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Conductivity cure time variation

Figure 6.33. Schematic plots of cure time variation of the conductivity measured (a) at constant cure temperature (b) at a constant low frequency at which charge migration dominates. An estimate of the time to reach gelation (fgei) for curing at a relatively low temperature Taae.i), based on the percolation model for the o(fc) function, is shown (see Section 6.7.2.2). For plots with actual experimental data following the dependencies described in this figure the reader is referred to the works of, for example, Eloundou et al. (1998b, 2002) and Nunez-Regueira et al. (2005). Figure 6.33. Schematic plots of cure time variation of the conductivity measured (a) at constant cure temperature (b) at a constant low frequency at which charge migration dominates. An estimate of the time to reach gelation (fgei) for curing at a relatively low temperature Taae.i), based on the percolation model for the o(fc) function, is shown (see Section 6.7.2.2). For plots with actual experimental data following the dependencies described in this figure the reader is referred to the works of, for example, Eloundou et al. (1998b, 2002) and Nunez-Regueira et al. (2005).
For continuous process systems, empirical models are used most often for control system development and implementation. Model predictive control strategies often make use of linear input-output models, developed through empirical identification steps conducted on the actual plant. Linear input-output models are obtained from a fit to input-output data from this plant. For batch processes such as autoclave curing, however, the time-dependent nature of these processes—and the extreme state variations that occur during them—prevent use of these models. Hence, one must use a nonlinear process model, obtained through a nonlinear regression technique for fitting data from many batch runs. [Pg.284]

Numerical results reported (2) on a typical TGDDM-DDS matrix laminate, assuming that the prepregs are suddenly expose to die cure temperature, are diown in Fig. 24 (a,b,c) as me variation of die tenqierature, decree of reaction and viscosity as a function of the processing time, bodi on the dam and on the core of the laminate. Input data of die full model are givmi in Table 9 (2). Due to the contribution of die thermal conductivity of the fibm the tenqierature at the center of die laminate nqiidly reaches the external inqiosed temperature and increases as a con uence of die imbalance between the rate of heat generation and the thermal diffiisivity of the composite (Fig. 24a). When these two quantities are comparable, the temperature profile reaches a maximum. [Pg.352]


See other pages where Conductivity cure time variation is mentioned: [Pg.39]    [Pg.101]    [Pg.309]    [Pg.581]    [Pg.588]    [Pg.93]    [Pg.105]    [Pg.135]    [Pg.2342]    [Pg.320]    [Pg.589]    [Pg.726]    [Pg.853]    [Pg.241]    [Pg.129]   
See also in sourсe #XX -- [ Pg.587 ]




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