Journal: Annals of Nuclear Energy

Abbreviation

Ann. nucl. energy

Publisher

Elsevier

Journal Volumes

ISSN

0306-4549

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Publications 1 - 10 of 19
  • Interpretation of experimental results from moderate-power in-pile testing of a Pu-Er-Zr-oxide inert matrix fuel
    Item type: Journal Article
    Hellwig, Christian; Kasemeyer, Uwe; Ledergerber, Guido; et al. (2003)
    Annals of Nuclear Energy
  • Verification of the new implementations in SHARKX against TSUNAMI to perform pinpower UQ and representativity analysis
    Item type: Journal Article
    Hursin, Mathieu; Perret, Gregory; Pautz, Andreas (2015)
    Annals of Nuclear Energy
  • Significance of Phebus-FP results for plant safety in Switzerland
    Item type: Journal Article
    Birchley, J.; Guntay, S. (2013)
    Annals of Nuclear Energy
  • Mixed convection study on the influence of low Prandtl numbers and buoyancy in turbulent heat transfer using DNS
    Item type: Journal Article
    Guo, Wentao; Prasser, Horst-Michael (2021)
    Annals of Nuclear Energy
    Direct numerical simulation (DNS) is performed to study turbulent heat transfer in Poiseuille-Rayleigh-Bénard (PRB) flows with low Prandtl numbers in this article. The mesh is Cartesian and a highly accurate finite difference sixth-order compact scheme is chosen to discretize the incompressible Navier–Stokes equations to perform DNS. Liquids with a fixed Richardson number of 0.25 and four different Prandtl number (Pr = 0.025, 0.05, 0.1, 0.71) are simulated and compared with Poiseuille flow to investigate the influence of Prandtl number and buoyancy on –PRB flows. Constant fluid properties and Boussinesq approximation are assumed. The obtained results are discussed and analysed in an extensive way in this study. Specifically, buoyancy initiate large scale circulation and the scale shrinks with the increasing of Prandtl number. Velocity fluctuations become stronger with PRB flow which indicate that buoyancy can strongly enhance the turbulent intensity. is increased in the cases of low Pr. Moreover, when Pr decreases, temperature distributions are found to be more homogeneous and mixing of the fluids is more sufficient in the middle of the channel. Additionally, the scale of the large-scale structures is enlarged in mixed convection compared with forced convection. This can be observed in the temperature field of low-Prandtl-number fluids. It is also observed that Reynolds analogy cannot be used to predict the thermal field under mixed convection or forced convection with low Prandtl number. The research results can be used for the R&D of Gen IV nuclear fast reactors.
  • Single-phase mixing studies by means of a directly coupled CFD/system-code tool
    Item type: Conference Paper
    Bertolotto, Davide; Manera, Annalisa; Frey, Simon; et al. (2009)
    Annals of Nuclear Energy
  • Simulation and experiments on the feasibility of using gamma tomography for void fraction measurements in nuclear fuel bundle mock-ups
    Item type: Journal Article
    Adams, Robert; Diaz, Julio; Petrov, Victor; et al. (2021)
    Annals of Nuclear Energy
    The High-Resolution Gamma Tomography System (HRGTS) facility is under development at the University of Michigan. This system is planned generally for application to two-phase flow loops representing nuclear fuel bundles. These measurements should provide insight into fluid dynamics phenomena and high resolution data for validation of computational fluid dynamics (CFD) codes. The HRGTS is planned to be deployed in the Michigan Advanced Rod Bundle fLow Experiment (MARBLE). an adiabatic test loop consisting of an 8x8 square lattice arrangement. Simulations modeling MARBLE were performed to study how the counting statistics of the imaging process propagate into void fraction uncertainty. The simulation results showed that an accuracy of around 1% is expected with imaging times of 1 min. Computed tomography measurements were performed on partial mockups of the bundle geometry, which confirmed that the technique can be used to obtain accurate subchannel void fraction data in complicated and challenging measurement scenarios.
  • Fuel cycle advantages and dynamics features of liquid fueled MSR
    Item type: Journal Article
    Krepel, Jiri; Hombourger, Boris; Fiorina, Carlo; et al. (2014)
    Annals of Nuclear Energy
  • Application of Continuous and Structural ARMA modeling for noise analysis of a BWR coupled core and plant instability event
    Item type: Journal Article
    Demeshko, M.; Dokhane, A.; Washio, T.; et al. (2015)
    Annals of Nuclear Energy
  • Uncertainty quantification of spent nuclear fuel with multifidelity Monte Carlo
    Item type: Journal Article
    Albà, Arnau; Adelmann, Andreas; Rochman, Dimitri (2024)
    Annals of Nuclear Energy
    Uncertainty quantification (UQ) of spent nuclear fuel (SNF) is a crucial task that provides predictions and confidence bounds for important quantities of interest, such as decay heat, nuclide content, or k-effective. An accurate estimation of these quantities and their uncertainties is essential to reduce the risks and costs of storing and transporting SNF. The most accurate and robust method employed in reactor physics computations for UQ is Monte Carlo, however it is computationally intensive, to the point where it is unfeasible to carry out UQ for all the high-level waste expected in Switzerland. In this work, Multifidelity Monte Carlo (MFMC) is applied for the first time to the UQ of SNF, and a novel model management strategy to alleviate the training costs of MFMC is introduced. It is shown that MFMC drastically reduces the computational costs of UQ, with speedups between 5 and 1500 with respect to simple Monte Carlo.
  • Fast uncertainty quantification of spent nuclear fuel with neural networks
    Item type: Journal Article
    Albà, Arnau; Adelmann, Andreas; Münster, Lucas; et al. (2024)
    Annals of Nuclear Energy
    The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards. State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics with reduced computational costs compared to physics-based models. An NN is trained using data generated from CASMO5 lattice calculations. The trained NN accurately predicts decay heat and nuclide concentrations of SNF, as a function of key input parameters, such as enrichment, burnup, cooling time between cycles, mean boron concentration and fuel temperature. The model is validated against physics-based decay heat simulations and measurements of different uranium oxide fuel assemblies from two different pressurized water reactors. In addition, the NN is used to perform sensitivity analysis and uncertainty quantification. The results are in very good alignment to CASMO5, while the computational costs (taking into account the costs of generating training samples) are reduced by a factor of 10 or more. Our findings demonstrate the feasibility of using NNs as surrogate models for fast characterization of SNF, providing a promising avenue for improving computational efficiency in assessing nuclear fuel behavior and associated risks.
Publications 1 - 10 of 19