RAVEN.

• How uncertain is a figure of merit given distributions on the inputs, and which inputs drive that uncertainty?
• Where in the input space does a system fail, and what is the failure probability?
• Can a fast surrogate of an expensive code support thousands of evaluations for optimisation or reliability studies?

• Forward uncertainty propagation (Monte Carlo, Latin hypercube, polynomial chaos)
• Adaptive sampling, limit-surface search, dynamic event trees
• Surrogate and reduced-order models
• Sensitivity and statistical post-processing
• Optimisation and model calibration
• Code coupling and HPC job management

Limitations

Produces no physics of its own; every result depends on an external code the user installs and wraps, and fusion codes are not among the bundled interfaces. The XML input is verbose and the reference documentation is distributed as PDFs. Some wiki pages are stale.

A general nuclear and scientific-computing uncertainty-quantification framework with a fission safety origin; no official page describes a fusion application. Its generic external-code and Python model interfaces let a fusion team wrap their own neutronics, thermal-hydraulics or systems codes, so any fusion use is the user's own coupling.

Published by the project itself. These are the project's own tutorials, not Morton Labs runs.

• Getting started — An ordered path through the workshop modules and manuals.
• Workshop slide sets — Twelve modules from forward sampling to code coupling and data mining.

No reference workflow documents this tool yet. Its evidence status is “Listed”: A reviewed catalog entry with upstream links.

Developed by Idaho National Laboratory (copyright Battelle Energy Alliance, LLC). No individual profiles are published for this project yet.

Roles name a specific relationship with evidence. Authorship of a paper is not maintainership, and a listed person is not a Morton Labs partner or endorser.

• Awesome Nuclear — Curated by Paul Romano and contributors