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Are tube viscometer data valid for suspension flows?
Lionel Pullum,Paul Slatter,Lachlan Graham,Andrew Chryss 한국유변학회 2010 Korea-Australia rheology journal Vol.22 No.3
Successful rheological characterization of mineral slurries is fraught with many problems and the need to pump higher concentrations, especially in tailings disposal, has meant that more and more slurries exhibit non-Newtonian behaviour. Capillary tube viscometry is the preferred method of testing and apparently credible results obtained from stable "homogeneous" suspensions, containing particles with diameters of 10s and 100s of microns, are the rule rather than the exception. Settleable solids suspended in visco-plastic fluids may be stable in the un-sheared condition, but will stratify when the fluid is sheared. This behaviour results in an unobserved stratified flow within the conveying line. For small pipes, such as those used for rheological characterization, the stratified bed flow effect is small and is masked by the viscous nature of the suspending medium. Unfortunately, the stratified bed flow dominates the transport pressure gradients in larger pipes, resulting in often gross under-prediction of full size behaviour. To illustrate this effect, tests conducted with a non-Newtonian carrier fluid in 12 and 25NB (nominal bore) pipes were found to be insensitive to the addition of large (-1mm) particles at concentrations up to 20% w/w. Conversely, transport characteristics for these suspensions are a strong function of solids concentration in larger pipes, e.g 100NB. Analysis presented in the paper shows that such behaviour is consistent with the behaviour of high viscosity stratified flows. In practice, tests would not be attempted with such large particles, which make the results obtained even more surprising. The behaviour casts doubts upon the validity of much of the capillary tube data obtained with "normal" slurry size distributions. This phenomenon needs to be understood, if the design of high concentration pumping systems for industrial slurries and pastes is to be performed with any certainty.
Papamarkou, Theodore,Guy, Hayley,Kroencke, Bryce,Miller, Jordan,Robinette, Preston,Schultz, Daniel,Hinkle, Jacob,Pullum, Laura,Schuman, Catherine,Renshaw, Jeremy,Chatzidakis, Stylianos Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.2
Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability of such methods. This is important for inspecting high value assets or assets with a high consequence of failure, such as aerospace and nuclear components. Recent advances in convolution neural networks can support and automate these inspection efforts. This paper proposes using residual neural networks (ResNets) for real-time detection of corrosion, including iron oxide discoloration, pitting and stress corrosion cracking, in dry storage stainless steel canisters housing used nuclear fuel. The proposed approach crops nuclear canister images into smaller tiles, trains a ResNet on these tiles, and classifies images as corroded or intact using the per-image count of tiles predicted as corroded by the ResNet. The results demonstrate that such a deep learning approach allows to detect the locus of corrosion via smaller tiles, and at the same time to infer with high accuracy whether an image comes from a corroded canister. Thereby, the proposed approach holds promise to automate and speed up nuclear fuel canister inspections, to minimize inspection costs, and to partially replace human-conducted onsite inspections, thus reducing radiation doses to personnel.