• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2084-2093
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• 2022

Probabilistic safety assessment is widely used to quantify the risks of nuclear power plants and their uncertainties. When the lognormal distribution describes the uncertainties of basic events, the uncertainty of the top event in a fault tree is approximated with the sum of lognormal random variables after minimal cutsets are obtained, and rare-event approximation is applied. As handling complicated analytic expressions for the sum of lognormal random variables is challenging, several approximation methods, especially Monte Carlo simulation, are widely used in practice for uncertainty analysis. In this study, a theoretical approach for analyzing the sum of lognormal random variables using an efficient numerical integration method is proposed for uncertainty analysis in probability safety assessments. The change of variables from correlated random variables with a complicated region of integration to independent random variables with a unit hypercube region of integration is applied to obtain an efficient numerical integration. The theoretical advantages of the proposed method over other approximation methods are shown through a benchmark problem. The proposed method provides an accurate and efficient approach to calculate the uncertainty of the top event in probabilistic safety assessment when the uncertainties of basic events are described with lognormal random variables.

• Nuclear Engineering and Technology
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• v.37 no.2
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• pp.139-150
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• 2005

A robust control design procedure for a nuclear reactor has been developed and experimentally validated on the Penn State TRIGA research reactor. The utilization of the robust controller as a component of an autonomous control system is also demonstrated. Two methods of specifying a low order (fourth-order) nominal-plant model for a robust control design were evaluated: 1) by approximation based on the 'physics' of the process and 2) by an optimal Hankel approximation of a higher order plant model. The uncertainty between the nominal plant models and the higher order plant model is supplied as a specification to the ,u-synthesis robust control design procedure. Two methods of quantifying uncertainty were evaluated: 1) a combination of additive and multiplicative uncertainty and 2) multiplicative uncertainty alone. The conclusions are that the optimal Hankel approximation and a combination of additive and multiplicative uncertainty are the best approach to design robust control for this application. The results from nonlinear simulation testing and the physical experiments are consistent and thus help to confirm the correctness of the robust control design procedures and conclusions.

• Journal of the Korean Society of Propulsion Engineers
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• v.24 no.3
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• pp.18-30
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• 2020

This study deals an estimation method of thrust measurement uncertainty in solid rocket motors. Guidelines of the force measurement uncertainty estimation have been provided by ISO, domestic and international organizations. However, all of them are described by focusing on the force calibration machines and force transducers with a conceptually-driven way. Thus the guidelines cannot be directly applicable to uncertainty estimation of calibration equation and its linear approximation, which are critical error sources in the thrust measurement. In this paper, the equations taking into account effects of both error sources are derived based on fundamental concepts of measurement uncertainty. These are applied to the real thrust measurement system where a relatively simple estimation method for the thrust measurement uncertainty is proposed.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.12
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• pp.89-99
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• 2008

In this paper, the position tracking control problem of the servo system with nonlinear dynamic friction is issued. The nonlinear dynamic friction contains a directly immeasurable friction state variable and the uncertainty caused by incomplete parameter modeling and its variations. In order to provide the efficient solution to these control problems, we propose the composite control scheme, which consists of the robust friction state observer, the FNN approximator and the approximation error estimator with sliding mode control. In first, the sliding mode controller and the robust friction state observer is designed to estimate the unknown internal state of the LuGre friction model. Next, the FNN estimator is adopted to approximate the unknown lumped friction uncertainty. Finally, the adaptive approximation error estimator is designed to compensate the approximation error of the FNN estimator. Some simulations and experiments on the servo system assembled with ball-screw and DC servo motor are presented. Results show the remarkable performance of the proposed control scheme. The robust friction state observer can successfully identify immeasurable friction state and the FNN estimator and adaptive approximation error estimator give the robustness to the proposed control scheme against the uncertainty of the friction parameters.

