Sean Ziegeler

Visualization of fluid flows in virtual environments

Visualization of Fluid Flows in Virtual Environments Ziegele , S. B. r r 1), Gopal, G. P. 1), Blades, E. 2), Moo head, R. J. 1), Marcum, D. L. 2) and Guan, Y. 1) 1) ERC GRI Visualization, Analysis and Imaging Laboratory, Mississippi State University, MS, U.S.A. E-mail : {sean | gopi | rjm | guanyl } @gri.msstate.edu 2) ERC SimCenter, Mississippi State University, MS, U.S.A., E-mail : { blades | marcum } @simcenter.msstate.edu

The MetVR case study: meteorological visualization in an immersive virtual environment

Traditional methods for displaying weather products are generally two-dimensional (2D) plots or just text format. It is hard for forecasters to get the entire picture of the atmosphere using these methods. The problems apparent in 2D with comparing and correlating multiple layers are overcome simply by adding a dimension. This is important because pertinent features in the data sets may lie in multiple layers and span several time steps. However, simply using a three-dimensional (3D) approach is not enough. The capacity for analysis of small-scale, but important, features in 2D are lost when transitioning to 3D. We propose that 3Ds advantages can be incorporated with 2Ds small-scale analysis by using an immersive virtual environment. In this case study, we evaluate our current standing with the project: have we met our goals, and how should we proceed from this point? To evaluate our application, we invited meteorologists to use the application to explore a data set. Then we presented our goals and asked which ones had we met, from a meteorologists perspective. The results qualitatively reflected that our application was effective and further research would be worthwhile.

Spatial Error Metrics for Oceanographic Model Verification

AbstractA common problem with modern numerical oceanographic models is spatial displacement, including misplacement and misshapenness of ocean circulation features. Traditional error metrics, such as least squares methods, are ineffective in many such cases; for example, only small errors in the location of a frontal pattern are translated to large differences in least squares of intensities. Such problems are common in meteorological forecast verification as well, so the application of spatial error metrics have been a recently popular topic there. Spatial error metrics separate model error into a displacement component and an intensity component, providing a more reliable assessment of model biases and a more descriptive portrayal of numerical model prediction skill. The application of spatial error metrics to oceanographic models has been sparse, and further advances for both meteorology and oceanography exist in the medical imaging field. These advances are presented, along with modifications necessar...

Unitary qubit lattice simulations of multiscale phenomena in quantum turbulence

A unitary qubit lattice algorithm, which scales almost perfectly to the full number of cores available (e.g., 216000 cores on a CRAY XT5), is used to examine quantum turbulence and its interrelationship to classical turbulence with production runs on grids up to 57603. The maximal grids achievable by conventional CFD for quantum turbulence is just 20483, and artificial dissipation had to be introduced. Our unitary algorithms preserve the Hamiltonian structure of the Gross-Pitaevskii equation which describes quantum turbulence in a zero-temperature Bose-Einstein condensate (BEC). As a result, parameter regimes have been uncovered which exhibit very short Poincare recurrence time, as well as a strong triple cascade structure in the kinetic energy spectrum. Moreover, a detailed examination of the incompressible kinetic energy spectrum has revealed for the first time within a turbulence simulation the k-17/5 quantum Kelvin wave cascade. By generalizing the unitary entanglement operators on the 2 qubits, a finite temperature BEC system is examined. These unitary qubit lattice algorithms are directly applicable to quantum computers as they become available.

SSH-Enabled ParaView

ParaView is a very powerful visualization tool used by many in the Department of Defense (DoD) high performance computing (HPC) community. It is both fast and flexible. It performs well on a user’s desktop, but it can also scale to take advantage of clusters and large shared memory machines. Recently, ParaView has been adapted to run on the Linux clusters at the US Army Research Laboratory DoD Supercomputing Resource Center (ARL DSRC) using the Load Sharing Facility (LSF) batch queues. This method makes heavy use of Secure Shell (SSH) port forwarding to move data between the cluster nodes and the user’s desktop. The method is fast, convenient, and secure. Unfortunately, not everyone can use SSH port forwarding. For example, some users may not have access to servers that allow port-forwarded traffic. Also, there are users that are specifically banned from initiating a port forward from their desktop. To solve this problem, we have developed a version of ParaView that does not use TCP/IP sockets between the client and the server. Instead, the data is passed through the SSH standard in/out. If the user wishes to use a batch queue, a helper script handles the communication between the login node and the nodes that are allocated to the user. This paper describes the implementation of an SSHenabled ParaView. It then empirically compares our version to various other methods of running ParaView in the DoD HPC environment. Finally, it helps guide HPC users to determine the method that best fits their needs. This work benefits the DoD HPC community by making ParaView client/server available to users that have been previously unable to use it.

