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

点击翻译按钮获取中文摘要

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.