Validation of two-layer model for underexpanded hydrogen jets

Previous studies have shown that the two-layer model more accurately predicts hydrogen dispersion than the conventional notional nozzle models without significantly increasing the computational expense. However, the model was only validated for predicting the concentration distribution and has not been adequately validated for predicting the velocity distributions. In the present study, particle imaging velocimetry (PIV) was used to measure the velocity field of an underexpanded hydrogen jet released at 10 bar from a 1.5 mm diameter orifice. The two-layer model was the used to calculate the inlet conditions for a two-dimensional axisymmetric CFD model to simulate the hydrogen jet downstream of the Mach disk. The predicted velocity spreading and centerline decay rates agreed well with the PIV measurements. The predicted concentration distribution was consistent with data from previous planar Rayleigh scattering measurements used to verify the concentration distribution predictions in an earlier study. The jet spreading was also simulated using several widely used notional nozzle models combined with the integral plume model for comparison. These results show that the velocity and concentration distributions are both better predicted by the two-layer model than the notional nozzle models to complement previous studies verifying only the predicted concentration profiles. Thus, this study shows that the two-layer model can accurately predict the jet velocity distributions as well as the concentration distributions as verified earlier. Though more validation studies are needed to improve confidence in the model and increase the range of validity, the present work indicates that the two-layer model is a promising tool for fast, accurate predictions of the flow fields of underexpanded hydrogen jets.     

Investigation of Pt and Pd Modified WO<Subscript>3</Subscript> and ZnO Based Thin Film Sensors for Ethanol Sensing

Ethanol sensors based on different WO₃ and ZnO structures are studied in the present work. The XRD and SEM processes are used to characterize the sensing layer’s surface morphology which reveals the presence of nanoparticle in sensing layer. Further reducing the nanoparticle diameter by the addition of palladium (Pd) and platinum (Pt) for both the sensors (ZnO and WO₃) gives good results on sensitivity, operating temperature, response time and recovery time. Nanoparticle diameter for undoped WO₃, Pd-modified WO₃ and Pt-modified WO₃ based sensors is 11.8, 6 and 5.4 nm, whereas nanoparticle diameter for undoped, Pd-modified and Pt-modified ZnO based sensors is 20, 14 and 11 nm, respectively. Analysis of dynamic response of the sensors when exposed to different concentrations of ethanol vapour (from 500 to 10,000 ppm) and various temperatures indicate the improvement in sensitivity up to 77.2% for WO₃ and 74.6% for ZnO based sensors.     

Thermodynamic optimization of the K2O-Al2O3-SiO2 system

A critical evaluation and thermodynamic optimization of experimental phase diagrams and thermodynamic properties of the K₂O-Al₂O₃-SiO₂ system was performed at 1?bar total pressure. A set of self-consistent thermodynamic functions of all phases in the K₂O-Al₂O₃-SiO₂ system was obtained. The liquid phase was described using the Modified Quasichemical Model with the KAlO₂ associate component. The set of optimized model parameters obtained for all phases reproduces available and reliable thermodynamic properties and phase diagram data as well as the melt structure of the K₂O-Al₂O₃-SiO₂ system within the experimental error limits.     

Gold-silver alloy nanoshells: a new candidate for nanotherapeutics and diagnostics

We have developed novel gold-silver alloy nanoshells as magnetic resonance imaging (MRI) dual T₁ (positive) and T₂ (negative) contrast agents as an alternative to typical gadolinium (Gd)-based contrast agents. Specifically, we have doped iron oxide nanoparticles with Gd ions and sequestered the ions within the core by coating the nanoparticles with an alloy of gold and silver. Thus, these nanoparticles are very innovative and have the potential to overcome toxicities related to renal clearance of contrast agents such as nephrogenic systemic fibrosis. The morphology of the attained nanoparticles was characterized by XRD which demonstrated the successful incorporation of Gd(III) ions into the structure of the magnetite, with no major alterations of the spinel structure, as well as the growth of the gold-silver alloy shells. This was supported by TEM, ICP-AES, and SEM/EDS data. The nanoshells showed a saturation magnetization of 38 emu/g because of the presence of Gd ions within the crystalline structure with r₁ and r₂ values of 0.0119 and 0.9229 mL mg^-1 s^-1, respectively (Au:Ag alloy = 1:1). T₁- and T₂-weighted images of the nanoshells showed that these agents can both increase the surrounding water proton signals in the T₁-weighted image and reduce the signal in T₂-weighted images. The as-synthesized nanoparticles exhibited strong absorption in the range of 600-800 nm, their optical properties being strongly dependent upon the thickness of the gold-silver alloy shell. Thus, these nanoshells have the potential to be utilized for tumor cell ablation because of their absorption as well as an imaging agent.     

