Abstract
To provide the information on severe accident progression is very important for advanced or new type of nuclear power plant (NPP) design. A parametric study, therefore, was performed to investigate the effect of thermal hydraulic design parameters on severe accident progression of pressurized water reactors (PWRs). Nine parameters, which are considered important in NPP design or severe accident progression, were selected among the various thermal hydraulic design parameters. The backpropagation neural network (BPN) was used to determine parameters, which might more strongly affect the severe accident progression, among nine parameters. For training, different input patterns were generated by the latin hypercube sampling (LHS) technique and then different target patterns that contain core uncovery time and vessel failure time were obtained for Young Gwang Nuclear (YGN) Units 3 and 4 using modular accident analysis program (MAAP) 3.0B code. Three different severe accident scenarios, such as two loss of coolant accidents (LOCAs) and station blackout (SBO), were considered in this analysis. Results indicated that design parameters related to refueling water storage tank (RWST), accumulator and steam generator (S/G) have more dominant effects on the progression of severe accidents investigated, compared to the other six parameters. 9 refs., 5 tabs. (Author)
Roh, Chang Hyun;
Chang, Soon Heung;
[1]
Chang, Keun Sun
[2]
- Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)
- Sunmoon University, Asan (Korea, Republic of)
Citation Formats
Roh, Chang Hyun, Chang, Soon Heung, and Chang, Keun Sun.
Thermal Hydraulic design parameters study for severe accidents using neural networks.
Korea, Republic of: N. p.,
1997.
Web.
Roh, Chang Hyun, Chang, Soon Heung, & Chang, Keun Sun.
Thermal Hydraulic design parameters study for severe accidents using neural networks.
Korea, Republic of.
Roh, Chang Hyun, Chang, Soon Heung, and Chang, Keun Sun.
1997.
"Thermal Hydraulic design parameters study for severe accidents using neural networks."
Korea, Republic of.
@misc{etde_324138,
title = {Thermal Hydraulic design parameters study for severe accidents using neural networks}
author = {Roh, Chang Hyun, Chang, Soon Heung, and Chang, Keun Sun}
abstractNote = {To provide the information on severe accident progression is very important for advanced or new type of nuclear power plant (NPP) design. A parametric study, therefore, was performed to investigate the effect of thermal hydraulic design parameters on severe accident progression of pressurized water reactors (PWRs). Nine parameters, which are considered important in NPP design or severe accident progression, were selected among the various thermal hydraulic design parameters. The backpropagation neural network (BPN) was used to determine parameters, which might more strongly affect the severe accident progression, among nine parameters. For training, different input patterns were generated by the latin hypercube sampling (LHS) technique and then different target patterns that contain core uncovery time and vessel failure time were obtained for Young Gwang Nuclear (YGN) Units 3 and 4 using modular accident analysis program (MAAP) 3.0B code. Three different severe accident scenarios, such as two loss of coolant accidents (LOCAs) and station blackout (SBO), were considered in this analysis. Results indicated that design parameters related to refueling water storage tank (RWST), accumulator and steam generator (S/G) have more dominant effects on the progression of severe accidents investigated, compared to the other six parameters. 9 refs., 5 tabs. (Author)}
place = {Korea, Republic of}
year = {1997}
month = {Dec}
}
title = {Thermal Hydraulic design parameters study for severe accidents using neural networks}
author = {Roh, Chang Hyun, Chang, Soon Heung, and Chang, Keun Sun}
abstractNote = {To provide the information on severe accident progression is very important for advanced or new type of nuclear power plant (NPP) design. A parametric study, therefore, was performed to investigate the effect of thermal hydraulic design parameters on severe accident progression of pressurized water reactors (PWRs). Nine parameters, which are considered important in NPP design or severe accident progression, were selected among the various thermal hydraulic design parameters. The backpropagation neural network (BPN) was used to determine parameters, which might more strongly affect the severe accident progression, among nine parameters. For training, different input patterns were generated by the latin hypercube sampling (LHS) technique and then different target patterns that contain core uncovery time and vessel failure time were obtained for Young Gwang Nuclear (YGN) Units 3 and 4 using modular accident analysis program (MAAP) 3.0B code. Three different severe accident scenarios, such as two loss of coolant accidents (LOCAs) and station blackout (SBO), were considered in this analysis. Results indicated that design parameters related to refueling water storage tank (RWST), accumulator and steam generator (S/G) have more dominant effects on the progression of severe accidents investigated, compared to the other six parameters. 9 refs., 5 tabs. (Author)}
place = {Korea, Republic of}
year = {1997}
month = {Dec}
}