A New Viscoelastic Model for Polycarbonate Compressing Flow

Background: Prediction models, e.g. for prediction of response to growth hormone treatment, need validation in appropriate independent cohorts, comparing predicted and observed outcomes. In a previous validation of a model for predicting the first-year response to growth hormone treatment in children with idiopathic growth hormone deficiency, overfitting was observed. We modified the prediction formula and now report validation of this modified model. Patients and Methods: The modified and original prediction models were applied to a group of patients selected from Lilly’s GeNeSIS database using the same inclusion and exclusion criteria as for the original model. For both prediction methods, observed first-year height velocity was plotted vs. predicted height velocity in a calibration plot. For a valid prediction, the regression line should correspond to the line of identity (observed outcome is equal to predicted outcome); the regression lines for each prediction model were tested for significant differences from this line of identity. Results: The number of patients fulfilling the criteria was 226. The regression line in the calibration plot of the modified model was not significantly different from the line of identity (p = 0.43), in contrast to the original model (p < 0.001). For the modified model the mean (SD) prediction error was –0.11 (2.05) cm/year and for the original model 0.28 (2.11) cm/year. Conclusion: The modified prediction method, obtained after calibration of the original model, performs well in an independent patient sample and gives more accurate predictions than the original model.

Numerical prediction of pressure drop and particle separation efficiency of some vane-type dust filters

Liquid sloshing is a common phenomenon in the transporting of liquid tanks. Liquid waves lead to fluctuating forces on the tank wall. If these fluctuations are not predicted or controlled, they can lead to large forces and momentum. Baffles can control liquid sloshing fluctuations. One numerical method, widely used to model the liquid sloshing phenomena is Smoothed Particle Hydrodynamics (SPH). Because of its Lagrangian nature, SPH is suitable for simulating free surface flow. In the present study, a relatively accurate Incompressible SPH (ISPH) method improved by kernel gradient correction tensors, particle shifting algorithms, turbulence viscosity calculations, and free surface particle detectors is applied for the free surface flow modeling. In comparison to the other SPH Simulations and experimental data, these results show that the present algorithm is effective for simulating free surface problems. The present algorithm has been applied to simulate liquid sloshing phenomena, while the aim of this study is the investigation of vertical and horizontal baffle effects on the control and damping of liquid sloshing. Results show that for vertical baffles, baffle size has a major role in sloshing fluctuation damping. For horizontal baffles, also including size, the baffle base position has a significant role in liquid sloshing fluctuation damping. When horizontal baffle is near the free surface, sloshing fluctuation-damping increases.

The relation between slump flow and yield stress of ultra-high performance concrete (UHPC) mixtures was studied with theoretical analysis and experimentation. The relational expression between slump flow and yield stress of UHPC mixtures was built and then verified with a rheological test. The results showed that the prediction model, as a function of cone geometry of dimensionless slump flow and dimensionless yield stress of the UHPC mixtures, was constructed based on Tresca criteria, considering the geometric relation of morphological characterization parameters before and after slump of the UHPC mixtures. The rationality and applicability of the dimensionless prediction model was verified with a rheological test and a slump test of UHPC mixtures with different dosages of polycarboxylate superplasticizer. With increase in polycarboxylate superplasticizer dosage, yield stress of the two series of UHPC mixtures (large/small binding material consumption) gradually decreased, leading to a gradual increase in slump flow. Based on the prediction model of dimensionless slump flow and dimensionless yield stress, the relational expression between slump flow and yield stress of the UHPC mixtures was built. The comparison result showed that the calculated data was consistent with the experimental data, which provided a new method for predicting yield stress of UHPC mixtures with a slump test.

A strategy to establish Food Safety Model Repositories

Several aspects of the melting temperatures (MT) of linear chain homologs are discussed. It is shown that with some reasonable modifications of Einstein's model of simple solids, the MT of n-alkanes and their derivatives can be successfully modeled. Results are compared with those obtained by previous authors who have used numerical methods and thermodynamics. Results of investigation of quadratic recurrence relations of the second order and forms including exponential functions are reported. Finally, the correlation of MT with density at T = 298.15 K gives the limiting value, 0.86 gcm-3 , at the temperature of nearly 420 K. It is expected that proper correlations should use densities of liquids just at the MT, because some available data suggest a linear dependence of MT on density. All results obtained are mutually consistent

Warpage reduction, dimensional tolerance accomplishment and time and cost saving are some of the most important problems in the injection molding production process. In this study, a real case of a thermoplastic injected part is analyzed. The mold for a pipeline connecting element has been designed, according to known technical and economical criterion to fulfill customer requirements. Particularly for this element, warpage of a specified surface and two diameter sizes are fundamental for correct part functionality and assembling. The analysis is centered on the effects of four process parameters, i. e. packing pressure, packing time, melt temperature and cooling time that heavily influence final results. A finite element model has been used to evaluate their effects on four important final objectives that are the geometrical entities and production cycle time. Each variable has been varied into its proper range, in accordance with a central composite design DoE plan; 25 simulations have been executed and results have been represented using response surfaces. Pareto Front for above listed objectives has been extracted using a genetic algorithm and the best set of parameters has been determined after application of specific selection criteria and a weighted objective function. After numerical evaluation, the CCD DoE plan has been experimentally repeated. Results have been measured on real components, and then represented with response surfaces as well. The same algorithm and objective function have been used for optimization, to determine the experimental optimum parameter set. Finally, the two parameter sets have been compared.

