Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “MCNP6”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Simulations of Neutron Time-of-Flight Method by Inelastic Scattering Carbon-12 using MCNP6 and Geant4

A neutron spectroscopy technique using neutron time-of-flight (TOF) with gamma tagging from inelastic scattering on carbon-12 was examined using MCNP6 and Geant4. Experimental data for the 12 C(n,n'$γ$) reaction from D. Spaargaren and C.C. Jonken was used to supplement the simulations due to their limitations with modeling correlated particles on an event-by-event basis. The MCNP and Geant4 models relied on different methods to determine the efficiencies and were compared against each other for validation of the results. The total efficiency of coincident detection as determined by MCNP is around 6.32E-11. That is, around 6 events will be detected per 100 billion source neutrons. It was found that the amount of neutrons which lose energy to elastic scattering is not negligible, and efforts to reconstruct the beam energy from the detected neutrons must take this into account. Running on 40 computer cores in multi-threading mode, the Geant4 model could simulate about half a billion source neutrons per hour. In the absence of biasing methods, Geant4 can not simulate the efficiency in any reasonable amount of time given current computing power. Further efforts will be made to introduce biasing to the simulation to verify the MCNP results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

MCNP6 High-energy Event Generators

This report presents a brief description of all high-energy event generators used by MCNP6, namely, of the intranuclear cascade models (INC); Fermi breakup models; coalescence models; preequilibrium nuclear reactions; evaporation reactions; and of the fission reaction models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

A New MCNP6 Electron-Photon Transport Validation Test: The Lockwood Energy Deposition Experiment (V.1.0)

This memo announces the availability of a new validation test for quantifying the accuracy of the MCNP6 electron-photon transport algorithm for use in energy-deposition calculations. Specifically, energy-deposition results are compared with the Lockwood energy-deposition experiment. The comparison includes energy-deposition profiles in a variety of different single-element materials including beryllium, aluminum, carbon, copper, iron, molybdenum, tantalum, and uranium for pencil beam electron sources with energies including 0.05-, 0.1-, 0.3-, 0.5, and 1-MeV and angles of incidence including normal, 30°, and 60° off-normal. The purpose of this memo is to discuss the contents of the Lockwood validation directory and to outline the procedure for generating the input files, running the tests, processing the results, and comparing results to the experimental and numerical benchmark. Each step is mostly automated by a makefile that executes the necessary perl script.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

(U) Calculating First-Order Sensitivities to Material Density and Composition in Fixed-Source Problems Using the MCNP6 Perturbation Capability

The Taylor series (differential operator) perturbation method, as implemented in the PERT capability in MCNP6 (Ref. 1), can be used for first-order sensitivity analyses in fixed-source problems.2 Sensitivities of a response with respect to material and nuclide densities can be used for efficient first-order uncertainty quantification. This report discusses how to compute sensitivities of material densities and compositions in order to propagate uncertainties using the standard uncertainty propagation formula.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

(U) Correlated Sampling Using Batch Statistics to Reduce the Uncertainty of Combinations of KSEN Outputs with MCNP6

The relative sensitivity of k eff to the densities of nuclides in a material are combined to compute relative sensitivities to other inputs. When computed in a single Monte Carlo run, the nuclide density sensitivities are correlated, and the statistical uncertainties propagated to other inputs will be incorrect unless those correlations are accounted for. Equations are presented to apply correlated sampling using batch statistics for the sum of an arbitrary number of random tallies and the difference of two random tallies when each is multiplied by a different constant. When correlated sampling is used on a recent benchmark evaluation, the correct statistical uncertainties for certain combinations of sensitivities are dramatically smaller than the incorrect uncertainties. A user-controlled, regular output of MCNP6’s KSEN sensitivities that allows batch statistics to be applied to combinations would be extremely valuable.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

MCNP6 Cosmic-Source Option

MCNP is a Monte Carlo radiation transport code that has been under development for over half a century. Over the last decade, the development team of a high-energy offshoot of MCNP, called MCNPX, has implemented several physics and algorithm improvements important for modeling galactic cosmic-ray (GCR) interactions with matter. In this presentation, we discuss the latest of these improvements, a new Cosmic-Source option, that has been implemented in MCNP6.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2012

Features of MCNP6 Relevant to Medical Radiation Physics

MCNP (Monte Carlo N-Particle) is a general-purpose Monte Carlo code for simulating the transport of neutrons, photons, electrons, positrons, and more recently other fundamental particles and heavy ions. Over many years MCNP has found a wide range of applications in many different fields, including medical radiation physics. In this presentation we will describe and illustrate a number of significant recently-developed features in the current version of the code, MCNP6, having particular utility for medical physics. Among these are major extensions of the ability to simulate large, complex geometries, improvement in memory requirements and speed for large lattices, introduction of mesh-based isotopic reaction tallies, advances in radiography simulation, expanded variance-reduction capabilities, especially for pulse-height tallies, and a large number of enhancements in photon/electron transport.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2014

