Combustion parameters are the important aspects in the development and research of an internal combustion engines. Hence present work is an attempt to develop an artificial neural network (ANN) model for anticipating the combustion characteristics of computerized CI DI VCR, four strokes, single cylinder, and water cooled engine using mixture of two biodiesel and diesel blends as a fuel. The two biodiesel selected are Jatropha and Simarouba and mixed in the ratio of 75:25. Two biodiesel mixtures is used for the preparation of various blends with diesel. Experiments are carried out at different compression ratio, at different % load and blends B20, B40, B60and neat diesel (B00) fuel to accumulate the assorted combustion data. The input variables are % load, compression ratio, blends and crank angle. The outputs well thought-out are cylinder pressure (CP), net heat release rate (NHRR), cumulative heat release (CHR), rate of pressure rise (RPR), mass fraction burned (MFB) and maximum cylinder pressure. The assessment is focused on developing artificial neural network for the prediction and verifying the combustion characteristics of engine. MATLAB software is used for analysis of different combustion aspects of the engine. Multilayer neural network perceptions are used with Feed-Forward Back Propagation algorithm with performance function for mapping the input and output parameters. The experimental outcomes are compared with ANN estimated results for the analysis. The outcome from the study authenticate that experimental results are in match with ANN results. The accuracy of the ANN model shows the developed model can effectively predict the engine combustion characteristics with excellent regression coefficients ‘R’ and its values are close to unity and the mean square errors are extremely less. ANN model approach is competent towards predicting the combustion aspect of diesel engine with good accuracy

This article presents analysis of carbon Swan, C$_2$, laser-plasma emission records using line strength data, C$_2$-Swan-lsf, and the ExoMol astrophysical database. Line-strength data fitting of 0.25-nanometer spectral resolution ExoMol-computed spectra for a 6,000-Kelvin temperature C$_2$ Swan system, ${\rm d}\ ^3\,\Pi_{\rm g} \longrightarrow {\rm a} \ ^3\,\Pi_{\rm u}$, $\Delta {\rm v} = 0, \pm 1$, reveals a temperature of 5,640 Kelvin. The six per cent lower temperature is associated primarily with the accuracy of the transition wavelengths in the ExoMol vs. C$_2$-Swan-lsf data. The analysis of experiment data examines spectra that are recorded following laser-induced optical breakdown in carbon monoxide. The laser plasma data are recorded with 0.35-nm spectral resolution. The temperature inferences are elaborated when using non-linear fitting with both databases. The results show temperatures in excess of 6,000 Kelvin for the $\Delta {\rm v} = -1$ sequence, and for a time delay of 30\,$\upmu$s from laser plasma initiation. The accuracy of the C$_2$ Swan bands line strength data is of the order of 1 picometer. These line strength data are also utilized for analysis of laser-induced fluorescence experiments that employ a spectral resolution of 5.5 picometer, and a temperature of 2,716 Kelvin is inferred. Accurate C$_2$ databases show many applications in laboratory diagnosis and interpretation of astrophysical plasma records.

This study investigates the effect of oil viscosity on pollutant emissions and fuel consumption of an internal combustion engine (ICE) in high-altitude using Response Surface Methodology (RSM). A Chevrolet Corsa Evolution 1.5 SOHC gasoline engine was used in Cuenca, Ecuador (2560 meters above sea level), testing three lubricating oils with kinematic viscosities of 9.66, 14.08, and 18.5 cSt under various engine speeds and loads. Key findings include: hydrocarbon (HC) emissions were minimized to 7.25 ppm with low viscosity and load; carbon dioxide (CO2) emissions peaked at 15.2% vol with high viscosity and load; carbon monoxide (CO) ranged from 0.04% to 3.74% depending on viscosity and load; nitrogen oxides (NOx) were significantly influenced by viscosity, RPM, and load, indicating a need for model refinement; and fuel consumption was significantly affected by load and viscosity. RSM-based optimization identified optimal operational conditions with a viscosity of 13 sCt, 1473 rpm, and a load of 78%, resulting in 52.35 ppm of HC, 13.97% vol of CO2, 1.2% vol of CO, 0 ppm of NOx, and a fuel consumption of 6.66 l/h. These conditions demonstrate the ability to adjust operational variables to maximize fuel efficiency and minimize emissions. This study underscores the critical role of optimizing lubricant viscosity and operational conditions to mitigate environmental impact and enhance engine performance in high-altitude environments.

