Ignition timing is very important for improving performance of modern SI Engines . In an ideal four stroke SI engine, compression and expansion take place during 180° of crank rotation and combustion takes place instantaneously at TDC. During combustion the volume remains constant and there is a sudden pressure rise . However, in an actual spark ignition engine, combustion does not occur instantaneously. It is initiated by a spark produced before TDC at a definite time which affects the maximum pressure generated and the corresponding crank angle, indicated mean effective pressure, and consequentially the work done in cycle and so the thermal efficiency. Studies have been conducted to demonstrate some of these effects previously . This paper essentially aims at generating MATLAB programs which will take input of sparktiming and will give output of the required engine parameters so that salient conclusions can be drawn. Computer models of engine processes are important tools for analysis of engine performance .
DOI: 10.4236/oalib.1104184 8 Open Access Library Journal stoichiometric mixture using a single cylinder research engine. The engine was operated at various sparktiming and varied from 20˚CA BTDC until knock is detected in incremental steps of 2˚CA. As can be noticed in investigation results, in some cases, the engine could not be operated at retarded and/or advanced ST; this is due to that fuel with higher octane number enhanced knock tolerance and allowed wider range of operation at high compression ratio and advanced sparktiming that allow more favorable variables. As a result, fewer measurements are obtained for the case of high compression ratio and low octane number. Fur- thermore, in order to distinct the effect of compression ratio on the engine per- formance and exhaust emissions, the optimal sparktiming of 32˚CA BTDC was selected. The fuel delivery system is port injection with injection pressure of 2.3 bar and injection starting (SOI) at 360˚ crank angle BTDC. Thermal efficiency, bp, BSFC, in-cylinder pressure, heat release, and exhaust emissions were ob- tained and analyzed. In the case of changing testing fuel, adequate time of opera- tion was allowed before taking measurements to make sure that the fuel utilized beforehand is removed from the fuel system.
significant and affects the average cycles shown in Figures 5 and 6. At a later sparktiming of 4 CA bTDC (Figure 6), the TRF mixture displayed initially a slightly higher burning rate (i.e. higher peak pressure) compared to the other fuel mixtures due to the influence of knock. Figure 8 presents the cycle-by-cycle analysis of the peak pressures as a function of the crank angle at which they occur at a sparktiming of 2 CA bTDC. In general, the relationship between the measured peak pressures and the crank angle of occurrence is linear across the set of fuels investigated with the higher peak pressures occurring at a corresponding earlier crank angle. 31 It is worth noting that addition of n-butanol to gasoline reduces the magnitude
1. Spark production – the ignition system must be able to quickly build enough high voltage sufficient to satisfy the requirements to ignite the air fuel mixture and maintain the adequate burn time for complete combustion. 2. Sparktiming control – the ignition system must be able to alter the delivery
46 The above figures reflect the pressure variations observed for each of the corresponding cases. As observed in the pressure trace of the 50dBTDC sparking case, the autoignition starts later than TDC, represented by the heat release peaks around 7dATDC. Secondly, the heat released is not very high, compared to other cases, especially during autoignition. This could be attributed to the extended flame propagation phase, which begins around 40dBTDC, earlier than the other cases. This extended burning due to flame front propagation could result in a large part of the fuel being consumed before TDC is reached. This phenomenon was also observed and explained by Yun . Also, the conditions favorable for autoignition of the limited lean fuel mixture remaining in the cylinder are reached after TDC, resulting in a late autoignition. In the case of 30dBTDC sparking, the pressure in the cylinder during the combustion cycle is very high, so is the heat releases as seen in Fig. 3.13. The autoignition phase begins very early, around 8 degrees before TDC. This early autoignition during compression stroke, causes a significant increment in heat release rate, which is reflected in the pressure curve as well. Although this high expulsion of heat energy may lead to higher pressures, this case however is not a very favorable one. The multiple spikes, which occur after autoignition begins, signifies instability in the ignition process. The sharp spike of heat release following this section signifies a very high energy release in short time-span, which is possible grounds for knocking. A reason for such an immense energy release for this sparktiming could be that the kernel formation occurs at elevated temperature and pressure conditions, which might accelerate the reaction process during the flame propagation stage, causing autoignition to occur earlier.
