Effects of a Nd—Fe—B magnetic filler on the crystallization of poly(phenylene sulfide)

The crystallization of poly(phenylene sulfide) (PPS) in a polymer–magnetic Nd—Fe—B powder suspension was studied. Isothermal crystallization behavior was analyzed by way of differential scanning calorimetry, and the kinetics were described via the Avrami equation. The Avrami parameters and the crystallization times were strongly affected by both the particle size and the presence of a coupling agent coated on the filler particles. The small Nd—Fe—B particles exhibited long induction and half‐times, whereas the large particles tended to have short crystallization times. Particles ranging from 38 to 150 μ appeared to have similar crystallization times and to have no significant change in the value of Avrami index with melt crystallization temperature. As a result of these analyses, the dynamic mechanical properties were determined to correlate the fundamental polymer crystallization characteristics and the physical properties of the PPS binder. The enhancement of the wetting of the filler to the binder was promoted through the coupling agent, as confirmed by dynamic mechanical testing performed on the samples. The storage modulus typically decreased because of the presence of the uncoated small particles. Conversely, the loss modulus was enhanced because of the presence of the coated small particles in the PPS binder. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 83: 1091–1102, 2002     

A neural network-based power system stabilizer using power flowcharacteristics

A neural network-based power system stabilizer (neuro-PSS) is designed for a generator connected to a multi-machine power system utilizing the nonlinear power flow dynamics. The use of power flow dynamics provides a PSS for a wide range of operation with reduced size neural networks. The neuro-PSS consists of two neural networks: neuro-identifier and neuro-controller. The low-frequency oscillation is modeled by the neuro-identifier using the power flow dynamics, then a generalized backpropagation-through-time (GBTT) algorithm is developed to train the neuro-controller. The simulation results show that the neuro-PSS designed in this paper performs well with good damping in a wide operation range compared with the conventional PSS     

Application of condition-based HRA method for a manual actuation of the safety features in a nuclear power Plant

A practical approach to develop a more realistic fault-tree model with a consideration of various conditions endured by a human operator is proposed. In safety-critical systems, the generation failure of an actuation signal is caused by the concurrent failures of the automated systems and an operator action. These two sources of safety signals are complicatedly correlated. The failures of sensors or automated systems will cause a lack of necessary information for a human operator and result in error-forcing contexts such as the loss of corresponding alarms and indications. It is well known that the error-forcing contexts largely affect the operator's performance. An automated system which consists of multiple processing channels and complex components is also affected by the availability of the sensors. This paper proposes a condition-based human reliability assessment (CBHRA) method in order to address these complicated conditions in a practical way. We apply the CBHRA method to the manual actuation of the safety features such as a reactor trip and auxiliary feedwater actuation in Korean Standard Nuclear Power Plants. Even the human error probability of each given condition is simply assumed, the application results prove that the CBHRA effectively accommodates the complicated error-forcing contexts into the fault trees.     

Effects of various post-treatments on carbon nanotube films for reliable field emission

In this report, the FE characteristics of carbon nanotubes (CNTs) treated using both thermal annealing and mechanical coatings on the as-grown CNTs systematically studied. It was found that in the high temperature annealed samples, CNTs were attacked at its root during annealing due to a small amount of oxygen, and were pulled out of the substrate in places after FE measurements because of the contact resistance. However, for the mechanically coated samples both with spin on glass (SOG) and polymethyl methacrylate (PMMA), CNTs were found to be nearly intact after FE measurements and showed reliable FE characteristics over repeatable voltage scan. The reliability of CNTs during FE could be owing to the strong adhesion of CNTs to the substrate both by SOG and PMMA coatings.     

Reliability-based failure cause assessment of collapsed bridge during construction

Until now, in many forensic reports, the failure cause assessments are usually carried out by a deterministic approach so far. However, it may be possible for the forensic investigation to lead to unreasonable results far from the real collapse scenario, because the deterministic approach does not systematically take into account any information on the uncertainties involved in the failures of structures.Reliability-based failure cause assessment (reliability-based forensic engineering) methodology is developed which can incorporate the uncertainties involved in structural failures and structures, and to apply them to the collapsed bridge in order to identify the most critical failure scenario and find the cause that triggered the bridge collapse. Moreover, to save the time and cost of evaluation, an algorithm of automated event tree analysis (ETA) is proposed and possible to automatically calculate the failure probabilities of the failure events and the occurrence probabilities of failure scenarios. Also, for reliability analysis, uncertainties are estimated more reasonably by using the Bayesian approach based on the experimental laboratory testing data in the forensic report. For the applicability, the proposed approach is applied to the Hang-ju Grand Bridge, which collapsed during construction, and compared with deterministic approach.     

A study on the degree of deliberation and meditation of cyber poll respondents for nuclear-related decision making

Public participation is essential in each step of the nuclear-related decision-making process. Recently, the electronic systems using the Internet have become quite popular, and have emerged as a good medium for communicating with the public. In this study, a comprehensive utilization of electronic public participation was used to analyze public opinion on a given nuclear-related decision-making process. The degree of deliberation and meditation of the public participating in an electronic poll survey was evaluated, and the decision-making factors representing the personal characteristics of the poll respondents such as age, income, education, residence, degree of knowledge and concern were carefully incorporated when preparing the poll-survey questionnaire for its evaluation. Fuzzy analysis was used to assess and aggregate the responses to each decision-making factor. As a case study, this procedure was used to analyzing public opinion on the location of a low-level radioactive waste disposal facility on the campus of Seoul National University. The results show that there is a tendency of respondents who are on the negative side of the argument to be more deliberate and meditative in their decision-making process than those on the positive side. Knowledge and residence were found to be important decision-making factors.