Effects of operating and design parameters on the performance of a solid oxide fuel cell–gas turbine system

We present a steady‐state thermodynamic model of a hybrid solid oxide fuel cell (SOFC)–gas turbine (GT) cycle developed using a commercial process simulation software, AspenPlus?. The hybrid cycle model incorporates a zero‐dimensional macro‐level SOFC model. A parametric study was carried out using the developed model to study the effects of system pressure, SOFC operating temperature, turbine inlet temperature, steam‐to‐carbon ratio, SOFC fuel utilization factor, and GT isentropic efficiency on the specific work output and efficiency of a generic hybrid cycle with and without anode recirculation. The results show that system pressure and SOFC operating temperature increase the cycle efficiency regardless of the presence of anode recirculation. On the other hand, the specific work decreases with operating temperature. Overall, the model can successfully capture the complex performance trends observed in hybrid cycles. Copyright © 2010 John Wiley & Sons, Ltd.     

Synthesis and swelling properties of crosslinked poly(orthosilicate)s from cyclohexanedimethanols

Condensations of cyclohexanedimethanol derivatives with tetraethyl orthosilicate in the proper stoichiometric ratio produce crosslinked poly(orthosilicate)s. Synthesized crosslinked polymers have swelling abilities in common organic solvents such as tetrahydrofuran (THF), dichloromethane, benzene and acetone. All these polymers are moderately thermally stable and possess solvent uptake abilities that are not only good and fast but are also regenerable. FTIR, solid‐state ¹³C, ²⁹Si‐NMR and thermal methods were used to characterize these synthesized polymers. The effects of different cyclohexanedimethanol derivatives on the properties of polymers were also examined. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci 122:1182–1189, 2011     

Predicting Construction Company Decline

The main goal of this research is to develop a model that construction company executives can use to determine whether their company is healthy, whether decline is setting in, or whether decline has reached an advanced stage. The construction company decline model is a statistical model that was developed by making use of nonfinancial data collected from construction companies that have filed bankruptcy under Chapter 11 and construction companies that have been functioning without bankruptcy protection. The company profile survey provided information about 21 organizational, human capital, and strategic posture characteristics of construction companies. Factor analysis was used to reduce the number of variables. The factors obtained by performing factor analysis were regressed against decline ratings using multinomial logistic regression. The model allows a user to determine the condition of a company relative to decline. The model was validated by testing it using randomly selected data from the sample. The paper is of relevance to researchers because it contributes to the rather slim body of research in this area. It is of relevance to practitioners too as it provides them with an early warning tool that allows them to conduct a timely turnaround in case their company is in decline.     

Adaptive-Network-Based Fuzzy Inference System Application to Estimate the Flashover Voltage on Insulator

Abstract

This paper presents application of adaptive network based fuzzy inference system (ANFIS) to estimate critical flashover voltage on polluted insulators. Diameter, height, creepage distance, form factor and equivalent salt deposit density were used as input variables for ANFIS, and critical flashover voltage was estimated. In order to train the network and to test its performance, the data sets are derived from experimental results obtained from the literature and a mathematical model. Obtained results were given in both tabulated and graphical form for various cases studies, separately. Satisfactory and more accurate results obtained by using ANFIS to estimate the critical flashover voltage for the considered conditions compared with the previous works. Both test and validation stages were explained in detail and it is observed that estimated results rather close to experimental results.     

Circulating fluidized bed hydrodynamics with air staging: an experimental study

The influence of secondary air injection (SA) on the hydrodynamics of circulating fluidized beds was studied in a 0.23-m ID riser. The secondary to primary air ratio, the vertical position, and the mode of injection (radial, tangential, and 45° entrance) are considered to be the key parameters of SA injection. It was found that the amount and location of SA have direct influence on the solids holdup and the segregation patterns in the riser. The SA divided the riser into two different flow zones: a dense turbulent zone below and a relatively dilute bed above the injection port. The mean solids velocity is found to be upwards with a greater magnitude in the center region. It is downwards with a smaller magnitude along the walls, suggesting core-annular flow structures for both above and below the SA injection region.     

