利用简单的预测工具估算高压下的空气比热容比
作者:Alireza Bahadori*,Hari B. Vuthaluru
国籍:澳大利亚
出处:ScienceDirect
中文译文:
多年来,人们在广泛的温度范围内对空气的热物性和输运性质的评价方面投入了大量的研究工作。然而,对高压空气比热比的研究相对有限。本文提出了一种简单的预测工具,它比现有的模型简单,计算量小,适用于工艺工程师。它利用一种新的有意义的Arrhenius型渐近指数函数和Vandermonde矩阵,预测了空气在高压下的比热比随温度和压力的变化。该方法以Vandermonde矩阵为基础,具有较高的精度和清晰的数值背景,在有更多数据的情况下,可以快速调整相关系数。提出的关联式预测了温度高达1000K、压力高达1000bar ( 10万k Pa )的空气比热比。估计结果与文献中的可靠数据非常吻合,平均绝对偏差小于0.2 %。本研究开发的工具可以在不选择任何实验测量的情况下,快速检测各种工况下的压缩空气比热比,对工程技术人员具有巨大的实用价值。特别是,化学和工艺工程师将发现该方法对用户友好,计算透明,不涉及复杂的表达式。
关键字:公式化、比热比、压缩空气、范德蒙矩阵、Arrhenius函数
1.介绍
空气性能评价方法的发展是许多早期研究的主题,这些研究被用来在计量和校准以及空调等一系列科学技术应用中感兴趣的特定温度区域进行性能评价计算[1],这些科学应用领域和相应的研究主要是指像Giacomo [2,3]那样的低温,或者像Melling等人那样的较高温度。[4],他研究了100 - 200℃温度范围内的空气特性。然而,对于某些其他技术领域,如干燥,了解广泛温度范围内的特性对于准确预测所涉及的物理过程中的传热和传质现象至关重要[1]。
热容比或绝热指数或比热容比,是恒压热容()与定容热容()的比值,有时也称为等熵膨胀因子[5]:
(1)
对于来说,查找列表信息可能相当困难,因为更常用于列表。以下关系,可用于确定[5]:
(2)
基于近似的值(特别是)在许多情况下对于实际工程计算如管道和阀门的流量不够准确。在可能的情况下,应该使用一个实验值,而不是基于这个近似的实验值。还可以通过从表示为[6]的残馀性质中确定来计算热容比的一个严格值:
(3)
的值很容易获取和记录,但的值需要通过这些关系来确定。Gaskell [7]推导了热容之间的热力学关系。上述定义是用来从状态方程(如:
命名法 |
|||
A |
调谐系数 |
R |
通用气体常数(R)8.314 J/(kmol K) |
B |
调谐系数 |
T |
温度 (K) |
C |
调谐系数 |
特性极限温度 (K) |
|
D |
调谐系数 |
V |
范德蒙德矩阵 |
定压比热(J/(kgK)) |
V |
体积 (m3) |
|
定容比热(J/(kgK)) |
x |
多项式的自变量 |
|
E |
引起参数变化的过程活化能(J/kmol) |
alpha; |
热膨胀系数 |
适当定义与温度有关的参数,为某一特性单独确定的单元 |
等温压缩性 |
||
指前系数,具有相同单位的利息性质 |
等熵可压缩性 |
||
P |
压力(杆) |
rho; |
密度 (kg/m3) |
kappa; |
比热比,无量纲 |
||
psi; |
多项式 |
Peng-Robinson ),值也可以通过数值导数(扰动T和P (独立)计算和来确定[7] 。在热力学中,定容热容和定压热容是广泛的性质,可以写成能量/度性质。热力学定律表明这两种热容之间存在如下关系[7]:
(4)
(5)
这里是热膨胀系数
(6)
为等温压缩性:
(7)
并且 是等熵压缩:
(8)
在恒定体积和恒定压力下,特定热容量(密集特性)差异的相应表示是:
(9)
式中:为适用条件下物质的密度[7],比热容比的对应表达式保持不变,因为比热容是密集性质,不论是在质量还是摩尔基础上,热力学系统尺寸相关量都可以在比热容比中剔除[7]:
(10)
针对上述问题,有必要开发一种准确、理论上有意义、简单的预测工具,它比现有方法更容易实现,计算量更少,可以预测空气比热比随温度和压力的变化。本文以系统的方式对这类预测工具进行了描述,并结合一个实例说明了模型的简单性和工具的有用性。
2. 新型预测工具的开发方法
本研究的首要目的是准确地拟合空气在高压下的比热比随温度和压力的变化关系。这是由一个简单的预测工具使用一个Arrhenius型渐近指数函数来完成的,对Vogel-Tammann-Fulcher ( VTF )进行了小的修改[8-10]。
这一点很重要,因为在快速的工程计算中,经常需要将高压空气的比热比作为温度和压力的函数进行精确和数学上的简单关联,以避免复杂计算的额外计算负担。Vogel-Tammann-Fulcher ( VTF )方程是以下列一般形式给出的渐近指数函数[11-13]:
(11)
在Eq . ( 11 )式中,f为适当定义的温度相关参数,为某一性质单独确定的单位,fc为指前系数,具有相同的感兴趣性质单位,T和Tc分别为实际温度和特征极限温度(均以开尔文度给出),参考引起参数变化过程的活化能(以J / kmol单位给出),R为通用气体常数( R ) 8.314 J / ( kmol K )。对于Tc = 0的Vogel-Tammann-Fulcher ( VTF )方程的一个特例是著名的Arrhenius [14]方程。
为了研究空气比热比随温度和压力变化的关系,对Vogel - Tammann - Fulcher ( VTF )方程进行了二阶和三阶修正[13]:
(12)
在Eq . ( 12 ),Tc一直被认为是零来转换Eq。( 12 )到著名的Arrhenius方程[14]型(见Eq . ( 13 ) ):
(13)
发展这种相关性所需的数据包括已报道的空气比热比随温度和压力变化的数据[15]。下面的方法论被应用于发展这种相关性。
2.1、Vandermonde矩阵
Vandermonde矩阵是每行中具有几何级数项的矩阵,即一个mtimes;n矩阵[16]。
(14)
或者
(15)
对于所有下标i和j。一个平方Vandermonde矩阵(其中m = n )的行列式可表示为[16]:
Vandermonde矩阵在一组点上计算多项式,形式上,它将多项式的系数转换为多项式在该点处的值。Vandermonde定理对于不同的点的非零性表明,对于不同的点,在这些点上系数到值的映射是一一对应的,因此多项式插值问题是唯一解可解的,这个结果称为不等式定理[17]。
它们在多项式插值中非常有用,因为用V和m times; n的Vander - monde矩阵求解线性方程组Vu = y等价于求多项式( s )的系数 [17]。
当le;n-1时才具有该性质
Vandermonde矩阵可以很容易地用拉格朗日基多项式来反演:每列都是拉格朗日基多项式的系数,随着阶数的增加,系数下降。由此得到的插值问题的解称为拉格朗日多项式[17]。
2.2、开发预测工具
预测工具是针对低压和高压两种工况开发的。
表1.Eqs中使用了调整系数。( 20 ) - ( 23 )用于压力小于100bar空气比热比的预测。
系数 |
值 |
|||||||||||||||||||||||||||||||||||||||||
A1 |
2.13879130290102times;10-1 |
|||||||||||||||||||||||||||||||||||||||||
B1 |
2.82131393663459times;10-4 |
|||||||||||||||||||||||||||||||||||||||||
C1 |
1.2488865910034times;10-5 |
|||||||||||||||||||||||||||||||||||||||||
D1 |
6.27433500214741times;10-8 |
|||||||||||||||||||||||||||||||||||||||||
A2 |
1.01842474952184times;102 |
|||||||||||||||||||||||||||||||||||||||||
B2 |
5.31113787399755times;10-1 |
|||||||||||||||||||||||||||||||||||||||||
C2 |
2.22974989456912times;10-2 |
|||||||||||||||||||||||||||||||||||||||||
D2 |
1.16407115910221times;10-4 |
|||||||||||||||||||||||||||||||||||||||||
A3 |
2.78259248326215times;104 |
|||||||||||||||||||||||||||||||||||||||||
