Engineering topics

McKinney, G. W.

Publications and source records attributed to McKinney, G. W..

7 records

DOE OSTI2017

Neutron and Gamma Correlations using CGM in MCNP 6.2.0

The general-purpose Monte Carlo radiation-transport code MCNP6{sup TM} came from the integration of MCNP5{sup TM} and MCNPX{sup TM}. The most recent release of MCNP, version 6.2.0, is scheduled for release in early 2017, which includes a variety of bug fixes and new features to the code. MCNP traditionally models nuclear reactions using Monte Carlo sampling techniques on measured and modeled cross-sectional data contained within ACE (A Compact Evaluated Nuclear Data File) libraries. In addition to reaction cross-sections, ACE libraries contain information relating to the production of secondary particles, such as secondary neutrons and photons. For example, an incident neutron on a nucleus can produce a nuclear reaction in the form (n, M{sub n}n'), where n is the incident neutron and M{sub n} is the number of secondary neutrons n' produced. Using this notation, different reactions can be described based on their neutron multiplicity, such as neutron capture (M{sub n} = 0), elastic and inelastic scattering (M{sub n} = 1), and reactions with multiple secondary neutrons (M{sub n} > 1). Accounting for secondary γ-rays emitted from the residual nuclei, this reaction can further be generalized as n, Mnn'M to describe a reaction producing M{sub n} neutrons and M -rays. These values M{sub n} and M are referred to as the neutron and multiplicities, respectively. ACE libraries contain the cross-sectional data for each reaction, and their corresponding statistically averaged multiplicities for neutrons and photons, M-bar{sub n} and M-bar{sub γ}. However, there are several limitations to reaction sampling using ACE libraries. First, the data contained within ACE libraries is limited by the accuracy of the model and/or experiment, which out of necessity can greatly simplify the true physics behind a reaction. For example, the value M{sub γ} in a (n, M{sub n}n'M{sub γ}γ) reaction is determined by the statistically averaged multiplicity M-bar{sub γ} instead of a distribution. Consequently, MCNP, unable to produce a continuous distribution, will utilizes a binary sampling around M-bar{sub γ}. While this sampling method provides a statistically average M{sub γ} over a large simulation, it is an inaccurate representation of an individual reaction and its corresponding secondary particles. Secondary neutrons are affected in a similar manner, such that different kinds of secondary neutrons can be sampled from the same interaction (eg., an inelastic neutron with an [n, 2n] neutron). Lastly, the random sampling technique for secondary particles remains completely independent of the sampled reaction, eliminating the ability to correlate secondary particles for a given reaction. To supplement these data libraries, additional codes and models describing certain nuclear reactions are employed. These codes can fill in the information missing from ACE, provide a better model to the true physics distribution, and can provide true correlated secondary particles, which has many useful applications in research and industry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2017

Speed and Memory Improvements to MCNP6 Delayed-Gamma Line Treatment

The delayed-gamma (DG) line-emission (line) sampling was first introduced into MCNPX in 2005. At the time, storage demands for problems with large numbers of DG lines was a major concern. In order to accommodate large amounts of line data while keeping the memory footprint of such a problem from exceeding memory limits, complicated logic was included to sift through various storage options. In mid-2015 concerns arose over the adequacy of the MCNP6 DG source option (inherited from MCNPX) with line sampling for modeling high-fidelity problems. One indication that there was a problem came when studying the MCNP6 produced DG spectra of U-238 and its progeny using high-fidelity spectroscopic tallies when compared to DG line amplitudes directly from ENDF/B-VII.1 emission data. Despite producing a correct integral number of gammas, the comparison to ENDF data showed a broadening in the energy lines and a reduction in the amplitude of more prominent lines. It was later found that the cause of this broadening was one of the memory-saving techniques used to store DG line data when problems required many DG lines. Specifically, the DG lines were integrated into fine energy bins for sampling. When a DG line was to be sampled, a random number would be used to choose which energy bin to sample based on emission probability. Once chosen, a second random number was used to perform nearest-line sampling in that bin. The integration of lines led to an oversampling of low-probability lines, especially when in close proximity to high-probability lines. This treatment also caused a similar issue with the time-dependent behavior of DG line emission which occurred when low-probability lines were sampled near high probability lines with the former decay constant being used. The effect on time-dependent gamma production was demonstrated in analytic benchmarks performed by Weldon et al.. To address these issues a new gamma line storage and sampling treatment was introduced in 2015. This new treatment, stores gamma emission data in a line-by-line CDF which is sampled directly, thereby resolving both the line-broadening issue, and the time-dependence issue as was demonstrated by Tutt el al.. In addition, the new DG sampling treatment was written to use dynamically dimensioned storage arrays as opposed to large fixed-dimensioned arrays, as in the former treatment, and also makes more efficient use of line data that is stored by reducing or eliminating repeated data. As a result, storage arrays are generated only for DG data relevant to the problem being executed. The result should be a reduction in both memory requirements and runtime. However, these reductions could not be realized until the logic for the old treatment could be removed. This paper provides an update on recent work which has focused on the removal of the old DG sampling logic and attempts to quantify the memory reduction and speed up of selected problems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

