Abstract

Due to experimental data insufficiency for results validation realized by Computation Fluid Dynamics method (CFD), we are proposed new numerical simulations to determined concentration distribution of fine particles in indoor air for transient regime. The ANN model approach of multi-layer perceptron type with three layers is applied successfully. This model requires learning through a database which deduced from the bibliographic literature, composed by 2271 measurement points of which 80% assigned to ANN model training, 10% to test model and so the remaining (10%) assigned to validation part. The ANN model developed in this paper is beneficial and easy to predict fine particles distribution in air indoor when compared to the CFD method. The results average error found by this model does not reach 5%, when compared to the CFD method with an error of 16%. This model is used to treat the effect of the velocity and air exhaust section positions on the stability and flow regime establishment time.

摘要

由于实验数据不足，无法通过计算流体动力学方法（CFD）进行结果验证，因此，我们提出了新的数值模拟方法来确定瞬态状态下室内空气中细颗粒的浓度分布。成功应用了三层多层感知器类型的ANN模型方法。此模型需要通过从书目文献中得出的数据库进行学习，该数据库由2271个测量点组成，其中80％分配给ANN模型训练，10％分配给测试模型，其余（10％）分配给验证部分。与CFD方法相比，本文开发的ANN模型是有益的，并且易于预测室内空气中的细颗粒分布。该模型发现的结果平均误差未达到5％，与CFD方法相比，误差为16％。该模型用于处理速度和排气段位置对稳定性和流态建立时间的影响。