Validation of the time–temperature superposition principle for crack propagation in bituminous mixtures

The time–temperature superposition principle (TTSP) is known to be valid in the small strain domain where the behaviour of bituminous mixtures is linear viscoelastic (LVE). The behaviour is then called thermorheologically simple. In this work, an experimental campaign was performed at University of Lyon/ENTPE (France) to check the validity of the TTSP in the linear domain in the tridimensional case and also when cracks occur and propagate in bituminous mixture. A four-point bending test, which has been designed at University of Lyon/ENTPE, was used as crack propagation test. First, a complex modulus test is performed on cylindrical specimen in the LVE domain. Then, a series of crack propagation tests are carried out at different temperatures and different imposed displacement rates. The same shift factors obtained for master curve of complex modulus is also applied for the crack propagation tests analysis. The results allow obtaining a unique curve, for identical loadings when plotting as a function of reduced time. This result confirms that the TTSP is also valid for crack propagation in bituminous mixtures.     

Applicability condition of time–temperature superposition principle (TTSP) to a multi-phase system

The applicability condition of the time–temperature superposition principle (TTSP) to a multi-phase system is analytically discussed assuming a mixture law. It was concluded that the TTSP does not hold for a multi-phase system in general but does hold for a multi-component system in which some components have the same temperature dependence and the others have no temperature dependence. On the basis of the results, the application of the TTSP to plant materials such as wood and bamboo was examined using a mixture law and a stretched-exponential function having a characteristic relaxation time τ ₀ and a stretching parameter β. Wood can be treated as a multi-phase system consisting of a framework (f) and matrix (m). In this case, it was expected that the TTSP holds for the matrix in the shorter time region t?τ _0f under T<T _gf , while the TTSP holds for the framework in the longer time region t?τ _0m under T>T _gm , where t and T _g is a measurement time and the glass transition temperature, respectively.     

Application of time–temperature–stress superposition on creep of wood–plastic composites

Time–temperature–stress superposition principle (TTSSP) was widely applied in studies of viscoelastic properties of materials. It involves shifting curves at various conditions to construct master curves. To extend the application of this principle, a temperature–stress hybrid shift factor and a modified Williams–Landel–Ferry (WLF) equation that incorporated variables of stress and temperature for the shift factor fitting were studied. A wood–plastic composite (WPC) was selected as the test subject to conduct a series of short-term creep tests. The results indicate that the WPC were rheologically simple materials and merely a horizontal shift was needed for the time–temperature superposition, whereas vertical shifting would be needed for time–stress superposition. The shift factor was independent of the stress for horizontal shifts in time–temperature superposition. In addition, the temperature- and stress-shift factors used to construct master curves were well fitted with the WLF equation. Furthermore, the parameters of the modified WLF equation were also successfully calibrated. The application of this method and equation can be extended to curve shifting that involves the effects of both temperature and stress simultaneously.     

Application of Kramers–Kronig relations to time–temperature superposition for viscoelastic materials

Dynamical mechanical analysis (DMA) is an experimental technique commonly used to study the frequency and temperature dependence of the mechanical properties of viscoelastic materials. The measured data are traditionally shifted by application of the time–temperature superposition principle to obtain the master curves of the viscoelastic material. The goal of this work is to present a methodology to determine the horizontal and vertical shift coefficients to be applied to the isotherms of storage and loss moduli measured. The originality lies in the calculation of the shift coefficients by a method requiring fulfilment of the Kramers–Kronig relations conveying the causality condition. The computed vertical shift coefficients are compared to the coefficients predicted by the Bueche–Rouse theory.     

Flexural creep behavior of discontinuous thermoplastic composites: Non-linear viscoelastic modeling and time–temperature–stress superposition

Flexural creep behavior of nylon 6/6, polypropylene and high-density polyethylene long fiber thermoplastic (LFT) composites was studied according to ASTM D-2990. Neat polymers were tested for baseline data and compared with the 40 wt.% E-glass reinforced LFTs, all processed by compression molding. All materials exhibited non-linear viscoelasticity and showed a succession in creep resistance consistent with static flexural yield strength. A four parameter empirical model used for short fiber thermoplastics (SFT), proposed by Hadid et al., was found to provide an excellent fit to the experimental data. Time-compliance data from flexural creep and dynamic mechanical analysis (DMA) were combined to utilize short-term flexural creep tests to predict lifetime of the composites. A time–temperature–stress superposition (TTSSP) procedure was used, where stress-based vertical shifts were applied in addition to horizontal shifts used in a traditional time–temperature superposition (TTSP). Master curves obtained by this method projected the long-term creep properties, the order of creep resistance being consistent with the flexural creep data.     