The paper presents data from a monthly campaign for the elemental composition of PM10 in a specific receptor Kotórz Mały (Opole Voivodeship) located in the vicinity of a moderately inhabited rural area, measured in one-hour samples with the use of the PX-375 analyzer by the Horiba company. The hourly variability of SO2, NO, NO2, CO, O3 concentrations as well as the variability of meteorological parameters were also determined. On average, during the entire measurement period, the elements related to PM10 can be arranged in the following order: As<V<Ni<Pb<Cr<Mn<Cu<Ti<Zn<K<Fe<Ca<Al<Si<S. Trace elements, including toxic elements, such as As, V, Ni, Pb, Cr, Mn, were present in low concentrations, not exceeding 10 ng/m3 (average daily value). These elements had fairly even concentrations, daily and hourly. The concentrations of the main elements in the PM10 in the receptor are subject to strong hourly changes related not only to changes in the structure of the sources identified in the statistical analysis but mainly to wind speed and direction changes (soil and sand particles pick-up and inflow of pollutants from coal combustion). It has been shown that the transport emission in the receptor can have an intense effect on PM10 in the afternoon.

This article discusses laser-induced laboratory-air plasma measurements and analysis of hydroxyl (OH) ultraviolet spectra. New experiments with Q-switched laser pulses illustrate occurrence of molecular recombination spectra for time delays of the order of several dozen of microseconds after plasma initiation. The computation of the emission spectra utilizes line strength data that are communicated as a supplementary file. Applications of detailed OH computations include laser-induced plasma and combustion analyses, to name but two applications.

In the study at hand a systemic investigation regarding the tribochemical effects of crankcase soot is presented. Sooted oils were generated via an engine dynamometer test. Both conventional as well as advanced oil condition monitoring methods indicated a mild degradation of additives. The wear volume was greatly increased with the sooted oils in model tribometer tests, despite the high residual zinc dialkyl dithiophosphate (ZDDP) antiwear (AW) levels. Once the soot was removed via ultracentrifugation, the wear volume returned to levels comparable to the fresh oil. Surface investigations revealed that ZDDP tribofilms could not form in the sooted oils, as only a thin sulfide layer was present on the metal surfaces. Meanwhile, typical tribofilms were observable with centrifuged oils. The results indicated that a tribocorrosive mechanism is most likely responsible for the elevated wear in the sooted oils, where only the iron sulfide base layer of ZDDP films is formed, which then is rapidly removed by the soot particles in an abrasive manner.

Electric vehicles (EV) are getting more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace the internal combustion engine (ICE) vehicles in near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system can face huge instabilities with enough EV penetration; but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of smart grid. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emission from the transportation sector. However, there are some major obstacles for EVs to overcome before replacing the ICE vehicles totally. This paper is focused on reviewing all the useful data available on EV configurations, energy sources, motors, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector.

Early in the middle of the 1800s. Up until about 1900, the automotive land speed record 1 was held by an electrical vehicle. Unfortunately, internal combustion engine vehicles did not do 2 well in the market due to their high cost, limited speed, and short range in the 20th century, which 3 resulted in a global fall in their use as private motor vehicles. Since there are no fuel emissions into 4 the environment, there are important uses for electrical vehicles, Electric cars, or EVs, have a closed 5 circuit, cheap operating costs, no maintenance requirements, and excellent performance. The goal 6 of this project is to assess the current state of electric vehicles and the issues surrounding the global 7 advancement of electric vehicle technology. Because of the changing climate.8