Nick Croft

Mesh-Driven Vector Field Clustering and Visualization: An Image-Based Approach

Vector field visualization techniques have evolved very rapidly over the last two decades, however, visualizing vector fields on complex boundary surfaces from computational flow dynamics (CFD) still remains a challenging task. In part, this is due to the large, unstructured, adaptive resolution characteristics of the meshes used in the modeling and simulation process. Out of the wide variety of existing flow field visualization techniques, vector field clustering algorithms offer the advantage of capturing a detailed picture of important areas of the domain while presenting a simplified view of areas of less importance. This paper presents a novel, robust, automatic vector field clustering algorithm that produces intuitive and insightful images of vector fields on large, unstructured, adaptive resolution boundary meshes from CFD. Our bottom-up, hierarchical approach is the first to combine the properties of the underlying vector field and mesh into a unified error-driven representation. The motivation behind the approach is the fact that CFD engineers may increase the resolution of model meshes according to importance. The algorithm has several advantages. Clusters are generated automatically, no surface parameterization is required, and large meshes are processed efficiently. The most suggestive and important information contained in the meshes and vector fields is preserved while less important areas are simplified in the visualization. Users can interactively control the level of detail by adjusting a range of clustering distance measure parameters. We describe two data structures to accelerate the clustering process. We also introduce novel visualizations of clusters inspired by statistical methods. We apply our method to a series of synthetic and complex, real-world CFD meshes to demonstrate the clustering algorithm results.

Assessing the Scalability of Multiphysics Tools for Modeling Solidification and Melting Processes on Parallel Clusters

A comprehensive solution of solidification/melting processes requires the simultaneous representation of free surface fluid flow, heat transfer, phase change, nonlinear solid mechanics and, possibly, electromagnetics together with their interactions, in what is now known as multiphysics simulation. Such simulations are computationally intensive and the implementation of solution strategies for multiphysics calculations must embed their effective parallelization. For some years, together with our collaborators, we have been involved in the development of numerical software tools for multiphysics modeling on parallel cluster systems. This research has involved a combination of algorithmic procedures, parallel strategies and tools, plus the design of a computational modeling software environment and its deployment in a range of real world applications. One output from this research is the three-dimensional parallel multiphysics code, PHYSICA. In this paper we report on an assessment of its parallel scalability on a range of increasingly complex models drawn from actual industrial problems, on three contemporary parallel cluster systems.

Finite volume method for the solution of flow on distorted meshes

Purpose – To improve flow solutions on meshes with cells/elements which are distorted/ non‐orthogonal.Design/methodology/approach – The cell‐centred finite volume (FV) discretisation method is well established in computational fluid dynamics analysis for modelling physical processes and is typically employed in most commercial tools. This method is computationally efficient, but its accuracy and convergence behaviour may be compromised on meshes which feature cells with non‐orthogonal shapes, as can occur when modelling very complex geometries. A co‐located vertex‐based (VB) discretisation and partially staggered, VB/cell‐centred (CC), discretisation of the hydrodynamic variables are investigated and compared with purely CC solutions on a number of increasingly distorted meshes.Findings – The co‐located CC method fails to produce solutions on all the distorted meshes investigated. Although more expensive computationally, the co‐located VB simulation results always converge whilst its accuracy appears to g...

Development of a radial ventricular assist device using numerical predictions and experimental haemolysis.

The objective of this study was to demonstrate the potential of Computational Fluid Dynamics (CFD) simulations in predicting the levels of haemolysis in ventricular assist devices (VADs). Three different prototypes of a radial flow VAD have been examined experimentally and computationally using CFD modelling to assess device haemolysis. Numerical computations of the flow field were computed using a CFD model developed with the use of the commercial software Ansys CFX 13 and a set of custom haemolysis analysis tools. Experimental values for the Normalised Index of Haemolysis (NIH) have been calculated as 0.020 g/100 L, 0.014 g/100 L and 0.0042 g/100 L for the three designs. Numerical analysis predicts an NIH of 0.021 g/100 L, 0.017 g/100 L and 0.0057 g/100 L, respectively. The actual differences between experimental and numerical results vary between 0.0012 and 0.003 g/100 L, with a variation of 5% for Pump 1 and slightly larger percentage differences for the other pumps. The work detailed herein demonstrates how CFD simulation and, more importantly, the numerical prediction of haemolysis may be used as an effective tool in order to help the designers of VADs manage the flow paths within pumps resulting in a less haemolytic device.

Visualization of flow past a marine turbine: the information-assisted search for sustainable energy

Interest in renewable, green, and sustainable energy has risen sharply in recent years. The use of marine turbines to extract kinetic energy from the tidal current is gaining popularity. CFD modeling is carried out to investigate the surrounding flow behavior and thus develop effective marine turbine systems. However, visualizing the simulation results remains a challenging task for engineers. In this paper, we develop, explore and present customized visualization techniques in order to help engineers gain a fast overview and intuitive insight into the flow past the marine turbine. The system exploits multiple-coordinated information-assisted views of the CFD simulation data. Our application consists of a tabular histogram, velocity histogram, parallel coordinate plot, streamline plot and spatial views. Information-based streamline seeding is used to investigate the behavior of the flow deemed interesting to the engineer. Specialized, application-specific information based on swirling flow is derived and visualized in order to evaluate turbine blade design. To demonstrate the usage of our system, a selection of specialized case scenarios designed to answer the core questions brought out by engineers is described. We also report feedback on our system from CFD experts researching marine turbine simulations.

Computational Modelling of Metallurgical Processes: Achievements and Challenges

Extractive metallurgical processes rate amongst the most complex from the perspective of computational modeling. They typically involve multi-phase and multi-component fluid flow in very complex geometries, heat transfer driven by a number of interacting phenomena, solid-liquid-gaseous phase change and mass transfer together with complex thermodynamics and associated chemical reactions. Beyond the model building itself, key challenges have always involved being able to identify the phenomena present together with interactions to characterize processes and experimental laboratory and plant data to parameterize the arising models — it is in this milieu, which require considerable process understanding and subtlety of thought, that David Robertson has made his contributions.