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An improved particle swarm optimization method for locating time-varying indoor particle sources

机译：改进的粒子群算法在室内时变粒子源定位中的应用

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The indoor transmission of airborne particles can spread disease and have health-related and even life-threatening effects on occupants, thus necessitating effective ways to locate indoor particle sources. The identification of particle sources from concentration distributions is a difficult task because particles are often released at a time-varying rate, and particle transport mechanisms are more complex than those of gas. This study proposes an improved multi-robot olfactory search method for locating two types of time-varying indoor particle sources: 1) periodic sources such as occupants' respiratory activities and 2) decaying sources such as laboratory leaky containers with hazardous chemicals. The method considers both particle concentrations and indoor air velocities by including an upwind term in the standard particle swarm optimization (PSO) algorithm, preventing robots from becoming trapped into a local optimum, which occurs when using other algorithms. We also considered two ventilation types (mixing ventilation and displacement ventilation) when particles are emitted from different source types, comprising four scenarios. For each scenario, particle concentration and air velocity were simulated using computational fluid dynamics (CFD) and then fed to the PSO algorithm for source localization. In addition, we validated the CFD approach for one scenario by comparing experimental data (e.g., velocities and particle concentrations) under laboratory settings. The results showed that the proposed method can locate the two types of particle sources within approximately 55 s, and the success rates of source localization exceeding 96%, which is a much higher level than levels achieved from the standard PSO and wind utilization II algorithms.
机译：空气中颗粒物在室内的传播可以传播疾病，并对乘员产生与健康相关的危害甚至威胁生命的影响，因此需要找到室内颗粒物源的有效方法。从浓度分布中识别颗粒来源是一项艰巨的任务，因为颗粒通常以随时间变化的速率释放，并且颗粒传输机制比气体传输机制更为复杂。这项研究提出了一种改进的多机器人嗅觉搜索方法，用于定位两种类型的时变室内颗粒物源：1）周期性源，例如乘员的呼吸活动； 2）衰变源，例如实验室装有危险化学品的泄漏容器。该方法通过在标准粒子群优化（PSO）算法中包括一个迎风项来同时考虑粒子浓度和室内空气速度，从而防止机器人陷入使用其他算法时出现的局部最优状态。当粒子从不同类型的源发出时，我们还考虑了两种通风类型（混合通风和置换通风），包括四种情况。对于每种情况，都使用计算流体动力学（CFD）对颗粒浓度和空气速度进行了模拟，然后将其输入PSO算法进行源定位。此外，我们通过比较实验室设置下的实验数据（例如速度和颗粒浓度）验证了一种情况下的CFD方法。结果表明，所提出的方法可以在大约55 s内找到两种类型的粒子源，并且源定位成功率超过96％，这比标准PSO和风能利用II算法获得的水平要高得多。

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