Wheat plants of variety Spark and doubled haploid line SR3 from a Spark × Rialto mapping population  were grown from seed held at Rothamsted Research by Tanya Curtis and Nigel Halford. The seed were derived from lines originally supplied by the John Innes Centre Wheat Genetics Group, Norwich, UK. The plants were grown in vermiculite in a glasshouse with a 16 h day-length (supple- mental lighting was used as necessary) and a minimum temperature of 16 °C. Vermiculite does not retain nutri- ents, so the only nutrition available to the plants came from liquid feed solution. Feeding was started 3 weeks after potting and continued every 2 days until harvest. Plants were supplied with either a medium containing a full nutrient complement of potassium, phosphate, cal- cium, magnesium, sodium, iron, nitrate (2 mM Ca(NO 3 ) 2
Abstract— Taguchi design of Experiments Method is used to analyze the effect of three variables discharge current, spark on and spark off time on material removal rate, surface roughness and tool wear. AISI D2 steel with copper electrode is used. Taguchi Design of Experiments followed by taguchi analysis with the help of MINITAB is used. It is found that discharge current and pulse duration are significant factors. Taguchi L16 array is used .
(NOX), Hydrocarbons (HC), Lead Particles (PB), smoke and ash. These pollutants can cause interference in humans, animals, plants and other objects. The study was conducted to determine the effect of the main variables of the fuel system variables (Carburetor and EFI), type of fuel (Premium octane 88 and Pertamax octane 92) and the type of spark plugs (standard spark plugs and spark plugs Iridium) on CO gas exhaust. The test tool used to determine the value of the percentage of vehicle exhaust is a Gas Analyzer and the test vehicle used is a 4-step motorcycle. The research method uses factorial design and variable analysis using Yates's algorithm. The test results show that the effect of the fuel system is -1,272 (Carburetor), the effect of fuel type is -0,268 (Premium), and the type of spark plug is -0,018 (standard spark plug), so the most influential variable on CO gas output is the fuel system using the Carburetor.
Spark testing began around 1909 and continued on into the 1970’s. However, these studies required a special or dedicated grinder and an experienced metal worker. An unknown piece of steel would be placed against a grinding wheel and the experienced worker would observe the sparks, the worker would then compare the spark patterns to the spark patterns of known pieces of steel. This identification process took years of experience to perfect a technique that would give consistent results.
Figure 1. Basic operational principle of EDM  Tool has a shape of desired profile to be made on the workpiece . High value of voltage is applied across the gap. When potential difference turns out to be enough high, it releases through the gap in the form of the spark in interim of µs. The positive ions and electrons are quickened because of the heat, producing discharge channel which turns conductive. It is basically as of now when the spark bounces inflicting collisions amidst ions and electrons and making a channel of plasma. A sharp drop of the electrical resistance of the previous channel permits current density to achieve extremely high values producing a rise of ionization and therefore, setting up a strong magnetic field. The instant spark happens sufficient pressure is developed between workpiece and tool as a consequence a very high value of temperature is attained. At such high pressure and temperature, melting and erosion of some part of material takes place. Such localized high shoot up of temperature prompts material evacuation. Material evacuation happens in view of instantaneous vaporization of the material along with melting. This melted material is not expelled fully yet just mostly [19-21].
In the present simulation a relatively simple case of particular interest in connection with automotive engines is considered. The geometry is shown in figure 1. The combustion chamber is a right circular cylinder of radius ‘R’ with variable height ‘h’. The spark plug is eccentric by a distance ‘RS’ from the cylinder axis. It is assumed that the flame spreads as a spherical wave from the point of ignition. Tables 1 and 2 give the engine data for generating the simulated results and experimental pressure crank angle diagram for validation.
Weka began including wrappers for distributed processing on Hadoop in version 3.7 . distributedWekaBase is a package that provides map and reduce tasks that are not tied to a specific platform, and could potentially be used to add new platforms in the future. distributedWekaHadoop provides utilities specific to Hadoop, including loader and savers for HDFS and configuration for Hadoop tasks. This is only available for Hadoop version 1.1.2, which was pre-YARN, meaning it will only run on MapReduce. One reason for Weka’s appeal is the vast amount of tools in their library. While their classifiers and regressors are able to be used on Hadoop, most of them are not able to be parallelized. These algorithms are essentially trained as ensembles, in which smaller data subsets are trained individually but instead of merging them into a final model dur- ing the reduce phase, they are combined using voting techniques. This integration was introduced in 2013, but has not seen widespread adoption. This may be due to the lack of parallel algorithms. A similar package for Spark, distributedWekaSpark was intro- duced in March 2015. Currently it can only accept input from .csv files.
