以船舶机舱为研究对象，采用大涡模拟方法，以FDS软件为平台，计算了不同火灾功率、风机启动时间、风机流速和补风口面积条件下的火场温度变化过程。以FDS的数值模拟数据为样本建立了船舶机舱火灾温度的支持向量机计算模型，为提高模型预测的精确度，利用遗传算法对参数进行寻优。实验表明：应用本文模型预测结果与FDS计算结果基本一致，优于 SVM模型以及 BP 神经网络的预测结果，提出一种快速预测火场温度的工程计算方法。
The fire temperature change process under various conditions are calculated by large eddy simulation model based on FDS platform. The research takes ship engine room as the object, the conditions include the fire power , the activation time of exhaust system, the exhaust flow and the are of make-up air inlets. The support vector machine of fire temperature prediction model in ship engine room is built on the basis of FDS numerical simulation results sample. In order to improve the accuracy of the model predictions, the genetic algorithm is selected to optimize parameters . The experiments show that, the prediction results of the GA-SVM model are basically identical with the FDS calculation results , and are better than the prediction results of BP neural network and the SVM model, an engineering calculation approach for fast predicting the temperature of the fire is brought forth.
2018,(): 148-152 收稿日期：2016-08-01
DOI：10.3404/j.issn.1672-7649.2018.01.027
分类号：U665.1
作者简介：张茜(1990-),女,硕士研究生,研究方向为船舶自动化
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