Reports

Our report series is archived below

ARFC Technical Reports

  1. Bae, Jin Whan, Gwendolyn Chee, and Kathryn Huff. 2018. “Numerical Experiments for Verifying Demand Driven Deployment Algorithms.” Graduate Report UIUC-ARFC-2018-01. Urbana, IL: University of Illinois at Urbana-Champaign. https://github.com/arfc/ddca_numerical_exp.

    For many fuel cycle simulations, it is currently up to the user to define a deployment scheme, or facility parameters, to make sure that there’s no gap in the supply chain. Or, the same goal is achieved by setting the facility capacity to infinity, which does not reflect real-world conditions. The Demand-Driven Cycamore Archetype project (NEUP-FY16-10512) aims to develop Cycamore demand-driven deployment capabilities. The developed algorithm, in the form of Cyclus Institution agent, deploys Facilities to meet the front-end and back-end demands of the fuel cycle. This report describes numerical tests for non-optimizing, deterministic- optimizing and stochastic-optimizing prediction algorithms. These prediction models are being developed by the University of South Carolina. In this report, we discuss numerical experiments for testing the non-optimizing, deterministic optimizing and stochastic optimizing meth- ods. The numerical experiments will be designed for both the once through nuclear fuel cycle and advanced fuel cycles.

    @techreport{bae_numerical_2018,
      address = {Urbana, IL},
      type = {Graduate {Report}},
      title = {Numerical {Experiments} for {Verifying} {Demand} {Driven} {Deployment} {Algorithms}},
      url = {https://github.com/arfc/ddca_numerical_exp},
      number = {UIUC-ARFC-2018-01},
      institution = {University of Illinois at Urbana-Champaign},
      author = {Bae, Jin Whan and Chee, Gwendolyn and Huff, Kathryn},
      month = apr,
      year = {2018},
      keywords = {arfc, report},
      pages = {0--21},
      file = {Bae et al. - 2018 - Numerical Experiments for Verifying Demand Driven .pdf:/Users/khuff/Zotero/storage/5X9YWIQW/Bae et al. - 2018 - Numerical Experiments for Verifying Demand Driven .pdf:application/pdf}
    }
    
  2. Ridley, Gavin, Alexander Lindsay, Matthew Turk, and Kathryn Huff. 2017. “Multiphysics Analysis of Molten Salt Reactor Transients.” Undergraduate Report UIUC-ARFC-2017-01. Urbana, IL: University of Illinois at Urbana-Champaign. https://github.com/arfc/publications/tree/2017-ridley-msrTransients.

    Molten salt nuclear reactor technology has not yet been constructed for industrial scale. High fidelity simulation capability of both transients and steady-state behavior must be developed for reactor licensing. The simulations should make use of high performance computing (HPC) in order to come to realistic results while minimizing assumptions. High resolution simulation of limiting reactor transients using open-source software can inform reproducible results suitable for preliminary licensing activity. We present example results of the new code.

    @techreport{ridley_multiphysics_2017,
      address = {Urbana, IL},
      type = {Undergraduate {Report}},
      title = {Multiphysics {Analysis} of {Molten} {Salt} {Reactor} {Transients}},
      url = {https://github.com/arfc/publications/tree/2017-ridley-msrTransients},
      number = {UIUC-ARFC-2017-01},
      institution = {University of Illinois at Urbana-Champaign},
      author = {Ridley, Gavin and Lindsay, Alexander and Turk, Matthew and Huff, Kathryn},
      month = aug,
      year = {2017},
      note = {DOI 10.5281/zenodo.1145437},
      keywords = {arfc, report},
      pages = {0--12},
      file = {uiuc-arfc-2017-01.pdf:/Users/khuff/Zotero/storage/ASUYNNXT/uiuc-arfc-2017-01.pdf:application/pdf}
    }
    
  3. Bae, Jin Whan, and Kathryn D. Huff. 2017. “Non-Algorithmic Capability Gaps for Cyclus and Cycamore Transition Analyses.” Graduate Report UIUC-ARFC-2017-02. Urbana, IL: University of Illinois at Urbana-Champaign. https://doi.org/10.5281/zenodo.1145439.

    As part of NEUP-FY16-10512, fuel cycle transition scenarios were simulated using Cyclus and existing Cycamore archetypes. The purpose of this study is to identify current non-algorithmic gaps in the capabilities necessary for key transition scenarios. The gaps identified through this exercise mainly pertain to the greedy exchange model, and the manual, static parameter of fuel cycle facilities. The scenarios are from the Idaho National Laboratory Nuclear Fuel Cycle Evaluation and Screening Report. The transition scenarios begin with EG01 and transition to EG23, EG24, EG29, EG30, separately.

    @techreport{bae_non-algorithmic_2017,
      address = {Urbana, IL},
      type = {Graduate {Report}},
      title = {Non-algorithmic {Capability} {Gaps} for {Cyclus} and {Cycamore} transition analyses},
      url = {https://github.com/arfc/transition-scenarios},
      number = {UIUC-ARFC-2017-02},
      institution = {University of Illinois at Urbana-Champaign},
      author = {Bae, Jin Whan and Huff, Kathryn D.},
      month = nov,
      year = {2017},
      doi = {10.5281/zenodo.1145439},
      keywords = {arfc, report},
      pages = {0--12},
      file = {uiuc-arfc-2017-02.pdf:/Users/khuff/Zotero/storage/3GFDQRRG/uiuc-arfc-2017-02.pdf:application/pdf}
    }