Volume Visualization For CFD Unstructured-Grid Flow Fields

Volume visualization, which plays an important role in flow visualization, hasbeen taken as the leading and preferred method to visualize 3D scalar fields. It producesthe final image from the discrete 3D fields by re-sampling and synthesizing, makingusers gain a direct insight into the whole state and the details of the fields. Compared tostructured-grid data, unstructured-grid data with complicated topology result indifficulties in designing and implementing the volume visualization algorithms,especially for the 3D unstructured-grid flow fields from the CFD simulation. It is achallenging work to achieve the accurate, available and real-time visualization of the 3Dunstructured-grid flows with different formats, complicated physical features andunsteady behaviors. To overcome the deficiencies of the existing volume visualizationmethods for the 3D unstructured-grid flows, this dissertation focuses on the followingissues in the practical CFD applications: high-accuracy visualization of the cell-centereddata, exact extraction of the important physical features, volume illumination of the 3Dunstructured-grid data and efficient rendering of the unsteady flows. The majorcontributions of this paper are as follows.1. To visualize unstructured cell-centered data, the existing methods can onlyperform indirect visualization, which depress the rendering accuracy and violate thediscontinuity constraint. To solve the problems, this paper proposes a direct method tovisualize unstructured cell-centered data. High accuracy is achieved via datareconstruction performed directly on the original cell-centered data. To keep thediscontinuity, the field at a sample is reconstructed using the double control volumesand the Roe-average computation inspired by an Upwind-FVM solver. Analysis andexperiments demonstrate that the proposed approach gains a high-accuracyreconstruction which is more accordant with the numerical solution. In addition, theidea of direct reconstruction is not only fit for volume rendering, but also can be appliedto other visualization methods (such as isosurface extraction and streamline tracking)and helps render high-accuracy images for CFD simulation.2. To deal with the accuracy, availability and adaptability deficiency of the existingmethods on extracting the 3D shocks in flows, a two-level sampling method ispresented for shock detection and noise filtering based on the shock attributes with theaid of ray casting. The normal Mach at the first-level sample is computed with thepressure gradient to detect the shock and the contact discontinuity feature is eliminatedaccordingly. To identify and filter the noise, the velocity magnitude at the second-levelsample is projected on the tangent of the shock to evaluate the noise, which isindependent of the data set. This work is performed on GPU for the 3D unstructuredgriddata with complicated topology. Experimental results show that the approach can automatically filter the noise. The adaptability and accuracy are much better than theexisting methods even for the multi-shock flows.3. It is difficult to estimate the sample gradient for unstructured-grid volumeillumination due to the complicated topology, which makes the real-time volumeillumination be hardly achieved because of the computation complexity and thedifficulty of implementation on GPU. This paper proposes an efficient illuminationcomputation and implementation approach for unstructured-grid volume. The vertexgradient is accurately estimated using the Green-Gauss theorem and the inverse-distanceextrapolation (or the volume-weighted extrapolation), which can be applied to theunstructured cells with various shapes. Furthermore, the sample gradient is obtained byan efficient method based on the cell gradient tensor and the computation cost islowered consequently. With the aid of a well-designed data structure, the real-timeperformance of the algorithm can be achieved on GPU even for the large data sets.Experiments show that this approach can lead to a clear insight into the details andstructures of the 3D flows.4. Although the temporal and spatial coherence plays an essential role invisualizing unstructured time-varying fields, the existing approaches do not pay enoughattention to it and thus depresses the performence. This paper presents an efficientapproach for volume rendering of unstructured time-varying flows by utilizing thetemporal and spatial coherence. The temporal coherence of both the cell and the vertexdata is analyzed to build the temporal tables, which is used to accelerate the dynamic resampling.The spatial coherence between the face-adjacencies is exploited to speed upthe ray traversal. We also design a novel texture structure that separates the vertex datafrom the cell data and a smart gradient matrix to reduce the pressure of GPU memory. Abasic unit containing 16-step data is used in data compression and management to avoidrendering stalls and lead to a compact and efficient storage. Analysis and experimentsdemonstrate that the approach gains a much lower cost of both time and space than theexisting methods, which allows rendering time-varying data on a larger mesh scale inreal time.