CFD analysis of thermodynamic cycles in a pulse tube refrigerator

The objectives of this paper are to study the thermodynamic cycles in an inertance tube pulse tube refrigerator (ITPTR) by means of CFD method The simulation results show that gas parcels working in different parts of ITPTR undergo different thermodynamic cycles The net effects of those thermodynamic cycles are pumping heat from the low temperature part to the high temperature part of the system The simulation results also show that under different frequencies of piston movement the gas parcels working in the same part of the system will undergo the same type of thermodynamic cycles The simulated thermal cycles are compared with those thermodynamic analysis results from a reference Comparisons show that both CFD simulations and theoretical analysis predict the same type of thermal cycles at the same location However only CFD simulation can give the quantitative results while the thermodynamic analysis is still remaining in quality (C) 2010 Elsevier Ltd All rights reserved

Experimental and numerical study on power consumptions in a double-tube-socket pneumatic conveying system

A new methodology is proposed in this paper to predict the lowest power consumption for a double-tube-socket (DTS) pneumatic conveying system. This methodology is established on both experimental work and numerical simulation. After parametric studies by numerical simulation, the desired conveying cases which have the lowest power consumption were obtained. Finally those cases were carried out in our experimental system. The measured power consumption was close to that predicted. In this paper the experimental work is discussed and the numerical simulation introduced. (c) 2010 Elsevier B.V. All rights reserved.

Study on peak overpressure and flame propagation speed of gas deflagration in the tube with obstacles

The on-way peak overpressure and flame propagation speed of gas deflagration in the tube with obstacles are important data for process safety. Based on carbon monoxide deflagration experiments, the paper presents a multi-zone integration model for calculation of on-way peak overpressure, in which the tube with obstacles is considered as a series of venting explosion enclosures which link each others. The analysis of experimental data indicates that the on-way peak overpressure of gas deflagration can be correlated as an empirical formula with equivalence ratio of carbon monoxide oxidation, expansion ratio, flame path length, etc., and that the on-way peak overpressure exhibits a linear relationship with turbulence factor and flame propagation speed. An empirical formula of flame propagation speed is given.

CFD analysis of pneumatic conveying in a double-tube-socket (DTS (R)) pipe

A pipeline with a bypass is widely used for the pneumatic conveying of material. The double-tube-socket (DTS (R)) technology, which uses a special inner bypass, represents the current state of the art. Here, a new methodology is proposed based on the use of computational fluid dynamics (CFD) to predict the energy consumption of DTS conveying. The predicted results are in good agreement with those from pilot-scale experiments. (C) 2010 Elsevier Inc. All rights reserved.

NEW METHODOLOGY FOR THE ESTIMATION OF ENERGY CONSUMPTION IN A DOUBLE-TUBE-SOCKET (DTS (R)) PNEUMATIC CONVEYING

A new methodology based on the use of CFD is proposed to estimate the energy consumptions in a DTS (DOUBLE-TUBE-SOCKET) pneumatic conveying. A simple computational program based on this methodology is developed. It can directly give the lowest energy consumption and the compatible gas consumption by only input the distance of conveying and the conveying tonnage. This computational program has been validated through our experimental work.

Effects of Naotan Pill on repair of neural cells and cognitive disorders in juvenile rats following hypoxia and ischemia

