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Articles 1 - 4 of 4
Full-Text Articles in Physics
Numerical Investigation And Statistical Analysis Of The Flow Patterns Behind Square Cylinders Arranged In A Staggered Configuration Utilizing The Lattice Boltzmann Method, M. Abid, N. Yasin, M. Saqlain, S. Ul-Islam, S. Ahmad
Numerical Investigation And Statistical Analysis Of The Flow Patterns Behind Square Cylinders Arranged In A Staggered Configuration Utilizing The Lattice Boltzmann Method, M. Abid, N. Yasin, M. Saqlain, S. Ul-Islam, S. Ahmad
Mathematics & Statistics Faculty Publications
Flow past bluff bodies like square cylinders is important in engineering applications, but flow patterns behind staggered cylinder arrangements remain poorly understood. Existing studies have focused on tandem or side-by-side configurations, while offset orientations have received less attention. The aim of this paper is to numerically investigate flow dynamics and force characteristics behind two offset square cylinders using the single relaxation time lattice Boltzmann method. The effects of changing both the Reynolds number (Re = 1-150) and gap spacing ratio (g* = 0.5-5) between the cylinders are analyzed. Instantaneous vorticity contours, time histories of drag and lift coefficients, power spectra …
M-Cubes: An Efficient And Portable Implementation Of Multi-Dimensional Integration For Gpus, Ioannis Sakiotis, Kamesh Arumugam, Marc Paterno, Desh Ranjan, Balŝa Terzić, Mohammad Zubair
M-Cubes: An Efficient And Portable Implementation Of Multi-Dimensional Integration For Gpus, Ioannis Sakiotis, Kamesh Arumugam, Marc Paterno, Desh Ranjan, Balŝa Terzić, Mohammad Zubair
Computer Science Faculty Publications
The task of multi-dimensional numerical integration is frequently encountered in physics and other scientific fields, e.g., in modeling the effects of systematic uncertainties in physical systems and in Bayesian parameter estimation. Multi-dimensional integration is often time-prohibitive on CPUs. Efficient implementation on many-core architectures is challenging as the workload across the integration space cannot be predicted a priori. We propose m-Cubes, a novel implementation of the well-known Vegas algorithm for execution on GPUs. Vegas transforms integration variables followed by calculation of a Monte Carlo integral estimate using adaptive partitioning of the resulting space. mCubes improves performance on GPUs by maintaining relatively …
Modeling Martian Planetary Entry Descent And Landing Using Monte Carlo Driven Response Surface Methodology, Narcrisha S. Norman
Modeling Martian Planetary Entry Descent And Landing Using Monte Carlo Driven Response Surface Methodology, Narcrisha S. Norman
Mechanical & Aerospace Engineering Theses & Dissertations
Response surface methodology (RSM) is a statistical method that explores the relationships between several descriptive variables and one or more response variables. For over sixty years, among other areas, it has been utilized in quality engineering, process engineering, aircraft engineering, economics, chemical engineering, automotive engineering and design/technique optimization. In this dissertation, RSM is utilized to produce regression models that represent the planetary entry, descent and landing (EDL) process. A complete understanding of EDL process is an essential component of any planetary exploration. Research in this area is ongoing and confidence in the ability to explore known celestial bodies is growing. …
Limit Theorems In The Area Of Large Deviations For Some Dependent Random Variables, Narasinga Rao Chaganty, Jayaram Sethuraman
Limit Theorems In The Area Of Large Deviations For Some Dependent Random Variables, Narasinga Rao Chaganty, Jayaram Sethuraman
Mathematics & Statistics Faculty Publications
A magnetic body can be considered to consist of n sites, where n is large. The magnetic spins at these n sites, whose sum is the total magnetization present in the body, can be modelled by a triangular array of random variables (X(n) 1,..., X(n) n). Standard theory of physics would dictate that the joint distribution of the spins can be modelled by dQn(x) = zn-1 exp[ -Hn(x)]Π dP(xj), where x = (x1,..., xn) ∈ Rn, where Hn is the Hamiltonian, zn is …