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Estimation of air specific heat ratio at elevated pressures using simple predictive tool Keywords: Formulation Specific heat ratio Compressed air Vandermonde matrix Arrhenius function Over the years, considerable research effort has been expended towards evaluation of the thermophysical and transport properties of air for a wide range of temperatures. However, a relatively limited attention was oriented towards investigation of air specific heat ratios at elevated pressures. In this work, a simple predictive tool, which is easier than current available models, less complicated with fewer computations and suitable for process engineers, is presented here for the prediction of specific heat ratio of air at elevated pressures as a function of temperature and pressure using a novel and theoretically based meaningful Arrhenius-type asymptotic exponential function combined with Vandermonde matrix. The proposed method is superior owing to its accuracy and clear numerical background based on Vandermonde matrix, wherein the relevant coefficients can be retuned quickly if more data are available. The proposed correlation predicts the specific heat ratios of air for temperatures up to 1000 K, and pressures up to 1000 bar (100,000 kPa). Estimations are found to be in excellent agreement with the reliable data in the literature with average absolute deviations being less than 0.2%. The tool developed in this study can be of immense practical value for the engineers and scientists to have a quick check on the compressed air specific heat ratios at various conditions without opting for any experimental measurements. In particular, chemical and process engineers would find the approach to be user-friendly with transparent calculations involving no complex expressions. 1.Introduction The development of methods for evaluation of air properties was the subject of a number of earlier investigations, which were employed to conduct property evaluation calculations at specific temperature regions of interest in a certain range of scientific and technological applications, like metrology and calibration as well as for air conditioning [1]. These scientific fields of application and the corresponding investigations mainly refer either to low temperatures like those carried out by Giacomo [2,3], or to relatively higher temperatures as those by Melling et al.[4], who investigated air properties in the temperature range between 100 and 200 °C. However, the knowledge of properties at wide range of temperature levels is vital for certain other technological fields, like drying, to allow accurate prediction of heat and mass transfer phenomena during the physical processes involved [1]. The heat capacity ratio or adiabatic index or ratio of specific heats, is the ratio of heat capacity at constant pressure () to heat capacity at constant volume (). It is sometimes also known as the isentropic expansion factor [5]: (1) It can be rather difficult to find tabulated information for , since is more commonly tabulated. The following relation, can be used to determine [5]: (2) Values based on approximations (particularly — = R ) are in many cases not sufficiently accurate for practical engineering calculations such as flow rates through pipes and valves. An experimental value should be used rather than one based on this approximation, where possible. A rigorous value for the heat capacity ratiocan also be calculated by determining from the residual properties expressed as [6]: (3) Values for are readily available and recorded, but values for need to be determined via relations such as these. Gaskell [7] referred to the derivation of the thermodynamic relations between the heat capacities. The above definition is the approach used to develop rigorous expressions from equations of state (such as
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