BACKGROUND: Hypoxia and ischemia induce neuronal damage, decreased neuronal numbers and synaptophysin levels, and deficits in learning and memory functions. Previous studies have shown that lycium barbarum polysaccharide, the most effective component of barbary wolfberry fruit, has protective effects on neural cells in hypoxia-ischemia. OBJECTIVE: To investigate the effects of Naotan Pill on glutamate-treated neural cells and on cognitive function in juvenile rats following hypoxia-ischemia. DESIGN, TIME AND SETTING: The randomized, controlled, in vivo study was performed at the Cell Laboratory of Lanzhou University, Lanzhou Institute of Modern Physics of Chinese Academy of Sciences, and Department of Traditional Chinese Medicine of Gansu Provincial Rehabilitation Center Hospital, China from December 2005 to August 2006. The cellular neurobiology, in vitro experiment was conducted at the Institute of Human Anatomy, Histology, Embryology and Neuroscience, School of Basic Medical Sciences, Lanzhou University, and Department of Traditional Chinese Medicine of Gansu Provincial Rehabilitation Center Hospital, China from March 2007 to January 2008. MATERIALS: Naotan Pill, composed of barbary wolfberry fruit, danshen root, grassleaf sweetflag rhizome, and glossy privet fruit, was prepared by Gansu Provincial Rehabilitation Center, China. Rabbit anti-synaptophysin, choline acetyl transferase polyclonal antibody, streptavidin-biotin complex kit and diaminobenzidine kit (Boster, Wuhan, China), as well as glutamate (Hualian, Shanghai, China) were used in this study. METHODS: Cortical neural cells were isolated from neonatal Wistar rats. Neural cell damage models were induced using glutamate, and administered Naotan Pill prior to and following damage. A total of 54 juvenile Wistar rats were equally and randomly assigned into model, Naotan Pill, and sham operation groups. The left common carotid artery was ligated, and then rat models of hypoxic-ischemic injury were assigned to the model and Naotan Pill groups. At 2 days following model induction, rats in the Naotan Pill group were administered Naotan Pill suspension for 21 days. In the model and sham operation groups, rats received an equal volume of saline. MAIN OUTCOME MEASURES: Neural cell morphology was observed using an inverted phase contrast microscope. Survival rate of neural cells was measured by MTT assay. Synaptophysin and choline acetyl transferase expression was observed in the hippocampal CA1 region of juvenile rats using immunohistochemistry. Cognitive function was tested by the Morris water maze. RESULTS: Pathological changes were detected in glutamate-treated neural cells. Neural cell morphology remained normal after Naotan Pill intervention. Absorbance and survival rate of neural cells were significantly greater following Naotan Pill intervention, compared to glutamate-treated neural cells (P < 0.05). Synaptophysin and choline acetyl transferase expression was lowest in the hippocampal CA1 region in the model group and highest in the sham operation group. Significant differences among groups were observed (P < 0.05). Escape latency and swimming distance were significantly longer in the model group compared to the Naotan Pill group (P < 0.05). CONCLUSION: Naotan Pill exhibited protective and repair effects on glutamate-treated neural cells. Naotan Pill upregulated synaptophysin and choline acetyl transferase expression in the hippocampus and improved cognitive function in rats following hypoxia-ischemia.

Preparation of palladium membrane over porous stainless steel tube modified with zirconium oxide

A palladium membrane has been prepared by electroless plating on the surface of a porous stainless steel tube. Since the large surface pores of the tube are obstacle for preparation of a defect-free palladium film on the surface, zirconium oxide particles were deposited inside the pores. The mean thickness of the resulting Pd membrane on the modified tube was ca. 10 mum. It is suggested that the permeability of hydrogen is partly governed by gas diffusion in the pores. (C) 2004 Elsevier B.V. All rights reserved.

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP-HPLC using feed-forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP-HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two-dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An "early stopping" strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.

Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.

Retention modeling and optimization of pH value and solvent composition in HPLC using back-propagation neural networks and uniform design

A novel method for the optimization of pH value and composition of mobile phase in HPLC using artificial neural networks and uniform design is proposed. As the first step. seven initial experiments were arranged and run according to uniform design. Then the retention behavior of the solutes is modeled using back-propagation neural networks. A trial method is used to ensure the predicting capability of neural networks. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for both basic and acidic samples.

Simultaneous determination of methylene blue and new methylene blue by slab optical waveguide spectroscopy and artificial neural networks

A slab optical waveguide (SOWG) has been used for study of adsorption of both methylene blue (MB) and new methylene blue (NMB) in liquid-solid interface. Adsorption characteristics of MB and NMB on both bare SOWG and silanized SOWG by octadecyltrichlorosilane (ODS) were compared. The simultaneous determinations of both MB and NMB were explored by flow injection SOWG spectrophotometric analysis and artificial neural networks (ANNs) for the first time. Concentrations of MB and NMB were estimated simultaneously with the ANNs. Results obtained with SOWG were compared with those got by conventional UV-visible spectrophotometry. (C) 2003 Elsevier Science B.V All rights reserved.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.