In hazardous situations involving the dispersion of chemical, biological, radiological, and nuclear pollutants, timely containment of the emission is critical. A contaminant disperses as a dynamically evolving plume into the atmosphere, introducing complex difficulties in predicting the dispersion trajectory and potential evacuation sites. Strategies for predictive modeling of rapid contaminant dispersion demand localization of the emission source, a task performed effectively via unmanned mobile-sensing platforms. With vast possibilities in sensor configurations and source-seeking algorithms, platform deployment in real-world applications involves much uncertainty alongside opportunity. This work aims to develop a plume source detection simulator to offer a reliable comparison of source-seeking approaches and performance testing of ground-based mobile-sensing platform configurations prior to experimental field testing. Utilizing ROS, Gazebo, MATLAB, and Simulink, a virtual environment is developed for an unmanned ground vehicle with a configurable array of sensors capable of measuring plume dispersion model data mapped into the domain. For selected configurations, gradient-based and adaptive exploration algorithms were tested for source localization using Gaussian dispersion models in addition to large eddy simulation models incorporating the effects of atmospheric turbulence. A unique global search algorithm was developed to locate the true source with overall success allowing for further evaluation in field experiments. From the observations obtained in simulation, it is evident that source-seeking performance can improve drastically by designing algorithms for global exploration while incorporating measurements of meteorological parameters beyond solely concentration (e.g. wind velocity and vorticity) made possible by the inclusion of high-resolution large eddy simulation plume data.

在涉及化学、生物、放射性和核污染物扩散的危险情况下，及时控制排放至关重要。污染物作为动态演变的羽流扩散到大气中，这给预测扩散轨迹和潜在疏散地点带来了复杂的困难。快速污染物扩散的预测建模策略需要排放源的本地化，这是一项通过无人移动传感平台有效执行的任务。由于传感器配置和寻源算法的巨大可能性，实际应用中的平台部署涉及很多不确定性和机会。这项工作旨在开发一种羽流源检测模拟器，以便在实验现场测试之前对源搜索方法和地面移动传感平台配置的性能测试提供可靠的比较。利用 ROS、Gazebo、MATLAB 和 Simulink，为无人驾驶地面车辆开发了一个虚拟环境，该车辆具有可配置的传感器阵列，能够测量映射到域中的羽流扩散模型数据。对于选定的配置，除了包含大气湍流影响的大涡模拟模型外，还使用高斯色散模型测试了基于梯度和自适应探索算法的源定位。开发了一种独特的全局搜索算法，以整体成功定位真正的来源，从而可以在现场实验中进行进一步评估。