Predicting Solar Modulation Potentials for Modeling Cosmic Background Radiation

The modeling of the detectability of special nuclear material (SNM) at ports and border crossings requires accurate knowledge of the background radiation at those locations. Background radiation originates from two main sources, cosmic and terrestrial. Cosmic background is produced by high-energy galactic cosmic rays (GCR) entering the atmosphere inducing a cascade of particles that eventually impact the earth's surface. The solar modulation potential represents one of the primary inputs to modeling cosmic background radiation. Usoskin et al. formally define solar modulation potential as 'the mean energy loss [per unit charge, in units of megavolts (MV),] of a cosmic ray particle inside the heliosphere...'. Modulation potential, a function of elevation, location, and time, shares an inverse relationship with cosmic background radiation. As a result, radiation detector background thresholds require adjustment to account for differing background levels, caused in part by differing solar modulations. Failure to do this can result in higher rates of false positives and failed detection of SNM during low and high levels of solar modulation potential, respectively. This study focuses on solar modulation's time dependence and seeks the best method to predict modulation for future dates. The best method will be incorporated into NINESIM, a discrete event simulator. An in-depth description of NINESIM is beyond the scope and purpose of this paper. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2016

Delayed-Gamma Energy Biasing with Exact Energy Sampling in MCNP 6.2.0

Characterization of delayed-particle signatures is of the utmost importance for detecting, identifying, and quantifying special nuclear material (SNM). Various non-destructive assay (NDA) techniques used in nuclear material safeguards rely on characteristic gamma-ray or neutron emissions to infer properties of a material. The all-particle, all-energy Monte-Carlo radiation transport code MCNP6 is capable of simulating delayed-particle signatures from SNM in addition to modeling the transport of emitted particles to a detector or other instrument. In some cases, NDA techniques require the measurement of low probability emissions from a given material. In physical scenarios, this problem can be bypassed by longer count-times or may be simply irrelevant due to the mass, and therefore the total activity, of the material being measured. However, when applying the Monte-Carlo method to such scenarios, sampling of low probability emissions requires a large number of histories to converge to a statistically acceptable solution. To address this issue, the delayed-neutron and gamma energy biasing feature was introduced in MCNP6 by H. Armstrong et al. in 2013 which allows the user to set up energy windows in which delayed-particle emission sampling can be adjusted. For delayed-gamma sampling, this biasing method was based on the bin-wise structure of gamma emission data for both the multi-bin gamma and line-emission sampling modes. Recent improvements have been made to the line-emission sampling algorithm which upgrade the previous mini-bin sampling treatment to an exact energy-line treatment. This upgrade was not compatible with the biasing method implemented by H. Armstrong et al. due to the sampled data no longer having a bin-wise structure in line-emission mode. For this reason the delayed-gamma energy biasing feature has been rewritten to support the exact line-sampling treatment. This paper provides a description of the delayed-gamma energy biasing (DGEB) feature in MCNP6 as well as results and discussion for a gamma-ray spectroscopy example used to demonstrate the biasing utility. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

