In the present work, a online data assimilation approach, based on the Kalman filter algorithm, is proposed for the source term reconstruction in accidental events with dispersion of radioactive agents in air. For this purpose a Gaussian plume model of dispersion in air is embedded in the Kalman filter algorithm to estimate unknown scenario parameters, such as the coordinates and the intensity of the source, on the basis of measurements collected by a mobile sensor. The approach was tested against pseudo-experimental data produced with both the Gaussian plume model and the Lagrangian puff model SCIPUFF. The results show the good capabilities of the proposed approach in retrieving the values of the unknown parameters when (i) one or more release parameters are poorly known and (ii) a sufficient number of experimental measurements describing the evolution of the dispersion process can be collected in a short time by means of mobile sensors. Thanks to its flexibility and computational efficiency, and due to the exploitation of the Kalman filter potentialities through the use of a simplified model of dispersion in air, the proposed approach can constitute a useful tool for the management of emergency scenarios.

中文翻译：

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基于卡尔曼滤波器的意外放射性扩散事件在线源项估计方法

在目前的工作中，提出了一种基于卡尔曼滤波器算法的在线数据同化方法，用于放射性物质在空气中弥散的意外事件中的源项重建。为此，在卡尔曼滤波器算法中嵌入了空气中弥散的高斯羽流模型，以根据移动传感器收集的测量结果来估计未知场景参数，例如源的坐标和强度。该方法针对使用高斯羽流模型和拉格朗日喷流模型 SCIPUFF 产生的伪实验数据进行了测试。结果表明，当 (i) 一个或多个释放参数知之甚少和 (ii) 可以收集足够数量的描述分散过程演变的实验测量值时，所提出的方法在检索未知参数值方面具有良好的能力在短时间内通过移动传感器。由于其灵活性和计算效率，并且由于通过使用简化的空气扩散模型来利用卡尔曼滤波器的潜力，所提出的方法可以成为管理紧急情况的有用工具。