• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1997년도 춘계학술발표회논문집(2)
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• pp.489-494
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• 1997

주조 스테인레스강으로 제작된 원자로 냉각재배관은 고온에서 장기간 운전됨에 따라 열취화의 영향을 받을 수 있다. 장기간의 열취화는 재료의 연성 및 파괴인성을 저하시킬 수 있으며, 배관에 균열이 존재하는 경우 건전성을 위협할 수 있다. 따라서 본 논문에서는 원전수명연장을 위한 타당성 검토 측면에서 Chopra의 방법 등을 이용한 원자로 냉각재배관의 열취화 평가 및 민감도 분석을 수행하였다. 이를 통해 원자로 냉각재배관의 열취화 수준을 정량화하였고, 건전성 평가에 활용될 수 있는 J$_{IC}$ 값을 예측하였으며, 열취화에 영향을 미치는 주요 인자를 도출하였다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.96-101
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• 2004

Cast austenitic stainless steel is used for several components, such as primary coolant piping, elbow, pump casing and valve bodies in light water reactors. These components are subject to thermal embrittlement at the reactor operating temperature. The objective of this study is to summarize the method of estimating ferrite content, Charpy impact energy and J-R curve and to evaluate the thermal embrittlement of the cast austenitic stainless steel piping used in the domestic nuclear power plants. The result of evaluation, two domestic nuclear power plants used CF-8M and CF-8A material has adequate fracture toughness after saturation.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1174-1179
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• 2003

Cast austenitic stainless steel is used for several components, such as primary coolant piping, elbow, pump casing and valve bodies in light water reactors. These components are subject to thermal aging at the reactor operating temperature. Thermal aging results in spinodal decomposition of the delta-ferrite leading to increased strength and decreased toughness. This study shows that ferrite content can be predicted by use of the artificial neural network. The neural network has trained learning data of chemical components and ferrite contents using backpropagation learning process. The predicted results of the ferrite content using trained neural network are in good agreement with experimental ones.

• 대한기계학회논문집A
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• 제28권4호
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• pp.460-466
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• 2004

Cast austenitic stainless steel is used for several components, such as primary coolant piping, elbow, pump casing and valve bodies in light water reactors. These components are subject to thermal aging at the reactor operating temperature. Thermal aging results in spinodal decomposition of the delta-ferrite leading to increased strength and decreased toughness. This study shows that ferrite content can be predicted by use of the artificial neural network. The neural network has trained teaming data of chemical components and ferrite contents using backpropagation learning process. The predicted results of the ferrite content using trained neural network are in good agreement with experimental ones.