Stress prediction

Linton SJ (2004) Does work stress predicts insomnia A prospective study. Br J Health Psychol 2 127-136... 

Corn stover, a well-known example of lignocellulosic biomass, is a potential renewable feed for bioethanol production. Dilute sulfuric acid pretreatment removes hemicellulose and makes the cellulose more susceptible to bacterial digestion. The rheologic properties of corn stover pretreated in such a manner were studied. The Power Law parameters were sensitive to corn stover suspension concentration becoming more non-Newtonian with slope n, ranging from 0.92 to 0.05 between 5 and 30% solids. The Casson and the Power Law models described the experimental data with correlation coefficients ranging from 0.90 to 0.99 and 0.85 to 0.99, respectively. The yield stress predicted by direct data extrapolation and by the Herschel-Bulkley model was similar for each concentration of corn stover tested. 

The yield stress values given in Table 3 demonstrate that the yield stresses determined with the Herschel-Bulkley model were lower than the yield stresses determined with all the other methods at equal concentrations. The yield stress predicted by direct data extrapolation and by the Herschel-Bulkley model was similar for each concentration of corn stover. 

The problem with the Maxwell model shown in Figure 8.1 is that the stress predicted by the model decays to zero as shown in Equation (8.1),... 

Fig. 5.2 Comparison of creep behavior and time-dependent change in fiber and matrix stress predicted using a 1-D concentric cylinder model (ROM model) (solid lines) and a 2-D finite element analysis (dashed lines). In both approaches it was assumed that a unidirectional creep specimen was instantaneously loaded parallel to the fibers to a constant creep stress. The analyses, which assumed a creep temperature of 1200°C, were conducted assuming 40 vol.% SCS-6 SiC fibers in a hot-pressed SijN4 matrix. The constituents were assumed to undergo steady-state creep only, with perfect interfacial bonding. For the FEM analysis, Poisson s ratio was 0.17 for the fibers and 0.27 for the matrix, (a) Total composite strain (axial), (b) composite creep rate, and (c) transient redistribution in axial stress in the fibers and matrix (the initial loading transient has been ignored). Although the fibers and matrix were assumed to exhibit only steady-state creep behavior, the transient redistribution in stress gives rise to the transient creep response shown in parts (a) and (b). After Wu et al 1...

This scaling law, Eq. (9-48), implies that all components of the stress tensor are linear in the shear rate. Consider for example, a constant-shear-rate experiment. At steady state, not only is the shear stress predicted to be proportional to the shear rate, but so also is the first normal stress difference N This prediction has been nicely confirmed in recent experiments by Takahashi et al. (1994), who studied mixtures of silicon oil and hydrocarbon-formaldehyde resin. Both these fluids are Newtonian, and have the same viscosity, around 10 Pa s. Figure 9-18 shows that both the shear stress o and the first normal stress difference N = shear rate, so that the shear viscosity rj = aly and the so-called normal viscosity rjn = N /y are constants. The first normal stress difference in this mixture must be attributed entirely to the presence of interfaces, since the individual liquids in the mixture have no measurable normal stresses. A portion of the shear stress also comes from the interfacial stress. Figure 9-19 shows that the shear and normal viscosities are both maximized at a component ratio of roughly 50 50. At this component ratio, the interfacial term accounts for roughly half the total shear stress. 

Figure 12.18 (a) The solid line is the reduced shear stress oj Go as a function of reduced shear rate yx predicted by Eqs. (12-34)-(12-36). The long-dashed line is a hypothetical modification obtained by considering high-ftequency Rouse modes. The short-dashed line is the stress predicted by flow stratification, (b) Comparison of the theory with flow-stratification to experimental data of Rehage and Hoffiuann (1991). (From Spenley et al. 1993, reprinted with permission from the American Physical Society.)... 

Figure 7.27. Yield stress prediction for different particle sizes. [Adapted, by permission, from Pukanszky B, Voros G, Polym.Composites 17, No.3, 1996, 384-92.]...

Yield stresses can also be obtained by extrapolation of shear rate-shear stress data to zero shear rate according to one of several flow models. The application of several models was studied by Rao et al. (AS.) and Rao and Cooley (Al) The logarithm of the yield stresses predicted by each model and the total solids (TS) of the concentrates were related by quadratic equations. The equations for the yield stresses predicted by the Herschel-Bulkley model (Equation 4) which described very well the flow data of Nova and New Yorker tomato cultivars were ... 

