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B3. A qualification: the importance of measurement accuracy in .NET Use bar code 39 in .NET B3. A qualification: the importance of measurement accuracy




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B3. A qualification: the importance of measurement accuracy generate, create none none in none projects Visual Studio 2005/2008/2010 Overview One would imagi none for none ne that the fundamental model developed here could be used with typical manufacturer catalog data, in the manner presented in Section B2. Specifically, with the usual wide range of reported values of coolant temperatures, cooling rates and COP, we should be able to regress Equation (6.1) to obtain the 3 chiller characteristic parameters.

In fact, this is what we have done in the first part of our exercise above. It appears, however, that the operating points reported in manufacturer catalogs are not all measured points. Namely, a few points are measured, and the rest are extrapolated and/or interpolated.

This procedure produces nominal data at a level that may be tolerable for sizing air-conditioning systems, but may not be acceptable for determining chiller parameters via regression with the fundamental model. Specifically, when we used typical manufacturer catalog data (of the type described in detail in 10), we found that the best-fit parameters were not physically meaningful. Namely, although an excellent mathematical fit could be.

Cool Thermodynamics Mechanochemistry of Mater ials obtained, S in none for none t could turn out to be negative, or at the very least quite different from physically reasonable values for the rate of internal entropy leak production. The regressed values of R and Qeqv were also far from being physically tenable. This problem can be attributed to the inaccuracy of the nominal data.

Hence implementing the fundamental model demands accurate in-house measurements. Tutorial 6.2 highlights this point.

In 10, we ll develop a quasi-empirical analytic chiller model that is more robust and can work with the nominal data from manufacturer catalogs. The price paid, however, will be the inability to identify the parameters that characterize the chiller with specific irreversibility mechanisms, and hence the loss of an optimization capability. __________________________________________________________________________ Tutorial 6.

2. Recall the chil ler reported in Section B1 and the associated 30 data points tabulated in Table 6.1. In this tutorial, we ll demonstrate the price paid for producing nominal data by extrapolation from a few measured points.

We select 4 characteristic operating conditions from Table 6.1: points 8, 9, 14 and 15. Then we create the remaining 26 nominal data points in the following manner.

Via multiple linear regression (in a standard PC spreadsheet program), we empirically fit the input power P in as a linear function of in T in , T evap and Qevap. Then, based on this best-fit linear relation, we calculate cond P in at the coolant temperatures and cooling rates of the remaining 26 points. The resulting set of 30 data points (comprised of 4 actual measured points and 26 extrapolated points) is then used in the multiple-linear regression calculation prescribed and illustrated in Section B2, to generate the 3 leak characteristic chiller parameters: S int, R and Qeqv .

. The results of this exercise are based on the extrapolated nominal data set S int = 0.00390 kW K 1 R = 5.556 K kW 1 leak Qeqv = 23.66 kW based on the actual 30-point data set (for comparison) S int = 0.00555 kW K 1 R = 2.505 K kW 1 leak Qeqv = 4.38 kW with the rms er ror for correlating COP being well below the experimental error in both cases. Clearly, however, not only are the best-fit parameters inaccurate, but the negative value of S int is inadmissible..

_______________ none none ___________________________________________________________. Experimental Validation of the Fundamental Model and Optimization... C. WHERE ACTUAL none none CHILLER PERFORMANCE LIES ON THE CHARACTERISTIC CURVE The analytic chiller model provides a window through which we can view where chillers actually operate along their characteristic performance curves. Put another way, how close to the point of maximum COP is a chiller s rated operating condition Have chiller manufacturers empirically evolved configurations that are as efficient as possible for prescribed cooling needs The comprehensive and accurate data set summarized in Tables 6.

1 and 6.2 for a small typical commercial reciprocating chiller is well suited to the task. In Figure 6.

3, we plot the characteristic performance curve for this nominal 10.5 kW chiller at 5 different operating points that cover the nominal rated condition plus conditions for which the curves lie well above and below the rated condition curve. These correspond to points 3, 6, 15, 19 and 25 in Tables 6.

1 and 6.2. We see the basic performance features in Figure 6.

3: (a) a linear regime at the lower cooling rates, where chiller behavior is dominated by internal losses; (b) a region at higher cooling rates where COP changes rapidly with cooling rate, and finite-rate heat transfer is the key bottleneck; and (c) a point of maximum COP at the optimal balance between internal dissipation and heat transfer losses. For many chiller.
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