BDD: A Big-Data Computational Laboratory for the Optimization of Olfactory Search Algorithms in Turbulent Environments

自然灾害、污染源和恶意恐怖主义行为的有害影响超出了影响范围，威胁到更大区域的安全和保障。流体湍流是这些事件的同谋：它将污染物或从源释放到大气或海洋中的物质迅速分散，其旋转和涡流将分散的物质从源中带走。当源的位置未知时，湍流也模糊了遥感器推断源位置的能力。该项目利用大数据策略开发新的搜索算法，可以使用固定或移动的传感器识别湍流空气或海洋中释放的污染物或其他物质的来源。 其主要目标是直接加强我们在应对制剂释放威胁人类或环境安全的不利事件方面的准备。在复杂的湍流中识别污染源和扩散模式的能力具有重要的环境和安全意义。在源的强度未知的情况下，必须从远程测量中推断。当污染源的位置和强度都未知时，标量源的跟踪是一个不适定的问题，因此，最具挑战性。在应对环境和安全威胁方面，这些任务往往也是最紧迫的。在这种情况下的准备工作需要事先规划和适应性实时能力。因此，我们认为，设计了固定传感器的最佳放置和嗅觉搜索算法，以便准确有效地识别污染物的位置和强度-这是一项艰巨的工程挑战，因为检测器上的信号高度间歇性和前体湍流扩散路径的复杂性。使用大数据计算实验室制定了解码该信息的最佳策略，可以实时探测由高保真直接数值模拟计算的各种湍流的时间演变。源识别算法制定为一个变分问题，利用伴随场的属性来识别源的位置。变分的方法提供了一个自然的框架，以优化放置的固定传感器在感兴趣的区域，并引导运动的嗅觉机器人对源。重复的虚拟实验，使用公共访问的计算实验室，各种流配置，和不同水平的流场的保真度提供了一个准确的评估的搜索策略的性能存在的尺度依赖模型的不确定性。使用自主水下航行器测试了搜索策略的真实性能。

The detrimental effects of natural disasters, sources of pollution and malicious acts of terrorism spread beyond the point of impact and threaten safety and security in a much larger region. Fluid turbulence is complicit in these events: It quickly disperses pollutants or an agent released from a source into the atmosphere or the sea, and its whirls and eddies carry the dispersed agent away from the source. When the location of the source is unknown, turbulence also obfuscates the ability of a remote sensor to infer the source location. This project exploits big-data strategies to develop new search algorithms that can identify the source of pollutants or other agents released in turbulent air or in the sea, using stationary or moving sensors. The principal objective is to directly enhance our readiness in responding to adverse events where the release of an agent threatens human or environmental safety. The ability to identify a contaminant source and dispersion pattern in complex turbulent flows has significant environmental and security implications. In cases where the strength of the source is not known, it must be inferred from remote measurements. When both the location and strength of the contaminant source are unknown, tracking the scalar source is an ill-posed problem and, as a result, most challenging. These tasks are often also the most urgent in responding to environmental and security threats. Preparedness in such circumstances requires prior planning and adaptive real-time capabilities. Therefore, both optimal placement of stationary sensors and olfactory search algorithms are devised in order to accurately and efficiently identify the location and strength of contaminants - a formidable engineering challenge due to the highly intermittent signal at the detector and the complexity of the precursor turbulent dispersion path. Optimal strategies to decode that information are formulated using a big-data computational laboratory where the entire space-time evolution of various turbulent flows, computed from high-fidelity direct numerical simulations, can be probed in real-time. The source-identification algorithm is formulated as a variational problem where properties of the adjoint field are exploited to identify the source location. The variational approach provides a natural framework to optimize the placement of stationary sensors in a region of interest, and to guide the motion of an olfactory robot towards the source. Repeated virtual experiments using the publicly-accessible computational laboratory, the various flow configurations, and different levels of fidelity of the flow field provide an accurate assessment of the performance of the search strategy in presence of scale-dependent model uncertainty. Real-world performance of the search strategy is tested using an autonomous underwater vehicle.