The three fully coupled models (CNS, SKI, and SKB) behaved in general terms in a quite satisfactory manner. They predicted quite accurately the evolution of relative humidities inside the barrier. Stress prediction, however, has proved to be a more difficult task. There is always some concern about the actual reliability of measuring procedures. It appears that the measured radial stresses, which are essentially induced by the progressive hydration of the bentonite, are higher and develop faster than predictions, especially at the end of the considered period. 

Recorded values at a larger radial distance (r 7.10-7.80 m), not shown here, were significantly lower. Interestingly, a peak is recorded at early stages in all the three stress components, followed by a transient decay and a progressive increase at later dates. The stress cells measure total stress and the observed behavior is consistent with the expected change in pore water pressures, explained before. The stress predictions shown in the figures do not reproduce the measured transient, however. Some of the calculated values (especially for SKI) are quite close to actual absolute values. 

The rock stress predictions, based on database information and numerical modelling, were satisfactory and helped to focus attention on the key factors involved. In particular, the use of numerical modelling assists in evaluating potential stress changes in the vicinity of fracture zones. Methods of dealing with conceptual uncertainty and spatial variability of stress were successfully introduced into the rock stress characterization and predictive methodology. 

The stress predicted by Eqn (6.38) is very conservative. A laminate will have damage onset when the right-hand side of Eqn (6.38) exceeds the stress at which first damage will occur, but this damage onset almost never coincides with final failure. As mentioned above, some stress redistribution takes place as damage starts and evolves. This is shown schematically on the right of Figure 6.8. Therefore, Eqn (6.38) cannot be used to predict final failure of a laminate with a hole. For this reason, alternative methods have been proposed. 

Webster, G. Improving Letter-to-Pronunciation Accuracy with Automatic Morphologically-Based Stress Prediction. In Proceedings of Interspeech 2004 (2004). 

Fig. 9.19 Stress dependences of the activation energies of the half-loop nucleation in modes B and C under stress predicted by the models of Xu (2002) and Xu and Zhang (2003), respectively (from Argon et al. (2005) courtesy of Elsevier).

Figure 3. Plot showing stresses predicted in the ZrBi matrix (red) and SiC particles (blue) by compared to measured flexure strengths reported in recent literature.

The critical stress predicted by Eq. (14) depends only on the elastic modulus and not at all on the strength of the elastomer. In agreement with this, cavitation stresses in bonded rubber blocks under tension (Figs. 8 and 9) [35], and near rigid inclusions, at points where a triaxial tension is set up (Figs. 

Hg. 7.22, Shear stress predicted by the theory for monodisperse systems with... 

Product and reaction pathways in systems with multiple environmental stresses Prediction ... 

In this section, two analytical double-lap joint models are briefly introduced, highlighting their strengths and weaknesses in terms of accurate and realistic stress predictions, and finally validating their predicted lap-shear stress distributions for a specific double-strap CFRP/steel joint with experimentally acquired results. 

Lap-shear stress predictions are successfully achieved with Excel spreadsheets due to the continual iterations of parameters and formulas for each individual CFRP and adherend material, geometrical variations, and environmental preconditioning temperature usually involved in large-scale investigations. Figure 10.13(a) and (b) displays the lap-shear stress predictions... 

To anticipate accurate stress predictions from the FE model, it has to be representative of the actual experimental or in-situ application in terms of its dimensions, material thermo-mechanical properties, prescribed environmental conditions, degrees of freedom (i.e. constraints) and loading pattern, as well as the choice of the right element formulation suitable for the simulated application. As an example, the 2D FE model of Fig. 10.15 represents half... 

The advantage of including the adhesive s non-linear constitutive stress-strain model, in terms of the accuracy of the FE stress predictions, can be clearly demonstrated with reference to Fig. 10.16. This figure represents the stress-strain curve for a specific resin (i.e. Araldite 420 epoxy) bulk coupon tested experimentally in direct tension inside an environmental chamber where the temperature of the coupon reached 0°C. Path OAB represents the actual measured nominal stress-strain curve, which is characterized by two parts the first part (i.e. OA) is linear, whereas the second part (AB)... 

Figure 10.18(a) and (b) displays and validates the FE lap-shear stress predictions, with the same experimental double-strap CFRP/steel specimens discussed in Section 10.6.1, along the CFRP/steel joint bondline. The lap-shear stress divergence between the FE predictions and experimental results disclosed in Fig. 10.18(a) close to the x = 0 end is due to erroneous reading of the ERSG in this location, and is discussed in Al-Shawaf (2010). 

For joint capacity predictions of the CFRP/steel double-strap joint model of Fig. 10.15, the precise manner in which the failure criteria are applied and the consequent stress predictions extracted can be summarized as follows (Al-Shawaf, 2010) ... 

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