Rolls-Royce Motor Cars

About: Rolls-Royce Motor Cars is a based out in . It is known for research contribution in the topics: Turbine & Gas compressor. The organization has 3845 authors who have published 4934 publications receiving 58973 citations. The organization is also known as: Rolls-Royce Motor Cars Limited.

Load Partitioning and Strain-Induced Martensite Formation during Tensile Loading of a Metastable Austenitic Stainless Steel

Abstract: In-situ high-energy X-ray diffraction and material modeling are used to investigate the strain-rate dependence of the strain-induced martensitic transformation and the stress partitioning between austenite and α′ martensite in a metastable austenitic stainless steel during tensile loading. Moderate changes of the strain rate alter the strain-induced martensitic transformation, with a significantly lower α′ martensite fraction observed at fracture for a strain rate of 10−2 s−1, as compared to 10−3 s−1. This strain-rate sensitivity is attributed to the adiabatic heating of the samples and is found to be well predicted by the combination of an extended Olson–Cohen strain-induced martensite model and finite-element simulations for the evolving temperature distribution in the samples. In addition, the strain-rate sensitivity affects the deformation behavior of the steel. The α′ martensite transformation at high strains provides local strengthening and extends the time to neck formation. This reinforcement is witnessed by a load transfer from austenite to α′ martensite during loading.

Sensor fault detection, isolation and system reconfiguration based on extended Kalman filter for induction motor drives

Abstract: Induction motors (IMs) have been extensively used in industrial applications because of their inexpensiveness, ruggedness and reliability. Generally, to improve the performance of IM drives, one position sensor, one DC-link voltage sensor and at least two AC current sensors are necessary. However, failure of any of these sensors can cause degraded system performance or even instability. Consequently, it is very important to develop a sensor fault resilient control system for IMs drives so that continuous and normal operation is maintained even in cases of sensor faults. This study proposes a compact and robust sensor fault detection, isolation and system reconfiguration algorithm based on extended Kalman filter and reduced number of adaptive observers. A comprehensive set of experimental results are provided to verify the effectiveness of the proposed algorithm.

Ducted fan gas turbine power plant mounting

Abstract: A ducted fan gas turbine powerplant is mounted to aircraft structure via a single plane mounting which comprises a single peripheral array of struts which connect the core gas generator, the fan cowl and the aircraft structure. The structure which connects the struts to the core gas generator is extended downstream of the array of struts and terminates in a plain bearing. A downstream portion of the core gas generator includes a cylindrical portion which is in sliding engagement with the plain bearing, so that radial forces generated by the movement arm, the fulcrum of which coincides with the strut connection with the core gas generator are transmitting via the struts to the aircraft structure and relative axial expansion between the extension and the core gas generator is enabled.

Abradable layer including a rare earth silicate

Abstract: An abradable coating may include a rare earth silicate. The abradable coating may be deposited over a substrate, an environmental barrier coating, or a thermal barrier coating. The abradable coating may be deposited on a gas turbine blade track or a gas turbine blade shroud to form a seal between the gas turbine blade track or gas turbine blade shroud and a gas turbine blade. The abradable coating may also include a plurality of layers, such as alternating first and second layers including, respectively, a rare earth silicate and stabilized zirconia or stabilized hafnia.

An Automatic Identification System (AIS) Database for Maritime Trajectory Prediction and Data Mining

Abstract: In recent years, maritime safety and efficiency become very important across the world. Automatic Identification System (AIS) tracks vessel movement by onboard transceiver and terrestrial and/or satellite base stations. The data collected by AIS contain broadcast kinematic information and static information. Both of them are useful for maritime anomaly detection and vessel route prediction which are key techniques in maritime intelligence. This paper is devoted to construct a standard AIS database for maritime trajectory learning, prediction and data mining. A path prediction method based on Extreme Learning Machine (ELM) is tested on this AIS database and the testing results show this database can be used as a standardized training resource for different trajectory prediction algorithms and other AIS data based mining applications.