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The objectives of this study were to determine: the type of coating that minimized pipe surface roughness and how the choice of metrological instrument could influence pipe surface roughness data. The internal surface of pipe was coated with either phenolic, modified novalac, epoxy, or nylon material. Roughness of coated pipe was assessed with two linear surface profilers, a Dektak³ST^® and a Hommel T1000, and a Dimension 3000^® atomic force microscope (AFM). Arithmetic roughness (R_a), root mean square roughness (R_q), and mean peak‐to‐valley height (R_ZD), were statistically analyzed. The ability of R_ZD to focus on the extremes of height and depth on the surface made it a significant parameter for detecting features that would affect fluid flow in pipes.

A novel approach using artificial neural networks (ANNs) for predicting temperature profiles evaluated 27 wells in the Gulf of Mexico. Two artificial neural network models were developed that predict the temperature of the flowing fluid at any depth in flowing oil wells. Back propagation was used in training the networks. The networks were tested using measured temperature profiles from the 27 oil wells. Both neural network models successfully mapped the general temperature‐profile trends of naturally flowing oil wells. The highest accuracy was achieved with a mean absolute relative percentage error of 6.0 per cent. The accuracy of the proposed neural network models to predict the temperature profile is compared to that of existing correlations. Many correlations to predict temperature profiles of the wellbore fluid, for single‐phase or multiphase flow, in producing oil wells have been developed using theoretical principles such as energy, mass and momentum balances coupled with regression analysis. The Neural Network 2 model exhibited significantly lower mean absolute relative percentage error than other correlations. Furthermore, in order to test the accuracy of the neural network models to that of Kirkpatrick’s correlation, a mathematical model was developed for Kirkpatrick’s flowing temperature gradient chart.

In this study, a three‐dimensional (3D) flow model is used to approximate the crystallinity gradients of slowly crystallizing polymers developed in the injection molding process. A generalized second order parallel splitting formula is constructed to achieve both the accuracy and efficiency of the computation. Calculated values of flow‐wise (flow‐thickness plane) and width‐wise (width‐thickness plane) crystallinity distributions are obtained and compared with experimental results. The structure‐oriented simulation method developed is not only capable of describing moldability parameters, but is also able to predict the characteristics of ultimate properties of the final products.