Prediction of in vivo plasma concentration–time profile from in vitro release data of designed formulations of milnacipran using numerical convolution method

The aim of this study was to predict the in vivo plasma drug level of milnacipran (MIL) from in vitro dissolution data of immediate release (IR 50?mg and IR 100?mg) and matrix based controlled release (CR 100?mg) formulations. Plasma drug concentrations of these formulations were predicted by numerical convolution method. The convolution method uses in vitro dissolution data to derive plasma drug levels using reported pharmacokinetic (PK) parameters of a test product. The bioavailability parameters (C_max and AUC) predicted from convolution method were found to be 106.90?ng/mL, 1138.96?ng/mL?h for IR 50?mg and 209.80?ng/mL, 2280.61?ng/mL?h for IR 100?mg which are similar to those reported in the literature. The calculated PK parameters were validated with percentage predication error (% PE). The % PE values for C_max and AUC were found to be 7.04 and ?7.35 for IR 50?mg and 11.10 and ?8.21 for IR 100?mg formulations. The C_max, T_max, and AUC for CR 100?mg were found to be 120?ng/mL, 10?h and 2112.60?ng/mL?h, respectively. Predicted plasma profile of designed CR formulation compared with IR formulations which indicated that CR formulation can prolong the plasma concentration of MIL for 24?h. Thus, this convolution method is very useful for designing and selection of formulation before animal and human studies.     

Prediction of the temperature dependence of fracture toughness of 12Cr–2Ni–Mo refractory steel

We study the influence of temperature and the size of the specimens on the characteristics of static crack resistance of 12Cr–2Ni–Mo refractory steel. It is shown that, in the temperature range 20–450°C, the increase in the thickness of specimens leads to an insignificant increase in fracture toughness obtained along a 5% secant line according to the standards of evaluation of the characteristics of crack resistance. The evaluation of the characteristics of crack resistance of 12Cr–2Ni–Mo steel with regard for the scale effect according to an earlier developed numerical-experimental model reveals the existence of satisfactory agreement with the experimental data in the entire investigated temperature range. Translated from Problemy Prochnosti, No. 4, pp. 78–88, July–August, 2009.     

Shear failure characterization of time–temperature sensitive interfaces

Poor interlayer bonding can lead to early failures and thus to a reduction in service life of bituminous pavements. For this reason, it is important to identify the parameters influencing the interlayer shear failure and to characterize their effect by means of laboratory test. In particular, this study is focussed on the effects of test temperature and deformation rate on the interlayer shear strength (ISS) of double-layered asphalt concrete specimens. First, the ISS was measured at temperatures ranging from 0 °C to 30 °C and deformation rates ranging from 0.5 mm/min to 9 mm/min using the Ancona Shear Testing Research and Analysis (ASTRA) device. Then the experimental data were analyzed using a two-stage statistical modelling approach. In the first stage, the variation of ISS versus deformation rate, at each testing temperature, was modelled using both a power-law and a logarithmic function. In the investigated range of deformation rate, the models allowed to estimate the mean ISS with residual standard error varying from 0.062 MPa to 0.128 MPa. Moreover, the linear regression coefficients, which measure the influence of the deformation rate on ISS, changed with temperature. In the second stage, both temperature and deformation rate were used as joint predictors of ISS by using an approach based on the superposition of their effects. Results showed that the time–temperature superposition approach is applicable and a sigmoid-shaped master curve for ISS was obtained. The proposed approach was validated by using ISS measurements obtained on the same materials with different test devices.     

Prediction of the mechanical properties of the post-forged Ti–6Al–4V alloy using fuzzy neural network

Isothermal compression of the Ti–6Al–4V alloy was conducted at a 2500 ton isothermal hydrostatic press, and the mechanical properties including ultimate tensile strength, yield strength, elongation and area reduction of the post-forged Ti–6Al–4V alloy were measured at a ZWICK/Z150 testing machine. A fuzzy neural network (FNN) was applied to acquire the relationships between the mechanical properties and the processing parameters of post-forged Ti–6Al–4V alloy. In establishing those relationships, the forging temperature, strain and strain rate were taken as the inputs, whilst the ultimate tensile strength, yield strength, elongation and area reduction were taken as the output respectively. The predicted results using the present FNN model is in a good agreement with the experimental data of the post-forged Ti–6Al–4V alloy, and the optimum processing parameters can be quickly and conveniently selected to achieve the desired mechanical properties by means of the prediction based on the fuzzy neural network model.     

