MCNP&lt;sup&gt;®&lt;/sup&gt; Code Version 6.3.0 Theory &amp; User Manual

A., Kulesza, Joel; Adams, Terry R.; Armstrong, James C.; Bolding, Simon R.; Brown, Forrest B.; Bull, Jeffrey S.; Burke, Timothy P.; Clark, Alexander Rich; Forster, Robert; Giron, Jesse F.; Grieve, Tristan; Josey, Colin; Martz, Roger L.; McKinney, G.W.; Pearson, Eric; Rising, Michael Evan; Solomon, Clell J.; Swaminarayan, Sriram; Trahan, Travis John; Wilson, S. R.; Zukaitis, Anthony J.

doi:10.2172/1889957

Cited by 4 publications

(2 citation statements)

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“…gov/) is a general purpose radiation transport simulation code that is able to track 37 different particle types over a broad range of energies and up to 1 TeV/nucleon. 72 The MCNP code is available to the worldwide user community, subject to USA national security restrictions and distributed through the Radiation Safety Information Computational Center, part of Oak Ridge National Laboratory. MCNP has been applied to a wide range of medical physics problems 73 including the modelling of medical linear accelerators, [74][75][76][77][78] and imaging systems such as radiotherapy electronic portal imaging devices (EPIDs), [79][80][81] CT 82 and PET/CT.…”

Section: Mcnpmentioning

confidence: 99%

Monte-Carlo techniques for radiotherapy applications I: introduction and overview of the different Monte-Carlo codes

Fielding

2023

J Radiother Pract

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Introduction: The dose calculation plays a crucial role in many aspects of contemporary clinical radiotherapy treatment planning process. It therefore goes without saying that the accuracy of the dose calculation is of very high importance. The gold standard for absorbed dose calculation is the Monte-Carlo algorithm. Methods: This first of two papers gives an overview of the main openly available and supported codes that have been widely used for radiotherapy simulations. Results: The paper aims to provide an overview of Monte-Carlo in the field of radiotherapy and point the reader in the right direction of work that could help them get started or develop their existing understanding and use of Monte-Carlo algorithms in their practice. Conclusions: It also serves as a useful companion to a curated collection of papers on Monte-Carlo that have been published in this journal.

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Section: Mcnpmentioning

confidence: 99%

Monte-Carlo techniques for radiotherapy applications I: introduction and overview of the different Monte-Carlo codes

Fielding

2023

J Radiother Pract

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“…Monte Carlo simulations, for example using the Monte Carlo N-Particle (MCNP) code [14], allow the construction of a realistic and extended dataset of simulated cases and related sinograms; however, the computational cost is high. High-fidelity simulations, in fact, require the treatment of the full physics radiation transport in a detailed geometry of the PGET system.…”

Section: Introductionmentioning

confidence: 99%

Vanquishing the computational cost of passive gamma emission tomography simulations: a physics-aware reduced order modeling approach

Cavallini¹,

Ferretti²,

Boström³

et al. 2023

Preprint

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Passive Gamma Emission Tomography (PGET) has been developed by the International Atomic Energy Agency as a way to directly image the spatial distribution of individual fuel pins in a spent nuclear fuel assembly and so determine potential diversion.Constructing the analysis and interpretation of PGET measurements rely on the availability of comprehensive datasets. Experimental data are expensive, limited, and so are augmented by Monte Carlo simulations. The main issue concerning Monte Carlo simulations is the high computational cost to simulate the 360 angular views of the tomography. Similar challenges pervade numerical science.To address this challenge, we have developed a physics-aware reduced order modeling approach. It provides a framework to combine a small subset of the 360 angular views with a computationally inexpensive proxy solution, that brings the essence of the physics, to obtain a real-time high-fidelity solution at all angular views, but at a fraction of the computational cost.

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Vanquishing the computational cost of passive gamma emission tomography simulations leveraging physics-aware reduced order modeling

Cavallini,

Ferretti,

Bostrom

et al. 2023

Sci Rep

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Passive Gamma Emission Tomography (PGET) has been developed by the International Atomic Energy Agency to directly image the spatial distribution of individual fuel pins in a spent nuclear fuel assembly and determine potential diversion. The analysis and interpretation of PGET measurements rely on the availability of comprehensive datasets. Experimental data are expensive and limited, so Monte Carlo simulations are used to augment them. However, Monte Carlo simulations have a high computational cost to simulate the 360 angular views of the tomography. Similar challenges pervade numerical science. With the aim to create a large dataset of PGET simulated scenarios, we addressed the computational cost of Monte Carlo simulations by developing a physics-aware reduced order modeling approach. This approach combines a small subset of the 360 angular views (limited views approach) with a computationally inexpensive proxy solution (real-time forward model) that brings the essence of the physics to obtain a real-time high-fidelity solution at all angular views but at a fraction of the computational cost. The method’s ability to reconstruct 360 views with accuracy from a limited set of angular views is demonstrated by testing its performance for different types of reactor fuel assemblies.

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MCNP<sup>®</sup> Code Version 6.3.0 Theory & User Manual

Cited by 4 publications

References 0 publications

Monte-Carlo techniques for radiotherapy applications I: introduction and overview of the different Monte-Carlo codes

Monte-Carlo techniques for radiotherapy applications I: introduction and overview of the different Monte-Carlo codes

Vanquishing the computational cost of passive gamma emission tomography simulations: a physics-aware reduced order modeling approach

Vanquishing the computational cost of passive gamma emission tomography simulations leveraging physics-aware reduced order modeling

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