論文

流動場の基本的な特徴の 1 つとして渦が知られており，渦を解析することは，流れにおけるダイナミク スを理解する上で大変重要となってくる．しかし，非定常流データに対しては，従来の試行錯誤的な可視化方法では 常に変化しつづける複雑な渦構造を捉え続けることは困難である. そこで本論文では，時系列流動データにおける渦 の振舞いを分かりやすく可視化するための微分位相構造に基づくビジュアルデータマイニング手法を提案する．具体 的には，渦中心における圧力値が極小になることが知られていることから，圧力場の微分情報に基づいて渦中心を特 定し，位相情報から個々の渦領域を求め，時系列に沿ってそれらの渦を追跡する．また，すべての時刻で同一の色相 伝達関数を用い，時刻ごとに明度を調整することで，流動場が劇的に変化した場合でも，時刻ごとの渦の変化だけで なく，色相から流動場の変動を把握することができる伝達関数を設計する．本手法の有効性を，ハイブリッド風洞に おける計測融合シミュレーションによって得られたカルマン渦列データを用いて検証する．

This paper presents a method of alleviating such visual clutter by contracting multiple axes through the analysis of correlation between every pair of variables. In this method, we first construct a graph by connecting axis nodes with an edge weighted by data correlation between the corresponding pair of dimensions, and then reorder the multiple axes by projecting the nodes onto the primary axis obtained through the spectral graph analysis. This allows us to compose a dendrogram tree by recursively merging a pair of the closest axes one by one. Our visualization platform helps the visual interpretation of such axis contraction by plotting the principal component of each data sample along the composite axis. Smooth animation of the associated axis contraction and expansion has also been implemented to enhance the visual readability of behavior inherent in the given high-dimensional datasets.

In this paper, we propose a novel particle-based Lagrangian method for efficiently simulating viscous fluids by adopting position-based constraints. Our method uses the geometric configuration of particles for the positional constraints to approximate the dynamics of viscous fluids using position-based dynamics; thus the method can plausibly generate their motions while allowing for the use of much larger time steps than those previously adopted in the viscous fluid simulations. We also propose an associated boundary-handling scheme for position-based fluids to precisely specify boundary conditions for the constraints. Additionally, we reproduce elastic deformations of materials by controlling the constraints and incorporate thermal conduction into our framework to simulate resultant changes in particle properties and phase transition in the materials. By adjusting parameters, our method can encompass complex motions of fluids with different properties in a unified framework. Several examples demonstrate the effectiveness as well as versatility of our method.