Poincare recurrence and spectral cascades in three-dimensional quantum turbulence

The evolution of the ground state wave function of a zero-temperature Bose-Einstein condensate (BEC) is well described by the Hamiltonian Gross-Pitaevskii (GP) equation. Using a set of appropriately interleaved unitary collision-streaming operators, a quantum lattice gas algorithm is devised which on taking moments recovers the Gross-Pitaevskii (GP) equation in diffusion ordering (time scales as square of length). Unexpectedly, there is a class of initial conditions in which their Poincare recurrence is extremely short. Further it is shown that the Poincare recurrence time scales with diffusion ordering as the the grid is increased. The spectral results of Yepez et.al. [1] for quantum turbulence are corrected and it is found that it is the compressible kinetic energy spectrum that exhibits the 3 cascade regions: a small k classical Kolmogorov k^(-5/3) spectrum, a steep semi-classical cascade region, and a large k quantum Kelvin wave cascade k^(-3) spectrum. The incompressible kinetic energy spectrum exhibits basically a single cascade power law of k^(-3). For winding number 1 linear vortices it is also shown that there is an intermittent loss of Kelvin wave cascade with its signature seen in the time evolution of the kinetic energy, the loss of the k^(-3) spectrum in the incompressible kinetic energy spectrum as well as the minimization of the vortex core isosurfaces that inhibits the Kelvin wave cascade.

Lattice Boltzmann Algorithms for Fluid Turbulence

Lattice Boltzmann algorihms are a mesoscopic representation of nonlinear continuum physics (like Navier-Stokes, magnetohydro dynamics (MHD), Gross- Pitaevskii equations) which are ideal for parallel supercomputers because they transform the difficult nonlinear convective macroscopic derivatives into purely local moments of distribution functions. The macroscopic nonlinearities are recovered by relaxation distribution functions in the collision operator whose dependence on the macroscopic velocity is algebraically nonlinear and thus purely local. Unlike standard computational fluid dynamics codes, there is no loss in parallelization in handling arbitrary geometric boundaries, e.g., using bounce-back rules from kinetic theory. By encoding detailed balance into the collision operator through the introduction of discrete H-function, the lattice Boltzmann algorithm can be made unconditionally stable for arbitrary high Reynolds numbers. It is shown that this approach is a special case of a quantum lattice Boltzmann algorithm that entangles local qubits through unitary collision operators and which is ideally parallelized on quantum computer architectures. Here we consider turbulence simulations using 2,048 PEs on a 1,6003-spatial grid. A connection is found between the rate of change of enstrophy and the onset of laminar-to- turbulent flows.

Unitary Quantum Lattice Gas Algorithms for Quantum to Classical Turbulence

Using a set of interleaved unitary collision-stream operators, a three-dimensional (3D) quantum lattice gas algorithm is devised which, on taking moments, recovers the Gross-Pitaevskii (GP) equation. If a zero-temperature Bose-Einstein condensate (BEC) is trapped in an a magnetic well, the evolution of the ground-state wave function satisfies the scalar GP equation, while if the BEC is trapped in an optical trap the ground-state wave function satisfies spin or GP equations. Quantum turbulence is studied in a scalar GP system on 5,7603 grid yielding not only the classical Kolmogorov k-5/3 cascade but also the quantum vortex k-3 spectrum. For a certain class of initial conditions, one finds an intermittent loss of tangled quantum vortices as the vortex cores attain minimal size, and thus prevent the Kelvin wave cascade (due to helical wave-wave coupling on the vortex). A coupled set of GP equations are solved for spin or BEC. Skrymions, which describe topologically-linked quantum vortices, are examined. One finds, for certain initial conditions that the incompressible kinetic energy spectrum for the condensate component of a vortex ring core rapidly departs from the k-3 linear quantum vortex spectrum.

Poincare recurrence and intermittent destruction of the quantum Kelvin wave cascade in quantum turbulence

A quantum lattice gas algorithm, based on interleaved unitary collide-stream operators, is used to study quantum turbulence of the ground state wave function of a Bose-Einstein condensate (BEC). The Gross-Pitaevskii equation is a Hamiltonian system for a compressible, inviscid quantum fluid. From simulations on a 57603 grid it was observed that a multi-cascade existed for the incompressible kinetic energy spectrum with universal features: the large spatial scales exhibit a classical Kolmogorov k -5/3 spectrum while the very small scales exhibit a quantum Kelvin wave cascade k-3 spectrum. Under certain conditions one can explicitly determine the Poincare recurrence of initial conditions as well as the intermittent destruction of the Kelvin wave cascade.

Quantum Lattice-Gas Algorithm for Quantum Turbulence - CAP Simulations on 12,288 Cores of Cray XT-5 Einstein at NAVO

A novel unitary quantum lattice algorithm is developed to explore quantum turbulence. Because of its low memory requirements and its near perfect parallelization to the full 12,288 cores on the Cray XT5, simulations were run up to spatial grids of 5,7603. The Gross-Pitaevskii equation, which describes the ground state of a Bose Einstein condensate (BEC), is solved and it is found that the incompressible kinetic energy spectrum exhibits 3 distinct power laws: classical Kolmogorov k?5/3 spectrum at scales much larger than the individual quantum vortex cores, and a quantum Kelvin wave cascade spectrum of k?3 at scales of the order of the quantum cores. In the adjoining semiclassical regime, there is a steeper spectral decay transitioning between the classical and quantum regimes. However, its spectral exponent does not seem to be universal. This is the first, first-principle simulation yielding the universal quantum Kelvin cascade exponent.