김현민 교수

One of the benefits of the supercritical CO 2 Brayton cycle in Sodium-cooled Fast Reactors is an enhanced plant safety, since potential reactions of CO 2 with liquid sodium have been reported to be less stringent than a sodium-water reaction found in the Rankine cycle. However, moderate chemical interactions between CO 2 and liquid sodium make detecting CO 2 ingress accidents harder. Thus, this paper proposes a new physics-based detection algorithm by comparing the real-time pressure measurement

Probabilistic safety assessment (PSA) has had a significant role in quantitative decision-making by finding design and operational vulnerabilities and evaluating cost-benefit in improving such weak points. In particular, it has been widely used as the core methodology for risk-informed applications (RIAs). Even though the nature of PSA seeks realistic results, there are still “conservative” aspects. One of the sources for the conservatism is the assumptions of safety analysis and the estimation

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a promin

We present a holistic examination of tropospheric OH reactivity (OHR) in South Korea using comprehensive NASA DC-8 airborne measurements collected during the Korea–United States Air Quality field study and chemical transport models. The observed total OHR (tOHR) averaged in the planetary boundary layer (PBL, <2.0 km) and free troposphere was 5.2 s−1 and 2.0 s−1 during the campaign, respectively. These values were higher than the calculated OHR (cOHR, 3.4 s−1, 1.0 s−1) derived from trace-g

To obtain more accurate results of probabilistic safety assessment (PSA), it is necessary to reflect more complete dynamics of nuclear power plants. In analyzing these more realistic PSA models, numerous thermal-hydraulic code runs should be performed that typically take from a few minutes to several hours. This paper proposes a fast running model using deep learning techniques to obtain plausible accident scenarios while reducing the resources required to conduct PSA. The developed model is bui

The Zn-polyiodide redox flow battery is considered to be a promising aqueous energy storage system. However, in its charging process, the electrode kinetics of I^- oxidation often suffer from an intrinsically generated iodine film (I₂-F) on the cathode of the battery. Therefore, it is critical to both understand and enhance the observed slow electrode kinetics of I^- oxidation by an electrochemically generated I₂-F. In this article, we introduced an elec

Concerns over reliability assessments of the main components in nuclear power plants (NPPs) related to aging and continuous operation have increased. The conventional reliability assessment for main components uses experimental correlations under general conditions. Most NPPs have been operating in Korea for a long time, and it is predictable that NPPs operating for the same number of years would show varying extent of aging and degradation. The conventional reliability assessment does not adequ

The rapid advancement of artificial intelligence (AI) technology based on deep neural networks (DNNs) has spurred active development of DNN-based models in the nuclear domain. Due to the black-box nature of these models and the issue of low explainability, their practical application in safety-critical domains is hindered. To address this, numerous explainable AI (XAI) methods have been proposed. However, the selection of an appropriate XAI method is crucial as its performance significantly depe

Using traditional methods based on detection rules written by human security experts presents significant challenges for the accurate detection of network threats, which are becoming increasingly sophisticated. In order to deal with the limitations of traditional methods, network threat detection techniques utilizing artificial intelligence technologies such as machine learning are being extensively studied. Research has also been conducted on analyzing various string patterns in network packet