ANALYSIS OF RELIABILITY OF COOLING SYSTEM OF TEHRAN NUCLEAR RESEARCH REACTOR BY DETERMINATION OF IMPORTANCE AND SENSITIVITY

ABSTRACT Mechanical parameters such as dimensions, forces and stresses are never absolute and in reality are a function of process changes, human factors, operation and time. Therefore, their analysis and attaining the results of importance and sensitivity analyses can be used in determination of their safety category and enhancement of their reliability and maintenance. In this paper, using fault tree analysis method, the importance and sensitivity of Tehran Nuclear Research Reactor coolant system components are determined by analysis of four probable accidents: Loss of Coolant Accident (LOCA), Loss of Flow Accident (LOFA), Loss of Heat Sink Accident (LOHA) and Loss of Offsite Power Accident (LOOP) by the help of RELAB computer code. The results are used to identify the important and sensitive components. This recognition helps to optimize the repair and maintenance resources and enhancement of reliability and availability of the reactor components

Investigating the solder mask defects impact on leakage current on PCB under condensing humidity conditions

This paper focuses on conducting a thorough investigation into the solder mask defects and their influence on the leakage current development in PCB under operating conditions. A practical commercial PCBA has been used as a reference to construct a realistic numerical model, integrating the solder mask layer through COMSOL software. Comprehensive investigations cover scenarios with and without pinholes, conducting precise electrochemical reaction simulations under critical electrical conditions for validation against experimental data. The outcomes notably reveal how pinholes level up leakage current (LC) and reshape its paths. To validate the proposed method, experiments under real operating conditions and humidity were conducted. The comparison between the simulation and experiments demonstrated the effectiveness of the numerical model

A Novel Type-2 Fuzzy Logic for Improved Risk Analysis of Proton Exchange Membrane Fuel Cells in Marine Power Systems Application

A marine energy system, which is fundamentally not paired with electric grids, should work for an extended period with high reliability. To put it in another way, by employing electrical utilities on a ship, the electrical power demand has been increasing in recent years. Besides, fuel cells in marine power generation may reduce the loss of energy and weight in long cables and provide a platform such that each piece of marine equipment is supplied with its own isolated wire connection. Hence, fuel cells can be promising power generation equipment in the marine industry. Besides, failure modes and effects analysis (FMEA) is widely accepted throughout the industry as a valuable tool for identifying, ranking, and mitigating risks. The FMEA process can help to design safe hydrogen fueling stations. In this paper, a robust FMEA has been developed to identify the potentially hazardous conditions of the marine propulsion system by considering a general type-2 fuzzy logic set. The general type-2 fuzzy system is decomposed of several interval type-2 fuzzy logic systems to reduce the inherent highly computational burden of the general type-2 fuzzy systems. Linguistic rules are directly incorporated into the fuzzy system. Finally, the results demonstrate the success and effectiveness of the proposed approach in computing the risk priority number as compared to state-of-the-art methods

Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures

This paper presents recent contributions to the Marie Skłodowska-Curie Innovative Training Network titled INFRASTAR (Innovation and Networking for Fatigue and Reliability Analysis of Structures-Training for Assessment of Risk) in the field of reliability approaches for decision-making for wind turbines and bridges . Stochastic modeling of uncertainties for fatigue strength parameters is an important step as a basis for reliability analyses. In this paper, the Maximum Likelihood Method (MLM) is used for fitting the statistical parameters in a regression model for the fatigue strength of reinforcement bars. Furthermore, application of the Bootstrapping method is investigated. The results indicate that the latter methodology does not work well in the considered case study because of run-out tests within the test data. Moreover, the use of the Bayesian inference with the Markov Chain Monto Carlo approach is studied. These results indicate that a reduction in the statistical uncertainty can be obtained, and thus, better parameter estimates are obtained. The results are used for stochastic modelling in reliability assessment of a case study with a composite bridge. The reduction in statistical uncertainty shows high impact on the fatigue reliability in a case study on the Swiss viaduct Crêt De l’Anneau