A Review on Automotive Car Composite Leaf Spring Design and Optimization

Authors: Prasad P.Kunzarkar, Prof. Tushar V.Gujrathi

Abstract- Nowadays growth in competition and innovation in automotive sector tends to modify the existing products or replace old and outdated products by newly innovated and advanced material products.A suspension system of any vehicle is also an area where these types of innovations are carried out consistantly.To increasing the comfort of user there are lots of efforts are taken.Basically leaf springs are mostly used as a suspension system in automotive vehicles.The leaf spring design optimization will be performed periodically by changing from conventional steel to composite material.generally the composite materials are mostly used in various areas such as marine,automobile,aerospace structure etc. Due to high strength they are widely used in the low weight applications and also as an alternate for metals to reduce the material cost.This paper presents literature review on, compatibility of composite material for leaf spring in automobile.Comparison with the conventional leaf spring. Also the design and analysis of composite leafspring.

Keywords— Ansys, Carbon fiber material, composite material, F.E.A, Static analysis.

References-

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Prediction of Permeability Characteristics of Fine Grained Soils and its Validation

Authors: Dr. R Chitra, Dr. Manish Gupta, Harendra Prakash, Shahid Noor

Abstract— Permeability of soils forms one of the important soil parameters required in the design of water retaining structures like Dams. Grain size distribution and density are known to influence the permeability of soils. It is one of the most important physical properties of soil used in geotechnical engineering. Though many correlations have been developed by many researchers in the past, the applicability of these correlations to the real practice have been always doubtful. Also these studies have concentrated on mostly the coarse grained soils only. Fine grained soils are complex in itself with the presence of finer particles. The behavior of the fine grained soil depends mostly on the clay and silt contents. The present study focuses on the applicability of the existing empirical relationship to the permeability of fine grained soils. The data obtained from the investigations carried out by CSMRS for various river valley projects is used in the present study. An attempt is also made to develop correlation/empirical relations between k and grain sizes, index properties, and compaction characteristics. Attempts are also made to validate the equations thus obtained.

Keywords—Coefficient of Permeability, Grain Sizes, Void Ratio, Maximum Dry Density, Atterberg Limits, Specific Gravity.

References-

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Sensor Integration in BS VI Exhaust after Treatment System for Automobiles

Authors: Mylaudy Dr. S. Rajadurai, S. Shibu Anand, P. Matcharaja

Abstract - The demanding regulatory requirement mandates the need for new engine-management system and its integration with exhaust-treatment technologies strategy. Different types of sensors play vital role on the engine out and the efficient performance of the exhaust after treatment system. Location, geometry and the orientation of the sensor installation is discussed in detail. The sensitivity of the sensor with the HEGO index guidelines provide the required signal without the noise interference The impact of various particles such as conductive water particles and engine particulates are implemented during the design of exhaust after treatment system. This improves the life expectancy, accuracy level of sensing of the sensors. Advancement of gas sensor technology over the past few decades has led to significant progress in pollution control and thereby environmental protection.

Keywords - Exhaust after treatment, Oxygen/Lambda, Temperature, pressure, Nox, PM, Emission Norms, HEGO Index, DOC, DPF, SCR, ASC, LNT, Hydro Carbon, Oxides of carbon and Oxides of Nitrogen.

References-

[1] Jonathan Zhang “Catalytic Converter – Part I of Automotive Aftertreatment System”

[2] C. Scott Nelson, David Chen, Joseph Ralph and Eric D’Herde “The Development of a RTD Temperature Sensor for Exhaust Applications”

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Improving the Performance of Hive by Parallel Processing of Massive Data

Authors: Ankul Barman, Deepak Paranjpe

Abstract- Data refers to technologies and initiatives that involve data that is too diverse, fast-changing or massive for conventional technologies, skills and infra- structure to address efficiently is called Big data. Hive is a data warehouse infrastructure tool, well suited for query processing and data analysis. Hive is gaining popularity for its SQL like query language HiveQL and for supporting majority of the SQL operations in relational database management systems (RDBMS). Being the expensive operation in RDBMS, join has been the focus of many query optimization techniques to improve performance of database systems. This work proposes the use of Numerous Query Optimization (NQO) techniques to improve the overall performance of Hive. During parallel execution of numerous queries, many opportunities can arise for conjoint scan and/or computation tasks. Executing frequent jobs only once can reduce the total execution time of all queries remarkably. Our framework, transforms a set of interrelated HiveQL queries into new global queries that can produce the same results in remarkably smaller total execution times. It is experimentally shown that ConjointHive outperforms the conventional Hive by 30-60% reduction, depending on the number of queries and percentage of conjoint tasks, in the total execution time of correlated TPC-H queries.

Keywords- Big Data, Hive, HiveQL, Numerous Query Optimization, ConjointHive

References-

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Optimizing the Performance of HADOOP Using Index-Based Join Operation

Authors: Anshul Shrivas, Deepak Paranjpe

Abstract - Data refers to technologies and initiatives that involve data that is too diverse, fast-changing or massive for conventional technologies, skills and infra- structure to address efficiently is called Big Data. Hive is a data warehouse infrastructure tool, well suited for query processing and data analysis. Hive is gaining popularity for its SQL like query language HiveQL and for supporting majority of the SQL operations in relational database management systems (RDBMS). Being the expensive operation in RDBMS, join has been the focus of many query optimization techniques to improve performance of database systems. We investigate such techniques for join operations in Hive and develop a two-way join algorithm for queries in HiveQL. When a query requires only a small subset of data selected by a predicate in the WHERE clause, the brute-force method which scans the entire tables results in poor performance for redundant disk I/Os, and irrelevant maps initiation in case the query is issued using the MapReduce. In this work, we implement the proposed index-based join technique and integrate it in Hive. To add our extension, we obtain Hive architecture details by reverse engineering the code and map our design to the conceptual optimization flow. To evaluate the performance, after setting up the environment, we run relevant test queries on datasets generated using the industry standard benchmark, TPC-H. Our results indicate significant performance gain over relatively large data or highly selective queries.

Keywords : Big Data, Hive, Hadoop, Indexing, Join.

References-

[1] Antony, S., Chakka, P., Jain, N., J., Liu, Murthy, R., Sarma, J. S., Thusoo, A., Zhang, N “Hive – A Petabyte Scale Data Warehouse Using Hadoop,” IEEE 26th Intl. Conf. Data Engineering (ICDE), Long Beach, CA, 2010, pp. 996 – 1005.

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Load Balancing Techniques in Cloud Computing

Authors: Aakash Dhar Badgaiyan, Rajendra Arakh