Thermal analysis of a ceramic coating diesel engine piston using 3-D finite element method

In this study, firstly, thermal analyses are investigated on a conventional (uncoated) diesel piston, made of aluminum silicon alloy and steel. Secondly, thermal analyses are performed on pistons, coated with MgO-ZrO₂ material by means of using a commercial code, namely ANSYS. Finally, the results of four different pistons are compared with each other. The effects of coatings on the thermal behaviors of the pistons are investigated. It has been shown that the maximum surface temperature of the coated piston with material which has low thermal conductivity is improved approximately 48% for the AlSi alloy and 35% for the steel.     

CFD SIMULATION OF THE GAS-SOLID FLOW IN THE RISER OF A CIRCULATING FLUIDIZED BED WITH SECONDARY AIR INJECTION

A comprehensive investigation was carried out to study hydrodynamics aspects of secondary air injection in circulating fluidized beds. This article presents modeling and results of computational fluid dynamics simulations of gas-solid flow in the riser section of a laboratory-scale (ID = 0.23 m, height = 7.6 m) circulating fluidized bed with a radial secondary air injector. The gas-solid flow model is based on the two-fluid (Eulerian-Eulerian) approach, where both gas and solids phases are treated as interpenetrating continua. A granular kinetic theory model is used to describe the solids phase stresses. The simulation results are compared with measured pressure drop and axial particle velocity profiles; reasonable agreement is obtained. Qualitatively, excellent agreement is obtained in predicting the increase in solids volume fraction below secondary air ports, the accumulation of solids around the center of the riser due to momentum of secondary air jets, and the absence of the solids down-flow near the wall above the secondary air injection ports, which are the prominent features of secondary air injection observed in the experiments.     

A Model for Cross-Domain Opinion Target Extraction in Sentiment Analysis

Opinion target extraction is one of the core tasks in sentiment analysis on text data. In recent years, dependency parser–based approaches have been commonly studied for opinion target extraction. However, dependency parsers are limited by language and grammatical constraints. Therefore, in this work, a sequential pattern-based rule mining model, which does not have such constraints, is proposed for cross-domain opinion target extraction from product reviews in unknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also reveals the difference between the concepts of opinion target and aspect, which are commonly confused in the literature. The model consists of two stages. In the first stage, the aspects of reviews are extracted from the target domain using the rules automatically generated from source domains. The aspects are also transferred from the source domains to a target domain. Moreover, aspect pruning is applied to further improve the performance of aspect extraction. In the second stage, the opinion target is extracted among the aspects extracted at the former stage using the rules automatically generated for opinion target extraction. The proposed model was evaluated on several benchmark datasets in different domains and compared against the literature. The experimental results revealed that the opinion targets of the reviews in unknown domains can be extracted with higher accuracy than those of the previous works.     

Mobile multi-view object image search

High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.     

Developing robust vision modules for microsystems applications

In this work, several robust vision modules are developed and implemented for fully automated micromanipulation. These are autofocusing, object and end-effector detection, real-time tracking and optical system calibration modules. An image based visual servoing architecture and a path planning algorithm are also proposed based on the developed vision modules. Experimental results are provided to assess the performance of the proposed visual servoing approach in positioning and trajectory tracking tasks. Proposed path planning algorithm in conjunction with visual servoing imply successful micromanipulation tasks.     

Hypergraph Models and Algorithms for Data-Pattern-Based Clustering

In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.     

Freezing Range,Melt Quality,and Hot Tearing in Al-Si Alloys

Metallurgical and Materials Transactions A - In this study, three different aluminum-silicon alloys (A356, A413, and A380) that have different solidification morphology and solidification ranges...