Experimental determination of the retention time of reduced temperature of gas–vapor mixture in trace of water droplets moving in counterflow of combustion products

Technical Physics Letters - We have experimentally studied temporal variation of the temperature of gas–vapor mixture in the trace of water droplets moving in the counterflow of...     

A partial least squares solution to the problem of multicollinearity when predicting the high temperature properties of 1Cr–1Mo–0.25V steel using parametric models

Recently there has been renewed interest in assessing the predictive accuracy of existing parametric models of creep properties, with the recently develop Wilshire methodology being largely responsible for this revival. Without exception, these studies have used multiple linear regression analysis (MLRA) to estimate the unknown parameters of the models, but such a technique is not suited to data sets where the predictor variables are all highly correlated (a situation termed multicollinearity). Unfortunately, because all existing long-term creep data sets incorporate accelerated tests, multicollinearity will be an issue (when temperature is held high, stress is always set low yielding a negative correlation). This article quantifies the severity of this potential problem in terms of its effect on predictive accuracy and suggests a neat solution to the problem in the form of partial least squares analysis (PLSA). When applied to 1Cr–1Mo–0.25V steel, it was found that when using MLRA nearly all the predictor variables in various parametric models appeared to be statistically insignificant despite these variables accounting for over 90% of the variation in log times to failure. More importantly, the same linear relationship appeared to exist between the first PLS component and the log time to failure in both short and long times to failure and this enabled more accurate extrapolations to be made of the time to failure, compared to when the models were estimated using MLRA.     

Growth of phases in the solid-state from room temperature to an elevated temperature in the Pd–Sn and the Pt–Sn systems

The solid-state growth of the product phases in bulk and electroplated diffusion couples of the Pd–Sn and the Pt–Sn systems is reported at various temperatures, ranging from room temperature to 215 °C. The growth rate of the product phase in the Pt–Sn system is found to be much lower compared to the Pd–Sn system and the Au–Sn system also, which is currently used in the microelectronics industry. The time dependent experiments indicate that the growth rate in the Pd–Sn system is parabolic in nature, i.e., it is controlled by the diffusion rates of components through the product phases. However, the growth rate is linear and hence reaction-controlled in the Pt–Sn system, which indicates that the formation of the compound is the rate-limiting step rather than the diffusion rates of components. The PdSn₄ phase covers almost whole interdiffusion zone in the Pd/Sn couple, while PtSn₄ is the only phase found in the Pt/Sn couple. The marker experiments indicate that both PdSn₄ and PtSn₄ grow mainly by the diffusion of Sn, with negligible diffusion of Pd and Pt, respectively. Furthermore, the analysis considering the same crystal structure (i.e., oC20) of these phases along with the concept of sublattice diffusion mechanism indicates that the diffusion rates of both Pd and Pt are negligible via both the lattice and the grain boundaries.     

Prediction of the mechanical properties of forged Ti–10V–2Fe–3Al titanium alloy using FNN

In this paper, a fuzzy neural network (FNN) prediction model has been employed to establish the relationship between processing parameters and mechanical properties of Ti–10V–2Fe–3Al titanium alloy. In establishing these relationships, deformation temperature, degree of deformation, solution temperature and aging temperature are entered as input variables while the ultimate tensile strength, yield strength, elongation and area reduction are used as outputs, respectively. After the training process of the network, the accuracy of fuzzy model was tested by the test samples and compared with regression method. The obtained results with fuzzy neural network show that the predicted results are much better agreement with the experimental results than regression method and the maximum relative error is less than 7%. And the optimum matching processing parameters can be quickly selected to achieve the desired mechanical property based on the fuzzy model. It proved that the model has a good precision and excellent ability of predicting.     

