Model nearly constant loss

Nearly Constant Loss Models. Nearly constant loss (NCL) is evidenced by a power-law dependence of <7 ( ) on frequency with an exponent very close to unity, leading to e (< ) loss response that varies only slightly over a snbstantial frequency... 

Although a PCPE may be used to model NCL data with equal slopes for both a (0) and " (0) since they both involve the same jpc 1 exponent, some data may be better represented by such power-law response for cr ((u) but by a function that yields a very close approximation to constant loss for the el (o) part of the response (Nowick et al. [1998]). In the absence of hopping, just the series combination of an ideal capacitor and a CPE can yield such behavior with very nearly constant loss over several decades of frequency (Macdonald [2001a]). 

K1 The Kohlrausch frequency response model derived from the KO model see Section 4.2, Eq. (1) with Cz = ci , Eq. (3), and Eq. (4). Some composite models are the CKO, CKl, PKl, CKOS, CKIS, EMKl, CKIEL, CPKl. Parallel elements appear on the left side of KO or Kl, and series ones on the right. C denotes a parallel capacitance or dielectric constant. NCL Nearly constant loss e"((0) nearly independent of frequency over a finite range... 

Many current models treat ventilation loss based on the assumption of a well-mixed space. Furtaw et al. (1996) conducted experiments with pre-set ventilation rates and constant source strengths. These authors showed that rooms with high ventilation rates behave as well-mixed spaces, and that the ventilation rate accurately accounts for steady-state levels and ventilation loss when the source is turned off. At lower ventilation rates, mixing is not uniform and concentrations near the source deviate from those further away. However, once the source is turned off, the ventilation rate accurately accounts for the observed decrease in air concentration. The assumption of a well-mixed room is questionable in the case in which there are few activities and no mixing of air currents. In such cases, diffusion and multiple-zone models can be used to more realistically capture spatial heterogeneity (Furtaw et al., 1996 Nicas, 1996, 1998). Another approach for estimation is a fluid dynamics model utilizing a supercomputer (Matoba et al., 1994a). 

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