• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2104-2125
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• 2021

This paper presents the verification and validation (V&V) of a calculation module for isotope inventory prediction to control the back-end cycle of spent nuclear fuel (SNF). The calculation method presented herein was implemented in a two-step code system of a lattice code STREAM and a nodal diffusion code RAST-K. STREAM generates a cross section and provides the number density information using branch/history depletion branch calculations, whereas RAST-K supplies the power history and three history indices (boron concentration, moderator temperature, and fuel temperature). As its primary feature, this method can directly consider three-dimensional core simulation conditions using history indices of the operating conditions. Therefore, this method reduces the computation time by avoiding a recalculation of the fuel depletion. The module for isotope inventory calculates the number densities using the Lagrange interpolation method and power history correction factors, which are applied to correct the effects of the decay and fission products generated at different power levels. To assess the reliability of the developed code system for back-end cycle analysis, validation study was performed with 58 measured samples of pressurized water reactor (PWR) SNF, and code-to-code comparison was conducted with STREAM-SNF, HELIOS-1.6 and SCALE 5.1. The V&V results presented that the developed code system can provide reasonable results with comparable confidence intervals. As a result, this paper successfully demonstrates that the isotope inventory prediction code system can be used for spent nuclear fuel analysis.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1303-1314
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• 2019

This paper studies the harmonic characteristics of ship electric propulsion systems and their treatment methods. It also adopts effective measures to suppress and prevent ship power systems from affecting ship operation due to the serious damage caused by harmonics. Firstly, the harmonic characteristics of a ship electric propulsion system are reviewed and discussed. Secondly, aiming at problems such as resonant frequency and filter characteristics variations, resonance point migration, and unstable filtering performances in conventional passive filters, a method for fully tuning of a passive dynamic tunable filter (PDTF) is proposed to realize harmonic suppression. Thirdly, to address the problems of the uncontrollable inductance L of traditional air gap iron core reactors and the harmonics of power electronic impedance converters (PEICs), this paper proposes an electromagnetic coupling reactor with impedance transformation and harmonic suppression characteristics (ECRITHS), with the internal filter (IF) designed to suppress the harmonics generated by PEICs. The ECRITHS is characterized by both harmonic suppression and impedance change. Fourthly, the ECRITHS is investigated. This investigation includes the harmonic suppression characteristics and impedance transformation characteristics of the ECRITHS at the fundamental frequency, which shows the good performance of the ECRITHS. Simulation and experimental evaluations of the PDTF are carried out. Multiple PDTFs can be configured to realize multi-order simultaneous dynamic filtering, and can effectively eliminate the current harmonics of ship electric propulsion systems. This is done to reduce the total harmonic distortion (THD) of the supply currents to well below the 5% limit imposed by the IEEE-519 standard. The PDTF also can eliminate harmonic currents in different geographic places by using a low voltage distribution system. Finally, a detailed discussion is presented, with challenges and future implications discussed. The research results are intended to effectively eliminate the harmonics of ship electric power propulsion systems and to improve the power quality of ship power systems. This is of theoretical and practical significance for improving the power quality and power savings of ship power systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.165-171
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• 2019

The study of the computer architecture has shown that major improvements in price-to-energy performance stems from domain-specific hardware development. This paper analyzes the tensor processing unit (TPU) ASIC which can accelerate the reasoning of the artificial neural network (NN). The core device of the TPU is a MAC matrix multiplier capable of high-speed operation and software-managed on-chip memory. The execution model of the TPU can meet the reaction time requirements of the artificial neural network better than the existing CPU and the GPU execution models, with the small area and the low power consumption even though it has many MAC and large memory. Utilizing the TPU for the tensor flow benchmark framework, it can achieve higher performance and better power efficiency than the CPU or CPU. In this paper, we analyze TPU, simulate the Python modeled OpenTPU, and synthesize the matrix multiplication unit, which is the key hardware.

• Journal of Navigation and Port Research
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• v.44 no.6
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• pp.460-468
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• 2020

Research related to the technology developed for the Maritime Autonomous Surface Ship (MASS) is currently underway. Although one of those core technologies is collision-avoidance technology for ship operators at sea, no research has been done to objectively quantify its effectiveness. Therefore, this study was conducted to develop an evaluation model to examine the collision-avoidance ability of MASS. Ship-control experts performed a ship-handling simulation for each ship encounter type using the Full Mission Ship-handling Simulator (FMSS). We used the resulting data and technical statistics, to develop an evaluation model that utilized FMSS to quantify the operational capability of the collision-avoidance technology. This evaluation model also can be used at sea to assess deck officers' ability to use the technology and to improve and develop other MASS technologies.

