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ANALYSIS OF THE OECD PEACH BOTTOM TURBINE TRIP 2 TRANSIENT BENCHMARK WITH THE COUPLED NEUTRONIC AND THERMAL-HYDRAULICS CODE TRAC-M/PARCS

Deokjung Lee, Thomas Downar, Anthony P. Ulses, Bedirhan Akdeniz, Kostadin Ivanov

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Abstract

An analysis of the Peach Bottom Unit 2 Turbine Trip 2 (TT2) experiment has been performed using the U.S. NRC coupled thermal-hydraulics and neutronics code TRAC-M/PARCS. The objective of the analysis was to assess the performance of TRAC-M/PARCS on a BWR transient with significance in two phase flow and spatial variations of the neutron flux. TRAC-M/PARCS results are found to be in good agreement with measured plant data for both steady state and transient phases of the benchmark.

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What this paper is about

An analysis of the Peach Bottom Unit 2 Turbine Trip 2 (TT2) experiment has been performed using the U.S. NRC coupled thermal-hydraulics and neutronics code TRAC-M/PARCS. The objective of the analysis was to assess the performance of TRAC-M/PARCS on a BWR transient with significance in two phase flow and spatial variations of the neutron flux. TRAC-M/PARCS results are found to be in good agreement with measured plant data for both steady state and transient phases of the benchmark.

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Available abstract

An analysis of the Peach Bottom Unit 2 Turbine Trip 2 (TT2) experiment has been performed using the U.S. NRC coupled thermal-hydraulics and neutronics code TRAC-M/PARCS. The objective of the analysis was to assess the performance of TRAC-M/PARCS on a BWR transient with significance in two phase flow and spatial variations of the neutron flux. TRAC-M/PARCS results are found to be in good agreement with measured plant data for both steady state and transient phases of the benchmark.

Key concepts: TRAC, Neutron transport, Transient (computer programming), Hydraulics, Nuclear engineering, Thermal hydraulics, Environmental science, Turbine

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ANALYSIS OF THE OECD PEACH BOTTOM TURBINE TRIP 2 TRANSIENT BENCHMARK WITH THE COUPLED NEUTRONIC AND THERMAL-HYDRAULICS CODE TRAC-M/PARCS — Research Paper | ScholarLens