Projects / Programmes
Sensitivity of nuclear reactor physical parameters to thermal nuclear data
| Code |
Science |
Field |
Subfield |
| 2.03.02 |
Engineering sciences and technologies |
Energy engineering |
Fuels and energy conversion technology |
| Code |
Science |
Field |
| 2.02 |
Engineering and Technology |
Electrical engineering, Electronic engineering, Information engineering |
nuclear data, sensitivity and uncertainty analysis, nuclear reactor, stochastic particle transport, neutron transport
Data for the last 5 years (citations for the last 10 years) on
June 25, 2024;
A3 for period
2018-2022
Data for ARIS tenders (
04.04.2019 – Programme tender,
archive
)
| Database |
Linked records |
Citations |
Pure citations |
Average pure citations |
| WoS |
1,210 |
23,022 |
18,893 |
15.61 |
| Scopus |
1,206 |
25,573 |
21,340 |
17.69 |
Researchers (10)
| no. |
Code |
Name and surname |
Research area |
Role |
Period |
No. of publicationsNo. of publications |
| 1. |
39521 |
PhD Tanja Goričanec |
Computer intensive methods and applications |
Researcher |
2020 - 2024 |
0 |
| 2. |
03943 |
PhD Ivan Aleksander Kodeli |
Computer intensive methods and applications |
Researcher |
2020 - 2021 |
0 |
| 3. |
38202 |
PhD Bor Kos |
Energy engineering |
Researcher |
2020 - 2021 |
0 |
| 4. |
19167 |
PhD Igor Lengar |
Materials science and technology |
Researcher |
2020 - 2024 |
0 |
| 5. |
52752 |
Jan Malec |
Energy engineering |
Researcher |
2022 - 2024 |
0 |
| 6. |
27819 |
PhD Luka Snoj |
Energy engineering |
Researcher |
2020 - 2024 |
0 |
| 7. |
53533 |
Ingrid Švajger |
Energy engineering |
Junior researcher |
2020 - 2024 |
0 |
| 8. |
08557 |
PhD Andrej Trkov |
Energy engineering |
Head |
2020 - 2024 |
0 |
| 9. |
15742 |
Bojan Žefran |
|
Technical associate |
2020 - 2024 |
0 |
| 10. |
29546 |
PhD Gašper Žerovnik |
Computer intensive methods and applications |
Researcher |
2020 - 2024 |
0 |
Organisations (1)
| no. |
Code |
Research organisation |
City |
Registration number |
No. of publicationsNo. of publications |
| 1. |
0106 |
Jožef Stefan Institute |
Ljubljana |
5051606000 |
18 |
Abstract
In order to generate carbon-free electricity, radical innovations in renewable energy are required. Much of any country’s installed electricity production base exists solely to meet demand under “peak load” conditions, and the rise of variable-output renewable energy sources (wind, solar), and electric vehicles (which further complicate energy need predications and peak loads) brings difficult new challenges. These challenges can be mitigated using smaller, more flexible nuclear reactors – small modular reactors. Creating new small modular reactor concepts as needed for future sustainable energy needs depends not on breakthroughs in construction, but in data capture, modelling, and simulation, to create the control systems necessary for them to be optimally effective, efficient, and safe. To be able to analyse or give predictions using simulations about reactor system response parameters, which are dependent on the energy and spatial distribution of neutrons in a reactor, detailed knowledge of the thermal neutron scattering data is required, and methods for its measurement, calculation or estimation have to be developed. As with any measured, calculated or estimated quantity, knowledge of the quantity is of limited value, unless its uncertainty is assessed, and this is a critical point. The objective of the proposed research is to generate thermal neutron scattering cross sections and corresponding covariance data in a rigorous manner that is from first principles, by employing state-of-the-art atomistic simulations, which rely on density functional theory in combination with lattice or molecular dynamics calculations. A problem that persists throughout the field is that of storage and representation of thermal neutron scattering data uncertainties i.e. thermal neutron scattering cross section covariance matrices, because no format for thermal nuclear data covariances currently exists. Completely new data will be produced for reactor physics applications which will provide scientists all over the world with uncertainty information that was, until recently, regularly neglected. The results will open a whole new area in the field of nuclear data science as we will be able to evaluate nuclear data uncertainties that were previously not considered and for the first time, it will be possible to ascertain their impact.
