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Identification of multiple contamination sources using variational continuous assimilation

机译:使用变分连续同化识别多个污染源

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A variational continuous assimilation (VCA) method was proposed as an estimation method for multiple unknown contaminant sources in outdoor conditions. The VCA method is a data assimilation method that minimizes the difference between calculated and observed concentrations by adding a correction term to the governing equation of the computational fluid dynamics (CFD) model. The method was validated via a set of numerical experiments, in which single and multiple unknown source locations and their emission rates were estimated using the VCA method with limited observation data. The results showed that the VCA method could be used to roughly estimate source locations and emission rates, and could reproduce the correct concentration fields. The estimated concentration fields are, however, slightly more widely distributed than the actual concentration fields and are distributed slightly windward of the actual source locations. The estimation accuracy in the case with two sources was equivalent to the case with a single source.
机译:提出了一种变分连续同化(VCA)方法作为室外条件下多种未知污染物源的估算方法。 VCA方法是一种数据同化方法,通过在计算流体动力学(CFD)模型的控制方程式中添加一个校正项来最小化计算浓度和观测浓度之间的差异。通过一组数值实验验证了该方法,其中使用VCA方法在有限的观测数据的情况下估算了单个和多个未知源位置及其排放速率。结果表明,VCA方法可用于粗略估算源位置和排放速率,并可以再现正确的浓度场。但是,估计的浓度场比实际的浓度场分布得更广泛,并且分布在实际源位置的迎风位置稍偏上。具有两个来源的情况下的估计精度等同于具有单个来源的情况下的估计精度。

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