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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.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-T-682: In-Vivo Simulation of the Relative Biological Effectiveness in Proton Therapy Using a Monte Carlo Method

Purpose: In proton therapy, the relative biological effectiveness (RBE) – compared with conventional photon therapy – is routinely set to 1.1. However, experimental in vitro studies indicate evidence for the variability of the RBE. To clarify the impact on patient treatment, investigation of the RBE in a preclinical case study should be performed. Methods: The Monte Carlo software TOPAS was used to simulate the radiation field of an irradiation setup at the experimental beamline of the proton therapy facility (OncoRay) in Dresden, Germany. Simulations were performed on cone beam CT-data (CBCT) of a xenogeneous mouse with an orthotopic lung carcinoma obtained by an in-house developed small animal image-guided radiotherapy device. A homogeneous physical fraction dose of 1.8Gy was prescribed for the contoured tumor volume. Simulated dose and linear energy transfer distributions were used to estimate RBE values in the mouse based on an RBE model by Wedenberg et al. To characterize radiation sensitivity of normal and tumor tissue, α/β-ratios were taken from the literature for NB1RGB (10.1Gy) and human squamous lung cancer (6.2Gy) cell lines, respectively. Results: Good dose coverage of the target volume was achieved with a spread-out Bragg peak (SOBP). The contra-lateral lung was completely spared from receiving radiation. An increase in RBE towards the distal end of the SOBP from 1.07 to 1.35 and from 1.05 to 1.3 was observed when considering normal tissue and tumor, respectively, with the highest RBE values located distal to the target volume. Conclusion: Modeled RBE values simulated on CBCT for experimental preclinical proton therapy varied with tissue type and depth in a mouse and differed therefore from a constant value of 1.1. Further translational work will include, first, conducting preclinical experiments and, second, analogous RBE studies in patients using experimentally verified simulation settings for our clinically used patient-specific beam conforming technique.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Self-learning quantum Monte Carlo method in interacting fermion systems

We present the self-learning Monte Carlo method is a powerful general-purpose numerical method recently introduced to simulate many-body systems. In this work, we extend it to an interacting fermion quantum system in the framework of the widely used determinant quantum Monte Carlo. This method can generally reduce the computational complexity and moreover can greatly suppress the autocorrelation time near a critical point. This enables us to simulate an interacting fermion system on a $100 × 100$ lattice even at the critical point and obtain critical exponents with high precision.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optimized structure and electronic band gap of monolayer GeSe from quantum Monte Carlo methods

Here, we have used highly accurate quantum Monte Carlo methods to determine the chemical structure and electronic band gaps of monolayer GeSe. Two-dimensional (2D) monolayer GeSe has received a great deal of attention due to its unique thermoelectric, electronic, and optoelectronic properties with a wide range of potential applications. Density functional theory (DFT) methods have usually been applied to obtain optical and structural properties of bulk and 2D GeSe. For the monolayer, DFT typically yields a larger band-gap energy than for bulk GeSe but cannot conclusively determine if the monolayer has a direct or indirect gap. Moreover, the DFT-optimized lattice parameters and atomic coordinates for monolayer GeSe depend strongly on the choice of approximation for the exchange-correlation functional, which makes the ideal structure-and its electronic properties-unclear. In order to obtain accurate lattice parameters and atomic coordinates for the monolayer, we use a surrogate Hessian-based parallel line search within diffusion Monte Carlo to fully optimize the GeSe monolayer structure. The DMC-optimized structure is different from those obtained using DFT, as are calculated band gaps. The potential energy surface has a shallow minimum at the optimal structure. This, combined with the sensitivity of the electronic structure to strain, suggests that the optical properties of monolayer GeSe are highly tunable by strain.

36 MATERIALS SCIENCE↗
NASA NTRS1993

Successful combination of the stochastic linearization and Monte Carlo methods

A combination of a stochastic linearization and Monte Carlo techniques is presented for the first time in literature. A system with separable nonlinear damping and nonlinear restoring force is considered. The proposed combination of the energy-wise linearization with the Monte Carlo method yields an error under 5 percent, which corresponds to the error reduction associated with the conventional stochastic linearization by a factor of 4.6.

