Minimisation of divergence error in volumetric velocity measurements and implications for turbulence statistics
Minimisation of divergence error in volumetric velocity measurements and implications for turbulence statistics
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发表时间：
2013
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通讯作者：
Charitha M. de Silva;J. Philip;I. Marusic
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作者：
Charitha M. de Silva;J. Philip;I. Marusic
由于实验的不确定性，在不可压缩流体中进行的体积速度测量通常会受到非零发散误差的阻碍。在这里，我们提出了一种通过使用质量连续性作为约束来最小化发散误差的技术，并且对测量的速度场的变化最小。发散校正方案（DCS）是使用基于约束的非线性优化来实现的。 DCS 的评估是使用 DNS 速度场进行的，添加随机噪声以模拟实验不确定性，以及在通道流设施中测量的断层成像 PIV 数据集，雷诺数与 DNS 数据相匹配 (Re (cid:28) ≈ 937)。结果表明，修正后的速度场的散度减少到接近于零，并且流量统计数据有了明显的改善。特别是，使用空间梯度计算的统计数据得到了显着的改进，例如速度梯度张量、熵和耗散，其中零散度是最重要的。
Volumetric velocity measurements performed in incompressible fluids are typically hindered by a non zero divergence error due to experimental uncertainties. Here we present a technique to minimise divergence error by employing continuity of mass as a constraint, with minimal change to the measured velocity field. The divergence correction scheme (DCS) is implemented using a constraint based non-linear optimisation. An assessment of DCS is performed using DNS velocity fields with random noise added to emulate experimental uncertainties, together with a Tomo-graphic PIV data set measured in a channel flow facility at a matched Reynolds number to the DNS data ( Re (cid:28) ≈ 937). Results indicate that the divergence of the corrected velocity fields are reduced to near zero, and a clear improvement is evident in flow statistics. In particular, significant improvements are observed for statistics computed using spatial gradients such as the velocity gradient tensor, enstrophy and dissipation, where having zero divergence is most important.