Experimental studies on low pressure semi-direct fuel injection in a two stroke spark ignition engine

In this work a two-stroke scooter engine was modified to work with semi-direct injection of gasoline at a pressure of 8 bar from an injector in the cylinder barrel pointed toward the cylinder head. The influence of injection timing, injection pressure, spark plug location and air-fuel ratio, on performance, emissions and combustion characteristics has been investigated. In addition, a comparison has been made with manifold injection of gasoline on the same engine at a given speed and various outputs. A significant reduction in HC emissions and fuel consumption with no adverse effects on NOx emissions and combustion stability was observed. A small drop in power and increase in CO emission were observed disadvantages of the new injection system. Injection timing was found to be the most important factor and a balance between reduction in shortcircuited fuel by late injection, and time for mixture preparation by advancing the injection, was found to be essential.     

Development of an idle speed engine model using in-cylinder pressure data and an idle speed controller for a small capacity port fuel injected SI engine

An idle speed engine model has been proposed and applied for the development of an idle speed controller for a 125 cc two wheeler spark ignition engine. The procedure uses the measured Indicated Mean Effective Pressure (IMEP) at different speeds at a constant fuel rate and throttle position obtained by varying the spark timing. At idling conditions, IMEP corresponds to the friction mean effective pressure. A retardation test was conducted to determine the moment of inertia of the engine. Using these data, a model for simulating the idle speed fluctuations, when there are unknown torque disturbances, was developed. This model was successfully applied to the development of a closed loop idle speed controller based on spark timing. The controller was then implemented on a dSPACE Micro Autobox on the actual engine. The Proportional Derivative Integral (PID) controller parameters obtained from the model were found to match fairly well with the experimental values, indicating the usefulness of the developed idle speed model. Finally, the optimized idle speed control algorithm was embedded in and successfully demonstrated with an in-house built, low cost engine management system (EMS) specifically designed for two-wheeler applications.     

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

The travel decisions made by road users are more affected by the traffic conditions when they travel than the current conditions. Thus, accurate prediction of traffic parameters for giving reliable information about the future state of traffic conditions is very important. Mainly, this is an essential component of many advanced traveller information systems coming under the intelligent transportation systems umbrella. In India, the automated traffic data collection is in the beginning stage, with many of the cities still struggling with database generation and processing, and hence, a less‐data‐demanding approach will be attractive for such applications, if it is not going to reduce the prediction accuracy to a great extent. The present study explores this area and tries to answer this question using automated data collected from field. A data‐driven technique, namely, artificial neural networks (ANN), which is shown to be a good tool for prediction problems, is taken as an example for data‐driven approach. Grey model, GM(1,1), which is also reported as a good prediction tool, is selected as the less‐data‐demanding approach. Volume, classified volume, average speed and classified speed at a particular location were selected for the prediction. The results showed comparable performance by both the methods. However, ANN required around seven times data compared with GM for comparable performance. Thus, considering the comparatively lesser input requirement of GM, it can be considered over ANN in situations where the historic database is limited. Copyright © 2016 John Wiley & Sons, Ltd.     

Submerged Geosynthetic Reef as Shore Protection Measure for Islands

The Sagar Island, located north of the Bay of Bengal, intercepts the flow in the Hoogly estuary that comprises a network of several estuarine distributaries and...     

Surface prediction and control algorithms for anti-lock brake system

Anti-lock brake system (ABS) has been designed to achieve maximum deceleration by preventing the wheels from locking. The friction coefficient between tyre and road is a nonlinear function of slip ratio and varies for different road surfaces. In this paper, methods have been developed to predict these different surfaces and accordingly control the wheel slip to achieve maximum friction coefficient for different road surfaces. The surface prediction and control methods are based on a half car model to simulate high speed braking performance. The prediction methods have been compared with the results available in the literature. The results show the advantage of ABS with surface prediction as compared to ABS without proper surface identification. Finally, the performance of the controller developed in this paper has been compared with four different ABS control algorithms reported in the literature. The accuracy of prediction by the proposed methods is very high with error in prediction in a range of 0.17-2.4%. The stopping distance is reduced by more than 3% as a result of prediction for all surfaces.