A Practical Visualization Strategy for Large-Scale Supernovae CFD Simulations

彬彬

1 Overview

Simulating the expansion of a Type II supernova using an adaptive

computational fluid dynamics (CFD) engine yields a complex

mixture of turbulent flow with dozens of physical properties. The

dataset shown in this sketch was initially simulated on iVEC’s EPIC

supercomputer (a 9600 core Linux cluster) using FLASH [Fryxell

et al. 2000] to model the thermonuclear explosion, and later

post-processed using a novel integration technique to derive the radio

frequency emission spectra of the expanding shock-wave front

[Potter et al. 2011]. Model parameters have been chosen to simulate

the asymmetric properties of the SN 1987A remnant [Potter

et al. 2009].

This offline workflow takes several hundred machine-hours to complete,

and results in a volumetric time-series dataset stored on an

adaptive mesh refinement grid with a total storage allocation of >=

10 terabytes. This dataset consists of several thousand time-steps

(adaptively outputted at non-linear time intervals), each containing

a dozen simulation variables stored as floating-point vector fields.

Due to the intricate nature of the flow, visualizing these datasets

requires a rendering engine capable of high-quality image recons***ction

in order to maintain the underlying visual complexity.

In this sketch, we describe a practical approach we’ve developed

which enables explorative visualization for studying large-scale

time-series astrophysical CFD simulations. This is part of an ongoing

data-intensive research project within our group to support the

visualization of large-scale astrophysics datasets for the scientists at

the International Centre for Radio Astronomy Research (ICRAR).

In particular, we discuss the application of progressive stochastic

sampling and adjustable workloads to insure a consistent response

time and a fixed frame-rate to guarantee interactivity. The user is

permitted to adjust all rendering parameters while receiving continuous

visual feedback, facilitating explorative visualization of our

complex volumetric time-series datasets.

2 Approach

The main goal of our system was to enable users to quickly

search and isolate specific features within our large-scale timeseries

datasets, while providing an accurate and appropriate representation

of the underlying data. Our system uses a progressive

rendering approach and combines this with stochastic sampling to

enable a high-quality rendering and interactive control over all render

parameters including camera properties, clip-planes and transfer

function editing.

The image sequence in Fig. 1 demonstrates the capabilities of our

stochastic rendering approach in the context of our radio-frequency

simulation workflow. The figure highlights the quality of our system

for a given number of ray-interval samples and the corresponding

graphs summarize the rate of convergence for the listed quality

metrics. The rendered images provide a unique view of the

radio frequency emission at 843 MHz for day 4136 after the SN

1987A explosion, as represented by the radio power per unit volume

(in log-scale), modulated by the spectral index from the aforementioned

simulation dataset

彬彬

tc

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頂。。。

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我看看就走，你们聊！

C.R.CAN

好铁多多发，感激分享

tc

无聊时可以刷屏幕 灌水 也可以试试 帖子的标题究竟可以写多长

tc

很经典，很实用，学习了!

晃晃

已阵亡的 蝶 随 风 舞 说过  偶尔按一下 CTRL A 会发现 世界还有另一面

菜刀吻电线

既来之，则看之！