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QMCPACK is an open-source production level many-body ab initio Quantum Monte Carlo code for computing the electronic structure of atoms, molecules, and solids with full performance portable GPU support.
QMCPACK is an open-source production level many-body ab initio Quantum Monte Carlo code for computing the electronic structure of atoms, molecules, and solids with full performance portable GPU support.
QMCPACK is an open-source production level many-body ab initio Quantum Monte Carlo code for computing the electronic structure of atoms, molecules, and solids with full performance portable GPU support.
A Monte Carlo method implemented in the PRIZMA-DSP code to calculate multiplying systems, including weakly coupled systems, is described. In the code, the basis of the method is a sequential calculation of a prescribed chain of active generations, which is described by, first and foremost, the dominant ratio of the system. The fission points of the running generation serve as a source for constructing neutron trajectories, new fission points arising on a trajectory are transmitted unchanged to calculate the next generation, and so on. In the calculation of the active generation, K{sub eff} and made-to-order linear-fractional functions are evaluated. In order for the distribution to become established, a passive calculation with prescribed chain length is made before the active calculation. In addition, after the calculation of each generation the operation of random mixing of the entire set of fission points and their distribution over the nuclei is performed in order to obtain a uniform distribution. At the start of the calculation, a special rejection procedure performs a pre-set number of preliminary computational iterations so that an arbitrary zeroth distribution over all nuclei goes to a distribution obtained via a characteristic function.
Continuing efforts toward validating the buildup factor method and the BRYNTRN code, which use the deterministic approach in solving radiation transport problems and are the candidate engineering tools in space radiation shielding analyses, are presented. A simplified theory of proton buildup factors assuming no neutron coupling is derived to verify a previously chosen form for parameterizing the dose conversion factor that includes the secondary particle buildup effect. Estimates of dose in tissue made by the two deterministic approaches and the Monte Carlo method are intercompared for cases with various thicknesses of shields and various types of proton spectra. The results are found to be in reasonable agreement but with some overestimation by the buildup factor method when the effect of neutron production in the shield is significant. Future improvement to include neutron coupling in the buildup factor theory is suggested to alleviate this shortcoming. Impressive agreement for individual components of doses, such as those from the secondaries and heavy particle recoils, are obtained between BRYNTRN and Monte Carlo results.
The α eigenvalue has implications for time-dependent problems where the system is sub- or supercritical. We present methods and results from calculating the α-eigenvalue spectrum for a continuous-energy infinite medium with a simplified Monte Carlo transport code. We formulate the α-eigenvalue problem, detail the Monte Carlo code physics, and provide verification and results. We have a method for calculating the α-eigenvalue spectrum in a continuous-energy infinite-medium. The continuous-time Markov process described by the transition rate matrix provides a way of obtaining the α-eigenvalue spectrum and kinetic modes. These are useful for the approximation of the time dependence of the system.
ITS (Integrated TIGER Series) is a suite of fully coupled electron/photon Monte Carlo radiation transport codes. The package contains programs to perform 1D, 2D and 3D simulations. The 3D geometry representations include the traditional combinatorial geometry (CG), facet-based models, CAD models for those users who have a license for the ACIS{sup R} CAD libraries, and hybrid models of any combination. There is a multigroup version which permits adjoint analysis. The availability of source code permits the more sophisticated user to tailor the code to specific applications and to extend the capabilities of the codes to more complex applications. ITS is presently available only through a U.S.-government-use license. It is available once again through the Radiation Safety Information Computational Center (RSICC). (authors)
The use of Monte Carlo simulations in diagnostic medical imaging research is widespread due to its flexibility and ability to estimate quantities that are challenging to measure empirically. However, any new Monte Carlo simulation code needs to be validated before it can be used reliably. The type and degree of validation required depends on the goals of the research project, but, typically, such validation involves either comparison of simulation results to physical measurements or to previously published results obtained with established Monte Carlo codes. The former is complicated due to nuances of experimental conditions and uncertainty, while the latter is challenging due to typical graphical presentation and lack of simulation details in previous publications. In addition, entering the field of Monte Carlo simulations in general involves a steep learning curve. It is not a simple task to learn how to program and interpret a Monte Carlo simulation, even when using one of the publicly available code packages. This Task Group report provides a common reference for benchmarking Monte Carlo simulations across a range of Monte Carlo codes and simulation scenarios. In the report, all simulation conditions are provided for six different Monte Carlo simulation cases that involve common x‐ray based imaging research areas. The results obtained for the six cases using four publicly available Monte Carlo software packages are included in tabular form. In addition to a full description of all simulation conditions and results, a discussion and comparison of results among the Monte Carlo packages and the lessons learned during the compilation of these results are included. This abridged version of the report includes only an introductory description of the six cases and a brief example of the results of one of the cases. This work provides an investigator the necessary information to benchmark his/her Monte Carlo simulation software against the reference cases included here before performing his/her own novel research. In addition, an investigator entering the field of Monte Carlo simulations can use these descriptions and results as a self‐teaching tool to ensure that he/she is able to perform a specific simulation correctly. Finally, educators can assign these cases as learning projects as part of course objectives or training programs.
