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Impacts of Discretization Error, Flow Modeling Error, and Measurement Noise on Inverse Transport-Diffusion-Reaction in a T-Junction

机译:离散化误差,流量模型误差和测量噪声对T界逆传输扩散反应的影响

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摘要

By combining a physical model and sensor outputs in an inverse transport-diffusion-reaction strategy, an accurate cartography of the concentration field may be obtained. The paper addresses the influence of discretization errors, flow uncertainties and measurement noise on the reconstruction process of the concentration field. We consider a key element of a drinking water network that is a pipe junction where Reynolds and Peclet numbers are approximately 2000 and 1000 respectively. We show that a 10% error between the reference concentration field and the reconstructed concentration field may be obtained using a coarse discretization. Nevertheless, to keep the error below 10%, a fine concentration discretization is required. The study also details the influence of the flow approximation on the concentration reconstruction process. The flow modeling error obtained when the exact Navier-Stokes flow is approximated by a Stokes flow may lead to a 40% error in the reconstructed concentration. However if the flow field is obtained from the full set of Navier-Stokes equations, we show that the error may be less than 5%. Then, we observe that the quality of the reconstructed concentration field obtained with the proposed inverse technique is not deteriorated when sensor outputs have a normal distribution noise variance of few percents. Lastly, a good engineering practice would be to stop the reconstruction process according to an extended discrepancy principle including modeling and measurement errors. As shown in the article, the quality of the reconstructed field declines after reaching the threshold of the modeling error.
机译:通过组合逆传输扩散反应策略中的物理模型和传感器输出,可以获得浓度场的精确制图。本文解决了离散化误差,流量不确定性和测量噪声对浓度场的重建过程的影响。我们考虑一个饮用水网络的关键要素,该饮用水网络是雷诺和Peclet数分别约为2000和1000的管道连接。我们表明,可以使用粗略离散化获得参考浓度字段和重建浓度字段之间的10%误差。然而,为了保持10%以下的误差,需要精细的浓度离散化。该研究还详述了流动近似对浓度重建过程的影响。当斯托克斯流程近似时,获得的流量建模误差可以导致重建浓度的40%误差。然而,如果流场从全套Navier-Stokes方程获得,则显示错误可能小于5%。然后,我们观察到,当传感器输出具有少数百分比的正常分布噪声方差时,用所提出的逆技术获得的重建浓度场的质量不会恶化。最后,良好的工程学实践是根据延长的差异原理来停止重建过程,包括建模和测量误差。如图所示,在达到建模误差的阈值后,重建场的质量下降。

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