openmc_uq_sinbad

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

openmc_uq_sinbad

(★ 12)

Repository for running SINBAD benchmarks with OpenMC and UQ

openmc_uq_sinbad 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# SINBAD fusion benchmarks with UQ and openmc
Repository for running SINBAD benchmarks with OpenMC and nuclear data uncertainty propagation. Nuclear data is propagated with Monte Carlo simulation, à la Total Monte Carlo (TMC).

[Fusion Science and Technology article](https://www.tandfonline.com/doi/full/10.1080/15361055.2021.1895667?scroll=top&needAccess=true)

Required:

* [openmc](https://github.com/openmc-dev/openmc)
* [openmc/data](https://github.com/openmc-dev/data)
* [NJOY](https://github.com/njoy/NJOY21)
* [SANDY](https://github.com/luca-fiorito-11/sandy)

Post processing:

* [ProbabilityBoundsAnalysis.jl](https://github.com/AnderGray/ProbabilityBoundsAnalysis.jl)

Useage:
---
**Nuclear Data sampling:**
* Download relevant endf and hdf5 data with [openmc/data](https://github.com/openmc-dev/data) scripts (NNDC, TENDL and JEFF used)
* Set relevant environmental variables in environment.sh
* (If TENDL) Set names of TENDL endf files with _[random_data/rename_tendl_lib.py](https://github.com/AnderGray/openmc_uq_benchmarks/blob/main/randomData/rename_tendl_lib.py)_
* Sample benchmark data with scripts in _[random_data](https://github.com/AnderGray/openmc_uq_benchmarks/blob/main/randomData)_
  * SANDY used for sampling
  * Slurm submission scripts provided, appropriate changes required
  * Default number of samples: 500
  * Some covariance files missing for some nuclides, see _[random_data/README.MD](https://github.com/AnderGray/openmc_uq_sinbad/blob/main/random_data/README.md)_
  * Variance reduction cannot be used with these cross sections due to [issue #1699](https://github.com/openmc-dev/openmc/issues/1699)
  
**Benchmarks and Uncertainty Propagation:**
* Set appropriate submission script for _[TMC_tools/openmc_sub.sh](https://github.com/AnderGray/openmc_uq_sinbad/blob/main/TMC_tools/openmc_sub.sh)_
* For Ni and FNG, build source with _[build_source.sh](https://github.com/AnderGray/openmc_uq_sinbad/blob/main/oktav_ni/buildSource.sh)_
* Various uncertain libraries may be propagated with provided scripts, ie _[oktav_fe/run_endf.sh](https://github.com/AnderGray/openmc_uq_sinbad/blob/main/oktav_fe/runEndf.sh)_
 * One job is submitted per random evaluation, i.e if 500 random samples, 500 jobs
 * Multiple random nuclides are ran simultaneously. Inter-nuclide dependence is assumed to be independent.

**Sensitivity Analysis (todo):**
* Not yet supplied, but quite simple.
* If you would like to know the sensitivity of eg Fe56, run the simulation will all other nuclides uncertain except this one
* Then measure the drop in uncertainty with either difference in variances (Saltelli), entropy (information based), area metric (pinching), ...

**Post processing and plotting (todo):**
* For tally arithmetic, we use [ProbabilityBoundsAnalysis.jl](https://github.com/AnderGray/ProbabilityBoundsAnalysis.jl)
  * Allows you to perform arithmetic considering both ND and statsitical uncertainties.
  * Arithmetic with independence, dependent (with correlation), or unknown correlation.
* Plotting script available in _[TMC_tools](https://github.com/AnderGray/openmc_uq_sinbad/tree/main/TMC_tools)_

Details:
---
In TMC random evaluations of the nuclear data quantities are created either by using the endf covaraiance information (produces gaussian random evalutations) or by using a bayesian method with a nuclear reaction model code (TENDL library produced this way). The particle transport simulation is then repeated with each random evaluation. Each transport simulation has statistical uncertainty assosiated with it, which also must be considered. If the individual statistical uncertainty is negliable compared to the ND uncertainty, then standard probability theory may be applied. If this is not the case and both need to be considered, we use [imprecise probabilities](https://en.wikipedia.org/wiki/Imprecise_probability) to perform further analysis. This is especially relevent in "Fast-TMC", where larger statistical uncertainty is exchanged for compute time.

![alt text](https://imgur.com/kAVOSfd.png "Oktav Fe tendl and endf")
![alt text](https://imgur.com/AQvX6JN.png "Difference with uncertainty")

openmc_uq_sinbad

openmc_uq_sinbad 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

openmc_uq_sinbad

openmc_uq_sinbad 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README