On the construction and use of linear low-dimensional ventilation models.
On the construction and use of linear low-dimensional ventilation models.
复制标题
DOI：
10.1111/j.1600-0668.2012.00771.x
复制
发表时间：
2012-10
期刊：
影响因子：
5.8
通讯作者：
Shi-Jie Cao;J. Meyers
中科院分区：
文献类型：
--
作者：
Shi-Jie Cao;J. Meyers
未标记的快速可靠的低维模型对于监视和控制通风应用很重要。补充了用于室内污染物浓度的传输方程的Stokes方程。然后，可以根据数值模拟或实验来构建空气质量的污染物质量速率。允许由任何类型的污染物源分布产生的浓度场的重建模型。我们将浓度的离散线性通风模型放在简单的基准通气情况下，并将其与耦合的RANS模拟分析离散模型中的（低维）分辨率进行了量化，并且还研究了α值太低的误差论文介绍了线性低维通气模型的推导和构建，该模型允许通过构建任何类型的任何类型的室内 - 污染物分布产生的重建浓度场。因此，CFD模拟方法。
UNLABELLED The construction of fast reliable low-dimensional models is important for monitoring and control of ventilation applications. We employ a discrete Green's function approach to derive a linear low-dimensional ventilation model directly from the governing equations for indoor ventilation (i.e., the Navier-Stokes equations supplemented with a transport equation for indoor-pollutant concentration). It is shown that the flow equations decouple from the concentration equation when the ratio α of air-mass-flow rate to pollutant-mass-flow rate increases to infinity. A low-dimensional discrete representation of the Green's function of the concentration equation can then be constructed, based on either numerical simulations or experiments. This serves as a linear model that allows for the reconstruction of concentration fields resulting from any type of pollutant-source distribution. We employ a suite of Reynolds-averaged Navier-Stokes (RANS) simulations to illustrate the methodology. We focus on a simple benchmark ventilation case under constant-density conditions. Discrete linear ventilation models for the concentration are then derived and compared with coupled RANS simulations. An analysis of errors in the discrete linear model is presented: dependence of the error on the (low-dimensional) resolution in the discrete model is quantified, and errors introduced by too low values of α are also investigated. PRACTICAL IMPLICATIONS The paper introduces the derivation and construction of linear low-dimensional ventilation models, which allow reconstructing concentration fields resulting from any type of indoor-pollutant-source distribution. Once constructed, these ventilation models are very efficient to estimate indoor contaminant concentration distributions, compared to direct CFD simulation approaches. Therefore, these models can facilitate monitoring and control of ventilation systems, to remove indoor contaminants.