• Nuclear Engineering and Technology
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• 제45권6호
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• pp.731-744
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• 2013

Energy security is a topic of high importance to many countries throughout the world. Countries with access to vast energy supplies enjoy all of the economic and political benefits that come with controlling a highly sought after commodity. Given the desire to diversify away from fossil fuels due to rising environmental and economic concerns, there are limited technology options available for baseload electricity generation. Further complicating this issue is the desire for energy sources to be sustainable and globally scalable in addition to being economic and environmentally benign. Nuclear energy in its current form meets many but not all of these attributes. In order to address these limitations, TerraPower, LLC has developed the Traveling Wave Reactor (TWR) which is a near-term deployable and truly sustainable energy solution that is globally scalable for the indefinite future. The fast neutron spectrum allows up to a ~30-fold gain in fuel utilization efficiency when compared to conventional light water reactors utilizing enriched fuel. When compared to other fast reactors, TWRs represent the lowest cost alternative to enjoy the energy security benefits of an advanced nuclear fuel cycle without the associated proliferation concerns of chemical reprocessing. On a country level, this represents a significant savings in the energy generation infrastructure for several reasons 1) no reprocessing plants need to be built, 2) a reduced number of enrichment plants need to be built, 3) reduced waste production results in a lower repository capacity requirement and reduced waste transportation costs and 4) less uranium ore needs to be mined or purchased since natural or depleted uranium can be used directly as fuel. With advanced technological development and added cost, TWRs are also capable of reusing both their own used fuel and used fuel from LWRs, thereby eliminating the need for enrichment in the longer term and reducing the overall societal waste burden. This paper describes the origins and current status of the TWR development program at TerraPower, LLC. Some of the areas covered include the key TWR design challenges and brief descriptions of TWR-Prototype (TWR-P) reactor. Selected information on the TWR-P core designs are also provided in the areas of neutronic, thermal hydraulic and fuel performance. The TWR-P plant design is also described in such areas as; system design descriptions, mechanical design, and safety performance.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1995년도 추계학술발표회논문집(1)
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• pp.191-196
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• 1995

The intelligent human-machine interface (HMI) has been developed to enhance the safety and availability of a nuclear power plant by improving operational reliability The key elements of the HMI are the large display panels which present synopsis of the plant status and the compact, digital work stations for the primary operator control and monitoring functions. The work station consists of four consoles such as a dynamic alarm console (DAC), a system information console (SIC), a computerized operating-procedure console (COC), and a safety related information console (SRIC). The DAC provides clean alarm pictures, in which information overlapping is excluded and alarm impacts are discriminated, for quick situation awareness. The SIC covers a normal operation by offering all necessary plant information and control functions. In addition, it is closely linked with the DAC and the COC to automatically display related system information under the request of these consoles. The COC aids the operator with proper emergency operation guidelines so as to shutdown the plant safely, and it also reduces his physical/mental burden by automating the operating procedures. The SRIC continuously displays safety related information to allow the operator to assess the plant status focusing on plant safety. The proposed HMI has been validated and demonstrated with on-line data obtained from the full-scope simulator for Yonggwang Units 1,2.

• Journal of Radiation Protection and Research
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• 제41권3호
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• pp.237-244
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• 2016

Background: Korea Atomic Energy Research Institute is developing a fission product transport module for predicting the behavior of radioactive materials in the primary cooling system of a nuclear power plant as a separate module, which will be connected to a severe accident analysis code, Core Meltdown Progression Accident Simulation Software (COMPASS). Materials and Methods: This fission product transport (COMPASS-FP) module consists of a fission product release model, an aerosol generation model, and an aerosol transport model. In the fission product release model there are three submodels based on empirical correlations, and they are used to simulate the fission product gases release from the reactor core. In the aerosol generation model, the mass conservation law and Raoult's law are applied to the mixture of vapors and droplets of the fission products in a specified control volume to find the generation of the aerosol droplet. In the aerosol transport model, empirical correlations available from the open literature are used to simulate the aerosol removal processes owing to the gravitational settling, inertia impaction, diffusiophoresis, and thermophoresis. Results and Discussion: The COMPASS-FP module was validated against Aerosol Behavior Code Validation and Evaluation (ABCOVE-5) test performed by Hanford Engineering Development Laboratory for comparing the prediction and test data. The comparison results assuming a non-spherical aerosol shape for the suspended aerosol mass concentration showed a good agreement with an error range of about ${\pm}6%$. Conclusion: It was found that the COMPASS-FP module produced the reasonable results of the fission product gases release, the aerosol generation, and the gravitational settling in the aerosol removal processes for ABCOVE-5. However, more validation for other aerosol removal models needs to be performed.