Elishakoff, I.↗
NASA NTRS2011

An Efficient Monte Carlo Method for Modeling Radiative Transfer in Protoplanetary Disks

Monte Carlo methods have been shown to be effective and versatile in modeling radiative transfer processes to calculate model temperature profiles for protoplanetary disks. Temperatures profiles are important for connecting physical structure to observation and for understanding the conditions for planet formation and migration. However, certain areas of the disk such as the optically thick disk interior are under-sampled, or are of particular interest such as the snow line (where water vapor condenses into ice) and the area surrounding a protoplanet. To improve the sampling, photon packets can be preferentially scattered and reemitted toward the preferred locations at the cost of weighting packet energies to conserve the average energy flux. Here I report on the weighting schemes developed, how they can be applied to various models, and how they affect simulation mechanics and results. We find that improvements in sampling do not always imply similar improvements in temperature accuracies and calculation speeds.

radiative transfer↗
DOE OSTI2024

Magnetic structure of A ≤ 10 nuclei using the Norfolk nuclear models with quantum Monte Carlo methods

Here we present quantum Monte Carlo calculations of magnetic moments, form factors, and densities of A ≤ 10 nuclei within a chiral effective field theory approach. We use the Norfolk two- and three-body chiral potentials and their consistent electromagnetic one- and two-nucleon current operators. We find that two-body contributions to the magnetic moment can be large (up to ≈ 33% in A = 9 systems). We study the model dependence of these observables and place particular emphasis on investigating their sensitivity to using different cutoffs to regulate the many-nucleon operators. Calculations of elastic magnetic form factors for A ≤ 10 nuclei show excellent agreement with the data out to momentum transfers q ≈ 3 fm -1 .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

SU-E-T-805: Verification of the Simplified Monte Carlo Method for Simulation in An Inhomogeneous Phantom

Purpose: A simplified Monte Carlo (SMC) method has been developed to obtain fast and accurate dose calculation in heterogeneous tissues to improve the accuracy of dose calculation. We have applied the SMC method to calculation of dose kernels for the pencil beam scanning. While the SMC method tracks individual primary protons in medium, it simplifies the dose calculation by using a measured depth-dose distribution instead of considering nuclear reactions and tracking secondary particles. To verify the accuracy of SMC calculation, we compared a dose-calculation Result using the SMC method with that using the Full Monte Carlo (FMC) method in an inhomogeneous phantom. Methods: As a model of the inhomogeneous media, we considered a phantom complied of a rectangular acrylic block of size 150*300*200 mm {sup 3} immersed in water within a virtual container with a size of 300*300*400 mm{sup 3}. Results: We found excellent agreement of overall dose distributions between the SMC and FMC methods. As for the laterally integrated depth-dose distributions, slight difference was found in front of the second Bragg-Peak between the two algorithms. While it took 25717.0 s on 2.7 GHz Intel Core i7 for the FMC method to complete the calculation, it took 15.4 s for the SMC method to complete it. The SMC method can calculate the dose distribution approximately 1675 times faster than the FMC method. Conclusion: The dose distribution obtained by SMC method agreed well that obtained by the FMC method in a simple inhomogeneous phantom. In contrast, calculation time by the SMC method has been reduced by three orders in magnitude compared with the FMC method.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

High Accuracy Transition Metal Effective Cores for the Many-Body Diffusion Monte Carlo Method