MCNP6 Monte Carlo Code Optimization [Slides]

The MCNP6.1 Monte Carlo code, released in 2013, offers many features not available in previous versions, but runs 20-30% slower for most problems and 2-5x slower for some problems. The MCNP 2020 initiative was established in mid-2013 to address code performance and other issues, with 3 basic components: (1) Improve MCNP performance, with the specific goal of achieving a 2x performance gain within 2 years; (2) Upgrade the core MCNP software and algorithms in an evolutionary fashion, with the goal of achieving more agile and sustainable code; (3) Prepare for future exascale systems that will include massive parallelism and limited memory per core, with the goal of providing flexible and adaptable coding for possible exascale systems. This talk reviews the initial performance improvements to MCNP6.1 that have been incorporated into the 2014 update, MCNP6.1.1. The performance improvements to date have included both classic code optimizations and algorithm improvements. Testing on a variety of problems has demonstrated that the performance improvements were effective, yielding speedups by factors of 1.2x - 4x compared to MCNP6.1, depending on the type of problem. For criticality problems, MCNP6.1.1 runs 1.5x - 1.7x faster than MCNP6.1. Much more work is planned for improving MCNP6 performance, structure, and algorithms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Semi-Analytical Benchmarks for MCNP6 [Slides]

This presentation begins with an Introduction and some background on semi-analytical benchmarks for MCNP6. Discussion topics include: verification & validation, MCNP history of V&V, semi-analytic benchmarks, numerical Results, comparison of semi-analytic to MCNP, and tips for proper comparison. This presentation ends with conclusions & future Work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

MCNP6 Study of Spatial Fidelity Required for ROC Curve Convergence

The detection of special nuclear material at ports and borders is one of the primary missions of the Department of Homeland Security's (DHS) Domestic Nuclear Detection Office (DNDO). In support of this mission a large-scale modeling and simulation effort has been undertaken with the goal of providing a comprehensive tool-kit that addresses the complexity of problems involved with developing and maintaining technologies deployed to global nuclear detection architecture (GNDA) nodes. An important objective of DNDO's mission is to characterize detector system performance before deployment. In the effort to support this mission, the latest version of MCNP has been used to perform detectability studies of highly enriched uranium (HEU) to populate a database that will be used for the tool-kit. The characterization method used in the implementation of the database is the receiver operating characteristic (ROC). A ROC curve shows the limits of detectability of a given system as well as an overall metric of the efficacy of a detection system. This is accomplished by comparing total detector count distributions to the count distribution produced by background alone. For an in-depth explanation of the theory behind the MCNP production of ROC curves please refer to the paper, 'How To Use The Receiver Operating Characteristic Tally Option in MCNP6'. Building a useful database requires an expansive parameter space. The current tool-kit use nine parameters, which if varied with just 5 options each, results in nearly two million simulations to run. Depending on the set of parameters these simulations can take a few hours or a few weeks to complete. This computational cost necessitates the use of scaling algorithms to interpolate between the data contained in the database. The tool-kit employs a discrete event method to simulate the movement and detector response of special nuclear material (SNM) through a GNDA node. Given the nature of a discrete event simulation, constant motion of SNM must be split up in to discrete spatial detection points. The fidelity required of the spatial splitting to achieve a properly converged answer is investigated here for a neutron detection system. The detector used was a SNAP-3 neutron detector in front of a sphere of SNM in the presence of background radiation. A parameter study has been performed to determine the spatial fidelity required for ROC curve convergence for moving objects in a discrete event simulator. It appears that 5 points is the minimum number of detection points with 7 points needed for one of the parameter sets. The convergence worsens with increases to detection length but appears to improve with increasing detector distance. These results will help future work involving quantifying the accuracy based on given tolerances and parameters. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

EBR-II Static Neutronic Calculations by PHISICS / MCNP6 codes

The International Atomic Energy Agency (IAEA) launched a Coordinated Research Project (CRP) on the Shutdown Heat Removal Tests (SHRT) performed in the 80's at the Experimental fast Breeder Reactor EBR-II, USA. The scope of the CRP is to improve and validate the simulation tools for the study and the design of the liquid metal cooled fast reactors. Moreover, training of the next generation of fast reactor analysts is being also considered the other scope of the CRP. In this framework, a static neutronic model was developed, using state-of-the art neutron transport codes like SCALE/PHISICS (deterministic solution) and MCNP6 (stochastic solution). Preliminary criticality calculations have been performed using both models (deterministic and stochastic). Results show the good agreement between different models. It should also be noted that deterministic solution could still be further improved by implementing super-homogenization factors and by increasing the transport calculation order.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Comparison of the MCNP6 and FLUKA Codes in Shielding Calculation at ProNova Proton Therapy Facility

