Sensitivity analysis of the LMT-006 benchmark experiments with OpenMC
Ali, Nour (2024)
Diplomityö
Ali, Nour
2024
School of Energy Systems, Energiatekniikka
Kaikki oikeudet pidätetään.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe20241213102542
https://urn.fi/URN:NBN:fi-fe20241213102542
Tiivistelmä
The LEU-MET-THERM-006 (Low Enriched Uranium fissile material, METal physical form, THERMal spectrum, LMT-006 for short) series of experiments was carried out in 1969-1970 by the CEA (Commissariat à l’Energie Atomique), France in the experimental criticality facility of Valduc. The experiments are included in the ICSBEP (International Criticality Safety Benchmark Evaluation Project) handbook. The experiments consist of water-moderated and water-reflected metal tubes of low-enriched uranium (1.6%) arranged in arrays. A correlation of the reactivity with the fraction of epi-thermal fissions reveals a gradient wherein reactivity is higher for softer neutron spectra. This thesis models the LMT-006 benchmarks in OpenMC, verifies the results using the previously obtained MCNP6 results, and investigates the cause of the swing in reactivity through select sensitivity analysis and cross section perturbation.
Criticality benchmarks are a valuable resource for nuclear data and code validation. The obtained results of the LMT-006 benchmark allows the validation of key cross sections with various nuclear data libraries. OpenMC, a free, open-source Monte Carlo neutron transport code, facilitates the sensitivity analysis and nuclear data validation with different libraries through its integrated Python API and NJOY interface. The results of this thesis contribute to the overall body of work of benchmark models, results, and nuclear data validation.
Criticality benchmarks are a valuable resource for nuclear data and code validation. The obtained results of the LMT-006 benchmark allows the validation of key cross sections with various nuclear data libraries. OpenMC, a free, open-source Monte Carlo neutron transport code, facilitates the sensitivity analysis and nuclear data validation with different libraries through its integrated Python API and NJOY interface. The results of this thesis contribute to the overall body of work of benchmark models, results, and nuclear data validation.
