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Computed tomography and optical remote sensing: Development for the study of indoor air pollutant transport and dispersion.

机译:计算机断层扫描和光学遥感:用于研究室内空气污染物迁移和扩散的研究进展。

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This thesis investigates the mixing and dispersion of indoor air pollutants under a variety of conditions using standard experimental methods. It also extensively tests and improves a novel technique for measuring contaminant concentrations that has the potential for more rapid, non-intrusive measurements with higher spatial resolution than previously possible.; Experiments conducted in a sealed room support the hypothesis that the mixing time of an instantaneously released tracer gas is inversely proportional to the cube root of the mechanical power transferred to the room air. The constant of proportionality is determined and the relationship is used to predict mixing times for two indoor air flow scenarios: forced ventilation and strong natural convection. Predicted mixing times agree well with observation. The empirical relationship helps bridge the gap between the common but mostly untested assumption of instantaneous, perfect mixing and complex computational models for simulating pollutant transport and dispersion.; One table-top and several room-scale experiments are performed to test the concept of employing optical remote sensing (ORS) and computed tomography (CT) to measure steady-state gas concentrations in a horizontal plane. Various remote sensing instruments, scanning geometries and reconstruction algorithms are employed. Reconstructed concentration distributions based on existing iterative CT techniques contain a high degree of unrealistic spatial variability and do not agree well with simultaneously gathered point-sample data. A new reconstruction method, Smooth Basis Function Minimization (SBFM), is developed. SBFM treats concentration distributions as continuous and representable by a superposition of asymmetric smooth functions--bivariate Gaussians are used in this thesis. SBFM reconstructions of synthetic and experimental data show much better agreement with measured concentration profiles than reconstructions based on standard techniques.; The dispersion of tracer gas in a chamber is examined using a scanning open-path FTIR and SBFM computed tomography. Interpolation of successive path-integral concentration measurements for each ray yields input data for CT reconstructions at specific times during the experiment. This allows one to follow the evolution of the tracer gas concentration distribution in time. The results demonstrate significant potential for CT/ORS as a fast, accurate and relatively non-invasive technique to measure time-varying gas concentrations in a variety of applications.
机译:本文采用标准实验方法研究了在各种条件下室内空气污染物的混合和扩散。它还广泛地测试和改进了一种用于测量污染物浓度的新技术,该技术具有比以前可能的更快,更非侵入式的测量方式,并且具有更高的空间分辨率。在密封室内进行的实验支持以下假设:瞬时释放的示踪气体的混合时间与传递到室内空气的机械能的立方根成反比。确定比例常数,并将该关系用于预测两种室内空气流场景的混合时间:强制通风和强自然对流。预计的混合时间与观察结果非常吻合。经验关系有助于弥合瞬态,完美混合的常见但大多数未经测试的假设与模拟污染物迁移和扩散的复杂计算模型之间的差距。进行了一个桌面和几个房间规模的实验,以测试采用光学遥感(ORS)和计算机断层扫描(CT)来测量水平面上的稳态气体浓度的概念。采用了各种遥感仪器,扫描几何形状和重建算法。基于现有迭代CT技术的重构浓度分布包含高度不现实的空间变异性,与同时收集的点样本数据不太吻合。开发了一种新的重建方法,即平滑基函数最小化(SBFM)。 SBFM将浓度分布视为连续的并且可以通过不对称平滑函数的叠加来表示-本论文使用双变量高斯分布。与基于标准技术的重建相比,合成和实验数据的SBFM重建与测得的浓度曲线具有更好的一致性。使用扫描开放路径FTIR和SBFM计算机断层扫描技术检查示踪气体在室内的分散情况。对每条射线进行连续的路径积分浓度测量值的插值可得出实验期间特定时间CT重建的输入数据。这使人们能够及时追踪示踪气体浓度分布的变化。结果表明,CT / ORS作为一种快速,准确且相对非侵入性的技术,具有在各种应用中测量随时间变化的气体浓度的巨大潜力。

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