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Next: Neglecting pressure dependence of Up: Derivation of curve fits Previous: Viscosity of steam at Contents Index

Thermal conductivity of water at saturation

It turns out that a quadratic model function

m(t) = a + bt + bt²

(B6)

fits data very well.

If we perform a least square fit of m(t) to the data of [25, Table 6, Db 5]^B3 in the temperature range from 110 ${}^\circ\mbox{C}$ to 300 ${}^\circ\mbox{C}$ we obtain the following coefficients

$\begin{eqnarray*} a &=& 0.5916900889\\ b &=& 1.383087264\cdot 10^{-3}\\ c &=& -5.118478013\cdot 10^{-6}\\ \end{eqnarray*}$

$\textstyle \parbox{1.50cm}{\begin{eqnarray} \end{eqnarray}}$

The resulting fit has an error less than 0.2 percent in the temperature range [140 ${}^\circ\mbox{C}$ ;300 ${}^\circ\mbox{C}$ ].

There is no reason for reducing the error even further (by limiting the temperature interval) since the measurement error for the data in general exceed 1 percent [28, p. 565].

In the figures B.7 and B.8 we see the a comparison of the fit and the data and the error of the fit as a function of temperature respectively.

$\begin{figure} % latex2html id marker 47085\rule{\textwidth}{0.2mm} \rule{0cm}... ...k_f(t)$\ (\raise0.5ex\hbox{$.....$}) and the data points ($\cdot$).}\end{figure}$

$\begin{figure} % latex2html id marker 47105\rule{\textwidth}{0.2mm} \rule{0cm}... ...rve fit formula of the thermal conductivity at saturation $k_f(t)$.}\end{figure}$

Next: Neglecting pressure dependence of Up: Derivation of curve fits Previous: Viscosity of steam at Contents Index

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