The Monte Carlo method is a powerful tool to simulate particle transport in materials, and can be effectively used to design radiation shielding. The purpose of this study is to compare the results from two Monte Carlo codes (MCNP6 and FLUKA ) in shielding design. The calculation was also compared with the measured results from the ProNova Superconducting 360 degrees (SC360) installation at the Knoxville Provision Center for Proton Therapy (PCPT) facility. The SC360 230 MeV continue wave (CW) proton beam is produced by an isochronous cyclotron, the Sumitomo P235. The maximum beam that is extracted into the beamline is 300 nA, which is of 60% of the full current generated in cyclotron. The SC360 facility has an energy selection system for adjusting the proton range, one fixed beam and one gantry room for rotating the beam around the stationary patient. At the entrance to each treatment room beamline, a thickness adjustable Beryllium degrader inserted in the beam path is used to achieve the desirable beam energy. When protons interact with beamline components, the secondary neutrons become the dominant source to determine the radiation shielding. In this study, the dose rate outside the lateral wall of the beamline vault was discussed. The calculations from two Monte Carlo codes were compared and they agreed very well. The dose rates measured by a Wendi-II Neutron Detector agreed within a factor of less than seven compared with the calculation results. The difference may be explained by a variety of factors such as the uncertainty in knowing the exact composition of shielding materials, geometry difference, and the measurement uncertainty due to instrumentation. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2014

Evaluation of Low Power ARM Processors for Monte Carlo Particle Transport: MCNP6 on the ARM Cortex-A8 - Paper 83

In the push towards exascale computing, the limiting factors are power requirements and heat dissipation. Using current technology, the power required to drive an exascale supercomputer would near 400-MW. Clearly, future computing hardware will have to be more energy efficient in order to achieve exascale performance. Most of the current research in advanced computing architectures is in the use of standard CPUs with accelerators. However, these accelerators are not generally supported by the traditional scientific programming languages (C, C++, and Fortran) and the scientific software must be tailored specifically to take advantage of the accelerators. Large scale scientific software (hundreds-of-thousand to millions of lines of code) can not be easily ported to the computing platform du jour. The widespread use of low power ARM based processors in the mobile computing market offer a low power alternative to accelerators that are also supported by the traditional scientific programming languages and programming paradigms such as MPI and OpenMP. Computing architectures for scientific computing are often assessed using standard benchmarks such as LINPACK benchmark or using suites of mini-applications that are greatly simplified stand-ins for scientific software. Often the real applications that will deployed on the scientific supercomputers are not ported or tested until delivery of the machines. In order to evaluate the use of ARM processors for scientific computing the Monte Carlo particle transport code, MCNP version 6 was compiled on the ARM Cortex-A8 and a set of benchmark problems were used to assess performance in comparison to an Intel Xeon E5-2670 and the low power Intel Atom N270. MCNP is a continuous energy Monte Carlo particle transport code in development at Los Alamos National Laboratory since 1977. MCNP is used to transport neutrons, photon, electrons, and charged particles using continuous energy interaction data. It is commonly used in fields of nuclear power, nuclear medicine, radiation protection, and high-energy physics through-out the world. MCNP version 6 is approximately 450 thousand lines of code, written primary in Fortran90, with some C and C++. MCNP supports distributed multiprocessing using MPI and threading with OpenMP. The ARM Cortex-A8 executes MCNP6 using about twice as much power as the Intel Xeon E5-2670 and about the same amount of power as the Intel Atom N270. However, these results are encouraging for the use of low-power ARM chips in future supercomputers. The ARM Cortex-A8 is a 32-bit CPU and it's NEON general-purpose SIMD (single-instruction multiple data) engine does not support double-precision floating-point arithmetic. More recent ARM processors (ARM Cortex-A53 and Cortex-A57) support 64-bit addressing and have NEON co-processors that support double-precision floating-point arithmetic. ARM claims that the A50 series delivers a performance increase of 15 times the Cortex-A8 performance for similar power usage. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

Enhancements to the MCNP6 background source

The particle transport code MCNP has been used to produce a background radiation data file on a worldwide grid that can easily be sampled as a source in the code. Location-dependent cosmic showers were modeled by Monte Carlo methods to produce the resulting neutron and photon background flux at 2054 locations around Earth. An improved galactic-cosmic-ray feature was used to model the source term as well as data from multiple sources to model the transport environment through atmosphere, soil, and seawater. A new elevation scaling feature was also added to the code to increase the accuracy of the cosmic neutron background for user locations with off-grid elevations. Furthermore, benchmarking has shown the neutron integral flux values to be within experimental error.

61 RADIATION PROTECTION AND DOSIMETRY↗