• Title/Summary/Keyword: plant uncertainty

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Fault Prediction and Diagnosis Using Fuzzy Expert System (퍼지 전문가 시스템을 이용한 고장 예측 및 진단)

  • 최성운;이영석
    • Journal of the Korea Safety Management & Science
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    • v.1 no.1
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    • pp.7-17
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    • 1999
  • As the loss from break-downs and errors, which became more frequent with the growth of elaborateness, complexity and in scale of the plant and equipments, are enormous, the improvement in the reliability, maintenance, safety, and qualify become to have interest. The fault diagnosis is a systematic and unified method to find errors, which is based on the interpretation that data, subconsciously, have noises. But, as most of the methods are inferences based on binomial logic, the uncertainty is not correctly reflected. In this study, we suggest, to manage the uncertainty in the system efficiently on the point of predictive maintenance, We should use fuzzy expert system, which make the decision considering uncertainty possible by taking linguistical variable and fixed quantity by using the fuzzy theory concepts on the basis of an expert's direct observation and experience.

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Hybrid position/force control of uncertain robotic systems using neural networks (신경회로망을 이용한 불확실한 로봇 시스템의 하이브리드 위치/힘 제어)

  • Kim, Seong-U;Lee, Ju-Jang
    • Journal of Institute of Control, Robotics and Systems
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    • v.3 no.3
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    • pp.252-258
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    • 1997
  • This paper presents neural networks for hybrid position/force control which is a type of position and force control for robot manipulators. The performance of conventional hybrid position/force control is excellent in the case of the exactly-known dynamic model of the robot, but degrades seriously as the uncertainty of the model increases. Hence, the neural network control scheme is presented here to overcome such shortcoming. The introduced neural term is designed to learn the uncertainty of the robot, and to control the robot through uncertainty compensation. Further more, the learning rule of the neural network is derived and is shown to be effective in the sense that it requires neither desired output of the network nor error back propagation through the plant. The proposed scheme is verified through the simulation of hybrid position/force control of a 6-dof robot manipulator.

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Parameter Uncertainty and Sensitivity Analysis on a Dose Calculation Model for Terrestrial Food-Chain Pathway (육상식품 섭취경로에 의한 선량계산 모델에서 파라메터의 불확실성 및 민감도 분석)

  • Lee, Chang-Woo;Choi, Yong-Ho;Chun, Ki-Jung;Lee, Jeong-Ho
    • Journal of Radiation Protection and Research
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    • v.16 no.2
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    • pp.67-74
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    • 1991
  • Parameter uncertainty and sensitivity of KFOOD model for calculating the ingestion dose via terrestrial food-chain pathway was analyzed with using Monte-Carlo approach. For the rice ingestion pathway, estimated values from KFOOD code were very conservative. Most sensitive input parameters in model were deposition velocities and soil-to-plant transfer coefficient of radionuclides.

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Use of Dynamic Reliability Method in Assessing Accident Management Strategy

  • Jae, Moosung
    • International Journal of Reliability and Applications
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    • v.2 no.1
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    • pp.27-36
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    • 2001
  • This Paper proposes a new methodology for assessing the reliability of an accident management, which Is based on the reliability physics and the scheme to generate dynamic event tree. The methodology consists of 3 main steps: screening; uncertainty propagation; and probability estimation. Sensitivity analysis is used for screening the variables of significance. Latin Hypercube sampling technique and MAAP code are used for uncertainty propagation, and the dynamic event tree generation method is used for the estimation of non-success probability of implementing an accident management strategy. This approach is applied in assessing the non-success probability of implementing a cavity flooding strategy, which is to supply water into the reactor cavity using emergency fire systems during the sequence of station blackout at the reference plant.

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Uncertainty reduction of seismic fragility of intake tower using Bayesian Inference and Markov Chain Monte Carlo simulation

  • Alam, Jahangir;Kim, Dookie;Choi, Byounghan
    • Structural Engineering and Mechanics
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    • v.63 no.1
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    • pp.47-53
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    • 2017
  • The fundamental goal of this study is to minimize the uncertainty of the median fragility curve and to assess the structural vulnerability under earthquake excitation. Bayesian Inference with Markov Chain Monte Carlo (MCMC) simulation has been presented for efficient collapse response assessment of the independent intake water tower. The intake tower is significantly used as a diversion type of the hydropower station for maintaining power plant, reservoir and spillway tunnel. Therefore, the seismic fragility assessment of the intake tower is a pivotal component for estimating total system risk of the reservoir. In this investigation, an asymmetrical independent slender reinforced concrete structure is considered. The Bayesian Inference method provides the flexibility to integrate the prior information of collapse response data with the numerical analysis results. The preliminary information of risk data can be obtained from various sources like experiments, existing studies, and simplified linear dynamic analysis or nonlinear static analysis. The conventional lognormal model is used for plotting the fragility curve using the data from time history simulation and nonlinear static pushover analysis respectively. The Bayesian Inference approach is applied for integrating the data from both analyses with the help of MCMC simulation. The method achieves meaningful improvement of uncertainty associated with the fragility curve, and provides significant statistical and computational efficiency.

