Fluid field estimation and source localization by dynamic positioning of autonomous underwater sensor nodes

在本研究方案的范围内，具有不确定源、边界和初始条件的流场应由移动传感器识别。因此，流场的模拟将与移动传感器节点的测量结果进行同化并进行预测。然后，对不确定流场的预测将被用于为移动传感器规划轨迹，移动传感器返回最佳测量结果，以提高流场估计的准确性。这样的系统可以用于定位主要未知流体区域的热源和污染源，但也可以用于验证流体动力学计算。通常，河流、港口、河口和海洋中的水流和污染物是由多个固定传感器测量的，并由载人船进行测量。通过一个集中控制的移动自主传感器网络，我们可以通过对传感器节点的优化定位来实现更高的动态分辨率。实时的全球数据同化对于发现哪些区域需要更多测量以及为移动传感器连续生成路径和测量位置是必不可少的。边界条件估计、源识别和海流测量的自动化过程确保了自组织水下机器人的高度灵活性和快速部署。此外，还开发了一种控制系统，该系统可以计算每个移动传感器的最佳轨迹，并确保网络的有效协调。此外，我们将与美国和巴西的合作机构在该项目的理论和实践部分进行合作。尤其是在源定位和边界条件估计方面，以及整个系统的动力学方面，还需要进一步的研究。由于整个系统包含许多无法建模的不确定性和部件，因此实际实现是该研究项目的一部分。这一概念的验证将通过一个实验装置来完成，在该装置中，配备温度传感器的移动水下机器人将识别和定位水箱内泵驱动的流场中的热源，并自动检测边界条件。估计的流场及其不确定性被用来生成车辆的最优轨迹。理论和模拟应在受控环境中通过该实验进行验证，并且在进行海试之前必须分析技术问题和可能性

In the scope of this research proposal fluid fields with uncertain sources, boundary and initial conditions shall be identified by mobile sensors. A simulation of the fluid field will therefore be assimilated with measurements from the mobile sensor nodes and predicted. The prediction of the uncertain fluid field will then be used to plan trajectories for the mobile sensors which return optimal measurements to improve the accuracy of the fluid field estimation.A system like that can be used to localize heat and pollution sources in primarily unknown fluid domains, but also to verify fluid dynamic calculations. Usually, currents and pollutants in rivers, harbors, estuaries, and oceans are measured by multiple stationary sensors, supported by measurements from manned vessels. With a centrally controlled mobile autonomous sensor network we can achieve a much higher dynamic resolution through optimal positioning of the sensor nodes. Global data assimilation in real-time is indispensable to discover in which areas more measurements are needed and to continuously generate paths and measurement locations for the mobile sensors.An automated process of boundary condition estimation, source identification, and current measurement assures a high flexibility and a fast deployment of the self-organizing underwater vehicles. Completely unknown fluid fields can be analyzed as long as all positions in the domain of interest can be reached by the vessels due to current conditions.At the Institute of Mechanics and Ocean Engineering several preliminary studies have been conducted about the theory of source identification and localization which were implemented successfully in simulations. Additionally, a control system has been developed which calculates the optimal trajectories for each mobile sensor and ensures an efficient coordination of the network. Furthermore, we will collaborate with partner institutions in the USA and Brazil in theoretical and practical parts of this project. Further research is especially needed in source localization and boundary condition estimation, but also the dynamic of the complete system. Because the complete system contains many uncertainties and parts, which cannot be modeled, a practical implementation is part of this research project.The verification of this concept will be done by an experimental setup, in which mobile underwater vehicles equipped with temperature sensors shall identify and localize a heat source in a pump driven flow field in a water tank and automatically detect boundary conditions. The estimated flow fields and its uncertainties are used to generate optimal trajectories for the vehicles. The theory and simulations shall be verified through this experiment in a controlled environment and technical issues and possibilities have to be analyzed before sea trials are conducted