Muhammad Zubair

Land Mine Detecting Robot Capable of Path Planning

The Landmine detecting robots are designed to cover maximum possible area of landmine field for detection of landmines. The detected landmines along with scanned and leftover area are represented on a visual map with accuracy in millimeters. This paper presents a prototype model of land mine detecting robot that is powerful yet low cost and easily controllable. A graphical user interface is developed for plotting the landmines, scanned & leftover area presentation, PID tuning and camera alignment. Emphasis is placed on the control of the differential drive robot in auto mode, semi-auto mode and the manual mode. Image processing technique is employed to find the accurate position of robot which provides the live reckoning feedback to the dead reckoning servo control of the robot. Metal detector is the sensor used to detect landmines. The graphical user interface for the remote terminal computer provides the effective control for the robot. The system is simple but powerful and intelligible to achieve the required results.

Sensitivity Study on Availability of I&C Components Using Bayesian Network

The objective of this study is to find out the impact of instrumentation and control (I&C) components on the availability of I&C systems in terms of sensitivity analysis using Bayesian network. The analysis has been performed on I&C architecture of reactor protection system. The analysis results would be applied to develop I&C architecture which will meet the desire reliability features and save cost. RPS architecture unavailability and availability were estimated to and for failure (0) and perfect (1) states, respectively. The impact of I&C components on overall system risk has been studied in terms of risk achievement worth (RAW) and risk reduction worth (RRW). It is found that circuit breaker failure (TCB), bi-stable processor (BP), sensor transmitter (TR), and pressure transmitter (PT) have high impact on risk. The study concludes and recommends that circuit breaker bi-stable processor should be given more consideration while designing I&C architecture.

Estimation of Surface Heat Flux and Surface Temperature during Inverse Heat Conduction under Varying Spray Parameters and Sample Initial Temperature

An experimental study was carried out to investigate the effects of inlet pressure, sample thickness, initial sample temperature, and temperature sensor location on the surface heat flux, surface temperature, and surface ultrafast cooling rate using stainless steel samples of diameter 27u2009mm and thickness (mm) 8.5, 13, 17.5, and 22, respectively. Inlet pressure was varied from 0.2u2009MPa to 1.8u2009MPa, while sample initial temperature varied from 600°C to 900°C. Becks sequential function specification method was utilized to estimate surface heat flux and surface temperature. Inlet pressure has a positive effect on surface heat flux (SHF) within a critical value of pressure. Thickness of the sample affects the maximum achieved SHF negatively. Surface heat flux as high as 0.4024u2009MW/m2 was estimated for a thickness of 8.5u2009mm. Insulation effects of vapor film become apparent in the sample initial temperature range of 900°C causing reduction in surface heat flux and cooling rate of the sample. A sensor location near to quenched surface is found to be a better choice to visualize the effects of spray parameters on surface heat flux and surface temperature. Cooling rate showed a profound increase for an inlet pressure of 0.8u2009MPa.

Study on Nuclear Accident Precursors Using AHP and BBN

Most of the nuclear accident reports used to indicate the implicit precursors which are not easily quantified as underlying factors. The current Probabilistic Safety Assessment (PSA) is capable of quantifying the importance of accident causes in limited scope. It was, therefore, difficult to achieve quantifiable decision-making for resource allocation. In this study, the methodology which facilitates quantifying these precursors and a case study were presented. First, four implicit precursors have been obtained by evaluating the causality and hierarchy structure of various accident factors. Eventually, it turned out that they represent the lack of knowledge. After four precursors are selected, subprecursors were investigated and their cause-consequence relationship was implemented by Bayesian Belief Network (BBN). To prioritize the precursors, the prior probability is initially estimated by expert judgment and updated upon observations. The pair-wise importance between precursors is calculated by Analytic Hierarchy Process (AHP) and the results are converted into node probability tables of the BBN model. Using this method, the sensitivity and the posterior probability of each precursor can be analyzed so that it enables making prioritization for the factors. We tried to prioritize the lessons learned from Fukushima accident to demonstrate the feasibility of the proposed methodology.

Modeling of common cause failures (CCFs) by using beta factor parametric model

Nuclear accidents and incidents such as Three Mile Island (TMI-2) accident (1979), Chernobyl disaster (1986) and the recent Fukushima nuclear disaster (2011) have caused people to be suspicious of the safety of nuclear energy, and have reduced the level of trust among public. Common cause failure (CCF) has been a major element of such accidents in terrestrial nuclear power reactors because of high redundancy built into the systems and susceptibility of these redundant systems to CCF mechanisms. For this purpose, ad hoc approaches used to be taken to address vulnerabilities to CCF by operating staff of the plants. A CCF event is a result of simultaneous failure of two or more individual components. Such an event can significantly affect the availability of safety systems and has long been recognized as an important issue in the probabilistic safety assessment (PSA). So a complicated and unresolved problem in the subject of safety and reliability is to model CCF in PSA. To overcome this problem the present research highlights a mathematical model to estimate system unavailability in nuclear power plants (NPPs) as well as in other industries. This mathematical model is based on Beta Factor parametric model. The motivation for development of this model lays in the fact that one of the most widespread software such as for fault tree (FT) and event tree (ET) modeling as part of the PSA does not comprise the option for simultaneous assignment of single failure event to multiple CCF groups. A significant finding from such modeling is that, in contrast to common expectations, a too early nuclear phase-out will not serve the deployment of renewable energy sources and rational use of energy. The proposed method can be seen as an advantage of the explicit modeling of CCF.

