Impact of hydrogen generated by splitting water with nano-silicon and nano-aluminum on diesel engine performance

Efficient utilization of hydrogen generated during the reactions of nano-silicon/water and nano-aluminum/water in internal combustion engine has been investigated in the current work. Engine performance and emission studies of formulated and stabilized nanoemulsion fuels (water in diesel W/D), nano-aluminum in water/diesel (W/DA) and water in nano-silicon/diesel (W/DS) have been compared with those of diesel. Experimental investigations showed reduction in brake specific fuel consumption (BSFC) by 21% and 37%; rise in brake thermal efficiency (BTE) by 16% and 14% when engine was fueled with W/DA and W/DS respectively. For nanoemulsion fuels an increase in induced power was also recorded. Brake mean effective pressure, BTE and NOx emission dropped for W/D due to reduced exhaust gas temperatures. Nevertheless due to elevated peak cylinder pressures and exhaust gas temperatures a marginal rise in NOx, CO, HC and radiative heat emissions was observed with W/DA and W/DS.     

Rolling element bearing design through genetic algorithms

The design of rolling element bearings has been a challenging task in the field of mechanical engineering. While most of the real aspects of the design are never disclosed by bearing manufacturers, the common engineer is left with no other alternative than to refer to standard tables and charts containing the bearing performance characteristics. This paper presents a more viable method to solve this problem using genetic algorithms (GAs). Since the algorithm is basically a guided random search, it weakens the chances of getting trapped in local maxima or minima. The method used has yielded improved performance parameters than those catalogued in standard tables.     

Selective greedy routing: exploring the path diversity in backbone mesh networks

This paper proposes a new routing protocol for IEEE 802.11s wireless mesh network to overcome the problems associated with the conventional proactive and reactive routing approaches. The proposed ‘Selective Greedy’ (SelG) routing protocol operates in two phases. In the first phase it exploits the proactive mode of Hybrid Wireless Mesh Protocol, the standard routing protocol for IEEE 802.11s mesh, and constructs a set of potential forwarders for every mesh point, that can act as the next-hop relay. In the second phase, during the actual data communication, a candidate is selected from the set of potential forwarders based on a local optimization. The optimization procedure considers the statistical effect of local link quality fluctuation and interference over the global routing path selection. This way the SelG protocol reduces the control packet flooding in the network (a major drawback for reactive protocols). At the same time, the optimization procedure captures the network dynamics, and thus avoids the possibility of routing based on stale information (a drawback for proactive protocols). The routing properties and the correctness of the SelG protocol is established theoretically, and the performance of the protocol is analyzed through simulation results. The proposed protocol is implemented in an indoor wireless mesh testbed, and the performance is evaluated and compared with other traditional approaches.     

Data-Driven DPPM Estimation and Adaptive Fault Coverage Calibration Using MATLAB?

A manufacturing defect is a finite chip area with electrically malfunctioning circuitry caused by fabrication errors. The fraction of defective chips that escapes to the customer is called the defect level, also known as defective parts per million (DPPM, or simply PPM) when normalized to one million units. This paper demonstrates a technique used to correlate coverage goals to DPPM based on test fallout data using a MATLAB^?-based error function minimization approach. This analysis is explained using regression models for DPPM yield versus fault/defect coverage. This approach is beneficial to semiconductor companies for calibrating their fault coverage goals to meet DPPM requirements from automotive or other customers that have very aggressive (i.e., ultra-low) DPPM demands.     

Aperture Field Estimation in E Plane and H-Plane T-Junctions Using Field Theoretic Analysis

Wireless Personal Communications - This paper presents method of moment based analysis of E Plane and H-Plane T-junctions to find the field distributions on different transverse and longitudinal...     

Optimization Process of an Air Dense Medium Fluidized Bed Separator for Treating High-Ash Non-coking Indian Coal

The performance of an air dense medium fluidized bed separator, used for coal cleaning, is significantly affected by the operating process parameters. Proper estimation of these parameters is of great importance from both operational and control standpoints. The existing literature reveals that the optimum operating range for these parameters has been experimentally determined by pursuing a traditional “one variable at a time approach” which fails to consider parameter interactions and sometimes misleading, especially when the behavior of two process parameters changes as a function of a third parameter. It is realized that the interrelationship among these process parameters have a significant effect on the overall performance of this separator. In this study, a detailed investigation is carried out to examine the interdependencies among these parameters and their interactional effect on the separation performance of an air dense medium fluidized bed separator. Response surface methodology is used for this purpose and empirical models for two key response parameters, namely, product ash content and combustible recovery, are developed. These empirical models are utilized to establish the new levels of the parameters to achieve the optimum performance of the equipment and subsequently these model predictions are validated against the experimental results. Preliminary results show a strong interaction among some operating parameters.     

Topology Management Ensuring Reliability in Delay Sensitive Sensor Networks with Arbitrary Node Failures

The lifetime of a sensor network is influenced by the efficient utilization of the resource constrained sensor nodes. The tree-based data gathering offers good quality of service (QoS) for the running applications. However, data gathering at the sink reduces the network lifetime due to a fast failure of highly loaded nodes. Loss of connectivity and sensing coverage affect the performance of the applications that demand critical QoS. In this paper, a data gathering tree management scheme has been proposed to deal with arbitrary node failures in delay-sensitive sensor networks. A load-balanced distributed BFS tree construction procedure has been introduced for an efficient data gathering. Based on the initial tree construction, a tree maintenance scheme and an application message handler have been designed to ensure the reliable delivery of the application messages. The correctness of the proposed scheme has been verified both theoretically and with the help of simulation. The proposed scheme offers low overhead, enhanced network lifetime and good QoS in terms of delay and reliability of the application messages.     

Analysis of Fatty Acid and Lignan Composition of Indian Germplasm of Sesame to Evaluate Their Nutritional Merits

An attempt was made to individually analyze a germplasm collection of 54 indigenous Indian sesame cultivars for fatty acid and lignan composition of their seed oil by gas chromatography and high performance liquid chromatography, respectively. The entries varied in their fatty acid and lignan composition. The mean percentage contents of palmitic, stearic, oleic, linoleic and α‐linolenic acids ranged between 10–22, 5–10, 38–50, 18–43 and less than 1 whereas sesamol, sesamin and sesamolin scored between 3–37, 27–67, 20–59 of the total percentage of lignan, respectively. The highest percentage of α‐linolenic acid (ALA) was obtained in Var‐9 (1.3 % of the total fatty acids). Among the lignans, high sesamin content is considered to be significant, particularly in terms of long shelf life and nutraceutical value of sesame seed oil. The study has broadened our understanding related to differential biochemical composition of the rich sesame germplasms, thereby providing us with a useful groundwork for identifying potential targets and suitable cultivars for genetic engineering approaches to be undertaken in order to improve the nutritional quality of sesame oil, which in turn would be beneficial towards human health.