• Nuclear Engineering and Technology
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• 제54권2호
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• pp.608-616
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• 2022

Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%-97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Nuclear Engineering and Technology
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• 제12권3호
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• pp.194-213
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• 1980

The paper describes and quantifies the wastes arising in the nuclear fuel cycle for Light Water Reactors, Heavy Water Reactors and Fast Breeder Reactors. The management and disposal technologies are indicated, together with their environmental impacts. Both once-through and uranium-plutonium recycle systems are evaluated, and comparisons are made on the basis of tingle reference technologies for waste management, and for one gigawatt/year of electricity generation. Environmental impacts are assessed, particularly that of health and safety, and a reference costing system is applied purely as a basis for comparing the fuel cycles. From this study it call be concluded generally that the relative differences of the impacts of waste management and disposal between the selected fuel cycles are not decisive factors in choosing a fuel cycle. Employing the technologies assumed, the radioactive wastes from any of the fuel cycles studied can be managed and disposed of with a high degree of safety and without undue risk to man or the environment. The cost of waste management and disposal is only a few percent of the value of the electricity generated and does not vary greatly between fuel cycles.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.103-105
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• 1992

The new approach using fuzzy linear programming with fuzzy multi-criterion is proposed for the best generation mix of a power system. A chracteristic feature of the presented approach is that not only cost but also reliability for goal function can be taken into account by using fuzzy multi-criterion and so more realistic solution can be obtained. The effectiveness of the proposed approach is demonstrated by the best generation mix problem of KEPCO-system size model which contains nuclear, coal, LNG, oil and pump-generator hydro plant in multi-years.

• 공업화학
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• 제33권4호
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• pp.352-366
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• 2022

Nuclear power plant operations for electricity generation, rare-earth mining, nuclear medical research, and nuclear weapons reprocessing considerably increase radioactive waste, necessitating massive efforts to eradicate radioactive waste from aquatic environments. Cobalt (⁵⁸Co) and strontium (⁹⁰Sr) radioactive elements have been extensively employed in energy generation, nuclear weapon testing, and the manufacture of healthcare products. The erroneous discharge of these elements as pollutants into the aquatic system, radiation emissions, and long-term disposal is extremely detrimental to humans and aquatic biota. Numerous methods for treating radioactive waste-contaminated water have emerged, among which the adsorption process has been promoted for its efficacy in eliminating radioactive waste from aquatic habitats. The current review discusses the adsorptive removal of radioactive waste from aqueous solutions using low-cost adsorbents, such as graphene oxide, metal-organic frameworks, and inorganic metal oxides, as well as their composites. The chemical modification of adsorbents to increase their removal efficiency is also discussed. Finally, the current state of ⁵⁸Co and ⁹⁰Sr removal performances is summarized and the efficiencies of various adsorbents are compared.

• 전기학회논문지
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• 제63권7호
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• pp.867-874
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• 2014

The pumped storage power plants have excellent load following characteristics. It can also be committed quickly for synchronous reserve when it is in the generating mode because it can readily increase its generating power and, consequently, increases the overall system reliability. There are strong incentives for standing the system reliability. Additionally, $CO_2$ emission can be typically impacted due to operation of pumped generators. The increase or decrease of $CO_2$ depends on the generation mix. This paper proposes evaluation of reliability, economy and environment of power system considering pumped generator. This paper describes three case studies of the reliability and economy and environment according to capacity factor and storage capacity of pumped generators. The probabilistic production simulation model is used in this paper. The practicality and effectiveness of the proposed approach are demonstrated by simulation studies for a real size power system model on the $5^{th}$ power plan in Korea.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1996년도 추계학술발표회논문집(1)
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• pp.73-78
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• 1996

In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller.