Search arXivSearch

arXiv · 2412.18314

On the improved performances of FLUKA v4-4.0 in out-of-field proton dosimetry

Abstract

A new model for the nuclear elastic scattering of protons below 250 MeV has been recently included in FLUKA v4-4.0, motivated by the evaluation of radiation effects in electronics. Nonetheless, proton nuclear elastic scattering plays a significant role also in proton dosimetry applications, for which the new model necessitated an explicit validation. Therefore, in this work a benchmark has been carried out against a recent measurement of radial-depth maps of absorbed dose in a water phantom under irradiation with protons of 100 MeV, 160 MeV, and 225 MeV. Two FLUKA versions have been employed to simulate these dose maps: v4-3.4, relying on a legacy model for proton nuclear elastic scattering, and v4-4.0, relying on the new model. The enhanced agreement with experimental absorbed doses obtained with FLUKA v4-4.0 is discussed, and the role played by proton nuclear elastic scattering, among other interaction mechanisms, in various regions of the radial-depth dose map is elucidated. Finally, the benchmark reported in this work is sensitive enough to showcase the importance of accurately characterizing beam parameters and the scattering geometry for Monte Carlo simulation purposes.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Alexandra-Gabriela Şerban, Juan Alejandro de la Torre González, Marta Anguiano, Antonio M. Lallena, Francesc Salvat-Pujol. 2024-12-24. On the improved performances of FLUKA v4-4.0 in out-of-field proton dosimetry. https://arxiv.org/abs/2412.18314

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Bi-Hamiltonian in Semiflexible Polymers built upon Overdamping Process

Quantifying the interaction between a system of interest and its ambient conditions, the memory effect links the states of two distinct Hamiltonians: one for the target system and one for the environment. In this paper, we propose the diffusion process derived from the Smoluchowski equation that can derive the evolution process described by the memory effect integration in a non Markovian regime. The Smoluchowski picture, within the framework of stochastic thermodynamics, justifies a diffusion process incorporated into the equations of motion, and the result of the derivation enables a coarse-grained molecular dynamics simulation with the modified equation of motion to reproduce attenuation from collisions between single walled carbon nanotubes (SWCNTs) under far from equilibrium conditions. The results of the numerical experiments on the collision confirm that heat diffusion compensates for the correlated momentum arising from the memory effect between the two Hamiltonians in both equilibrium and far from equilibrium states.

physics.comp-ph

Learning continuous reaction paths for transition-state prediction

Transition states are defined by reaction pathways, yet most machine-learning methods predict them as isolated geometries. We introduce MARC-TS, a two-stage framework that learns a continuous, endpoint-conditioned path, queries it at any resolution and uses local path context to refine a transition-state candidate. We construct T1x-IRC-8K, a dataset of 8,209 reactions and 1,088,725 path-resolved geometries. On held-out reactions, the path model reduced complete-path error by 48.4% relative to endpoint interpolation, and the localizer achieved a mean aligned structural error of 0.127 Å. Quantum-chemical optimization and vibrational analysis yielded 405 frequency-confirmed first-order saddle-point candidates from 410 predictions. In a 100-reaction nudged elastic band comparison, learned-path initialization reached a joint geometry-and-force target for 66% of reactions, compared with 12% for geometric interpolation after 100 optimizer steps. By treating the path as a reusable representation rather than an auxiliary output, MARC-TS connects transition-state prediction, mechanistic interpretation and quantum-chemical refinement.

physics.comp-ph

A subcell-refined entropy-residual-driven limiting strategy for high-order discontinuous Galerkin methods

Fine-grained, subcell-level dissipation control is essential for achieving robust high-order discontinuous Galerkin (DG) simulations of nonlinear hyperbolic systems in under-resolved regimes while preserving accuracy. This paper proposes a subcell-refined entropy-residual-driven limiting strategy for DG on Legendre-Gauss-Lobatto nodes. The limiter introduces only nearest-neighbor pairwise dissipation within each element, with closed-form coefficients that supply the minimal dissipation required to restore the element entropy inequality. The strategy is a diagonal, locally stable approximation of classical entropy-stable methods, and a generalized subcell framework reveals split-form DG and residual-distribution-based entropy correction schemes as particular choices of the limiting coefficients. For the Euler equations, a physically consistent jump operator separately models thermal and shear entropy production while preserving velocity and pressure equilibrium; a subcell refinement of the Zhang-Shu positivity limiter ensures pointwise positivity. Extensive numerical tests confirm that the scheme maintains optimal high-order accuracy, strictly enforces entropy dissipation, and significantly reduces the difficulty of a posteriori positivity-preserving procedures.

physics.comp-ph