• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Experiments in Fluids: Experimental Methods and Their Applications to Fluid Flow >Double-frame tomographic PTV at high seeding densities
【24h】

Double-frame tomographic PTV at high seeding densities

机译：高播种密度的双框架断层扫描PTV

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A novel method performing 3D PTV from double-frame multi-camera images is introduced. Particle velocities are estimated by following three steps: First, separate particle reconstructions with a sparsity based algorithm are performed on a fine grid. Second, they are expanded on a coarser grid on which 3D correlation is performed, yielding a predictor displacement field that allows to efficiently match particles at the two time instants. As these particles are still located on a voxel grid, the third, final step achieves particle position refinement to their actual subvoxel position by a global optimization process, also accounting for their intensities. As it strongly leverages on principles from tomographic reconstruction, the technique is termed Double-Frame Tomo-PTV (DF-TPTV). Standard synthetic tests on a complex turbulent flow show that the method achieves high particle and vector detection efficiency, up to seeding densities of around 0.08 particles per pixel (ppp). On these tests, it also shows a higher robustness to noise and lower root-mean-square errors on velocity estimation than similar state-of-the-art methods. Results from an experimental campaign on a transitional round air jet at Reynolds number 4600 are also presented. Average seeding density varies in time from 0.06 to 0.03 ppp during the considered run, with different densities and signal-to-noise ratios being observed with time in the jet and ambient air regions, supplied by two different seeding systems. The strong polydisperse nature of the seeding, as well as the coexistence of two spatial zones of significantly different particle densities and signal-to-noise ratios, are observed to be the most influential sources of limitation for DF-TPTV performance. However, the method still successfully reconstructs a large amount of particles, and, associated with an outlier rejection scheme based on temporal statistics, truthfully reconstructs the instantaneous jet dynamics. Further quantitative performance assessment is then provided by introducing statistics performed by bin averaging, upon assuming statistical axisymmetry of the jet. Mean and fluctuating axial velocity components in the jet near-field are compared with reference results obtained from planar PIV at higher seeding density, with an interrogation window of size comparable to that of the bins. Results are found to be in excellent agreement with one another, confirming the high performance of DF-TPTV to yield reliable volumetric vector fields at seeding densities usually considered for tomographic PIV processing.
机译：介绍了一种从双帧多摄像机图像执行3D PTV的新方法。通过以下三个步骤估计粒子速度：首先，在细网格上执行具有稀疏性算法的单独粒子重建。其次，它们在执行3D相关的粗略网格上扩展，产生允许在两个时间瞬间有效地匹配粒子的预测值位移场。由于这些颗粒仍然位于体素网格上，第三步是通过全球优化过程实现其实际子痫位置的粒子位置细化，也占他们的强度。由于它强烈利用了来自断层切断重建的原理，该技术被称为双帧致致致致汤 - PTV（DF-TPTV）。复杂湍流上的标准合成试验表明，该方法实现了高粒子和载体检测效率，达到每像素（PPP）约0.08颗粒的播种密度。在这些测试中，它还显示了比类似最先进的方法更高的噪声和较低的根均方误差的稳健性和较低的根均方误差。还提出了雷诺数4600的过渡圆形空气喷射的实验活动的结果。在考虑的运行期间，平均播种密度随时间从0.06到0.03ppp变化，并且在射流和环境空气区域中随时间观察不同的密度和信噪比，由两种不同的播种系统供应。播种的强多分性质以及两个空间区域的共存显着不同的颗粒密度和信噪比，是最有影响力的DF-TPTV性能的限制来源。然而，该方法仍然成功地重建了大量的粒子，并且与基于时间统计数据的异常抑制方案相关联，如实地重建瞬时射流动力学。然后通过在假设射流的统计轴对称时引入通过Bin平均执行的统计数据来提供进一步的定量性能评估。将喷射近场中的轴向速度分量与从较高播种密度的平面PIV获得的参考结果进行比较，尺寸与箱相当的询问窗口。结果发现，彼此非常一致，确认DF-TPTV的高性能，以在通常考虑的播种密度下产生可靠的体积矢量字段，通常考虑用于断层化PIV处理。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号