• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2803-2809
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• 2009

For a successful construction project not only construction engineering and project management technology but also economic evaluation technique is required. Design and construction technologies are necessary to receive a project order. However, construction management technology which can be apply from the project initial phase to the project operation and management phase is required to create a benefit from the project. Construction management technology is one of the effective factors for project success. Economical and efficient cost management from the planning phase influences the project success. This study investigated cost flow and cost factors of domestic Sewage Treatment Plant project for systematic analysis of cost items following the entire project phase. Particularly, data modeling based on domestic Sewage Treatment Equipment maintenance cost DB was performed, and maintenance cost estimation trend line is suggested using Monte carlo Simulation Method to decrease uncertainty of actual results DB and for feasibility study. Korea Academia-Industrial cooperation Society. The Korea Academia-Industrial cooperation Society. The Korea Academia-Industrial cooperation Society. The Korea Academia-Industrial cooperation Society.

• Nuclear Engineering and Technology
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• v.19 no.4
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• pp.292-309
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• 1987

A large dry PWR containment response analysis for postulated severe accidents was performed as part of the Zion Risk Rebaselining study for input to the U.S. NRC's "Reactor Risk Reference Document," NUREG-1150. The Methodologies used in the present work were developed as part of the Severe Accident Risk Reduction Program (SARRP) at Sandia National Laboratory specifically for the Surry Plant, but they were extrapolated to Zion. Major steps of the quantification of risk from a nuclear power plant are first outlined. Then, the methodologies of containment response analysis for severe accidents used for Zion are described in detail: major features of the containment event tree (CET) analysis codes and CET quantification procedures are summarized. In addition, plant specific features important to containment response analysis are presented along with the containment loading and performance issues included in the present uncertainty analysis. Finally, a brief summary of the results of deterministic and statistical containment event tree analysis is presented to provide a perspective on the large dry PWR containment response for postulated severe accidents.accidents.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.6
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• pp.20-29
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• 2003

When Arctic offshore development in the 1970's first led to the consideration of ice capable tankers, there was a high level of uncertainty over design requirements for both safety and ship performance. Also here was a lack of reliable methods to evaluate design proposals. Since that time, improved understanding of the ice environment has raised the confidence of design specifications. Parallel developments have resulted in a suite of engineering tools for ship performance evaluation at the design stage Recent development of offshore and near shore oil and gas reserves in several countries together with economic studies of increased transportation through the Russian Arctic has newly introduced the interest in ice capable tanker design. in response, Samsung Heavy Industries (SHI) applied its experience in tanker design and construction to the design of a specialized tanker with ice capability. SHI produced two prototype hull designs for further study. The performance of both hulls and of the propellers was evaluated at the Institute for Marine Dynamics (IMD) in St. John's, Newfoundland This paper discusses the development of the design, describes the model experiments to determine performance and variations, and presents the results.

• Nuclear Engineering and Technology
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• v.47 no.1
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• pp.11-25
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• 2015

The accident at Japan's Fukushima Daiichi nuclear power plant in March 2011, caused by an earthquake and a subsequent tsunami, resulted in a failure of the power systems that are needed to cool the reactors at the plant. The accident progression in the absence of heat removal systems caused Units 1-3 to undergo fuel melting. Containment pressurization and hydrogen explosions ultimately resulted in the escape of radioactivity from reactor containments into the atmosphere and ocean. Problems in containment venting operation, leakage from primary containment boundary to the reactor building, improper functioning of standby gas treatment system (SGTS), unmitigated hydrogen accumulation in the reactor building were identified as some of the reasons those added-up in the severity of the accident. The Fukushima accident not only initiated worldwide demand for installation of adequate control and mitigation measures to minimize the potential source term to the environment but also advocated assessment of the existing mitigation systems performance behavior under a wide range of postulated accident scenarios. The uncertainty in estimating the released fraction of the radionuclides due to the Fukushima accident also underlined the need for comprehensive understanding of fission product behavior as a function of the thermal hydraulic conditions and the type of gaseous, aqueous, and solid materials available for interaction, e.g., gas components, decontamination paint, aerosols, and water pools. In the light of the Fukushima accident, additional experimental needs identified for hydrogen and fission product issues need to be investigated in an integrated and optimized way. Additionally, as more and more passive safety systems, such as passive autocatalytic recombiners and filtered containment venting systems are being retrofitted in current reactors and also planned for future reactors, identified hydrogen and fission product issues will need to be coupled with the operation of passive safety systems in phenomena oriented and coupled effects experiments. In the present paper, potential hydrogen and fission product issues raised by the Fukushima accident are discussed. The discussion focuses on hydrogen and fission product behavior inside nuclear power plant containments under severe accident conditions. The relevant experimental investigations conducted in the technical scale containment THAI (thermal hydraulics, hydrogen, aerosols, and iodine) test facility (9.2 m high, 3.2 m in diameter, and $60m^3$ volume) are discussed in the light of the Fukushima accident.

