Di An , Derek Hollenbeck , Kai Cao , YangQuan Chen
{"title":"无人机蜂群生物炭覆盖抑制土壤甲烷排放的模拟研究","authors":"Di An , Derek Hollenbeck , Kai Cao , YangQuan Chen","doi":"10.1016/j.jiixd.2022.11.002","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we present a soil methane emissions suppression approach using swarms of unmanned aerial vehicles (UAVs), by spreading biochar mulch on top of the detected methane emissions area/source. Soil microorganisms can produce methane and release it into the atmosphere causing climate change such as global warming. However, people lack methods to manage soil methane emissions, especially quantification of methane emissions from the soil. Current measurement and suppression of methane methods are often limited due to the maintenance, installation, and calibration requirements of these sensing systems. To overcome these drawbacks, we present a new method called FADE-MAS2D (Fractional Advection Diffusion Mobile Actuator and Sensor) in which swarming UAVs are applied as optimal coverage control actuators to various methane release scenarios (from single to multi-source disturbances) utilizing an anomalous diffusion model with different time, and space fractional orders subject to wind fields. This strategy is based on the premise that methane diffusion can be modeled as an anomalous diffusion equation, and swarming UAVs can be applied to tackle the optimal coverage control issue. To simulate methane diffusion under the wind, we utilize the fractional calculus to solve the anomalous diffusion equation and define wind force with the drag equation. In addition, we integrated emissions control, UAV control efforts, and UAV location error in our cost function. Finally, we evaluated our approach using simulation experiments with methane diffusion and multiple methane emission sources in the time and space domain, respectively. The results show that when <em>α</em> = 0.8 and <em>β</em> = 1.8, the shape and emissions of methane perform well. Furthermore, our approach resulted in great control performance with multiple methane emission sources and different wind velocities and directions.</p></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"1 1","pages":"Pages 68-85"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Soil methane emission suppression control using unmanned aircraft vehicle swarm application of biochar mulch - A simulation study\",\"authors\":\"Di An , Derek Hollenbeck , Kai Cao , YangQuan Chen\",\"doi\":\"10.1016/j.jiixd.2022.11.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this paper, we present a soil methane emissions suppression approach using swarms of unmanned aerial vehicles (UAVs), by spreading biochar mulch on top of the detected methane emissions area/source. Soil microorganisms can produce methane and release it into the atmosphere causing climate change such as global warming. However, people lack methods to manage soil methane emissions, especially quantification of methane emissions from the soil. Current measurement and suppression of methane methods are often limited due to the maintenance, installation, and calibration requirements of these sensing systems. To overcome these drawbacks, we present a new method called FADE-MAS2D (Fractional Advection Diffusion Mobile Actuator and Sensor) in which swarming UAVs are applied as optimal coverage control actuators to various methane release scenarios (from single to multi-source disturbances) utilizing an anomalous diffusion model with different time, and space fractional orders subject to wind fields. This strategy is based on the premise that methane diffusion can be modeled as an anomalous diffusion equation, and swarming UAVs can be applied to tackle the optimal coverage control issue. To simulate methane diffusion under the wind, we utilize the fractional calculus to solve the anomalous diffusion equation and define wind force with the drag equation. In addition, we integrated emissions control, UAV control efforts, and UAV location error in our cost function. Finally, we evaluated our approach using simulation experiments with methane diffusion and multiple methane emission sources in the time and space domain, respectively. The results show that when <em>α</em> = 0.8 and <em>β</em> = 1.8, the shape and emissions of methane perform well. Furthermore, our approach resulted in great control performance with multiple methane emission sources and different wind velocities and directions.</p></div>\",\"PeriodicalId\":100790,\"journal\":{\"name\":\"Journal of Information and Intelligence\",\"volume\":\"1 1\",\"pages\":\"Pages 68-85\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Information and Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949715922000063\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Information and Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949715922000063","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
在本文中,我们提出了一种使用成群的无人机(UAV)的土壤甲烷排放抑制方法,通过在检测到的甲烷排放区域/源上铺设生物炭覆盖物。土壤微生物可以产生甲烷并将其释放到大气中,从而导致全球变暖等气候变化。然而,人们缺乏管理土壤甲烷排放的方法,尤其是对土壤甲烷排放量的量化。由于这些传感系统的维护、安装和校准要求,当前甲烷测量和抑制方法通常受到限制。为了克服这些缺点,我们提出了一种称为FADE-MAS2D(Fractional Advention Diffusion Mobile Actuator and Sensor)的新方法,在该方法中,群集无人机被应用为各种甲烷释放场景(从单一到多源扰动)的最佳覆盖控制致动器,利用风场下具有不同时间和空间分数阶的异常扩散模型。该策略的前提是甲烷扩散可以建模为异常扩散方程,群集无人机可以用于解决最佳覆盖控制问题。为了模拟甲烷在风下的扩散,我们利用分数演算来求解异常扩散方程,并用阻力方程来定义风力。此外,我们在成本函数中集成了排放控制、无人机控制工作和无人机位置误差。最后,我们分别在时间域和空间域中使用甲烷扩散和多个甲烷排放源的模拟实验来评估我们的方法。结果表明,当α=0.8和β=1.8,甲烷的形状和排放表现良好。此外,我们的方法在多种甲烷排放源和不同风速和风向的情况下取得了良好的控制性能。
Soil methane emission suppression control using unmanned aircraft vehicle swarm application of biochar mulch - A simulation study
In this paper, we present a soil methane emissions suppression approach using swarms of unmanned aerial vehicles (UAVs), by spreading biochar mulch on top of the detected methane emissions area/source. Soil microorganisms can produce methane and release it into the atmosphere causing climate change such as global warming. However, people lack methods to manage soil methane emissions, especially quantification of methane emissions from the soil. Current measurement and suppression of methane methods are often limited due to the maintenance, installation, and calibration requirements of these sensing systems. To overcome these drawbacks, we present a new method called FADE-MAS2D (Fractional Advection Diffusion Mobile Actuator and Sensor) in which swarming UAVs are applied as optimal coverage control actuators to various methane release scenarios (from single to multi-source disturbances) utilizing an anomalous diffusion model with different time, and space fractional orders subject to wind fields. This strategy is based on the premise that methane diffusion can be modeled as an anomalous diffusion equation, and swarming UAVs can be applied to tackle the optimal coverage control issue. To simulate methane diffusion under the wind, we utilize the fractional calculus to solve the anomalous diffusion equation and define wind force with the drag equation. In addition, we integrated emissions control, UAV control efforts, and UAV location error in our cost function. Finally, we evaluated our approach using simulation experiments with methane diffusion and multiple methane emission sources in the time and space domain, respectively. The results show that when α = 0.8 and β = 1.8, the shape and emissions of methane perform well. Furthermore, our approach resulted in great control performance with multiple methane emission sources and different wind velocities and directions.