A novel approach, which utilizes astigmatism-based depth codification and discrete dual-plane illumination, is developed to provide quasi-volumetric velocimetry (2.5D3C) by means of Stereo-PIV. The technique is referred as Dual-Plane Stereo-Astigmatism (DPSA). It provides the full characterization of the velocity gradient tensor by overcoming the limitation of out-of-plane gradient determination. The principle of the DPSA approach relies on the joint recording of two, consecutive, planar measurement domains and the subsequent allocation of particles based on the particle image shape. For the identification of particles, a correlation-based particle identification approach (CPI), which addresses non-overlapping particles, is introduced. With regard to dense particle fields, a method for the iterative particle reconstruction (IPR) is adapted for DPSA. For displacement analysis, an alternative PIV evaluation strategy is introduced, which utilizes derived information of particle locations. The DPSA approach is tested experimentally and synthetically by the investigation of a spray-induced flow and a generic test case, respectively. Separate planar velocity fields are obtained by means of PIV and PTV evaluation. A performance analysis is carried out to assess the influence of noise, particle density and particle image size on the procedure of particle identification and allocation. The CPI technique provides adequate results for low to medium particle densities and further features a high robustness regarding particle identification with respect to background noise, optical aberrations and inconsistent particle images. The adapted IPR method, on the other hand, shows viable results for particle densities of up to 0.1 ppp.

中文翻译：

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双平面立体像散——一种确定平面域全速梯度张量的新方法

开发了一种利用基于散光的深度编码和离散双平面照明的新方法，通过立体 PIV 提供准体积测速 (2.5D3C)。该技术被称为双平面立体散光 (DPSA)。它通过克服平面外梯度确定的限制来提供速度梯度张量的完整表征。DPSA 方法的原理依赖于两个连续的平面测量域的联合记录以及基于颗粒图像形状的后续颗粒分配。对于粒子的识别，引入了一种基于相关性的粒子识别方法（CPI），它解决了非重叠粒子。对于致密粒子场，迭代粒子重建 (IPR) 的方法适用于 DPSA。对于位移分析，引入了一种替代 PIV 评估策略，该策略利用粒子位置的派生信息。DPSA 方法分别通过对喷雾诱导流和通用测试案例的研究进行了实验和综合测试。单独的平面速度场是通过 PIV 和 PTV 评估获得的。进行性能分析以评估噪声、颗粒密度和颗粒图像尺寸对颗粒识别和分配过程的影响。CPI 技术为中低粒子密度提供了足够的结果，并进一步具有关于背景噪声、光学像差和不一致粒子图像的粒子识别的高鲁棒性。