Advancement in living probabilistic safety assessment to increase safety of nuclear power plants

Among the energy resources, the energy obtained from nuclear power plants is very important for the prosperity of any country. Living probabilistic safety assessment is a growing field that provides a high level of safety for nuclear power plants. Living probabilistic safety assessment consists of different techniques, among them this article presents a method to update reliability data. This method is based on Binomial likelihood function and its conjugate beta distribution for demand failure probability, and Poisson likelihood function and its conjugate gamma distribution for operational failure rate. The method uses generic data for beta and gamma prior distribution, which is updated by using the reliability data update method. Reliability data update is a computer-based program used to update nuclear power plant data according to changing conditions. By updating the living probabilistic safety assessment it is possible to get an online risk monitor system that can be helpful in severe accident conditions, as in Fukushima accident, to make the man–machine system friendly.

Reliability analysis of nuclear I&C architecture using Bayesian networks

The components and modules in an Instrumentation and Control (I&C) architecture have importance and their importance is being considered during design phase in qualitative manner. This study has been performed to simulate availability and importance analysis of various architecture configurations of nuclear Instrumentation & Control (I&C) systems by using Bayesian network models. A case study of four I&C architectures of a reactor protection system have been formulated and their Bayesian network models has been constructed to get the availability analysis and Risk Reduction Worth (RRW). The architecture of case 4 has been identified as highly reliable with availability of 0.9999996.

A Hybrid Approach for Reliability Analysis Based on Analytic Hierarchy Process and Bayesian Network

The investigation of the nuclear accidents reveals that the accumulation of various technical and nontechnical lapses compounded the nuclear disaster. By using Analytic Hierarchy Process (AHP) and Bayesian Network (BN) the present research signifies the technical and nontechnical issues of nuclear accidents. The study exposed that besides technical fixes such as enhanced engineering safety features and better siting choices, the critical ingredient for safe operation of nuclear reactors lie in the quality of human training and transparency of the nuclear regulatory process that keeps public interest at the forefront. In this study a hybrid approach for reliability analysis based on AHP and BN to increase Nuclear Power Plant (NPP) safety has been developed. By using AHP, best alternative to improve safety, design, operation, and to allocate budget for all technical and non-technical factors related with nuclear safety has been investigated. We use a special structure of Bayesian network (BN) based on the method AHP. The graphs of the BN and the probabilities associated with nodes are designed to translate the knowledge of experts on the selection of best alternative. The results show that the improvement in regulatory authorities will decrease failure probabilities and increase safety and reliability in industrial area.

Prioritization of Underlying Precursors in Nuclear Accidents

The facts that the implicit precursors which are not easily quantified are underlying factors are already known. The current Probabilistic Safety Assessment (PSA) is limited in its ability to quantify the importance of accident causes. It is, therefore, difficult to achieve quantifiable decision-making for resource allocation. In this study, the methodology which facilitates quantifying these precursors and a case study is presented. First, four implicit precursors have been obtained by evaluating the causality and hierarchy structure of various accident factors. Eventually it turned out they represent the lack of knowledge. After four precursors are selected, sub-precursors were investigated and their cause-consequence relationship was implemented by Bayesian Belief Network (BBN). To prioritize the precursors, the prior probability is initially estimated by expert judgment and updated upon observations. The pair-wise importance between precursors is calculated by Analytic Hierarchy Process (AHP) and the results are converted into node probability tables of the BBN model. Using this method, the sensitivity and the posterior probability of each precursor can be analyzed so that it enables to make prioritization for the factors. Authors tried to prioritize the lessons-learned from Fukushima accident to demonstrate the feasibility of the proposed methodology.Copyright

An Online Risk Monitor System (ORMS) to Increase Safety and Security Levels in Industry

The main idea of this research is to develop an Online Risk Monitor System (ORMS) based on Living Probabilistic Safety Assessment (LPSA). The article highlights the essential features and functions of ORMS. The basic models and modules such as, Reliability Data Update Model (RDUM), running time update, redundant system unavailability update, Engineered Safety Features (ESF) unavailability update and general system update have been described in this study. ORMS not only provides quantitative analysis but also highlights qualitative aspects of risk measures. ORMS is capable of automatically updating the online risk models and reliability parameters of equipment. ORMS can support in the decision making process of operators and managers in Nuclear Power Plants.