A Comparison of Two Shallow Water Models with Non-Conforming Adaptive Grids: classical tests

In an effort to study the applicability of adaptive mesh refinement (AMR) techniques to atmospheric models an interpolation-based spectral element shallow water model on a cubed-sphere grid is compared to a block-structured finite volume method in latitude-longitude geometry. Both models utilize a non-conforming adaptation approach which doubles the resolution at fine-coarse mesh interfaces. The underlying AMR libraries are quad-tree based and ensure that neighboring regions can only differ by one refinement level. The models are compared via selected test cases from a standard test suite for the shallow water equations. They include the advection of a cosine bell, a steady-state geostrophic flow, a flow over an idealized mountain and a Rossby-Haurwitz wave. Both static and dynamics adaptations are evaluated which reveal the strengths and weaknesses of the AMR techniques. Overall, the AMR simulations show that both models successfully place static and dynamic adaptations in local regions without requiring a fine grid in the global domain. The adaptive grids reliably track features of interests without visible distortions or noise at mesh interfaces. Simple threshold adaptation criteria for the geopotential height and the relative vorticity are assessed.Comment: 25 pages, 11 figures, preprin

Constructing Reference Metrics on Multicube Representations of Arbitrary Manifolds

Reference metrics are used to define the differential structure on multicube representations of manifolds, i.e., they provide a simple and practical way to define what it means globally for tensor fields and their derivatives to be continuous. This paper introduces a general procedure for constructing reference metrics automatically on multicube representations of manifolds with arbitrary topologies. The method is tested here by constructing reference metrics for compact, orientable two-dimensional manifolds with genera between zero and five. These metrics are shown to satisfy the Gauss-Bonnet identity numerically to the level of truncation error (which converges toward zero as the numerical resolution is increased). These reference metrics can be made smoother and more uniform by evolving them with Ricci flow. This smoothing procedure is tested on the two-dimensional reference metrics constructed here. These smoothing evolutions (using volume-normalized Ricci flow with DeTurck gauge fixing) are all shown to produce reference metrics with constant scalar curvatures (at the level of numerical truncation error).Comment: 37 pages, 16 figures; additional introductory material added in version accepted for publicatio

PERFORMANCE EVALUATION AND OPTIMIZATION OF THE UNSTRUCTURED CFD CODE UNCLE

Numerous advancements made in the field of computational sciences have made CFD a viable solution to the modern day fluid dynamics problems. Progress in computer performance allows us to solve a complex flow field in practical CPU time. Commodity clusters are also gaining popularity as computational research platform for various CFD communities. This research focuses on evaluating and enhancing the performance of an in-house, unstructured, 3D CFD code on modern commodity clusters. The fundamental idea is to tune the codes to optimize the cache behavior of the node on commodity clusters to achieve enhanced code performance. Accordingly, this work presents discussion of various available techniques for data access optimization and detailed description of those which yielded improved code performance. These techniques were tested on various steady, unsteady, laminar, and turbulent test cases and the results are presented. The critical hardware parameters which influenced the code performance were identified. A detailed study investigating the effect of these parameters on the code performance was conducted and the results are presented. The successful single node improvements were also efficiently tested on parallel platform. The modified version of the code was also ported to different hardware architectures with successful results. Loop blocking is established as a predictor of code performance

Methods for detecting and characterising clusters of galaxies

The main theme of this PhD-thesis is the observation of clusters of galaxies at submillimetric wavelengths. The Sunyaev-Zel'dovich (SZ) effect due to interaction of cosmic microwave background (CMB) photons with electrons of the hot intra-cluster medium causes a distinct modulation in the spectrum of the CMB and is a very promising tool for detecting clusters out to very large distances. Especially the European PLANCK-mission, a satellite dedicated to the mapping of CMB anisotropies, will be the first experiment to routinely detect clusters of galaxies by their SZ-signature. This thesis presents an extensive simulation of PLANCK's SZ-capabilities, that combines all-sky maps of the SZ-effect with a realisation of the fluctuating CMB and submillimetric emission components of the Milky Way and of the Solar system, and takes instrumental issues such as the satellite's point-spread function, the frequency response, scan paths and detector noise of the receivers into account. For isolating the weak SZ-signal in the presence of overwhelming spurious components with complicated correlation properties across PLANCK's channels, multifrequency filters based on matched and scale-adaptive filtering have been extended to spherical topologies and applied to simulated data. These filters were shown to efficiently amplify and extract the SZ-signal by combining spatial band-filtering and linear combination of observations at different frequencies, where the filter shapes and the linear combination coefficients follow from the cross- and autocorrelation properties of the sky maps, the anticipated profile of SZ clusters and the known SZ spectral dependence. The characterisation of the resulting SZ-sample yielded a total number of 6000 detections above a statistical significance of 3 sigma and the distribution of detected clusters in mass, redshift, and position on the sky. In a related project, a method of constructing morphological distance estimators for resolved SZ cluster images is proposed. This method measures a cluster's SZ-morphology by wavelet decomposition. It was shown that the spectrum of wavelet moments can be modeled by elementary functions and has characteristic properties that are non-degenerate and indicative of cluster distance. Distance accuracies following from a maximum likelihood approach yielded values as good as 5% for the relative deviation, and deteriorate only slightly when noise components such as instrumental noise or CMB fluctuations were added. Other complications like cool cores of clusters and finite instrumental resolution were shown not to affect the wavelet distance estimation method significantly. Another line of research is the Rees-Sciama (RS) effect, which is due to gravitational interaction of CMB photons with non-stationary potential wells. This effect was shown to be a second order gravitational lensing effect arising in the post-Newtonian expansion of general relativity and measures the divergence of gravitomagnetic potentials integrated along the line-of-sight. The spatial autocorrelation function of the Rees-Sciama effect was derived in perturbation theory and projected to yield the angular autocorrelation function while taking care of the differing time evolution of the various terms emerging in the perturbation expansion. The RS-effect was shown to be detectable by PLANCK as a correction to the primordial CMB power spectrum at low multipoles. Within the same perturbative formalism, the gravitomagnetic corrections to the autocorrelation function of weak gravitational lensing observables such as cosmic shear could be determined. It was shown that those corrections are most important on the largest scales beyond 1~Gpc, which are difficult to access observationally. For contemporary weak lensing surveys, gravitomagnetic corrections were confirmed not play a significant role. A byproduct of the simulation of CMB fluctuations on the basis of Gaussian random fields was a new way of generating coded mask patterns for X-ray and gamma-ray imaging. Coded mask cameras observe a source by recording the shadow cast by a mask onto a position-sensitive detector. The distribution of sources can be reconstructed from this shadowgram by correlation techniques. By using Gaussian random fields, coded mask patterns can be specifically tailored for a predefined point-spread function which yields significant advantages with respect to sensitivity in the observation of extended sources while providing a moderate performance compared to traditional mask generation schemes in the observation of point sources. Coded mask patterns encoding Gaussian point-spread functions have been subjected to extensive ray-tracing studies where their performance has been evaluated