• The Korea Journal of Herbology
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• v.28 no.3
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• pp.61-68
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• 2013

Objectives : A precise and simple system of nomenclature was required to avoid error, ambiguity or confusion. Although medicinal plants must be produced or distributed based on a pharmacopoeia described origin including scientific name, the Korean Pharmacopoeia tenth edition (KP 10) had many names against the nomenclature. Therefore, this study aimed at searching correct scientific names for 241 plants in KP 10. Methods : Authoritative databases - The Plant List, International Plant Name Index, YList, Tropicos, eFloras, World Checklist of Selected Plant Families, The Global Compositae Checklist, The International Legume Database and Information Service, et al. - and previously performed researches, floras were cross-checked. Results : The arrangement of this list was designed for four cases, errors including illegitimate, nomenclatural synonyms, recommended names and decision reserved names. Consideration about the scientific names produced nine correct names for ten misspellings and illegitimate, and thirty-six correct names for forty-one nomenclatural synonyms. These results should be reflected in the next of KP 10. Separately, ten recommended names were also suggested for taxonomic synonyms which had been used indiscriminately due to diverse taxonomic opinions. In addition to those, decision reserved names were suggested for thirteen species which had been corridor of uncertainty. Then again, there was need to study about authorship, because KP 10 did not keep recommendations for author citations. Conclusions : Correction of scientific names for some medicinal plants which violated the International Code of Nomenclature would be useful to improve the accuracy of a Pharmacopoeia as the criterional materials.

• Journal of the Korea Convergence Society
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• v.8 no.5
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• pp.1-11
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• 2017

With a given spring-mass-damper system, $H_{\infty}$ and ${\mu}$-synthesis control methods are used to build system controllers which minimize vibrations at two major natural frequencies in two cases; without uncertainty; with 20% uncertainty. In order to check the stability and performance of two controllers, those are examined using GM and PM values. The signal strength of output responses is compared using the concept of central numerical differentiation and then results are quantified using the RMS method. Lastly, 40 random samples of $H_{\infty}$ and ${\mu}$-synthesis controllers are obtained for three different $W_{per\;f1}$ weighting functions and drawn in the time domain in order to compare the stability. Overall, ${\mu}$-synthesis controller manages the vibrations much better than $H_{\infty}$ controller according to the robust stability and performance values obtained by simulating random samples of 40 plant models.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.12
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• pp.1351-1358
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• 2011

To approximate the threshold of the fault detection and diagnosis (FDD) system, validation of the measurements is mandatory. Naturally, the system shows uncertainties due to measuring sensors - mostly thermocouples or RTDs - and due to repeatability. The uncertainty of a thermocouple comes from natural variation or a drift of the thermocouple measurement. Considering the natural variation behaves like zero-mean white noise, its natural variation can be characterized closely by the steady-state standard deviation. However, residuals between measurements and no-fault references in FDD systems show a statistical distribution with various uncertainties. In this paper, steady-state variations of measurement residuals were investigated by utilizing built-in temperature sensors in a heat pump for the model development and the final application.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.97-109
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• 2020

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.