MCNP 6.2.0 Delayed-Particle Production Improvements

Development began on a delayed-particle physics package for the Monte Carlo Radiation transport code MCNPX in 2004 incorporating the CINDER90 depletion code and delayed particle libraries. When MCNP5 was merged with MCNPX to form MCNP6 in 2010, this delayed-particle capability was passed on and has been continuously improved in subsequent versions of MCNP6. Today, the delayed-particle feature in MCNP6 allows for the production of delayed neutrons, gammas, betas, and alpha particles. Future development includes the addition of delayed-positron production in the next release of MCNP6. Delayed particles can be sampled with MCNP using the ACT card in conjunction with the FISSION and NONFISS keywords. The user can specify one or more of the four available delayed-particle types mentioned above on one or both of the FISSION and NONFISS keywords. If a delayed-particle type is selected on the FISSION keyword, then delayed production of that particle from fission products and their progeny will be produced. Likewise, if a delayed-particle type is selected on the NONFISS keyword then delayed production of that particle from non-fission interactions and spontaneous decay will be produced. In some cases, the sheer number of delayed particles that can be produced from nuclear reactions and spontaneous decay (i.e. hundreds of delayed gamma lines when spontaneous fission is possible) can make it difficult to achieve converged solutions in an efficient and timely manner. Truncation methods are the simplest form of variance reduction and can be used to decrease convergence times by limiting calculations to the phase space of interest. This paper introduces several such truncation methods that allow the user to restrict the number of possible delayed particles available for sampling to only those of interest in the problem. It is important to stress that the use of any truncation method should be verified by first assessing the impact of the truncated particles (i.e. by disabling the truncation) for one or more baseline cases. In addition to these new methods, a new delayed-gamma sampling algorithm implemented in MCNP 6.2.0 is introduced. Comparisons to ENDF data and analytical benchmarking with the new algorithm show a substantial improvement in accuracy over previous versions. New improvements to the delayed-particle capability have been added to MCNP 6.2.0. These improvements include an updated treatment of the THRESH keyword which omits delayed-particle lines below a set amplitude, and addition of the PECUT and HLCUT keywords which allow the user to truncate delayed-particles lines based on energy and half-life, respectively. The delayed-gamma sampling algorithm has also been improved to allow exact energy sampling. Comparisons of MCNP calculations directly to ENDF data and analytical benchmarks have verified the significant improvement in the new delayed gamma sampling algorithm. (authors)

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

Predicting Future Solar Modulation and Implementation in MCNP6

The application of detecting low level radiation in the field requires quantification of the natural radiation background specific to that location. This background is made up two primary components: naturally decaying radionuclides in the soil and atmosphere known as terrestrial background, and the particle cascades caused by high energy cosmic radiation interacting within the earth's atmosphere known as cosmic background. The terrestrial component dominates the background spectrum below approximately 2 MeV, however, the cosmic component comprises the entirety of the high-energy spectrum. Furthermore, the terrestrial component is relatively constant for a given location while the cosmic component is heavily dependent on solar activity, an effect known as solar modulation. Annual values of the solar modulation parameter, φ, have been determined since 1936, and their fluctuations are seen to be roughly sinusoidal over time. If one wished to predict or simulate the outcome of an experiment in the future, a prediction for the solar modulation would be necessary to accurately determine the cosmic background contribution. The previous version 6.1.1 of MCNP contained solar modulation data for 46 years between 1960 and 2005. This data was updated with newer solar modulation data, from I. G. Usoskin et al., which reports solar modulation data for an additional 33 years between 1936 and 2014. In order to use past solar modulation data to predict cosmic background radiation fluctuations for dates beyond 2014 an algorithm was developed to forecast future solar modulation data based on the sinusoidal behavior exhibited in recorded data. This algorithm was implemented into MCNP 6.1.2 to be used for cosmic background simulations and tested against benchmark data to assess its accuracy. (authors)

54 ENVIRONMENTAL SCIENCES↗