The influence of heterogenous porosity on silicon nitride/steel wear in lubricated rolling contact

Heterogeneous porosity is detected on the surface and subsurface of hot isostatically pressed (HIPed) silicon nitride spherical rolling elements. The extent of the localised porosity accounts for an area of 6% of the rolling element surface and 4% of the material volume. An experimental investigation using a rotary tribometer is described to compare the lubricated rolling wear mechanisms and performance of HIPed silicon nitride with heterogeneous porosity defect in contact with steel. A brief review of previous investigations is presented. Localised porosity detection using white and violet light microscopy with post-test evaluation is described. Discussions, micro-hardness measurements and scanning electron microscopy illustrations are presented. Critical localised porosity size is evaluated from experimental results

Annular Seals of High Energy Centrifugal Pumps: Presentation of Full Scale Measurement

Prediction of rotordynamic behavior for high energy concentration centrifugal pumps is a challenging task which still imposes considerable difficulties. While the mechanical modeling of the rotor is solved most satisfactorily by finite element techniques, accurate boundary conditions for arbitrary operating conditions are known for journal bearings only. Little information is available on the reactive forces of annular seals, such as neck ring and interstage seals and balance pistons, and on the impeller interaction forces. The present focus is to establish reliable boundary conditions at annular seals. For this purpose, a full scale test machine was set up and smooth and serrated seal configurations measured. Dimensionless coefficients are presented and compared with a state of the art theory

PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development

This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. In the large, PlinyCompute presents the programmer with a very high-level, declarative interface, relying on automatic, relational-database style optimization to figure out how to stage distributed computations. However, in the small, PlinyCompute presents the capable systems programmer with a persistent object data model and API (the "PC object model") and associated memory management system that has been designed from the ground-up for high performance, distributed, data-intensive computing. This contrasts with most other Big Data systems, which are constructed on top of the Java Virtual Machine (JVM), and hence must at least partially cede performance-critical concerns such as memory management (including layout and de/allocation) and virtual method/function dispatch to the JVM. This hybrid approach---declarative in the large, trusting the programmer's ability to utilize PC object model efficiently in the small---results in a system that is ideal for the development of reusable, data-intensive tools and libraries. Through extensive benchmarking, we show that implementing complex objects manipulation and non-trivial, library-style computations on top of PlinyCompute can result in a speedup of 2x to more than 50x or more compared to equivalent implementations on Spark.Comment: 48 pages, including references and Appendi

Arm Mbed – AWS IoT System Integration [Open access]

This project explores the different Internet of Things (IoT) architectures and the available platforms to define a general IoT Architecture to connect Arm microcontrollers to Amazon Web Services. In order to accommodate the wide range of IoT applications, the architecture was defined with different routes that an Arm microcontroller can take to reach AWS. Once this Architecture was defined, a performance analysis on the different routes was performed in terms of communication speed and bandwidth. Finally, a Smart Home use case scenario is implemented to show the basic functionalities of an IoT system such as sending data to the device and data storage in the Cloud. Furthermore, a Cloud ML algorithm is triggered in real time by the Smart Home to receive a prediction of the current Comfort Level in the room