Correlation of Performance, Exhaust Gas Temperature and Speed of a Spark Ignition Engine Using Kiva4

The objective of this study was to investigate performance characteristics of a spark ignition engine, particularly, the correlation between performance, exhaust gas temperature and speed, using Kiva4. Test data to validate kiva4 simulation results were conducted on a 3-cylinder, four-stroke Volkswagen (VW) Polo 6 TSI 1.2 gasoline engine. Three different tests were, therefore, carried out. In one set, variations in exhaust gas temperature were studied by varying the engine load, while keeping the engine speed constant. In another test, exhaust gas temperature variations were studied by keeping the engine at idling whilst varying the speeds. A third test involved studying variations in exhaust gas temperature under a constant load with variable engine speeds. To study variations in exhaust gas temperatures under test conditions, a basic grid/mesh generator, K3PREP, was employed to write an itape17 file comprising of a 45° asymmetrical mesh. This was based on the symmetry of the combustion chamber of the engine used in carrying out experimental tests. Simulations were therefore performed based on the input parameters established in the conducted tests. Simulations with the kiva4 code showed a significant predictability of the performance characteristics of the engine. This was evident in the appreciable agreement obtained in the simulation results when compared with the test data, under the considered test conditions. A percentage error, between experimental results and results from simulations with the kiva4 code of only between 2% to 3% was observed.

A step parameters prediction model based on transfer process neural network for exhaust gas temperature estimation after washing aero-engines

The prediction of Exhaust Gas Temperature Margin(EGTM)after washing aeroengines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction,but also to formulate reasonable maintenance plans.However,the EGTM encounters step changes after washing aeroengines,while,in the traditional models,a persistence tendency exists between the prediction results and the previous data,resulting in low accuracy in prediction.In order to solve the problem,this paper develops a step parameters prediction model based on Transfer Process Neural Networks(TPNN).Especially,“step parameters”represent the parameters that can reflect EGTM step changes.They are analyzed in this study,and thus the model concentrates on the prediction of step changes rather than the extension of data trends.Transfer learning is used to handle the problem that few cleaning records result in few step changes for model learning.In comparison with Long Short-Term Memory(LSTM)and Kernel Extreme Learning Machine(KELM)models,the effectiveness of the proposed method is verified on CFM56-5B engine data.

Technical Measures and Selections for Reducing Flue Gas Heat Loss of Large Coal-Fired Boilers

The main technologies for reducing flue gas heat loss of pulverized coal-fired boilers are introduced, and the suitability of these technologies for boiler operation and the principles for selection of these technologies are explored. The main conclusions are： 1） the non-equilibrium control over flue gas flow rates at the inlet of the air heater and the reversal rotation of the air heater rotator should be popularized as regular technologies in large boilers; 2） increasing the area of the air heater to reduce the flue gas heat loss in pulverized coal-fired boilers should be the top option and increasing the area of the economizer be the next choice; 3） low- pressure economizer technology could save energy under special conditions and should be compared with the technology of increasing economizer area in terms of technical economics when the latter is feasible; 4） the hot primary air heater is only suitable to the pnlvefizing system with a large amount of cold air mixed.

Research and Development of Hot Primary Air Heater for Coal-Fired Boilers in Power Plant

The reasons of introducing cold air into pulverizer are analyzed for boilers with large capacity and high parameters. The temperature rises of the exhaust gas are calculated when varying the amount of the cold air. The hot primary air heater, a new technology, is developed to eliminate the cold air from the pulverized coal system. The applications, advantages and disadvantages are introduced in detail for the new device and system. It is concluded that introducing cold air into pulverizer is one of the major factors that causes the exhaust gas temperature of boilers with large capacity to be high. The amount of the cold air could be reduced signif icantly, even to zero in some cases by adopting the hot primary air heater, which drops the exhaust gas temperature of the boiler effectively. The hot primary air heater, which could play part roles of the steam-air heater or the hot air recirculation system, could also be used to adjust the exhaust gas temperature within the range of 20 ℃ by controlling the flow rate of the cooling medium. Moreover, the startup period of the steam-air heater or the hot air recirculation system will be shortened, which is a unique advantage of the hot primary air heater among the measures to drop the exhaust gas temperature.