• Journal of the Korean Physical Society
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• v.73 no.9
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• pp.1247-1254
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• 2018

We study the uncertainty stemming from different theoretical models in the spectroscopic factors extracted from experiments. We use three theoretical approaches, the distorted wave Born approximation (DWBA), the adiabatic distorted wave approximation (ADWA) and the continuum discretized coupled-channels method (CDCC), and analyze the $^{12}C(d,p)^{13}C$, $^{14}C(d,p)^{15}C$ reactions. We find that the uncertainty associated with the adopted theoretical models is less than 20%. We also investigate the contribution from the remnant term and observe that it gives less than 10% uncertainty. We finally make an attempt to explain the discrepancy in the spectroscopic factors of $^{17}C(\frac{3}{2}^+)$ between the ones extracted from experiments and from shell model calculations by analyzing the $^{16}C(d,p)^{17}C$ reaction.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.27-34
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• 2023

The optimal assignment problem between agents and tasks is known as one of the representative problems of combinatorial optimization and an NP-hard problem. This paper covers multi agent-multi task assignment problems with uncertain completion probability. The completion probabilities are generally uncertain due to endogenous (agent or task) or exogenous factors in the system. Assignment decisions without considering uncertainty can be ineffective in a real situation that has volatility. To consider uncertain completion probability mathematically, a mathematical formulation with stochastic programming is illustrated. We also present an algorithm by using the sample average approximation method to solve the problem efficiently. The algorithm can obtain an assignment decision and the upper and lower bounds of the assignment problem. Through numerical experiments, we present the optimality gap and the variance of the gap to confirm the performances of the results. This shows the excellence and robustness of the assignment decisions obtained by the algorithm in the problem with uncertainty.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.4
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• pp.665-672
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• 2010

This paper describes Monte Carlo Simulation(MCS) to assess the uncertainty of dynamic pressure calibrator and the expanded uncertainty results that were compared by GUM approximation and MCS. MCS uncertainties were computed using defining a domain of possible inputs, generating inputs randomly using probability distribution, performing a deterministic computation repeatedly and aggregating the results. It was revealed that the expanded uncertainty between GUM and MCS was different from each other. the expanded uncertainties were 0.5366%, 0.4856%, respectively. MCS is a suitable method for determining the uncertainty of simple and complex measurement systems. It should be more widely used and studied in measurement uncertainty calculations.

• Journal of Ship and Ocean Technology
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• v.9 no.1
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• pp.64-71
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• 2005

In probabilistic design, the challenge is to estimate the uncertainty propagation, since outputs of subsystems at lower levels could constitute inputs of other systems or at higher levels of the multilevel systems. Three uncertainty propagation estimation techniques are compared in this paper in terms of numerical efficiency and accuracy: root sum square (linearization), distribution-based moment approximation, and Taguchi-based integration. When applied to reliability-based design optimization (RBDO) under uncertainty, it is investigated which type of applications each method is best suitable for. Two nonlinear analytical examples and one vehicle crashworthiness for side-impact simulation example are employed to investigate the unique features of the presented techniques for uncertainty propagation. This study aims at helping potential users to identify appropriate techniques for their applications in the multilevel design.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.90-102
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• 2009

In this paper, a tracking control problem for a mechanical servo system with nonlinear dynamic friction is treated. The nonlinear friction model contains directly immeasurable friction state and the uncertainty caused by incomplete modeling and variations of its parameter. In order to provide the efficient solution to these control problems, we propose a hybrid control scheme, which consists of a robust friction state observer, a RFNN estimator and an approximation error estimator with sliding mode control. A sliding mode controller and a robust friction state observer is firstly designed to estimate the unknown infernal state of the LuGre friction model. Next, a RFNN estimator is introduced to approximate the unknown lumped friction uncertainty. Finally, an adaptive approximation error estimator is designed to compensate the approximation error of the RFNN estimator. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are presented. Results demonstrate the remarkable performance of the proposed control scheme.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.59-62
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• 1991

This paper describes an effective algorithm for evaluating the reliability indices and calculating the production cost for generation system with thermal, hydro and pumped storage plants. Using the Energy Invariance property, this algorithm doesn't need deconvolution process which gives large burden in computing time. In order to consider an adaptable load model, we consider the system load with forecasting uncertainty. The proposed algorithm is applied to the KEPCO system and its result shows high accuracy and less computing time.