Measured and predicted pressure distributions on the AFTI/F-111 mission adaptive wing

Flight tests have been conducted using an F-111 aircraft modified with a mission adaptive wing (MAW). The MAW has variable-camber leading and trailing edge surfaces that can change the wing camber in flight, while preserving smooth upper surface contours. This paper contains wing surface pressure measurements obtained during flight tests at Dryden Flight Research Facility of NASA Ames Research Center. Upper and lower surface steady pressure distributions were measured along four streamwise rows of static pressure orifices on the right wing for a leading-edge sweep angle of 26 deg. The airplane, wing, instrumentation, and test conditions are discussed. Steady pressure results are presented for selected wing camber deflections flown at subsonic Mach numbers up to 0.90 and an angle-of-attack range of 5 to 12 deg. The Reynolds number was 26 million, based on the mean aerodynamic chord. The MAW flight data are compared to MAW wind tunnel data, transonic aircraft technology (TACT) flight data, and predicted pressure distributions. The results provide a unique database for a smooth, variable-camber, advanced supercritical wing

Rapid Estimate of Wind Turbine Energy Loss due to Blade Leading Edge Delamination Using Artificial Neural Networks

Estimating reliably and rapidly the losses of wind turbine annual energy production due to blade surface damage is essential for optimizing maintenance planning and, in the case of leading edge erosion, assessing the need for protective coatings. These requirements prompted the development of the prototype system presented herein, using machine learning, wind turbine engineering codes and computational fluid dynamics to estimate annual energy production losses due to blade leading edge delamination. The power curve of a turbine with nominal and damaged blade surfaces is determined, respectively, with the open-source FAST and AeroDyn codes of the National Renewable Energy Laboratory, both using the blade element momentum theory for turbine aerodynamics. The loss prediction system is designed to map a given three-dimensional geometry of a damaged blade onto a damaged airfoil database, which, in this study, features 6000+ airfoil geometries, each analyzed with Navier-Stokes computational fluid dynamics over the working range of angles of attack. To avoid lengthy aerodynamic analyses to assess losses due to damages monitored during turbine operation, the airfoil force data of a damaged turbine required by AeroDyn are rapidly obtained using a machine learning method trained using the pre-existing airfoil database. Presented results demonstrate that realistic estimates of the annual energy production loss of a utility-scale offshore turbine due to leading edge delamination are obtained in just a few seconds using a standard desktop computer. This highlights viability and industrial impact of this new technology for managing wind farm energy losses due to blade erosion

2012 Grantmakers Information Technology Survey Report

Together the Technology Affinity Group (TAG) and Grants Managers Network (GMN) conducted an information technology survey of grantmaking organizations in July 2012. This survey serves as a follow?up to similar surveys TAG has conducted in collaboration with the Council on Foundation (The Council) in April 2003, July 2005, and June 2007, and then independently in 2010

Big Data Model Simulation on a Graph Database for Surveillance in Wireless Multimedia Sensor Networks

Sensors are present in various forms all around the world such as mobile phones, surveillance cameras, smart televisions, intelligent refrigerators and blood pressure monitors. Usually, most of the sensors are a part of some other system with similar sensors that compose a network. One of such networks is composed of millions of sensors connect to the Internet which is called Internet of things (IoT). With the advances in wireless communication technologies, multimedia sensors and their networks are expected to be major components in IoT. Many studies have already been done on wireless multimedia sensor networks in diverse domains like fire detection, city surveillance, early warning systems, etc. All those applications position sensor nodes and collect their data for a long time period with real-time data flow, which is considered as big data. Big data may be structured or unstructured and needs to be stored for further processing and analyzing. Analyzing multimedia big data is a challenging task requiring a high-level modeling to efficiently extract valuable information/knowledge from data. In this study, we propose a big database model based on graph database model for handling data generated by wireless multimedia sensor networks. We introduce a simulator to generate synthetic data and store and query big data using graph model as a big database. For this purpose, we evaluate the well-known graph-based NoSQL databases, Neo4j and OrientDB, and a relational database, MySQL.We have run a number of query experiments on our implemented simulator to show that which database system(s) for surveillance in wireless multimedia sensor networks is efficient and scalable

Microservices: Granularity vs. Performance

Microservice Architectures (MA) have the potential to increase the agility of software development. In an era where businesses require software applications to evolve to support software emerging requirements, particularly for Internet of Things (IoT) applications, we examine the issue of microservice granularity and explore its effect upon application latency. Two approaches to microservice deployment are simulated; the first with microservices in a single container, and the second with microservices partitioned across separate containers. We observed a neglibible increase in service latency for the multiple container deployment over a single container.Comment: 6 pages, conferenc

Overview of the JET results in support to ITER

The 2014–2016 JET results are reviewed in the light of their significance for optimising the ITER research plan for the active and non-active operation. More than 60 h of plasma operation with ITER first wall materials successfully took place since its installation in 2011. New multi-machine scaling of the type I-ELM divertor energy flux density to ITER is supported by first principle modelling. ITER relevant disruption experiments and first principle modelling are reported with a set of three disruption mitigation valves mimicking the ITER setup. Insights of the L–H power threshold in Deuterium and Hydrogen are given, stressing the importance of the magnetic configurations and the recent measurements of fine-scale structures in the edge radial electric. Dimensionless scans of the core and pedestal confinement provide new information to elucidate the importance of the first wall material on the fusion performance. H-mode plasmas at ITER triangularity (H = 1 at βN ~ 1.8 and n/nGW ~ 0.6) have been sustained at 2 MA during 5 s. The ITER neutronics codes have been validated on high performance experiments. Prospects for the coming D–T campaign and 14 MeV neutron calibration strategy are reviewed.European Commission (EUROfusion 633053

FogGIS: Fog Computing for Geospatial Big Data Analytics

Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (09-11 December, 2016) Indian Institute of Technology (Banaras Hindu University) Varanasi, Indi