Ice-slurry production using direct contact heat transfer

An ice slurry generation system was developed using direct contact heat transfer between water and the coolant, Fluroinert FC-84. The location of the coolant nozzle is an important design consideration to avoid clogging due to freezing of water. An ice fraction of up to about 40 percent was obtained with the nozzle located at the bottom of the ice slurry tank and the jet directed upwards into the water. Two simplified model were developed to extract the heat transfer coefficient between the coolant drops and the water. The first model requires as input the average drop diameter and the residence time while the second model uses the measured drop diameter distribution. The estimated heat transfer coefficients are much smaller than those computed using single-sphere correlations.     

Control of calcium hexaluminate grain morphology in in-situ toughened ceramic composites

The influence of processing conditions on the morphology of calcium hexaluminate (CA₆) grains in Al₂O₃: 30 vol% CaO·6Al₂O₃ (CA₆) ceramic composites was investigated. Specimens were prepared by in-situ reaction sintering using precursor powders of alumina, and either calcium carbonate or calcium oxide. In some samples, 1 vol% anorthite glass was added as a sintering aid. X-ray diffraction was used to study the phase development in the as-calcined and sintered states. The resultant microstructures were characterized using both scanning electron microscopy (SEM), and imaging secondary ion mass spectrometry (SIMS). It was found that the CA₆ grains developed a platelike morphology when CaCO₃ was used as the starting calcium-rich powder. In contrast, samples prepared using CaO resulted in equiaxed CA₆ grains. This result was observed to be independent of the anorthite glass addition. The findings are rationalized in terms of distinct CA₆ reaction mechanisms, resulting from differences in the reactivity of the powders during the early stages of calcining.Cement Nomenclature C CaO - A Al₂O₃     

Adaptive weighted outer-product learning associative memory

Associative-memory neural networks with adaptive weighted outer-product learning are proposed in this paper. For the correct recall of a fundamental memory (FM), a corresponding learning weight is attached and a parameter called signal-to-noise-ratio-gain (SNRG) is devised. The sufficient conditions for the learning weights and the SNRG's are derived. It is found both empirically and theoretically that the SNRG's have their own threshold values for correct recalls of the corresponding FM's. Based on the gradient-descent approach, several algorithms are constructed to adaptively find the optimal learning weights with reference to global- or local-error measure.     

An eight‐node hybrid‐stress solid‐shell element for geometric non‐linear analysis of elastic shells

This paper presents eight‐node solid‐shell elements for geometric non‐linear analysis of elastic shells. To subdue shear, trapezoidal and thickness locking, the assumed natural strain method and an ad hoc modified generalized laminate stiffness matrix are employed. A selectively reduced integrated element is formulated with its membrane and bending shear strain components taken to be constant and equal to the ones evaluated at the element centroid. With the generalized stresses arising from the modified generalized laminate stiffness matrix assumed to be independent from the ones obtained from the displacement, an extended Hellinger–Reissner functional can be derived. By choosing the assumed generalized stresses similar to the assumed stresses of a previous solid element, a hybrid‐stress solid‐shell element is formulated. Commonly employed geometric non‐linear homogeneous and laminated shell problems are attempted and our results are close to those of other state‐of‐the‐art elements. Moreover, the hybrid‐stress element converges more readily than the selectively reduced integrated element in all benchmark problems. Copyright © 2002 John Wiley & Sons, Ltd.     

用奇异模型描述的受限机器人的学习控制

本文了一类受限机器人的迭代学习控制方案，控制器的设计是基于机器人模型的降阶形式。在存在有界未知干扰的情况下，对末端操纵器受线性、无摩擦约束面的受限机器人。本文给出的控制方案保证了机器人系统的完全运动跟踪，同时保证了力跟踪误差有界的，且界的大小是可调节的。