Neural network model for estimating departure from nucleate boiling performance of pressurized water reactor core

Journal Article · Mon Feb 01 00:00:00 EST 1993 · Nuclear Technology; (United States)

OSTI ID:6757680

Kim, Hyunkoon; Lee, Seunghyuk ^[1]; Chang, Soonheung ^[2]

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1. Korea Inst. of Nuclear Safety, Taejon (Korea, Republic of)
2. Korea Advanced Inst. of Science and Technology, Taejon (Korea, Republic of)

A new approach for estimating the departure from nucleate boiling (DNB) performance of a pressurized water reactor core is proposed in which a neural network model is introduced to predict the DNB ratios (DNBRs) for given reactor operating conditions. This model is trained against the detailed simulation results of DNBRs obtained from optimized random input vectors that are generated by Latin hypercube sampling on a wide range of parameters. The trained network is examined to verify the generalized prediction capability of the model. The test results show that a higher level of accuracy in predicting the DNBR can be achieved with the neural network model for both steady-state and transient operating conditions. The neural network model can be developed as a viable tool for on-line DNBR estimation in a nuclear plant.

• OSTI ID: 6757680
• Journal Information: Nuclear Technology; (United States), Vol. 101:2; ISSN 0029-5450
• Country of Publication: United States
• Language: English

Related Subjects

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS
PWR TYPE REACTORS
REACTOR CORES
NUCLEATE BOILING
ACCURACY
MATHEMATICAL MODELS
NEURAL NETWORKS
NUCLEAR POWER PLANTS
PERFORMANCE
REACTOR MONITORING SYSTEMS
REACTOR OPERATION
STEADY-STATE CONDITIONS
TRANSIENTS
BOILING
ENRICHED URANIUM REACTORS
NUCLEAR FACILITIES
OPERATION
PHASE TRANSFORMATIONS
POWER PLANTS
POWER REACTORS
REACTOR COMPONENTS
REACTORS
THERMAL POWER PLANTS
THERMAL REACTORS
WATER COOLED REACTORS
WATER MODERATED REACTORS
210200* - Power Reactors
Nonbreeding
Light-Water Moderated
Nonboiling Water Cooled