SBIR Phase I: Extremely Low Frequency Characterization of High-Risk Lightning

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力包括显着减少由闪电引发的野火烧毁的区域。了解哪些雷击能够引发野火是至关重要的，因为在美国西部，在这些环境灾难中，超过70%的地区是由闪电引发的野火造成的。在全球范围内，野火每年造成64.5亿吨二氧化碳排放（占总排放量的18%）。探测高风险的雷击（那些能够点燃野火的）也可以显著减少生命、野生动物、栖息地、财产和森林的损失，因为目前美国闪电引发的野火每年摧毁4-6百万英亩。减少野火可以减少大规模疏散和与烟雾有关的健康状况，从而改善美国公众的健康和福利。由于野火破坏的风险降低，个人和企业都将从较低的保险费率中受益。大型野火是半数以上任务保障优先军事设施持续关注的问题，因为例行测试和训练活动是重要的点火源。拟议的项目也可能解决军事点火问题。当长持续电流（LCC）在有利于着火的环境条件下撞击地面时，火灾就会发生。lcc是那些持续40毫秒或更长时间，主要负责过度加热。该研究的变革性方面在于极低频（ELF）闪电发射的地面特征，以识别LCC打击，目标检测效率为95%，精度为40米。虽然对于大多数雷击来说，电流在几十微秒后停止流动，但一小部分雷击（不到10%）包含持续电流，持续时间比这长数千倍，从几十毫秒到几百毫秒不等。这可以看作是云电荷源和地面之间的准静止电弧，并且可以通过静电场变化和极低频发射检测到。第二个创新特征是使用机器学习算法，通过分析LCC打击位置的环境条件来精确定位高风险雷击点。这项技术可以在几秒钟内识别火灾，而不像目前的热量或烟雾识别系统需要数小时或数天才能识别火灾。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project includes a notable reduction in the area burned by lightning-initiated wildfires. Understanding which lightning strikes are capable of igniting wildfires is critical as in the Western U.S. lightning-initiated wildfires are responsible for over 70% of the area burned in these environmental catastrophes. Globally, wildfires are responsible for 6.45 gigatons of carbon dioxide (CO2) emissions annually (18% of total emissions). Detecting high risk lightning strikes (those capable of igniting wildfires) may also significantly reduce losses of life, wildlife, habitats, property, and forests as currently lightning-initiated wildfires in the US devastate 4-6 million acres per year. The reduction of wildfires can reduce large evacuations and smoke-related health conditions, thereby improving the health and welfare of the American public. Both people and businesses would benefit from lower insurance rates due to the decreased risk of wildfire damage. Large wildfires are a constant concern to more than half of the mission assurance priority military installations due to routine testing and training activities that are significant ignition sources. The proposed project may also address military ignition concerns. Wildfires start when a long continuing current (LCC) strikes the ground at a location where the environmental conditions are conducive for fire ignition. LCCs are those that last for 40 ms or longer and are essentially responsible for excessive heating. The transformative aspect of this research lies in the ground-based characterization of Extremely Low Frequency (ELF) lightning emissions to identify LCC strikes, with a 95% target detection efficiency and with 40 m accuracy. While for most lightning strikes the current ceases to flow after tens of microseconds, a small portion of lightning strikes (less than 10%) contain a continuing current that lasts thousands of times longer, from tens to hundreds of milliseconds. This can be viewed as a quasi-stationary arc between the cloud charge source and the ground and is detectable through electrostatic field changes and ELF emissions. A secondary innovative feature lies in the use of machine learning algorithms to pinpoint high risk lightning ignitions by analyzing the environmental conditions at the LCC strike location. This technology can identify a fire in seconds, unlike the present heat or smoke identification systems that can take hours or days to identify a fire.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.