Intake Ground Vortex Prediction Methods

For an aircraft turbofan engine in ground operations or during the take-off run a ground vortex can occur which is ingested and could potentially adversely affect the engine performance and operation. The vortex characteristics depend on the ground clearance, intake flow capture ratio and the relative wind vector. It is a complex flow for which there is currently very little appropriate quantitative preliminary design information. These aspects are addressed in this work where a range of models are developed to provide a method for estimating the key metrics such as the formation boundary and the ground vortex size and strength. Three techniques are presented which utilize empirical, analytical and semi-empirical approaches. The empirical methods are primarily based on a large dataset of model-scale experiments which quantitatively measured the ground vortex characteristics for a wide range of configurations. These include the effects of intake ground clearance, approaching boundary layer thickness, intake Mach number and capture velocity ratio. Overall the models are able to predict some of the key measured behaviours such as the velocity ratio for maximum vortex strength. With increasing empiricism for key sub-elements of the model construction, an increasing level of agreement is found with the experimental results. Overall the three techniques provide a relatively quick and easy method in establishing the important vortex characteristics for a given headwind configuration which is of significant use from a practical engineering perspective

Branch prediction apparatus, systems, and methods

An apparatus and a system, as well as a method and article, may operate to predict a branch within a first operating context, such as a user context, using a first strategy; and to predict a branch within a second operating context, such as an operating system context, using a second strategy. In some embodiments, apparatus and systems may comprise one or more first storage locations to store branch history information associated with a first operating context, and one ore more second storage locations to store branch history information associated with a second operating context.Board of Regents, University of Texas Syste

Mobile radio propagation prediction using ray tracing methods

The basic problem is to solve the two-dimensional scalar Helmholtz equation for a point source (the antenna) situated in the vicinity of an array of scatterers (such as the houses and any other relevant objects in 1 square km of urban environment). The wavelength is a few centimeters and the houses a few metres across, so there are three disparate length scales in the problem. The question posed by BT concerned ray counting on the assumptions that: (i) rays were subject to a reflection coefficient of about 0.5 when bouncing off a house wall and (ii) that diffraction at corners reduced their energy by 90%. The quantity of particular interest was the number of rays that need to be accounted for at any particular point in order for those neglected to only contribute 10% of the field at that point; a secondary question concerned the use of rays to predict regions where the field was less than 1% of that in the region directly illuminated by the antenna. The progress made in answering these two questions is described in the next two sections and possibly useful representations of the solution of the Helmholtz equations in terms other than rays are given in the final section

Methods of Prediction of Infantile Hemangioma Evolution

Cutaneous hemangioma is the most frequent benign tumor for children. This blood anomaly has a frequency of 10% for children and is more common for females than males. Infantile hemangioma appears soon after birth and, in general, after a period of evolution it regresses by itself. The hemangiomas appear with a high frequency on face and neck and, if they don’t regress completely, they may have psychological effects. Sometimes, depending on the size and location of the hemangioma (mainly on the face), a quick decision should be taken to proceed (or not) with surgery, so that the lesions do not cause permanent disfigurement of the patient. Yet, doctors do not know at the moment if, at a given moment of time, how much a hemangioma will progress in the near future. Therefore, an automatic monitoring system for the detection and evaluation of the evolution of hemangiomas would be a useful tool for physicians, helping them in their decision about treatment. The present lecture presents ongoing work on developing such a system. Based on a series of images of the same hemangioma acquired periodically (typically, one month passes between two successive medical controls for a single patient), we aim at firstly assessing the way the hemangioma evolves over time, and, secondly, at predicting its evolution in the near future. This involves segmenting the hemangioma area, which is made difficult by the variety of shapes and colors the tumor may take. Then, two parameters are computed for each hemangioma, namely, size and degree of redness, as being the factors of most significant importance for assessing the state of the tumor at a given time. A fuzzy system (which incorporates knowledge from experienced physicians) is then developed based on the two aforementioned parameters to assess the evolution of the tumor in time.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Compatible finite element methods for numerical weather prediction

This article takes the form of a tutorial on the use of a particular class of mixed finite element methods, which can be thought of as the finite element extension of the C-grid staggered finite difference method. The class is often referred to as compatible finite elements, mimetic finite elements, discrete differential forms or finite element exterior calculus. We provide an elementary introduction in the case of the one-dimensional wave equation, before summarising recent results in applications to the rotating shallow water equations on the sphere, before taking an outlook towards applications in three-dimensional compressible dynamical cores.Comment: To appear in ECMWF Seminar proceedings 201

Rule-based Machine Learning Methods for Functional Prediction

We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.Comment: See http://www.jair.org/ for any accompanying file

Finding missing edges in networks based on their community structure

Many edge prediction methods have been proposed, based on various local or global properties of the structure of an incomplete network. Community structure is another significant feature of networks: Vertices in a community are more densely connected than average. It is often true that vertices in the same community have "similar" properties, which suggests that missing edges are more likely to be found within communities than elsewhere. We use this insight to propose a strategy for edge prediction that combines existing edge prediction methods with community detection. We show that this method gives better prediction accuracy than existing edge prediction methods alone.Comment: 7 pages, 6 figure