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高温高压有机工质密度实验测量及预测方法
引用本文:朱霄珣,徐国强,全永凯,高炜,宋固,赵丽萍.高温高压有机工质密度实验测量及预测方法[J].航空动力学报,2016,31(7):1602-1609.
作者姓名:朱霄珣  徐国强  全永凯  高炜  宋固  赵丽萍
作者单位:北京航空航天大学 能源与动力工程学院, 北京 100191
摘    要:针对有机工质高温高压下密度的测量较为困难的问题,特别是超临界状态下,设计出高温高压下密度的测量方法,并提出了基于最小二乘支持向量机(LSSVM)的密度预测方法.该方法首先利用实验手段对有机工质在不同温度、压力点下的密度进行测量,并通过对该离散的实验段数据的学习,利用最小二乘支持向量机方法预测得到T-p面上密度的连续值,尤其是实验手段难以测量的超临界下的密度.基于该方法,以有机工质六甲基二硅氧烷为例,得到了其在T(600~850K)与p(1.3~2.25MPa)范围内的密度值及密度关于温度压力的函数公式,并将其结果与公布的密度数据对比,结果表明:两者的相对误差仅为2.4%,证明了方法的有效性. 

关 键 词:密度    最小二乘支持向量机    高温高压    预测    超临界
收稿时间:2014/10/27 0:00:00

Density experimental measurement and prediction method oforganic working fluid at high temperature and high pressure
ZHU Xiao-xun,XU Guo-qiang,QUAN Yong-kai,GAO Wei,SONG Gu and ZHAO Li-ping.Density experimental measurement and prediction method oforganic working fluid at high temperature and high pressure[J].Journal of Aerospace Power,2016,31(7):1602-1609.
Authors:ZHU Xiao-xun  XU Guo-qiang  QUAN Yong-kai  GAO Wei  SONG Gu and ZHAO Li-ping
Institution:School of Energy and Power Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
Abstract:In order to solve the problem of the density measurement at high temperature and high pressure (HTHP), particularly in a supercritical state, a density measurement method at HTHP was designed and a density prediction method based on least square support vector machine (LSSVM) was proposed. This experimental method was used to measure the density at different (T, p) points firstly. Then, by learning from the experimental data, the density prediction model based on LSSVM (DP-LSSVM) could be established, and the continuous density values in T-p plane could be predicted by DP-LSSVM, especially in supercritical region where the density was difficult to measure by experimental means. Hexamethyldisiloxane was taken for example. By using the proposed method, the density function on T (600-850K) and p (1.3-2.25MPa) was obtained, and density in this area could be calculated. By comparing the calculation results with the published data, it is found that the relative error is only 2.4%, the effectiveness of the method is proved.
Keywords:density  least square support vector machine (LSSVM)  high temperature and high pressure(HTHP)  prediction  supercritical
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