• 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.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2077-2083
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• 2022

In the unlikely event of core disruptive accident in sodium cooled fast reactors, the reactor containment building would be bottled up with sodium and fission product aerosols. The behavior of these aerosols is crucial to estimate the in-containment source term as a part of nuclear reactor safety analysis. In this work, the evolution of sodium aerosol characteristics (mass concentration and size) is simulated using HAARM-S code. The code is based on the method of moments to solve the integro-differential equation. The code is updated to FORTRAN-77 and run in Microsoft FORTRAN PowerStation 4.0 (on Desktop). The sodium aerosol characteristics simulated by HAARM-S code are compared with the measured values at Aerosol Test Facility. The maximum deviation between measured and simulated mass concentrations is 30% at initial period (up to 60 min) and around 50% in the later period. In addition, the influence of humidity on aerosol size growth for two different aerosol mass concentrations is studied. The measured and simulated growth factors of aerosol size (ratio of saturated size to initial size) are found to be matched at reasonable extent. Since sodium is highly reactive with atmospheric constituents, the aerosol growth factor depends on the hygroscopic growth, chemical transformation and density variations besides coagulation. Further, there is a scope for the improvement of the code to estimate the aerosol dynamics in confined environment.

• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.31-42
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• 2021

Recently, virtual reality (VR) and augmented reality (AR) technologies are attracting attention as core technologies in the era of the 4th industrial revolution. These virtual and augmented reality technologies are being used in a variety of industries, including the construction industry, healthcare industry, and manufacturing industry, to innovate in communication and collaboration, education and simulation, customer service and reinvention of the customer experience. In this paper, VR-based experiential safety education was conducted for workers of shipbuilding companies in Ulsan city, and for them, the educational effectiveness such as immersion, site applicability, safety accident prevention, education satisfaction, overall performance, and safety behavior in VR-based safety experience education were measured. In addition, we examined whether the immersion of VR-based safety experience education affects site applicability, safety accident prevention, educational satisfaction, overall performance, and safety behavior. Furthermore, it was analyzed whether site applicability plays a mediating role in the relationship between immersion and safety accident prevention. As a result, it was found that the immersion of VR-based safety experience education affects site applicability, safety accident prevention effect, education satisfaction, overall performance, and safety behavior, and that site applicability mediates between immersion and safety accident prevention. Based on these results, we suggests a direction for the development of VR-based contents in the field of safety and health and the transformation of safety and health education in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.905-912
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• 2022

Virtual reality, one of the core technologies of the 4th industrial revolution, is entering a new phase with the spread of low-cost wearable devices represented by Oculus. In the case of disaster evacuation drills, where practical training is almost impossible due to the risk of accidents, virtual reality is becoming a new alternative that enables effective training. In this paper, we propose a smoke modeling method that can be applied to fire evacuation drills implemented with virtual reality technology. In the event of a fire, smoke spreads along the aisle, and the density of the smoke changes over time. The proposed method models the smoke by applying a procedural function that can reflect the density of smoke calculated through simulation to the model in real-time. Implementation results in the background of the factory show that the proposed method produces models that can express the smoke according to the user's movement.

• Design & Manufacturing
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• v.16 no.4
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• pp.34-39
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• 2022

Sustained economic development around the world is accelerating resource depletion. Research and development of secondary batteries that can replace them is also being actively conducted. Secondary batteries are emerging as a key technology for carbon neutrality. The core of an electric vehicle is the battery (secondary battery). Therefore, in this study, the temperature change by the heat source of the hammer and the rotational speed (rpm) of the abrasive disc of the Classifier Separator Mill (CSM) was repeatedly calculated and analyzed using the heat flow simulation STAR-CCM+. As the rotational speed (rpm) of the abrasive disk increases, the convergence condition of the iteration increases. Under the condition that the inlet speed of the Classifier Separator Mill (CSM) and the heat source value of the disc hammer are the same, the disc rotation speed (rpm) and the hammer temperature are inversely proportional. As the rotational speed (rpm) of the disc increases, the hammer temperature decreases. However, since the wear rate of the secondary battery material increases due to the strong impact of the crushing rotational force, it is determined that an appropriate rpm setting is necessary. In CSM (Classifier Separator Mill), it is judged that the flow rate difference is not significantly different in the direction of the pressure outlet (Outlet 1) right above the classifier wheel with the fastest flow rate. Because the disc and hammer attachment technology is adhesive, the attachment point may deform when the temperature of the hammer rises. Therefore, it is considered necessary to develop high-performance adhesives and other adhesive technologies.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.95-109
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• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.