Mei Xiong, Zeng Chenni, Gong Guangcai
School of Energy and Power Engineering, Changsha University of Science and Technology, 960 Wanjiali South Road, Changsha, 410114 China.
School of Civil Engineering, Hunan University, 2 Lushan South Road, Changsha, 410082 China.
Build Simul. 2022;15(7):1243-1258. doi: 10.1007/s12273-021-0855-y. Epub 2021 Nov 25.
Mechanical and natural ventilations are effective measures to remove indoor airborne contaminants, thereby creating improved indoor air quality (IAQ). Among various simulation techniques, Markov chain model is a relatively new and efficient method in predicting indoor airborne pollutants. The existing Markov chain model (for indoor airborne pollutants) is basically assumed as first-order, which however is difficult to deal with airborne particles with non-negligible inertial. In this study, a novel weight-factor-based high-order (second-order and third-order) Markov chain model is developed to simulate particle dispersion and deposition indoors under fixed and dynamic ventilation modes. Flow fields under various ventilation modes are solved by computational fluid dynamics (CFD) tools in advance, and then the basic first-order Markov chain model is implemented and validated by both simulation results and experimental data from literature. Furthermore, different groups of weight factors are tested to estimate appropriate weight factors for both second-order and third-order Markov chain models. Finally, the calculation process is properly designed and controlled, so that the proposed high-order (second-order) Markov chain model can be used to perform particle-phase simulation under consecutively changed ventilation modes. Results indicate that the proposed second-order model does well in predicting particle dispersion and deposition under fixed ventilation mode as well as consecutively changed ventilation modes. Compared with traditional first-order Markov chain model, the proposed high-order model performs with more reasonable accuracy but without significant computing cost increment. The most suitable weight factors of the simulation case in this study are found to be (λ = 0.7, λ = 0.3, λ = 0) for second-order Markov chain model, and (λ = 0.8, λ = 0.1, λ = 0.1) for third-order Markov chain model in terms of reducing errors in particle deposition and escape prediction. With the improvements of the efficiency of state transfer matrix construction and flow field data acquisition/processing, the proposed high-order Markov chain model is expected to provide an alternative choice for fast prediction of indoor airborne particulate (as well as gaseous) pollutants under transient flows.
机械通风和自然通风是去除室内空气传播污染物的有效措施，从而改善室内空气质量（IAQ）。在各种模拟技术中，马尔可夫链模型是预测室内空气传播污染物的一种相对新颖且高效的方法。现有的（用于室内空气传播污染物的）马尔可夫链模型基本上被假定为一阶模型，然而，该模型难以处理惯性不可忽略的空气传播颗粒。在本研究中，开发了一种基于新型权重因子的高阶（二阶和三阶）马尔可夫链模型，以模拟固定和动态通风模式下室内颗粒的扩散和沉积。首先通过计算流体动力学（CFD）工具求解各种通风模式下的流场，然后根据模拟结果和文献中的实验数据对基本的一阶马尔可夫链模型进行实施和验证。此外，测试了不同组的权重因子，以估计二阶和三阶马尔可夫链模型的合适权重因子。最后，对计算过程进行了合理的设计和控制，以便所提出的高阶（二阶）马尔可夫链模型能够用于在连续变化的通风模式下进行颗粒相模拟。结果表明，所提出的二阶模型在预测固定通风模式以及连续变化通风模式下的颗粒扩散和沉积方面表现良好。与传统的一阶马尔可夫链模型相比，所提出的高阶模型具有更合理的精度，且计算成本没有显著增加。在减少颗粒沉积和逸出预测误差方面，本研究模拟案例中二阶马尔可夫链模型最合适的权重因子为（λ = 0.7，λ = 0.3，λ = 0），三阶马尔可夫链模型为（λ = 0.8，λ = 0.1，λ = 0.1）。随着状态转移矩阵构建效率以及流场数据采集/处理效率的提高，所提出的高阶马尔可夫链模型有望为快速预测瞬态流条件下室内空气传播颗粒（以及气态）污染物提供一种替代选择。
