Effect of teeth micro-geometrical form modification on contact kinematics and efficiency of high performance transmissions

Light weight, compactness and efficiency are key objectives in high performance vehicular transmission systems, which are subject to large variations in torque and power. Pitch line velocities of up to 52 m/s and teeth pair contact pressures of up to 3 GPa are routinely encountered under race conditions. Contact patch asymmetry due to angular misalignments between input and output shafts leads to the generation of high edge stress discontinuities on gear flanks, inducing fatigue spalling which affects system durability. Crowning is widely used as a palliative measure to mitigate these undesired effects. These problems can be further exacerbated by contact footprint truncation. The paper presents a new approach to modelling the kinematics and contact micro-geometry of meshing conjunctions of involute spur gears with profile and lead modifications. A time-efficient analytical method is presented to accurately determine the contact footprint and kinematics, leading to the solution of highly loaded non-Newtonian mixed thermo-elastohydrodynamic contact under the extreme prevalent conditions of high performance vehicular transmissions. The effect of tooth form modification on contact footprint truncation, contact kinematics and generated frictional power loss is investigated. This approach has not hitherto been reported in literature

CloudAligner: A fast and full-featured MapReduce based tool for sequence mapping

<p>Abstract</p> <p>Background</p> <p>Research in genetics has developed rapidly recently due to the aid of next generation sequencing (NGS). However, massively-parallel NGS produces enormous amounts of data, which leads to storage, compatibility, scalability, and performance issues. The Cloud Computing and MapReduce framework, which utilizes hundreds or thousands of shared computers to map sequencing reads quickly and efficiently to reference genome sequences, appears to be a very promising solution for these issues. Consequently, it has been adopted by many organizations recently, and the initial results are very promising. However, since these are only initial steps toward this trend, the developed software does not provide adequate primary functions like bisulfite, pair-end mapping, etc., in on-site software such as RMAP or BS Seeker. In addition, existing MapReduce-based applications were not designed to process the long reads produced by the most recent second-generation and third-generation NGS instruments and, therefore, are inefficient. Last, it is difficult for a majority of biologists untrained in programming skills to use these tools because most were developed on Linux with a command line interface.</p> <p>Results</p> <p>To urge the trend of using Cloud technologies in genomics and prepare for advances in second- and third-generation DNA sequencing, we have built a Hadoop MapReduce-based application, CloudAligner, which achieves higher performance, covers most primary features, is more accurate, and has a user-friendly interface. It was also designed to be able to deal with long sequences. The performance gain of CloudAligner over Cloud-based counterparts (35 to 80%) mainly comes from the omission of the reduce phase. In comparison to local-based approaches, the performance gain of CloudAligner is from the partition and parallel processing of the huge reference genome as well as the reads. The source code of CloudAligner is available at <url>http://cloudaligner.sourceforge.net/</url> and its web version is at <url>http://mine.cs.wayne.edu:8080/CloudAligner/.</url></p> <p>Conclusions</p> <p>Our results show that CloudAligner is faster than CloudBurst, provides more accurate results than RMAP, and supports various input as well as output formats. In addition, with the web-based interface, it is easier to use than its counterparts.</p