Purpose: Radium-223 ({sup 223}Ra), an α-emitting radionuclide, is used in unsealed radionuclide therapy for metastatic bone tumors. The demand for qualitative {sup 223}Ra imaging is growing to optimize dosimetry. The authors simulated {sup 223}Ra imaging using an in-house Monte Carlo simulation code and investigated the feasibility and utility of {sup 223}Ra imaging. Methods: The Monte Carlo code comprises two modules, HEXAGON and NAI. The HEXAGON code simulates the photon and electron interactions in the tissues and collimator, and the NAI code simulates the response of the NaI detector system. A 3D numeric phantom created using computed tomography images of a chest phantom was installed in the HEXAGON code. {sup 223}Ra accumulated in a part of the spine, and three x-rays and 19 γ rays between 80 and 450 keV were selected as the emitted photons. To evaluate the quality of the {sup 223}Ra imaging, the authors also simulated technetium-99m ({sup 99m}Tc) imaging under the same conditions and compared the results. Results: The sensitivities of the three photopeaks were 147 counts per unit of source activity (cps MBq{sup −1}; photopeak: 84 keV, full width of energy window: 20%), 166 cps MBq{sup −1} (154 keV, 15%), and 158 cps MBq{sup −1} (270 keV, 10%) for a low-energy general-purpose (LEGP) collimator, and those for the medium-energy general-purpose (MEGP) collimator were 33, 13, and 8.0 cps MBq{sup −1}, respectively. In the case of {sup 99m}Tc, the sensitivity was 55 cps MBq{sup −1} (141 keV, 20%) for LEGP and 52 cps MBq{sup −1} for MEGP. The fractions of unscattered photons of the total photons reflecting the image quality were 0.09 (84 keV), 0.03 (154 keV), and 0.02 (270 keV) for the LEGP collimator and 0.41, 0.25, and 0.50 for the MEGP collimator, respectively. Conversely, this fraction was approximately 0.65 for the simulated {sup 99m}Tc imaging. The sensitivity with the LEGP collimator appeared very high. However, almost all of the counts were because of photons that penetrated or were scattered in the collimator; therefore, the proportions of unscattered photons were small. Conclusions: Their simulation study revealed that the most promising scheme for {sup 223}Ra imaging is an 84-keV window using an MEGP collimator. The sensitivity of the photopeaks above 100 keV is too low for {sup 223}Ra imaging. A comparison of the fractions of unscattered photons reveals that the sensitivity and image quality are approximately two-thirds of those for {sup 99m}Tc imaging.
These slides are used to teach MCNP (Monte Carlo N-Particle) usage to nuclear criticality safety analysts. The following are the lecture topics: course information, introduction, MCNP basics, criticality calculations, advanced geometry, tallies, adjoint-weighted tallies and sensitivities, physics and nuclear data, parameter studies, NCS validation I, NCS validation II, NCS validation III, case study 1 - solution tanks, case study 2 - fuel vault, case study 3 - B&W core, case study 4 - simple TRIGA, case study 5 - fissile mat. vault, criticality accident alarm systems. After completion of this course, you should be able to: Develop an input model for MCNP; Describe how cross section data impact Monte Carlo and deterministic codes; Describe the importance of validation of computer codes and how it is accomplished; Describe the methodology supporting Monte Carlo codes and deterministic codes; Describe pitfalls of Monte Carlo calculations; Discuss the strengths and weaknesses of Monte Carlo and Discrete Ordinants codes; The diffusion theory model is not strictly valid for treating fissile systems in which neutron absorption, voids, and/or material boundaries are present. In the context of these limitations, identify a fissile system for which a diffusion theory solution would be adequate.
The Monte Carlo method is used in the MCU code-an application package for solving equations of transport of neutrons, photons, electrons, and positrons. The code has a modular structure, and every working version of the code is formed from modules and submodules depending on the problem in question. The submodules BETA and BEG, included in the SOFIZM compound physical module of the MCU-PD code, are described: BETA submodule simulates interaction of electrons and positrons with matter and BEG submodule generates photons in the electron and positron reactions with matter. The library of constants which is involved in the MCUDB50 database and supports execution of the MCU-PD code is briefly characterized.
