The 3-D Euler and Navier-Stokes calculations for aircraft components

An explicit multistage Runge-Kutta type of time-stepping scheme is used for solving transonic flow past a transport type wing/fuselage configuration. Solutions for both Euler and Navier-Stokes equations are obtained for quantitative assessment of boundary layer interaction effects. The viscous solutions are obtained on both a medium resolution grid of approximately 270,000 points and a find grid of 460,000 points to assess the effects of grid density on the solution. Computed pressure distributions are compared with the experimental data

Recent Updates to the CFD General Notation System (CGNS)

The CFD General Notation System (CGNS) - a general, portable, and extensible standard for the storage and retrieval of computational fluid dynamics (CFD) analysis data has been in existence for more than a decade (Version 1.0 was released in May 1998). Both structured and unstructured CFD data are covered by the standard, and CGNS can be easily extended to cover any sort of data imaginable, while retaining backward compatibility with existing CGNS data files and software. Although originally designed for CFD, it is readily extendable to any field of computational analysis. In early 2011, CGNS Version 3.1 was released, which added significant capabilities. This paper describes these recent enhancements and highlights the continued usefulness of the CGNS methodology

Recent advances in Runge-Kutta schemes for solving 3-D Navier-Stokes equations

A thin-layer Navier-Stokes has been developed for solving high Reynolds number, turbulent flows past aircraft components under transonic flow conditions. The computer code has been validated through data comparisons for flow past isolated wings, wing-body configurations, prolate spheroids and wings mounted inside wind-tunnels. The basic code employs an explicit Runge-Kutta time-stepping scheme to obtain steady state solution to the unsteady governing equations. Significant gain in the efficiency of the code has been obtained by implementing a multigrid acceleration technique to achieve steady-state solutions. The improved efficiency of the code has made it feasible to conduct grid-refinement and turbulence model studies in a reasonable amount of computer time. The non-equilibrium turbulence model of Johnson and King has been extended to three-dimensional flows and excellent agreement with pressure data has been obtained for transonic separated flow over a transport type of wing

Neighborhood Environment and Self-Rated Health among Adults in Southern Sri Lanka

The prevalence of different neighborhood environmental stressors and associations between the stressors and self-rated health are described in a representative sample of 2,077 individuals, aged 18–85 years, in southern Sri Lanka. Mosquito menace (69.4%), stray dog problems (26.8%), nuisance from neighbors (20.3%), and nuisance from drug users (18.7%) were found to be the most prevalent environmental stressors. None of the stressors investigated were associated with self-rated physical health, but nuisance from neighbors, nuisance from drug users, shortage of water and having poor water/sewage drainage system were associated with self-rated mental health among the respondents

Neurofeedback during Self-face Processing in Depressed versus Healthy Adolescents

Faculty advisor: Karina QuevedoThis research was supported by the Undergraduate Research Opportunities Program (UROP)

A Time-Warping Digital FIR Filter for Nuclear Magnetic Resonance Echoes Collected with Time-Varying Readout Gradients

This thesis pertains to medical diagnostic Magnetic Resonance Imaging (MRI). As the time required for pulse sequences in MRI examinations decreases, the non-ideal aspects associated with flat-top readout gradients (those attributed to a band-limited rectangular pulse) become of concern. Furthermore, the cost, in terms of design time, hardware, and reliability of such a flat-top system is excessive, since, with respect to the gradient-generating subsystem, an infinite bandwidth is the ultimate goal. Also, new pulse sequences have been devised that decrease scan time, utilizing sinusoidal readout gradients [18]. With that in mind, there is considerable advantage associated with an MRI system that allows for imaging- that is, receiving echoes-while the readout gradient amplitude varies. The goal of this thesis is to investigate the above-mentioned problem in detail and to propose a modification which, when performed on a current MR digital receiver, will transform the MR system from one which requires flat-top readout gradients to one which allows the readout gradient to assume any pre-defined function. With such an MR system, the readout gradient may be a sinusoid, in which case the bandwidth of the gradient-generating subsystem is minimized. The proposed modification is to replace a standard digital finite impulse response (FIR) filter with a new type of digital FIR filter that uses a warped time axis in the development of its coefficients and allows for these coefficients to change with every desired output point. Furthermore, the filter is specified such that the output sampling period (the time between output points) may be time varying and not, necessarily, a multiple of the input sampling frequency. We call this filter a time-warping digital FIR (TWF) filter

