serpentTools: A Python Package for Expediting Analysis with Serpent

Johnson, Amos; Kotlyar, Dan; Terlizzi, Stefano; Ridley, Gavin

doi:10.1080/00295639.2020.1723992

Cited by 23 publications

(4 citation statements)

References 4 publications

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“…Since Serpent2 does not compute the Monte-Carlo error for nuclide number densities on its own, an estimate of the computational deviation was obtained by generating fuel input with a different random number seeds a total of 100 times with the help of serpentTools (Johnson et al, 2020) package. Two distinct fuels with a realistic power history were chosen for this analysis, one with a more regular and contiguous (in time) power history while a second fuel had a discontinuous power history in the core.…”

Section: Error Quantification For Burnup Calculationsmentioning

confidence: 99%

Statistical Analysis of Fuel Cycle Data from Swedish Pressurized Water Reactors and the Impact of Simplifying Assumptions on Simulated Nuclide Inventories

Mishra

Elter²,

Branger³

et al. 2022

SSRN Journal

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Section: Error Quantification For Burnup Calculationsmentioning

confidence: 99%

Statistical Analysis of Fuel Cycle Data from Swedish Pressurized Water Reactors and the Impact of Simplifying Assumptions on Simulated Nuclide Inventories

Mishra

Elter²,

Branger³

et al. 2022

SSRN Journal

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“…The TFs were calculated using the python package transientGTF, a python-based code developed at the Georgia Institute of Technology for cross section storage and manipulation. The tool interfaces with serpentTools to read and automatically store cross section data from SERPENT output files [5,6]. The transientGTF is currently hosted on a private repository, however, the authors are planning to make it public in the near future.…”

Section: Gtf Calculationmentioning

confidence: 99%

Accelerated Coupled Monte Carlo-Thermal Hydraulic Calculations Using a Hybrid GTF-Diffusion-Based Prediction Block: First Results

Painter,

Terlizzi,

Kotlyar

2022

International Conference on Physics of Reactors 2022 (PHYSOR 2022)

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Accurate predictions of spatial power and temperature distributions require the coupling of a neutron transport solver with a thermal-hydraulic (TH) feedback. Nowadays, Monte Carlo (MC) codes are widely coupled to TH solvers, typically via a Picard iteration (PI) method, due to the higher fidelity that such frameworks can produce. To speed up a PI, a prediction step can produce an improved initial guess for a source distribution and feed it to the MC code. Recent work [1, 2] investigated a prediction step that uses generalized transfer functions (GTFs) to predict the macroscopic cross sections' variations following a perturbation in TH properties, such as coolant density. The previous method also relied on first order perturbation (FOP) theory to predict perturbed power profiles, rather than using an expensive MC iterate. The implemented FOP method relied on generating a fission matrix from which the forward and adjoint eigenmodes were extracted and later used to for power calculations. The generation of the fission matrix can introduce a significant computational overhead, therefore undermining the performance of the proposed hybrid technique when applied to high-dimensional problems, e.g., full core calculations. This work attempts to improve the GTF-FOP prediction step by replacing the FOP solver with a nodal diffusion solver, thus eliminating the need to calculate a fission matrix. The GTF-diffusion step was tested for various moderator density perturbations. In each case, the predicted power distribution showed good agreement with the reference case. The latter is attributed to the generally good prediction of most spatially distributed macroscopic cross sections, except the transport cross section, which will become the focus of future work.

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“…These capabilities were enabled to account for the gamma heating effect on the shape of the axial power profiles used in T/H sub-channel analysis. The post processing of SERPENT output files was conducted using the serpentTools python package [12].…”

Section: Tools and Modelling -Reactor Corementioning

confidence: 99%

A Coupled Approach to the Design Space Exploration of Nuclear Thermal Propulsion Systems

Petitgenet

Roper

Krecicki

et al. 2020

AIAA Propulsion and Energy 2020 Forum

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Nuclear Thermal Propulsion (NTP) is identified as one of the preferred propulsion technologies for future manned missions to Mars and other interplanetary destinations. NTP systems can improve the returns and mitigate the risks of such missions by reducing travel time and improving payload capacity as compared to traditional chemical propulsion systems. Due to the complexity and tightly coupled nature of the nuclear reactor and surrounding NTP subsystems, the traditional decoupled approach to NTP system analysis is inadequate. A new approach is needed to enable a high-fidelity design space exploration exercise for NTP systems. The approach outlined in this paper will address an integrated model of the reactor and supporting subsystems. This model, along with the incorporation of Design of Experiments and Surrogate Modeling, will allow for the exploration of the performance of a large number of NTP system designs with respect to metrics such as specific impulse and thrust to weight ratio. The subsystems analysis is handled by Numerical Propulsion Systems Simulation (NPSS) while reactor modeling is conducted using various numerical codes. This paper proposes and demonstrates a coupled design space exploration approach for NTP systems and uses these findings to consider vehicle-level implications.

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serpentTools: A Python Package for Expediting Analysis with Serpent

Cited by 23 publications

References 4 publications

Statistical Analysis of Fuel Cycle Data from Swedish Pressurized Water Reactors and the Impact of Simplifying Assumptions on Simulated Nuclide Inventories

Statistical Analysis of Fuel Cycle Data from Swedish Pressurized Water Reactors and the Impact of Simplifying Assumptions on Simulated Nuclide Inventories

Accelerated Coupled Monte Carlo-Thermal Hydraulic Calculations Using a Hybrid GTF-Diffusion-Based Prediction Block: First Results

A Coupled Approach to the Design Space Exploration of Nuclear Thermal Propulsion Systems

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