• Nuclear Engineering and Technology
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• 제54권3호
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• pp.834-841
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• 2022

System thermal-hydraulic (STH) code is adopted for nuclear safety analysis. The critical flow model (CFM) is significant for the accuracy of STH simulation. To overcome the defects of current CFMs (low precision or long calculation time), a CFM based on a genetic neural network (GNN) has been developed in this work. To build a powerful model, besides the critical mass flux, the critical pressure and critical quality were also considered in this model, which was seldom considered before. Comparing with the traditional homogeneous equilibrium model (HEM) and the Moody model, the GNN model can predict the critical mass flux with a higher accuracy (approximately 80% of results are within the ±20% error limit); comparing with the Leung model and the Shannak model for critical pressure prediction, the GNN model achieved the best results (more than 80% prediction results within the ±20% error limit). For the critical quality, similar precision is achieved. The GNN-based CFM in this work is meaningful for the STH code CFM development.

• Nuclear Engineering and Technology
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• 제51권8호
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• pp.1897-1904
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• 2019

Three-layer back propagation network (BPN) and genetic neural network (GNN) were developed in this study to predict the flow boiling heat transfer coefficient (HTC) in conventional and small-diameter channels. The GNN has higher precision than BPN (with root mean square errors of 17.16% and 20.50%, respectively) and other correlations. The inputs include vapor quality x, mass flux G, heat flux q, diameter D and physical parameter φ, and the predicted flow boiling HTC is set as the outputs. Influences of input parameters on the flow boiling HTC are discussed based on the trained GNN: nucleate boiling promoted by a larger saturated pressure, a larger heat flux and a smaller diameter is dominant in small channels; convective boiling improved by a larger mass flux and a larger vapor quality is more significant in conventional channels. The HTC increases with pressure both in conventional and small channels. The HTC in conventional channels rises when mass flux increases but remains almost unaffected in small channels. A larger heat flux leads to the HTC growth in small channels and an increase of HTC was observed in conventional channels at a higher vapor quality. HTC increases inversely with diameter before dry out.

• 조명전기설비학회논문지
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• 제17권5호
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• pp.29-35
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• 2003

본 논문에서는 유전 알고리즘과 신경 회로망을 이용하여 선형유도전동기의 최적화 설계 방법에 대하여 연구하였다. 최대 추력 및 추력/중량을 목적함수로 하여 유전알고리즘, 신경회로망, 유전알고리즘과 신경회로망의 합성에 의한 방법으로 선형유도전동기의 최적설계를 한 결과 제안한 방법이 가장 우수함을 확인하였다.