Gas-liquid two phase flow through a vertical 90deg elbow bend

Pressure drop data are reported for two phase air-water flow through a vertical to horizontal 90° elbow bend set in 0.026 m i.d. pipe. The pressure drop in the vertical inlet tangent showed some significant differences to that found for straight vertical pipe. This was caused by the elbow bend partially choking the inflow resulting in a build-up of pressure and liquid in the vertical inlet riser and differences in the structure of the flow regimes when compared to the straight vertical pipe. The horizontal outlet tangent by contrast gave data in general agreement with literature even to exhibiting a drag reduction region at low liquid rates and gas velocities between 1 and 2 m s -1. The elbow bend pressure drop was best correlated in terms of le/d determined using the actual pressure loss in the inlet vertical riser. The data showed a general increase with fluid rates that tapered off at high fluid rates and exhibited a negative pressure region at low rates. The latter was attributed to the flow being smoothly accommodated by the bend when it passed from slug flow in the riser to smooth stratified flow in the outlet tangent. A general correlation was presented for the elbow bend pressure drop in terms of total Reynolds numbers. A modified Lockhart-Martinelli model gave prediction of the data.

Improved Principal Component Monitoring of Large-Scale Processes

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Introduction of a Nonlinearity Measure for Principal Component Models

This is the first paper that introduces a nonlinearity test for principal component models. The methodology involves the division of the data space into disjunct regions that are analysed using principal component analysis using the cross-validation principle. Several toy examples have been successfully analysed and the nonlinearity test has subsequently been applied to data from an internal combustion engine.

Kv1.5 is a major component underlying the A-type potassium current in retinal arteriolar smooth muscle.

Little is known about the molecular characteristics of the voltage-activated K(+) (K(v)) channels that underlie the A-type K(+) current in vascular smooth muscle cells of the systemic circulation. We investigated the molecular identity of the A-type K(+) current in retinal arteriolar myocytes using patch-clamp techniques, RT-PCR, immunohistochemistry, and neutralizing antibody studies. The A-type K(+) current was resistant to the actions of specific inhibitors for K(v)3 and K(v)4 channels but was blocked by the K(v)1 antagonist correolide. No effects were observed with pharmacological agents against K(v)1.1/2/3/6 and 7 channels, but the current was partially blocked by riluzole, a K(v)1.4 and K(v)1.5 inhibitor. The current was not altered by the removal of extracellular K(+) but was abolished by flecainide, indicative of K(v)1.5 rather than K(v)1.4 channels. Transcripts encoding K(v)1.5 and not K(v)1.4 were identified in freshly isolated retinal arterioles. Immunofluorescence labeling confirmed a lack of K(v)1.4 expression and revealed K(v)1.5 to be localized to the plasma membrane of the arteriolar smooth muscle cells. Anti-K(v)1.5 antibody applied intracellularly inhibited the A-type K(+) current, whereas anti-K(v)1.4 antibody had no effect. Co-expression of K(v)1.5 with K(v)beta1 or K(v)beta3 accessory subunits is known to transform K(v)1.5 currents from delayed rectifers into A-type currents. K(v)beta1 mRNA expression was detected in retinal arterioles, but K(v)beta3 was not observed. K(v)beta1 immunofluorescence was detected on the plasma membrane of retinal arteriolar myocytes. The findings of this study suggest that K(v)1.5, most likely co-assembled with K(v)beta1 subunits, comprises a major component underlying the A-type K(+) current in retinal arteriolar smooth muscle cells

Performance of clay samples reinforced with vertical granular columns

This paper reports an experimental study in which samples of soft kaolin clay (100 mm in diameter and 200 mm in height) were reinforced with vertical columns of sand and tested under triaxial conditions. Samples were reinforced with either a single column of sand of 32 mm diameter or three columns of sand, each of 20 mm diameter. The replacement method was used to form the columns. The columns were installed in the clay to depths of 120 and 200 mm. Tests were also carried out on samples that were not reinforced with sand columns. The samples were compressed under both drained and undrained conditions. It was found that the undrained shear strength of samples containing full-depth columns was greatly improved compared with that of the unreinforced samples. In the fully drained tests, the sample installed with a single column of 32 mm diameter exhibited better performance than the sample with three columns of 20 mm diameter, although the area replacement ratio in the case of the three 20 mm diameter columns was higher than that of the single 32 mm diameter column. However, the undrained strength of the composite material was not particularly affected by the number of columns.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.