Institute for Computational Mechanics in Propulsion (ICOMP)

The Institute for Computational Mechanics in Propulsion (ICOMP) is a combined activity of Case Western Reserve University, Ohio Aerospace Institute (OAI) and NASA Lewis. The purpose of ICOMP is to develop techniques to improve problem solving capabilities in all aspects of computational mechanics related to propulsion. The activities at ICOMP during 1991 are described

Research in progress in applied mathematics, numerical analysis, fluid mechanics, and computer science

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science during the period October 1, 1993 through March 31, 1994. The major categories of the current ICASE research program are: (1) applied and numerical mathematics, including numerical analysis and algorithm development; (2) theoretical and computational research in fluid mechanics in selected areas of interest to LaRC, including acoustics and combustion; (3) experimental research in transition and turbulence and aerodynamics involving LaRC facilities and scientists; and (4) computer science

Electrical, Optical and acoustic diagnostics of atmospheric pressure gas discharges

This thesis presents original diagnostic investigations of atmospheric pressure gaseous discharges, operating in owing helium and helium with low concentrations (0.1 - 1 %) of gas admixtures, together with novel biomedical surface functionalisations. The initial body of this work focuses on comprehensive electrical and optical diagnostics of the operation of an industrial scale dielectric barrier discharge (DBD), maintained in a 10 l/min w of both helium and helium with 1% admixed oxygen. The experimental results reveal a coupling between the power dissipated in the discharge and the discharge homogeneity which, in turn, correlates to a shift in the power supply operating frequency and species optical emission intensities. The shift in the operating frequency is shown to be dependent upon increased charge deposition on the electrodes, as the input power is increased, thus changing the overall system capacitance through charge-voltage LIssajous gure analyses. Furthermore it is demonstrated that the gas temperature did not exceed approximately 380-410 K over the full parameter space, in the helium discharge, through model tting of the rst negative system of the N+2 band around 391.4 nm. A real-time, PC based monitoring diagnostic system has been developed which is used to perform long term analyses of a laboratory DBD chamber in helium and helium with 0.1% admixtures of both oxygen and nitrogen. Post analysis of the results, through multivariate analysis of the large experimental datasets, show that rapid system characterisations are faciltated using this method, the outcomes of which are compared with both global and uid model outputs within the literature. Passive acoustic diagnostics of a plasma jet system, together with signal analysis in the time, frequency and time-frequency domains are explored. Here it is found that ow induced mode transitions from buoyant, through laminar, to turbulent regimes may be identied using time-frequency scalogram plots. It is shown that the scalogram plots may also be used to identify the point at which power coupling together with the plume length are maximised. The transition into a fully turbulent plume structure is shown to be accompanied by low frequency acoustic signals which modulate the acoustic rst harmonic in the time-frequency domain. Moreover, a non invasive measurement of the power coupled into the discharge is demonstrated through passive acoustic sensing whilst the jet ow is laminar. Finally, surface modications of disposable plastics for biosensor applications are performed. In this work it is shown that the density of packing of both 40 nm and 80 nm gold nanoparticles may be tailored, through variation of the gas input ow rate of a linear eld jet, in order to enhance the optical signal according to the Mie theory for light extinction at a metal nanoparticle interface. The functionalisation using long chain polethyleneglycol has also been demonstrated to provide a protein repellent surface for non-specic protein binding reduction

Multivariate Pointwise Information-Driven Data Sampling and Visualization

With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.Comment: 25 page

[Activity of Institute for Computer Applications in Science and Engineering]

This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science

Iterative Solvers for Physics-based Simulations and Displays

La génération d’images et de simulations réalistes requiert des modèles complexes pour capturer tous les détails d’un phénomène physique. Les équations mathématiques qui composent ces modèles sont compliquées et ne peuvent pas être résolues analytiquement. Des procédures numériques doivent donc être employées pour obtenir des solutions approximatives à ces modèles. Ces procédures sont souvent des algorithmes itératifs, qui calculent une suite convergente vers la solution désirée à partir d’un essai initial. Ces méthodes sont une façon pratique et efficace de calculer des solutions à des systèmes complexes, et sont au coeur de la plupart des méthodes de simulation modernes. Dans cette thèse par article, nous présentons trois projets où les algorithmes itératifs jouent un rôle majeur dans une méthode de simulation ou de rendu. Premièrement, nous présentons une méthode pour améliorer la qualité visuelle de simulations fluides. En créant une surface de haute résolution autour d’une simulation existante, stabilisée par une méthode itérative, nous ajoutons des détails additionels à la simulation. Deuxièmement, nous décrivons une méthode de simulation fluide basée sur la réduction de modèle. En construisant une nouvelle base de champ de vecteurs pour représenter la vélocité d’un fluide, nous obtenons une méthode spécifiquement adaptée pour améliorer les composantes itératives de la simulation. Finalement, nous présentons un algorithme pour générer des images de haute qualité sur des écrans multicouches dans un contexte de réalité virtuelle. Présenter des images sur plusieurs couches demande des calculs additionels à coût élevé, mais nous formulons le problème de décomposition des images afin de le résoudre efficacement avec une méthode itérative simple.Realistic computer-generated images and simulations require complex models to properly capture the many subtle behaviors of each physical phenomenon. The mathematical equations underlying these models are complicated, and cannot be solved analytically. Numerical procedures must thus be used to obtain approximate solutions. These procedures are often iterative algorithms, where an initial guess is progressively improved to converge to a desired solution. Iterative methods are a convenient and efficient way to compute solutions to complex systems, and are at the core of most modern simulation methods. In this thesis by publication, we present three papers where iterative algorithms play a major role in a simulation or rendering method. First, we propose a method to improve the visual quality of fluid simulations. By creating a high-resolution surface representation around an input fluid simulation, stabilized with iterative methods, we introduce additional details atop of the simulation. Second, we describe a method to compute fluid simulations using model reduction. We design a novel vector field basis to represent fluid velocity, creating a method specifically tailored to improve all iterative components of the simulation. Finally, we present an algorithm to compute high-quality images for multifocal displays in a virtual reality context. Displaying images on multiple display layers incurs significant additional costs, but we formulate the image decomposition problem so as to allow an efficient solution using a simple iterative algorithm