TCP Over Optical Burst Switching: To Split or Not to Split?

TCP-based applications account for a majority of data traffic in the Internet; thus, understanding and improving the performance of TCP over optical burst switching (OBS) network is critical. In this paper, we identify the ill effects of implementing TCP over a hybrid network (IP-access and OBS-core). We propose a Split-TCP framework for the hybrid IP-OBS network to improve TCP performance. We propose two Split-TCP approaches, namely, 1:1:1 and N:1:N. We evaluate the performance of the proposed approaches over an IP-OBS hybrid network. Based on the simulation results, N:1:N Split-TCP approach outperforms all other approaches. We also develop an analytical model for end-to-end Split-TCP throughput and verify it with simulations.     

Horizontal collaboration in semiconductor manufacturing industry supply chain: An evaluation of collaboration intensity index

This paper aims to provide a generic quantitative model to comprehensively assess the degree of collaboration with individual horizontal collaboration initiatives with a view to check feasibility for satisfying the customer requirements. The analytic hierarchy process–fuzzy logic model (AHP–FLM) approach is chosen for developing the model, a method that is often used to tackle complex strategic decision making that calls for subjective judgment based on well-established logical reasoning, rather than on simple feeling and intuition. In the process, the complex and unstructured problem for ‘compatibility test’ is broken down into elements, and then a customized hierarchy structure is set up to demonstrate the relationship between different hierarchy levels and among these elements. Each element may have a different level of importance for the horizontal collaboration. A fuzzy rule based collaboration intensity index (CII) is developed to build up the relationships among these evaluation attributes. Synthesizing the generic relative importance and the forecasted degree of collaboration, the proposed approach can determine the success of the collaboration initiative. An illustrative example of a semiconductor industry supply chain (SSC) member that intends to partner with a potential and competing candidate enterprise is developed to demonstrate the applicability of the proposed fuzzy strategic alliance selection framework and to measure the effectiveness of a horizontal collaboration initiative.     

Design of exclusive or sum-of-products (ESP) logic arrays with universal tests for detecting stuck-at and bridging faults

The detection problem of bridging faults in AND-EXOR arrays is considered in this paper in a new framework. These AND-EXOR arrays are different from the arrays based on the so-called Reed-Muller canonic (RMC) expansion of functions. The multiple stuck-at fault detection test set in such arrays as already derived by Pradhan[1] has been utilized to detect bridging faults. One most important advantage of this test set is that it is independent of the function realized and it has a simple algebraic structure and hence can be generated easily. As this conventional test set is insufficient to detect all bridging faults, we propose a technique of augmenting the network with some additional observation points which take care of otherwise undetectable bridging faults.     

Robust manufacturing system design using multi objective genetic algorithms,Petri nets and Bayesian uncertainty representation

Decisions involving robust manufacturing system configuration design are often costly and involve long term allocation of resources. These decisions typically remain fixed for future planning horizons and failure to design a robust manufacturing system configuration can lead to high production and inventory costs, and lost sales costs. The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand). This paper presents a novel approach using multi objective genetic algorithms (GA), Petri nets and Bayesian model averaging (BMA) for robust design of manufacturing systems. The proposed approach is demonstrated on a manufacturing system configuration design problem to find optimal number of machines in different manufacturing cells for a manufacturing system producing multiple products. The objective function aims at minimizing makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs, and product demand. The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties.     

Cobalt(II) Phthalocyanine‐Catalyzed Highly Chemoselective Reductive Amination of Carbonyl Compounds in a Green Solvent

Cobalt phthalocyanine has been employed for the highly chemoselective reductive amination of aldehydes and ketones in ethanol as a green solvent. A large range of functional groups such as nitro, acid, amide, ester, nitrile, halogen, lactone, methoxy, hydroxy, alkene, N‐benzyl, O‐benzyl and heterocyclic rings were well tolerated under the present reaction conditions.