Prediction model of buoyancy-driven flow rate in inclined tunnels with a localized buoyancy source: Emphasis on stratification effects

Quantitative predictions of total dissolved gas (TDG) super-saturation are essential for developing operation schemes for high dams. Most TDG generation prediction models have various shortcomings that affect the accuracy of TDG super-saturation estimation, such as oversimplification of influencing factors and uncertainty in parameter values. In this study, the TDG generation process was divided into three parts, gas-liquid mass transfer process in the stilling phase, dilution resulting from the water jet plunging into the stilling phase, and outflow of TDG–super-saturated water from the stilling phase, while considering the water body and bubbles in the stilling phase as a whole. The residence time of the water in the stilling phase (Tr) was introduced to estimate mass transfer time, along with dimensional analysis methods. The properties of TDG generation were evaluated experimentally under varying Tr values. Based on the theoretical analysis and experimental results, a basic water renewal model was proposed and was validated using experimental data. Furthermore, prediction results of this model were compared with those of a classical empirical model and mechanical model based on observed data from a field survey at Xiluodu Dam. The results show that the relative errors between the predicted and experimental measurements were all less than 5%, indicating that the developed prediction model has a good performance. Compared with the mechanism model, the developed model could reduce the standard error (SE), normalized mean error (NME), and error of maximum (REMAX) by 60, 96, and 15%, respectively. Meanwhile, the developed model could reduce the SE, NME, REMAX by 17.4, 36, and 23%, respectively, compared with the empirical model. Considering all the error indexes, it can be concluded that the prediction performance of the water renewal model is the best among the three models. The proposed model was also more generically versatile than the existing models. Prediction results of water regeneration model for TDG could aid the drafting of governing strategies to minimize the risk of super-saturated TDG.

A prediction model for the flash point of binary liquid mixtures

High density polyethylene (HDPE) and polypropylene (PP) solubility in several pure and blend non-polar organic solvents was measured at 365–430 K temperature at atmospheric pressure, with polymer concentration of 0.5–25 g in 100 ml of solvent. The activity coefficients were estimated depending on the experimental solubility results for all the polymer-solvent systems. A non-ideal equation combined with activity coefficient was developed based on the crystallinity. A new correlation equation was attained, which is based on the melting temperature and heat of fusion using SSPS software. These two equations were used to predict the solid-liquid experimental data for the binary system polymer-solvent. The distinction between the experimental and model data was assessed by using mean absolute deviation percentage (MAD %). The non-ideal equation based on crystallinity and the new correlations showed low MAD %, displaying a close match with the experimental data.

Random effects regression models are routinely used for clustered data in etiological and intervention research. However, in prediction models, the random effects are either neglected or conventionally substituted with zero for new clusters after model development. In this study, we applied a Bayesian prediction modelling method to the subclinical ketosis data previously collected by Van der Drift et al. (2012). Using a dataset of 118 randomly selected Dutch dairy farms participating in a regular milk recording system, the authors proposed a prediction model with milk measures as well as available test-day information as predictors for the diagnosis of subclinical ketosis in dairy cows. While their original model included random effects to correct for the clustering, the random effect term was removed for their final prediction model. With the Bayesian prediction modelling approach, we first used non-informative priors for the random effects for model development as well as for prediction. This approach was evaluated by comparing it to the original frequentist model. In addition, herd level expert opinion was elicited from a bovine health specialist using three different scales of precision and incorporated in the prediction as informative priors for the random effects, resulting in three more Bayesian prediction models. Results showed that the Bayesian approach could naturally take the clustering structure of clusters into account by keeping the random effects in the prediction model. Expert opinion could be explicitly combined with individual level data for prediction. However in this dataset, when elicited expert opinion was incorporated, little improvement was seen at the individual level as well as at the herd level. When the prediction models were applied to the 118 herds, at the individual cow level, with the original frequentist approach we obtained a sensitivity of 82.4% and a specificity of 83.8% at the optimal cutoff, while with the three Bayesian models with elicited expert opinion, we obtained sensitivities ranged from 78.7% to 84.6% and specificities ranged from 75.0% to 83.6%. At the herd level, 30 out of 118 within herd prevalences were correctly predicted by the original frequentist approach, and 31 to 44 herds were correctly predicted by the three Bayesian models with elicited expert opinion. Further investigation in expert opinion and distributional assumption for the random effects was carried out and discussed.

A numerical method has been developed for the analysis of microwave devices made with superconducting materials or/and normal (perfect or imperfect) metal conductors and lossy dielectrics. This method, based on 2D and 3D Finite Element Method (F.E.M.) is applied here to evaluate the propagation characteristics of microstrip lines, coplanar waveguides, and the scattering parameters of planar circuits (resonators,. . .). The results given by F.E.M. are compared with those obtained by using other theoretical analysis or/and with experimental data.

To prevent SCC (stress corrosion cracking) of austenite stainless steel was sensitized in the heat-affected zone of a conventional welding bead. We developed a surface solution heat treatment technology using high-power YAG lasers and a prediction model using experimental data. The solution heat treatment (SHT) zone is influenced by several laser irradiation parameters included with intensity distribution. Because it was difficult to estimate the SHT zone by a simple moving heat source conduction model, the prediction model obtained by experimental data using four different optics became an useful method. We found that the equation for predicting of the depth (D) is D∼P/aV and the geometrical correlated width (W) is W∼aD.To prevent SCC (stress corrosion cracking) of austenite stainless steel was sensitized in the heat-affected zone of a conventional welding bead. We developed a surface solution heat treatment technology using high-power YAG lasers and a prediction model using experimental data. The solution heat treatment (SHT) zone is influenced by several laser irradiation parameters included with intensity distribution. Because it was difficult to estimate the SHT zone by a simple moving heat source conduction model, the prediction model obtained by experimental data using four different optics became an useful method. We found that the equation for predicting of the depth (D) is D∼P/aV and the geometrical correlated width (W) is W∼aD.

Flash-point estimation for binary partially miscible mixtures of flammable solvents by UNIFAC group contribution methods