Filter section input

The thermal stresses were computed by the finite element code ANSYS [29], taking full advantage of the axial symmetry of the filter see Fig. 16. Both the temperature-dependent physical properties of the EX-54 filter (Section V) and the time-dependent thermocouple data were used as inputs to stress analysis. The maximum stresses in the axial and tangential directions at the midsection are summarized in Table 14. It should be noted in Table 14 that the radial temperature gradient is the major contributor to thermal stresses those due to axial gradient are less than 20%. 

Assuming that the melt flow is laminar, its flow rate through a multilayer filter does not depend on time. But in the case when a molten metal contains dispersed particles with a size less than the section of the channel, the flow rate becomes dependent on time mainly due to the adhesion of the particles to the channel walls. With this, those particles which have the size larger than that of the capillary channel section are retained at the entrance of the filter in a form of a cake which increases the apparent length of the channel and decreases the active surface of the filter. The input of intensive ultrasonic oscillations in the mode of developed cavitation results in the appearance of active acoustic streams near the filter surface and in washing-out the cake. In the ideal case, the value of the flow rate through the filter can be sustained constant for a sufficiently long period of fine filtration due to the action of acoustic cavitation and streams. 

Our first task is to build a model where the complex vocal apparatus is broken down into a small number of independent components. One way of doing this is shown in Figure 11.1b, where we have modelled the lungs, glottis, pharynx cavity, mouth cavity, nasal cavity, nostrils and lips as a set of discrete, coimected systems. If we make the assumption that the entire system is linear (in the sense described in Section 10.4) we can then produce a model for each component separately, and determine the behaviour of the overall system fi om the appropriate combination of the components. While of course the shape of the vocal tract will be continuously varying in time when speaking, if we choose a sufficiently short time fi ame, we can consider the operation of the components to be constant over that short period time. This, coupled with the linear assumption then allows us to use the theory of linear time invariant (LTI) filters (Section 10.4) throughout. Hence we describe the pharynx cavity, mouth cavity and lip radiation as LTI filters, and so file speech production process can be stated as the operation of a series of z-domain transfer functions on the input. 

If crystallisation commences as soon as the solvent cools or if large quantities of hot solution are to be filtered, the funnel (and fluted filter paper) should be warmed externally during the filtration (hot water funnel). Three types of hot water funnel are illustrated in Fig. 11,1, 6 no flames should be present whilst inflammable solvents are being filtered through the funnel of Fig. 11, 1, 6, a. Alternatively, the funnel may be surrounded by an electric heating mantle (see Section 11,57) the heat input may be controlled by a variable transformer. When dealing with considerable volumes of aqueous or other solutions which do not deposit crystals rapidly on cooling, a Buchner funnel may be used for filtration (see detailed account in Section 11,1 and Fig. 11 1, 7, c). The filter paper... 

There are two types of input power buses. DC power buses are single-wire power connections such as found in automobiles and aircraft. The ground connection forms the other leg of the power system. The other form of input connection is the ac, or two or three-wire feed systems as found in ac power systems. The design of the EMI filter for dc systems is covered in Section 3.12 and takes the form of a simple L-C filter. All the noise is common-mode between the single power wire and the ground return. The dc filter is much more complicated, because of the parasitic behavior of the components involved. 

In this connection, it should be carefully noted that, even if X(t) is not a gaussian process, the mean and the autocorrelation function of the output of a linear, time-invariant filter are related to the mean and autocorrelation function of the input process according to Eqs. (3-293) and (3-294).64 This is an important fact of which use will be made in the next section. 

For simplicity of computer implementation, and in almost all practical cases, s(x) can be taken as zero outside some limited range of x. Using filter terminology, we may say that it has a finite impulse response. Let us consider the discrete version. For discretely sampled data, we write the sampled response function as sw. As in Sections V. A. 1-V. A.4, we take its output at the center of the filter. That is, the output corresponds to the Mth finite value, where M is the index at which sm is maximum. Because data are almost always sampled sequentially, we may take the index m as being directly proportional to time. Visualizing the convolution as in Section II.A of Chapter 1, we readily see that the filter s output lags its input by precisely M samples. 

Homomorphic filtering Described in Section 7.5. Zeros were removed from the input by transforming each channel with data in the range [0, 1] according to y = (255x + l)/256. We have used the homomorphic filter shown in Figure 7.23(b). Rescaling is done at the third percentile. (pwhite=0.03)... 

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