Latency, Energy and Carbon Aware Collaborative Resource Allocation with Consolidation and QoS Degradation Strategies in Edge Computing

Outstanding Paper AwardInternational audienceEdge Computing has emerged from the Cloud to tackle the increasingly stringent latency, reliability and scalability imperatives of modern applications, mainly in the Internet of Things arena. To this end, the data centers are pushed to the edge of the network to diversify and bring the services closer to the users. This spatial distribution offer a wide range of opportunities for allowing self-consumption from local renewable energy sources with regard to the local weather conditions. However, scheduling the users' tasks so as to meet the service restrictions while consuming the most renewable energy and reducing the carbon footprint remains a challenge. In this paper, we design a nationwide Edge infrastructure, and study its behavior under three typical electrical configurations including solar power plant, batteries and the grid. Then, we study a set of techniques that collaboratively allocates resources on the edge data centers to harvest renewable energy and reduce the environmental impact. These strategies also includes energy efficiency optimization by means of reasonable quality of service degradation and consolidation techniques at each data center in order to reduce the need for brown energy. The simulation results show that combining these techniques allows to increase the self-consumption of the platform by 7.83% and to reduce the carbon footprint by 35.7% compared to the baseline algorithm. The optimizations also outperform classical energy-aware resource management algorithms from the literature. Yet, these techniques do not equally contribute to these performances, consolidation being the most efficient

Latency, Energy and Carbon Aware Collaborative Resource Allocation with Consolidation and QoS Degradation Strategies in Edge Computing

International audienceEdge Computing has emerged from the Cloud to tackle the increasingly stringent latency, reliability and scalability imperatives of modern applications, mainly in the Internet of Things arena. To this end, the data centers are pushed to the edge of the network to diversify and bring the services closer to the users. This spatial distribution offer a wide range of opportunities for allowing self-consumption from local renewable energy sources with regard to the local weather conditions. However, scheduling the users' tasks so as to meet the service restrictions while consuming the most renewable energy and reducing the carbon footprint remains a challenge. In this paper, we design a nationwide Edge infrastructure, and study its behavior under three typical electrical configurations including solar power plant, batteries and the grid. Then, we study a set of techniques that collaboratively allocates resources on the edge data centers to harvest renewable energy and reduce the environmental impact. These strategies also includes energy efficiency optimization by means of reasonable quality of service degradation and consolidation techniques at each data center in order to reduce the need for brown energy. The simulation results show that combining these techniques allows to increase the self-consumption of the platform by 7.83% and to reduce the carbon footprint by 35.7% compared to the baseline algorithm. The optimizations also outperform classical energy-aware resource management algorithms from the literature. Yet, these techniques do not equally contribute to these performances, consolidation being the most efficient

Latency, Energy and Carbon Aware Collaborative Resource Allocation with Consolidation and QoS Degradation Strategies in Edge Computing

International audienceEdge Computing has emerged from the Cloud to tackle the increasingly stringent latency, reliability and scalability imperatives of modern applications, mainly in the Internet of Things arena. To this end, the data centers are pushed to the edge of the network to diversify and bring the services closer to the users. This spatial distribution offer a wide range of opportunities for allowing self-consumption from local renewable energy sources with regard to the local weather conditions. However, scheduling the users' tasks so as to meet the service restrictions while consuming the most renewable energy and reducing the carbon footprint remains a challenge. In this paper, we design a nationwide Edge infrastructure, and study its behavior under three typical electrical configurations including solar power plant, batteries and the grid. Then, we study a set of techniques that collaboratively allocates resources on the edge data centers to harvest renewable energy and reduce the environmental impact. These strategies also includes energy efficiency optimization by means of reasonable quality of service degradation and consolidation techniques at each data center in order to reduce the need for brown energy. The simulation results show that combining these techniques allows to increase the self-consumption of the platform by 7.83% and to reduce the carbon footprint by 35.7% compared to the baseline algorithm. The optimizations also outperform classical energy-aware resource management algorithms from the literature. Yet, these techniques do not equally contribute to these performances, consolidation being the most efficient