Side-chain motion of components in wood samples partially non-crystallized using NaOH–water solution

Wood samples (Picea jezoensis Carr.) were treated with solutions of aqueous NaOH (0–0.20 concentration fraction) and each treated samples evaluated by dynamic mechanical analyses (DMA). NaOH treatment was shown to affect the interactions between microfibrils and the surrounding matrix and, in particular, the dynamics of methylol groups in the microfibrils. The former is not dependent on the degree of crystallization but rather on the eluviation of the matrix. The latter depends on the degree of crystallization. Alkali treatment induces changes in the polymer domains as a result of matrix eluviation. This decreases the dynamics of methylol groups at NaOH concentrations less than 0.11. On the other hand, alkali treatment causes non-crystallization at concentrations greater than 0.11, which quantitatively increases the flexibility of methylol groups. Crystallinity decreased, and main-chain dynamics increased, following treatment with highly concentrated NaOH solutions. The dynamics of lignin also increased due to weakened interactions with microfibrils due to non-crystallization.     

Thermal diffusivity of gadolinium in the temperature range of 287–1277 K

New experimental data on the thermal diffusivity of gadolinium in the temperature interval from 287 to 1277 K obtained by the laser flash method with an error of 3–4% are presented. Results are compared with the available literature data. Reference tables on the heat transfer coefficients of gadolinium for scientific and practical use are developed. Critical indices for the thermal diffusivity of gadolinium above the Curie point are determined. The limitations of the laser flash method during measurement in the region of phase transformations are briefly discussed.     

Investigation of the heat conductivity of niobium in the temperature range 0.05–23 K

We report thermal conductivity measurements on a single-crystal niobium specimen of resistivity ratio 33,000 over the temperature range 0.05–23 K in the superconducting state and above 9.1 K in the normal state. The axis of the niobium rod was [110] oriented. The surface roughness was varied by sandblasting of the sample. The values of the thermal conductivity in the range from the lowest temperatures up to the maximal value covered a range of six orders of magnitude (=2×10^–5 W cm^–1 K^–1 at 50 mK to =22 W cm^–1 K^–1 at 9 K). Above 2 K the results for the untreated and the sandblasted sample are in accord, whereas below 2 K the influence of the sample surface is discernible. The various conduction and scattering mechanisms are discussed.     

Alumina formation and microstructural changes of aluminized CoNiCrAlY coating during high temperature exposure in the temperature range 925°C–1075°C

Abstract

MCrAlY (M = Ni, Co) coatings are commonly used on gas-turbine components as oxidation resistant overlay coatings and bondcoats for thermal barrier systems. In the present work the microstructural features and oxidation behavior of an aluminized Co-base MCrAlY-coating on a Ni-based superalloy have been investigated in the temperature range 925–1075 °C. Microstructural studies of the oxidized coatings by SEM/EBSD were complemented with numerical thermodynamic calculations using the software package ThermoCalc. In the as-received condition the outer part of the coating consisted mostly of β-(Ni,Co)Al. Formation of σ-CoCr was observed at the interface between the β-layer and the inner initial CoNiCrAlY. During high-temperature air exposure alumina based surface scales were formed but the oxidation induced Al depletion of the aluminized coating did not result in formation of the γ’-(Ni₃Al) phase. Rather, the subscale formation of Co/Cr-rich phases was observed and a direct transformation of β- into γ-Ni phase after longer times. It is expected that these subscale microstructural changes thus affect the alumina formation and growth as well as the critical aluminum depletion in a different manner as in the case of corresponding β-NiAl coatings, although a direct comparison between various coating systems was not possible on the basis of the present results.     

Prediction of flow stress in isothermal compression of Ti–6Al–4V alloy using fuzzy neural network

Isothermal compression of Ti–6Al–4V alloy at the deformation temperatures ranging from 1093 K to 1303 K with an interval 20 K, the strain rates ranging from 0.001 s⁻¹ to 10.0 s⁻¹ and the height reductions ranging from 20% to 60% with an interval 10% were carried out on a Thermecmaster-Z simulator. Based on the experimental results, a model for the flow stress in isothermal compression of Ti–6Al–4V alloy was established in terms of the fuzzy neural network (FNN) with a back-propagation learning algorithm using strain, strain rate and deformation temperature as inputs. The maximum difference and the average difference between the predicted and the experimental flow stress are 18.7% and 4.76%, respectively. The comparison between the predicted results based on the FNN model for flow stress and those using the regression method has illustrated that the FNN model is more efficient in predicting the flow stress of Ti–6Al–4V alloy.