Determination of optimal product styles by ordinal logistic regression versus conjoint analysis for kitchen faucets

In this study, a two-stage integrated approach is proposed and implemented to explore user perceptions about kitchen faucet styles and to find optimal levels of design parameters related to product appearance. At the first stage, a group of representative users have been asked to judge 38 systematically selected different faucet designs by using a semantic differential (SD) scale for 11 image (kansei) words about their visual perceptions. Then the relations between overall preference and kansei word scores of users are investigated by Ordinal Logistic Regression (OLOGREG). The model obtained helps understand how customers describe highly preferred designs. It can also be used to confirm if a product design will be highly preferred. At the second stage, the relations between overall preference scores and design parameter levels related to the product appearance are modeled using again OLOGREG, and then the best design parameter levels for visual perceptions are found by maximizing the overall preference scores. The results are confirmed and discussed. They are also compared to those of a commonly used approach in the literature, Conjoint Analysis (CA). This comparison has showed that the OLOGREG approach is superior to traditional CA. Finally, it is discussed how the product style design optimization approach presented and demonstrated in this study for the case of some kitchen faucets can be used in general for other products.Relevance to industry: Developing a user-centered product is an important policy of an enterprise in today's highly competitive marketplace. To reach this objective, a systematic method is proposed by integrating several techniques. This method will be useful to any industry that designs and produces consumer products. These products more fit the consumer needs and the competitiveness of them are improved.     

Pushback: A hidden Markov model based scheme for energy efficient data transmission in sensor networks

In sensor networks, application layer QoS requirements are critical to meet while conserving energy. One of the leading factors for energy wastage is failed transmission attempts due to channel dynamics and interference. Existing techniques are unaware of the channel dynamics and lead to suboptimal channel access patterns. We propose a MAC layer solution called pushback, that appropriately delays packet transmissions to overcome periods of poor channel quality and high interference, while ensuring that the throughput requirement of the node is met. It uses a hidden Markov model (HMM) based channel model that is maintained without any additional signaling overhead. The pushback algorithm is shown to improve the packet success rate by up to 71% and reduce the number of transmissions needed by up to 38% while ensuring the same throughput.     

Prediction of mechanical properties in magnesia based refractory materials using ANN

Refractory materials are heterogeneous materials having complex microstructures with different constituent’s properties. The mechanical properties of these materials change depending on their chemical composition and temperature. Therefore, it is important to select a refractory material, which is suitable for working conditions and is fit to place of use. Artificial neural network (ANN) model is established to investigate the relationship among processing parameters (chemical composition, temperature) and mechanical properties (bending strength, Young’s modulus) in magnesia based refractory materials. The mechanical properties of magnesia based refractory materials having four different chemical compositions were investigated using three point bending test at temperatures of 25, 400, 500, 600, 700, 800, 900, 1000 and 1400 °C.The bending strength (σ) and Young’s modulus (E) were theoretically calculated by ANN method and theoretical results were compared with experimental values for each temperature. There were insignificant differences between experimental values and ANN results meaning that ANN results can be used instead of experimental values. Thus, mechanical properties of refractory materials having different chemical composition can be predicted by using ANN method regardless of the treatment temperature.     

Hardness and yield strength of dentin from simulated nano-indentation tests

The finite element method (FEM) is applied for studying the hardness (H) and yield strength (Y) of dentin subjected to a nano-indentation process. The nano-indentation experiments were simulated with the ABAQUS finite element software package. This test, performed with a spherical indenter, was simulated by axisymmetric finite element analysis. The load versus displacement was calculated during loading-unloading sequence for different elastic modulus (E) and yield strength. Hardness and maximum principal compressive and tensile stresses were plotted for different elastic modulus depending on yield strength. The dentin was assumed to be isotropic, homogenous and elasto-plastic. The theoretical results outlined in this study were compared with the experimental works reported in the literature and then hardness and yield strength of dentin was estimated.     

On the applicability of linear elastic fracture mechanics to environmental stress cracking of low-density polyethylene

The primary aim of this work was to re-examine the range of applicability of linear elastic fracture mechanics (LEFM) to environmental stress cracking of low-density polyethylene. By use of a number of specimen types and a range of specimen dimensions and loads it is shown that theK _I-c relationship is unique only at lowK _Is. The material studied showed a region of constant crack velocity which was not caused by plasticity effects or the failure of LEFM. However, crack arrest, which occurred at highK _Is or loads, was shown to be caused by ductile yielding causing the crack to blunt.