Purpose: To test the validity of a windowless extrapolation chamber used to measure surface dose rate from planar ophthalmic applicators and to compare different Monte Carlo based codes for deriving correction factors. Methods: Dose rate measurements were performed using a windowless, planar extrapolation chamber with a {sup 90}Sr/{sup 90}Y Tracerlab RA-1 ophthalmic applicator previously calibrated at the National Institute of Standards and Technology (NIST). Capacitance measurements were performed to estimate the initial air gap width between the source face and collecting electrode. Current was measured as a function of air gap, and Bragg-Gray cavity theory was used to calculate the absorbed dose rate to water. To determine correction factors for backscatter, divergence, and attenuation from the Mylar entrance window found in the NIST extrapolation chamber, both EGSnrc Monte Carlo user code and Monte Carlo N-Particle Transport Code (MCNP) were utilized. Simulation results were compared with experimental current readings from the windowless extrapolation chamber as a function of air gap. Additionally, measured dose rate values were compared with the expected result from the NIST source calibration to test the validity of the windowless chamber design. Results: Better agreement was seen between EGSnrc simulated dose results and experimental current readings at very small air gaps (<100 µm) for the windowless extrapolation chamber, while MCNP results demonstrated divergence at these small gap widths. Three separate dose rate measurements were performed with the RA-1 applicator. The average observed difference from the expected result based on the NIST calibration was −1.88% with a statistical standard deviation of 0.39% (k=1). Conclusion: EGSnrc user code will be used during future work to derive correction factors for extrapolation chamber measurements. Additionally, experiment results suggest that an entrance window is not needed in order for an extrapolation chamber to provide accurate dose rate measurements for a planar ophthalmic applicator.
Prior to every ion implantation experiment a simulation of the ion range and other relevant parameters is performed using Monte-Carlo based codes. Although increasing computing power has improved the speed of these calculations, the demands on Monte-Carlo codes are also increasing, requiring evaluation of the optimal number of simulations while ensuring accuracy within threshold bounds. We evaluate the “Stopping and Range of Ions in Matter” (SRIM) code due to its widespread usage. We show how dividing simulations into multiple parallel simulations with different random seeds can lead to calculation speedup and find lower bounds for the required number of ion traces simulated based on an exemplar system of a Ga focused ion beam and a high energy C beam as used in high linear energy transfer testing. Here our results indicate simulations can yield results within the underlying data accuracy of SRIM at 10X and 100X shorter simulation time than the SRIM default values.
Analysis and modeling of nuclear reactors can lead to memory overload for a single core processor when it comes to refined modeling. A method to solve this problem is called 'domain decomposition'. In the current work, domain decomposition algorithms for a combinatorial geometry Monte Carlo transport code are developed on the JCOGIN (J Combinatorial Geometry Monte Carlo transport INfrastructure). Tree-based decomposition and asynchronous communication of particle information between domains are described in the paper. Combination of domain decomposition and domain replication (particle parallelism) is demonstrated and compared with that of MERCURY code. A full-core reactor model is simulated to verify the domain decomposition algorithms using the Monte Carlo particle transport code JMCT (J Monte Carlo Transport Code), which has being developed on the JCOGIN infrastructure. Besides, influences of the domain decomposition algorithms to tally variances are discussed. (authors)
Binary-pairing Monte-Carlo methods are widely used in particle-in-cell codes to capture effects of small angle Coulomb collisions. These methods preserve momentum and energy exactly when the simulation particles have equal weights. However, when the interacting particles are of varying weight, these physical conservation laws are only preserved on average. Here, we 1) extend these methods to weighted particles such that the scattering physics is correct on average, and 2) describe a new method for adjusting the particle velocities post scatter to restore exact conservation of momentum and energy. In conclusion, the efficacy of the model is illustrated with various test problems.
The 3-D Monte Carlo neutron and photon transport parallel code MCMG is developed. A new collision mechanism based on material but not nuclide is added in the code. Geometry cells and surfaces can be dynamically extended. Combination of multigroup and continuous cross-section transport is developed. The multigroup scattering is expansible to P5 and upper scattering is considered. Various multigroup libraries can be easily equipped in the code. The same results with the experiments and the MCNP code are obtained for a series of modes. The speedup of MCMG is a factor of 2-4 relative to the MCNP code in speed. (authors)
As part of the European NURISP research project, a single pin BWR benchmark problem was defined. The aim of this initiative is to test the coupling strategies between Monte Carlo and subchannel codes developed by different project participants. In this paper the results obtained by the Delft Univ. of Technology and Karlsruhe Inst. of Technology will be presented. The benchmark problem was simulated with the following coupled codes: TRIPOLI-SUBCHANFLOW, MCNP-FLICA, MCNP-SUBCHANFLOW, and KENO-SUBCHANFLOW. (authors)
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power make FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method’s inherent stochastic nature. Here, this study represents a community-wide effort to assess the reproducibility of the method, affirming that yes, FN-DMC is reproducible (when handled with care). Using the water–methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move, the determinant locality approximation, or the determinant T-move schemes, while the older locality approximation leads to considerable variability in results. These findings demonstrate that, with appropriate choices of algorithmic details, fixed-node DMC is reproducible across diverse community codes—highlighting the maturity and robustness of the method as a tool for open and reliable computational science.