Spark (.apache.org) is a processing framework that excels at iterative and near real-time processing. Created at UC Berkeley, it has been donated as an Apache project. Spark provides an abstraction that allows data in Hadoop to be viewed as a distributed data structure upon which a series of operations can be performed. The framework is based on the same concepts Tez draws inspiration from (Dryad), but excels with jobs that allow data to be held and processed in memory, and it can very efficiently schedule processing on the in-memory dataset across the cluster. Spark automatically controls replication of data across the cluster, ensuring that each element of the distributed dataset is held in memory on at least two machines, and provides replication-based fault tolerance somewhat akin to HDFS.
Since the development of internal combustion engines, inventions have been made to improve their efficiency. One of the primary aspects of which is to develop the understanding of the relationship between the engine and fuel that burns within it. In particular, the fuel’s propensity to auto-ignite is an important characteristic which can dictate the ability of an engine to work to its full thermodynamics potential. Fuel is desired to ignite rapidly after injection into combustion chamber when working with the compression ignition (CI) engine. In a spark ignition (SI) engine, a high resistance to auto ignition is favored mainly to prevent knocking. Knocking is an unwanted ringing sound caused by auto-ignition of the fuel/air mixture ahead of the turbulent flame front. Effects of engine knocking range from inconsequential to completely destructive and acts as the main limiting factor of thermodynamic efficiency.
Available Online at www.ijpret.com 36 Firstly 4 bulbs are lightened and then engine is started for single spark plug. By keeping the throttle constant and hence measuring the speed to be 3000 rpm, different apparatus readings are noted i.e. of ammeter, voltmeter, wattmeter, fuel gauge tube, is taken for every 10 sec for each 320 W load. The process is repeated for every 320 W load till all the 12 bulbs (960 W) is lightened.
For this work, Twitter data was considered for analysis and explored on Apache SPARK default configuration parameter, known as base system. My research shows that modifying the parameters values for MapReduce job. To have better outcomes, configuration parameter must be adjust as per the specific application till awaiting resource fully used . SPARK admin should be cautious when you selecting and rectifying the parameters value. Since negligence leads degrade the performance. So in my paper-research, proposes the ‘tuning approchment’ optimizes the entire performance with 38.5% over default configuration SPARK framework. As of now so many hundred of parameters present at SPARK; thus I took only a few seconds for my research consideration. In my future work, I must take rest of the parameters those may also impact the inputs and outputs. And enhance SPARK performance as well. For furthers, a large data sets can be worked with a bigger cluster size, and some of the methods like Scheduling algorithms, resource management plans can help to enhance the SPARK’s optimizing acute performance.
We have looked at four different systems, Spark Streaming, Storm/Heron, Google Dataflow/Beam and Flink. Each of these has been used in critical production deployments and proven successful for their intended applications. While we have only illustrated each with a trivial example we have seen that they all share some of the same concepts and create pipelines in very similar ways. One obvious difference is in the way Storm/Heron explicitly constructs graphs from nodes and edges and the others use a very functional style of pipeline composition. (Storm does have the Trident layer that allows a functional pipeline composition but it is not clear if this will be supported in the Heron version.)
Most spark plasma sintered samples investigated by some researchers are cylindrical with 20 mm diameter [13,14]. Such a small size could present difficulties in preparing suitable samples for measuring physical properties, such as tensile properties. Even more importantly, in real life applications, for national defence , aviation, and civil industries , much larger materials are required for these applications in the case of materials fabricated using the sintering method. Preparing much larger samples (>40 mm diameter) presents a difficulty when using SPS in getting homogenous properties within the samples due to poor heat distribution as the sample size gets bigger. Although, the SPS method has several advantages that distinguish it from the traditional sintering methods such as hot pressing and sintering of pre-compacted billets without pressure. Certain disadvantages of the standard SPS/FAST technology are observed (Fig. 1(a)), especially when sintering bigger samples, such as radial thermal gradient by thermal drain to the outside or non-heating of the material by too low electrical conductivity. Radial thermal gradient results in inhomogeneous radial microstructures. In the new hybrid spark plasma sintering system (HHPD-25 from FCT Sytem GmbH Germany) radial thermal gradient are eliminated, as the material can be heated additionally and/or exclusively by induction/resistance heating beside heating by pulsed direct current passage (Fig. 1(b)) . Significant increase of sintering activity for certain materials by hybrid heating (combination of SPS/FAST technology +