A Stochastic Model for Optimizing Offshore Oil Production Under Uncertainty (불확실성하의 해양석유생산 최적화를 위한 추계적 모형)

  • Ku, Ji-Hye;Kim, Si-Hwa
    • Journal of Navigation and Port Research
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    • v.43 no.6
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    • pp.462-468
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    • 2019
  • Offshore oil production faces several difficulties caused by oil price decline and unexpected changes in the global petroleum logistics. This paper suggests a stochastic model for optimizing the offshore oil production under uncertainty. The proposed model incorporates robust optimization and restricted recourse framework, and uses the lower partial mean as the measure of variability of the recourse profit. Some computational experiments and results based on the proposed model using scenario-based data on the crude oil price and demand under uncertainty are examined and presented. This study would be meaningful in decision-making for the offshore oil production problem considering risks under uncertainty.

Uncertainty analysis of containment dose rate for core damage assessment in nuclear power plants

  • Wu, Guohua;Tong, Jiejuan;Gao, Yan;Zhang, Liguo;Zhao, Yunfei
    • Nuclear Engineering and Technology
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    • v.50 no.5
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    • pp.673-682
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    • 2018
  • One of the most widely used methods to estimate core damage during a nuclear power plant accident is containment radiation measurement. The evolution of severe accidents is extremely complex, leading to uncertainty in the containment dose rate (CDR). Therefore, it is difficult to accurately determine core damage. This study proposes to conduct uncertainty analysis of CDR for core damage assessment. First, based on source term estimation, the Monte Carlo (MC) and point-kernel integration methods were used to estimate the probability density function of the CDR under different extents of core damage in accident scenarios with late containment failure. Second, the results were verified by comparing the results of both methods. The point-kernel integration method results were more dispersed than the MC results, and the MC method was used for both quantitative and qualitative analyses. Quantitative analysis indicated a linear relationship, rather than the expected proportional relationship, between the CDR and core damage fraction. The CDR distribution obeyed a logarithmic normal distribution in accidents with a small break in containment, but not in accidents with a large break in containment. A possible application of our analysis is a real-time core damage estimation program based on the CDR.

Effect of mitigation strategies in the severe accident uncertainty analysis of the OPR1000 short-term station blackout accident

  • Wonjun Choi;Kwang-Il Ahn;Sung Joong Kim
    • Nuclear Engineering and Technology
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    • v.54 no.12
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    • pp.4534-4550
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    • 2022
  • Integrated severe accident codes should be capable of simulating not only specific physical phenomena but also entire plant behaviors, and in a sufficiently fast time. However, significant uncertainty may exist owing to the numerous parametric models and interactions among the various phenomena. The primary objectives of this study are to present best-practice uncertainty and sensitivity analysis results regarding the evolutions of severe accidents (SAs) and fission product source terms and to determine the effects of mitigation measures on them, as expected during a short-term station blackout (STSBO) of a reference pressurized water reactor (optimized power reactor (OPR)1000). Three reference scenarios related to the STSBO accident are considered: one base and two mitigation scenarios, and the impacts of dedicated severe accident mitigation (SAM) actions on the results of interest are analyzed (such as flammable gas generation). The uncertainties are quantified based on a random set of Monte Carlo samples per case scenario. The relative importance values of the uncertain input parameters to the results of interest are quantitatively evaluated through a relevant sensitivity/importance analysis.

A generalized ANFIS controller for vibration mitigation of uncertain building structure

  • Javad Palizvan Zand;Javad Katebi;Saman Yaghmaei-Sabegh
    • Structural Engineering and Mechanics
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    • v.87 no.3
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    • pp.231-242
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    • 2023
  • A novel combinatorial type-2 adaptive neuro-fuzzy inference system (T2-ANFIS) and robust proportional integral derivative (PID) control framework for intelligent vibration mitigation of uncertain structural system is introduced. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-1 FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-2 FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In our approach, the PID control force is obtained using the generalized type-2 neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-2 neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-1 FLC, type-2 FLC, and type-1 neuro-fuzzy PID controllers.

Operating Criteria of Core Exit Temperature in Nuclear Power Plant with using Channel Statistical Allowance (총채널 불확실도를 적용한 원전 노심출구온도의 운전가능 판정기준)

  • Sung, Je Joong;Joo, Yoon Duk;Ha, Sang Jun
    • Journal of the Korean Society of Safety
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    • v.29 no.6
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    • pp.166-171
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    • 2014
  • Nuclear power plants are equipped with the reactor trip system (RTS) and the engineered safety features actuation system (ESFAS) to improve safety on the normal operation. In the event of the design basis accident (DBA), a various of post accident monitor(PAM)systems support to provide important details (e.g. Containment pressure, temperature and pressure of reactor cooling system and core exit temperature) to determine action of main control room (MCR). Operator should be immediately activated for the accident mitigation with the information. Especially, core exit temperature is a critical parameter because the operating mode converts from normal mode to emergency mode when the temperature of core exit reaches $649^{\circ}C$. In this study, uncertainty which was caused by exterior environment, characteristic of thermocouple/connector and accuracy of calibrator/indicator was evaluated in accordance with ANSI-ISA 67.04. The square root of the sum of square (SRSS) methodology for combining uncertainty terms that are random and independent was used in the synthesis. Every uncertainty that may exist in the hardware which is used to measure the core exit temperature was conservatively applied and the associative relation between the elements of uncertainty was considered simultaneously. As a result of uncertainty evaluation, the channel statistical allowance (CSA) of single channel of core exit temperature was +1.042%Span. The range of uncertainty, -0.35%Span ($-4.05^{\circ}C$) ~ +2.08%Span($24.25^{\circ}C$), was obtained as the operating criteria of core exit temperature.