Skip to main content

Burnup Code

This code was designed to solve the Bateman's equation: dN/dt = (Lambda+React*Phi)*N where N is the vector of nuclide density for each nuclide in each spatial cell, Lambda is the decay model matrix, that corresponds to the rate of decay of each nuclide to other nuclides, React is the reaction model matrix, that corresponds to the rate of transmutation of each isotope into other isotopes due to the neutron flux, and Phi is the absolute flux level in the core that has to be somehow normalized to make the reaction rates fit the actual physical processes.

Design Goals

The design goals for this package are:

  1. The code must be parallelizable to more than one computer (achieved).
  2. The code must pass benchmarks (partially achieved).
  3. Code runtime must be in the few minute scale. CPU time may be up to an hour for a large core. (Achieved! Full core at 7 minutes with 32 CPUs)
  4. Code must be stable for large time steps. (Partially Achieved, depends on power renormalization requirements.)
  5. Code must be abstract and use simple external API. (Under Review)
  6. Isotopic data must allow for both fudged and real database built data. (Achieved!)
  7. Code must pass high standards (Under Review and Not Finished): a. Code must be fully documented. Type hinting counts for simple functions. b. Code must be modular and modules should be as implementation independent of one another as possible. c. Code must pass code review regularly and Major versions in flying colors. d. Test coverage must be 90%+, and tests must include parallel tasked cases. Mock tests are required. Multiple input tests required for numerical tests. e. Code must be easily installable.

Ways to Contribute

  1. We need to work on proper automatic documentation. Currently not done.
  2. Code needs better quality and refactoring. You're welcome to try your hand.
  3. Other exponentiation methods than CRAM could potentially be faster. Please try your hand at them.
  4. The code currently takes explicit time steps with constant flux as the base method. The academic papers speak of using a predictor-corrector model to extend the correctness with lower cost. This should be investigated to reduce the number of internal steps, or a Lie algebra method can be used as well.
  5. Benchmarks for this code are required.

Metadata

Release files for ramp-batman 0.0.4

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for ramp-batman 0.0.4
File Size Uploaded Provenance Actions
ramp_batman-0.0.4.tar.gz 52.5 kB Without provenance Details for ramp_batman-0.0.4.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for ramp-batman 0.0.4
File Interpreter ABI Platform Actions
ramp_batman-0.0.4-py3-none-any.whl Python 3 none any Details for ramp_batman-0.0.4-py3-none-any.whl

Total release size: 101.0 kB

Release files / ramp_batman-0.0.4.tar.gz

Download URL ramp_batman-0.0.4.tar.gz
Size 52.5 kB
Tags Source
SHA-256 checksum
How to use checksums
32769ba99f9485933d05d38d2c875877e77c656f9b876ade86be6d54114bd9b6
BLAKE2b-256 checksum
How to use checksums
455eaf0f131af587a7d9de8abbafc143103403aa92b19f803109cfb9fa2bbf0d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.3

Release files / ramp_batman-0.0.4-py3-none-any.whl

Download URL ramp_batman-0.0.4-py3-none-any.whl
Size 48.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
2393e4ca36ac0fe960287a187017e860e677e7f66036ae14d5f4c0457b301ffc
BLAKE2b-256 checksum
How to use checksums
91aa3cd9e58ec41092b6a582a8376209a38740543df18febc7a4b518a058bcd4
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.14.3

Security for ramp-batman 0.0.4

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page