Effects of altitude on the soot emission and fuel consumption of a light-duty diesel engine

A four-cylinder, direct-injection (DI) diesel engine was used to study the effects of altitude on the variations of the exhaust soot emission and engine performance. The experiments were conducted in Mashhad, Iran, at an altitude of 975 m above sea level. A three-lobe rotary blower of Roots type was employed in order to simulate the altitudes down to 350 m by increasing the inlet manifold pressure of the engine. The tests were performed based on the ECE-R49 test cycle, and for each testing point, the experiments were repeated for five boosting pressures which correspond to five different altitudes. Results indicate that with increasing the altitude from 350 m to 975 m, the soot emission increases about 40%. This increase is due to the relatively lower the air density introduced into the cylinders in higher altitudes that leads to the increase of autoignition delay time which could shorten the late combustion phase; hence, the soot burnout process deteriorates. Also it was found that at low engine loads, the Brake-Specific Fuel Consumption (BSFC) increases about 20% with raising the altitude from 350 m to 975 m. At higher loads, the raising rate of fuel consumption is insignificant. The effects of altitude on the other engine parameters such as induced air mass flow rate, volumetric efficiency, equivalence ratio, and exhaust temperature were investigated as well. In addition, a sensitivity analysis was conducted and the results revealed that among the engine parameters, the soot emission alteration has the most sensitivity to the change of the altitude

On the performance of deep learning models for time series classification in streaming

Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, there have been recent efforts to adapt complex deep learning models for streaming tasks by reducing their processing rate. The design of the asynchronous dual-pipeline deep learning framework allows to predict over incoming instances and update the model simultaneously using two separate layers. The aim of this work is to assess the performance of different types of deep architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time-series datasets that are simulated as streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency.Comment: Paper submitted to the 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020

Semiempirical in-cylinder pressure based model for NOx prediction oriented to control applications

This work describes the development of a fast NO X predictive model oriented to engine control in diesel engines. The in-cylinder pressure is the only instantaneous input signal required, along with several mean variables that are available in the ECU during normal engine operation. The proposed model is based on the instantaneous evolution of the heat release rate and the adiabatic flame temperature (both obtained among other parameters from the in-cylinder pressure evolution). Corrections for considering the NO X reduction due to the re-burning mechanism are also included. Finally, the model is used for providing a model-based correction of tabulated values for the NO X emission at the reference conditions. The model exhibits a good behaviour when varying exhaust gas recirculation rate, boost pressure and intake temperature, while changes in the engine speed and injection settings are considered in the tabulated values. Concerning the calculation time, the model is optimised by proposing simplified sub-models to calculate the heat release and the adiabatic flame temperature. The final result is suitable for real time applications since it takes less than a cycle to complete the NO X prediction.Guardiola García, C.; López Sánchez, JJ.; Martín Díaz, J.; García Sarmiento, D. (2011). Semiempirical in-cylinder pressure based model for NOx prediction oriented to control applications. Applied Thermal Engineering. 31(16):3275-3286. doi:10.1016/j.applthermaleng.2011.05.048S32753286311

A Smart Post-Processing System for Forecasting the Climate Precipitation Based on Machine Learning Computations

Although many meteorological prediction models have been developed recently, their accuracy is still unreliable. Post-processing is a task for improving meteorological predictions. This study proposes a post-processing method for the Climate Forecast System Version 2 (CFSV2) model. The applicability of the proposed method is shown in Iran for observation data from 1982 to 2017. This study designs software to perform post-processing in meteorological organizations automatically. From another point of view, this study presents a decision support system (DSS) for controlling precipitation-based natural side effects such as flood disasters or drought phenomena. It goes without saying that the proposed DSS model can meet sustainable development goals (SDGs) with regards to a grantee of human health and environmental protection issues. The present study, for the first time, implemented a platform based on a graphical user interface due to the prediction of precipitation with the application of machine learning computations. The present research developed an academic idea into an industrial tool. The final finding of this paper is to introduce a set of efficient machine learning computations where the random forest (RF) algorithm has a great level of accuracy with more than a 0.87 correlation coefficient compared with other machine learning methods

Simulating the effects of turbocharging on the emission levels of a gasoline engine

The main objective of this work was to respond to the global concern for the rise of the emissions and the necessity of preventing them to form rather than dealing with their after-effects. Therefore, the production levels of four main emissions, namely NOx, CO2, CO and UHC in gasoline engine of Nissan Maxima 1994 is assessed via 1-D simulation with the GT-Power code. Then, a proper matching of turbine-compressor is carried out to propose a turbocharger for the engine, and the resultant emissions are compared to the naturally aspirated engine. It is found that the emission levels of NOx, CO, and CO2 are higher in terms of their concentration in the exhaust fume of the turbocharged engine, in comparison with the naturally aspirated engine. However, at the same time, the brake power and the brake specific emissions produced by the turbocharged engine are respectively higher and lower than those of the naturally aspirated engine. Therefore, it is concluded that, for a specific application, turbocharging provides the chance to achieve the performance of a potential naturally aspirated engine while producing lower emissions. Keywords: Emission, Gasoline SI engine, Turbocharging, GT-Power, 1-D simulation, Brake specifi

Experimental investigation of performance improving and emissions reducing in a two stroke SI engine by using ethanol additives

In present study, the operational parameters for a two stroke gasoline engine such as delivery ratio, scavenging efficiency, trapping efficiency, etc. have been investigated experimentally when its fuel is blended with ethanol additives. Also the amounts of emitted pollutants (HC, CO, CO2 and NOX) from this engine are measured in various engine velocity and loads. In experiments, ethanol is combined with gasoline in different percentages of 5%, 10% and 15%. The experiments have been done for 2500, 3000, 3500 and 4500 rpm. The results show that in most test cases, when alcoholic fuel is used, scavenging efficiency and delivery ratio increased due to rapid evaporation of ethanol, but fuel converging efficiency and brake specific fuel consumption (BSFC) decreased. The most outstanding result of using ethanol additive is significant reduction in pollutions emitted from engine and CO with 35% reduction has the most reduction percentage among other pollutants