Practical applications of the real-space diffusion Monte Carlo (DMC) method require the removal of core electrons, where currently localization approximations of semilocal potentials are generally used in the projector. Accurate calculations of complex solids and large molecules demand minimizing the impact of approximated atomic cores. Prior works have shown that the errors from such approximations can be sizable in both finite and periodic systems. In this work, we show that a class of differential pseudopotentials, known as pseudo-Hamiltonians, can be constructed for the 3d transition metal atoms, entirely removing the need for any localization scheme in the DMC projector. As a proof of principle, we demonstrate the approach for the case of Co. In order to minimize errors in the pseudo-Hamiltonian at the many-body level, we generalize the recently proposed correlation-consistent pseudopotential generation scheme to successively close semilocal representations of the differential potentials. Our generation scheme successfully produces potentials tailored specifically for real space projector quantum Monte Carlo methods with low error at the many-body level, i.e., with many-body scattering properties very close to relativistic all-electron results. In particular, we show that the agreement with respect to atomic and molecular quantities reach chemical accuracy in many cases-on par with the most accurate semilocal pseudopotentials available. Further, our pseudo-Hamiltonian generation scheme utilizes standard quantum chemistry codes designed only to work with semilocal pseudopotentials, enabling straightforward generation of pseudo-Hamiltonians for additional elements in future works.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Muon capture in nuclei: An ab initio approach based on Green's function Monte Carlo methods

An ab initio Green's function Monte Carlo (GFMC) method is introduced for calculating total rates of muon weak capture in light nuclei with mass number $A ≤ 12$. As a first application of the method, we perform a calculation of the rate in $^3$$\textbf{H}$ and $^4$$\textbf{He}$ in a dynamical framework based on realistic two- and three-nucleon interactions and realistic nuclear charge-changing weak currents. The currents include one- and two-body terms induced by $π$- and $ρ$-meson exchange, and $N$-to-$Δ$ excitation, and are constrained to reproduce the empirical value of the Gamow-Teller matrix element in tritium. We investigate the sensitivity of theoretical predictions to current parametrizations of the nucleon axial and induced pseudoscalar form factors as well as to two-body contributions in the weak currents. The large uncertainties in the measured $^4$$\textbf{He}$ rates obtained from bubble-chamber experiments (carried out over 50 years ago) prevent us from drawing any definite conclusions. No data exist for $^3$$\textbf{H}$, but results are compared to those of a recent Faddeev calculation as a validation of the present GFMC method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Monte-Carlo Method of Calculating Weakly Coupled Systems Using the PRIZMA-DSP Code

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.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Quantum Monte Carlo Methods for Astrophysical Applications

In recent years, new astrophysical observations have provided a wealth of exciting input for nuclear physics. For example, the observations of two-solar-mass neutron stars put strong constraints on possible phase transitions to exotic phases in strongly interacting matter at high densities. Furthermore, the recent observation of a neutron-star merger in both the electromagnetic spectrum and gravitational waves has provided compelling evidence that neutron-star mergers are an important site for the production of extremely neutron-rich nuclei within the r-process. In the coming years, an abundance of new observations is expected, which will continue to provide crucial constraints on the nuclear physics of these events. To reliably analyze such astrophysical observations and extract information on nuclear physics, it is very important that a consistent approach to nuclear systems is used. Such an approach consists of a precise and accurate method to solve the nuclear many-body problem in nuclei and nuclear matter, combined with modern nuclear Hamiltonians that allow to estimate the theoretical uncertainties. Quantum Monte Carlo methods are ideally suited for such an approach and have been successfully used to describe atomic nuclei and nuclear matter. In this contribution, I will present a detailed description of Quantum Monte Carlo methods focusing on the application of these methods to astrophysical problems. In particular, I will discuss how to use Quantum Monte Carlo methods to describe nuclear matter of relevance to the physics of neutron stars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Parallel Monte Carlo Methods for Heterogeneous Hardware Computer Systems Using GPUs and Coprocessors: Recent Development of ARCHER Code - Paper 54

