ventilation design scheme to improve the air quality in roadways: they should increase the ventilation volume to dilute the emitted pollutants; in addition, the positions of underground gas monitoring devices should be adjusted to prevent interference from exhaust pollutants emitted by vehicles. This paper provides the theoretical basis and results of a preliminary investigation of the dispersion and transportation characteristics of exhaust pollutants emitted by vehicles in roadways. The results in this paper can serve as guidance for reducing the risk of occupational diseases.
Journal Article