VIRTUE : integrating CFD ship design

Novel ship concepts, increasing size and speed, and strong competition in the global maritime market require that a ship's hydrodynamic performance be studied at the highest level of sophistication. All hydrodynamic aspects need to be considered so as to optimize trade-offs between resistance, propulsion (and cavitation), seakeeping or manoeuvring. VIRTUE takes a holistic approach to hydrodynamic design and focuses on integrating advanced CFD tools in a software platform that can control and launch multi-objective hydrodynamic design projects. In this paper current practice, future requirements and a potential software integration platform are presented. The necessity of parametric modelling as a means of effectively generating and efficiently varying geometry, and the added-value of advanced visualization, is discussed. An illustrating example is given as a test case, a container carrier investigation, and the requirements and a proposed architecture for the platform are outlined

Integration-free Learning of Flow Maps

We present a method for learning neural representations of flow maps from time-varying vector field data. The flow map is pervasive within the area of flow visualization, as it is foundational to numerous visualization techniques, e.g. integral curve computation for pathlines or streaklines, as well as computing separation/attraction structures within the flow field. Yet bottlenecks in flow map computation, namely the numerical integration of vector fields, can easily inhibit their use within interactive visualization settings. In response, in our work we seek neural representations of flow maps that are efficient to evaluate, while remaining scalable to optimize, both in computation cost and data requirements. A key aspect of our approach is that we can frame the process of representation learning not in optimizing for samples of the flow map, but rather, a self-consistency criterion on flow map derivatives that eliminates the need for flow map samples, and thus numerical integration, altogether. Central to realizing this is a novel neural network design for flow maps, coupled with an optimization scheme, wherein our representation only requires the time-varying vector field for learning, encoded as instantaneous velocity. We show the benefits of our method over prior works in terms of accuracy and efficiency across a range of 2D and 3D time-varying vector fields, while showing how our neural representation of flow maps can benefit unsteady flow visualization techniques such as streaklines, and the finite-time Lyapunov exponent

A texture-based framework for improving CFD data visualization in a virtual environment

In the field of computational fluid dynamics (CFD) accurate representations of fluid phenomena can be simulated but require large amounts of data to represent the flow domain. Inefficient handling and access of the data at initialization and runtime can limit the ability of the engineering to quickly visualize and investigate the entire flow simulation, and thus hampering the ability to make a quality engineering decision in a timely manner. This problem is amplified n-fold if the solution set is time dependent, or transient. To visualize the data efficiently, dataset access should be decreased if not eliminated at runtime to provide an interactive environment to the end user. Also a reduction in the size of the initial datasets should be reduced as much as possible while maintaining validity of the solution so that larger (i.e. transient) solution datasets can be visualized. To accomplish this, the format in which the dataset is stored should be changed from conventional formats. With the recent advancements of graphical processor unit (GPU) technology, current research in the computer graphics community has lead a novel approach for efficiently storing and accessing flow field data as texture data during a visualization. A so-called texture-based solution for visualization of flow fields allows the end user to visualize complex three-dimensional flow fields in an intuitive fashion while remaining interactive. This work presents a framework for incorporating texture-based analysis techniques into a current CFD visualization application to improve the capabilities for investigating flow fields. The framework presented easily extendible to allow for research and incorporation of progressive visualization methods, in keeping with current technology. Comparisons of the current framework with the texture-based framework are shown to effectively visualize a dataset that could not be visualized in its entirety with the current framework. Comparisons of common visualization techniques, such as contour planes and streamlines, are made to show how the texture-based framework out performs the current framework

Fine-grained visualization pipelines and lazy functional languages

The pipeline model in visualization has evolved from a conceptual model of data processing into a widely used architecture for implementing visualization systems. In the process, a number of capabilities have been introduced, including streaming of data in chunks, distributed pipelines, and demand-driven processing. Visualization systems have invariably built on stateful programming technologies, and these capabilities have had to be implemented explicitly within the lower layers of a complex hierarchy of services. The good news for developers is that applications built on top of this hierarchy can access these capabilities without concern for how they are implemented. The bad news is that by freezing capabilities into low-level services expressive power and flexibility is lost. In this paper we express visualization systems in a programming language that more naturally supports this kind of processing model. Lazy functional languages support fine-grained demand-driven processing, a natural form of streaming, and pipeline-like function composition for assembling applications. The technology thus appears well suited to visualization applications. Using surface extraction algorithms as illustrative examples, and the lazy functional language Haskell, we argue the benefits of clear and concise expression combined with fine-grained, demand-driven computation. Just as visualization provides insight into data, functional abstraction provides new insight into visualization

VtkSMP: Task-based Parallel Operators for VTK Filters

International audienceNUMA nodes are potentially powerful but taking benefit of their capabilities is challenging due to their architec- ture (multiple computing cores, advanced memory hierarchy). They are nonetheless one of the key components to enable processing the ever growing amount of data produced by scientific simulations. In this paper we study the parallelization of patterns commonly used in VTK algorithms and propose a new multi- threaded plugin for VTK that eases the development of parallel multi-core VTK filters. We specifically focus on task-based approaches and show that with a limited code refactoring effort we can take advantage of NUMA node capabilities. We experiment our patterns on a transform filter, base isosurface extraction filter and a min/max tree accelerated isosurface extraction. We support 3 programming environments, OpenMP, Intel TBB and X-KAAPI, and propose different algorithmic refinements according to the capabilities of the target environment. Results show that we can speed execution up to 30 times on a 48-core machine

The Dynamics of Liquid Drops and their Interaction with Solids of Varying Wettabilites

Microdrop impact and spreading phenomena are explored as an interface formation process using a recently developed computational framework. The accuracy of the results obtained from this framework for the simulation of high deformation free-surface flows is confirmed by a comparison with previous numerical studies for the large amplitude oscillations of free liquid drops. Our code's ability to produce high resolution benchmark calculations for dynamic wetting flows is then demonstrated by simulating microdrop impact and spreading on surfaces of greatly differing wettability. The simulations allow one to see features of the process which go beyond the resolution available to experimental analysis. Strong interfacial effects which are observed at the microfluidic scale are then harnessed by designing surfaces of varying wettability that allow new methods of flow control to be developed