Traditional methods of accelerating Monte Carlo (MC) calculations typically use CPUs as the computing unit. The parallelism is based upon multithreading on a multi-core processor, and message passing interface (MPI) across many processors and nodes. In recent years, the hardware accelerators have been introduced by the high performance computing industry as an alternative approach to achieve massive parallelism. Hardware accelerators have the advantage of high Floating-point Operations Per Second (FLOPS) and high energy efficiency. Examples include graphics processing units (GPUs) and coprocessors. Compute-intensive programs can be off-loaded to them to be concurrently executed by hundreds or thousands of active threads. Existing production MC codes, however, cannot be directly compiled or executed on the new platforms. It is therefore necessary to rewrite or redevelop the code using the hardware accelerator specific programming models and APIs. Several studies have been conducted on using GPUs to accelerate MC calculations in reactor analysis and medical physics. In this paper, we present the recent progress at Rensselaer Polytechnic Institute (RPI) in developing a new parallel MC code called ARCHER (Accelerated Radiation-transport Computations in Heterogeneous Environments) for coupled photon-electron transport simulations. The code is designed as a test bed to evaluate the efficacy of heterogeneous computing systems for low-cost MC-based radiation dosimetric calculations. The code has three variants corresponding to three parallel hardware platforms: ARCHER-CPU (multi-core CPU), ARCHER-GPU (Nvidia GPU) and ARCHER-COP (Intel Xeon Phi coprocessor). The code has been used for several applications, including CT imaging dosimetry, radiotherapy dosimetry and radiation shielding design. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Simulation of Nuclear Reactor Kinetics by the Monte Carlo Method

The KIR computer code intended for calculations of nuclear reactor kinetics using the Monte Carlo method is described. The algorithm implemented in the code is described in detail. Some results of test calculations are given.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Adaptive, Active Learning, and Multifidelity Monte Carlo Methods in the MOOSE Stochastic Tools Module

MOOSE is an open-source computational platform for constructing multi-physics models and executing them in a massively parallel fashion. It has a stochastic tools module (STM) for forward/inverse uncertainty quantification (UQ) and surrogate modeling. This presentation details some recent developments to the STM with respect to the implementation of adaptive, active learning, and multifidelity Monte Carlo methods for forward UQ of computational models. Specifically, the adaptive Monte Carlo methods include Markov Chain Monte Carlo (MCMC)-driven algorithms like adaptive importance sampling and parallelized subset simulation for statistical QoI estimation, rare events analysis, and stochastic gradient-free optimization. The active learning methods include Gaussian Process (GP) surrogates and their training via Adam optimization, design of acquisition functions, and integration with samplers like Monte Carlo, adaptive importance, and parallelized subset simulation. These active learning methods are also designed to work in a batch mode, wherein, the required calls to the full computational model are executed in parallel whenever a user-specified batch size is met. The multifidelity methods in STM are broadly divided into two categories: hierarchical, where a defined hierarchy exists among the low-fidelity models, and peer, where all the low-fidelity models are treated equally. A GP surrogate is used to learn the differences between the low- and high-fidelity models in both multifidelity categories, and acquisition functions from the active learning classes are used to decide whether to rely on a low-fidelity model or call the expensive high-fidelity model. Alongside the software description and usage, applications are also presented to nuclear engineering computational models including a TRISO nuclear fuel particle, a reactor pressure vessel, and a heat-pipe microreactor.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

A Fast Monte Carlo Method for Model-Based Prognostics Based on Stochastic Calculus

This work proposes a fast Monte Carlo method to solve differential equations utilized in model-based prognostics. The methodology is derived from the theory of stochastic calculus, and the goal of such a method is to speed up the estimation of the probability density functions describing the independent variable evolution over time. In the prognostic scenarios presented in this paper, the stochastic differential equations describe variables directly or indirectly related to the degradation of a monitored system. The method allows the estimation of the probability density functions by solving the deterministic equation and approximating the stochastic integrals using samples of the model noise. By so doing, the prognostic problem is solved without the Monte Carlo simulation based on Euler's forward method, which is typically the most time consuming task of the prediction stage. Three different prognostic scenarios are presented as proof of concept: (i) life prediction of electrolytic capacitors, (ii) remaining time to discharge of Lithium-ion batteries, and (iii) prognostic of cracked structures under fatigue loading. The paper shows how the method produces probability density functions that are statistically indistinguishable from the distributions estimated with Euler's forward Monte Carlo simulation. However, the proposed solution is orders of magnitude faster when computing the time-to-failure distribution of the monitored system. The approach may enable complex real-time prognostics and health management solutions with limited computing power.

Corbetta, M.↗