SEARCH · Engineering Papers
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.
Monte Carlo method applied to pigment particle clusters, and relevance of Mie scattering function to reflected light
Monte Carlo method to predict solid propellant variability effects on Algol ID motor performance, for Little Joe II project
Conventional Bayesian optimization methods implicitly assume that the data and model being optimized are “perfect.” This assumption leads to inaccurate posterior probability distribution functions (PDFs) when applied to “imperfect” data or models. The new Bayesian optimization framework presented in this report provides a way to parameterize the effect of imperfections usually encountered in a prior PDF of generalized data or a model on the posterior PDF. The effects of imperfections are parameterized by a set of constraints imposed on the posterior expectation values of deviations between the data and the model and on their covariance matrix elements. A particular set of values for these constraints conveys an evaluator’s best estimate of the effect of imperfections on the corresponding posterior expectation values. When a prior PDF of generalized data is assumed to be normal, an expression for a posterior PDF satisfying an arbitrary set of constraints is derived analytically for linear models. An analogous iterative algorithm is given for nonlinear models. The corresponding posterior PDF should be used to estimate any posterior expectation values in the presence of imperfections parameterized by that set of constraints. A posterior PDF of a conventional Bayesian optimization method is recovered analytically when all evaluator-specified constraints are set to zero (i.e., in the absence of any imperfections). The analytical expressions derived in this report for normal PDFs and linear models were verified numerically by a Metropolis–Hastings Monte Carlo method. The methods presented herein could be applied to any kind of data or models, including differential cross-section data or integral benchmark experiments.
Radiotherapy using ion beams has gained momentum in radiation oncology. While the number of proton therapy facilities that have been established or are under construction is rapidly growing, interest in heavy ion (whose atomic number Z is greater than one) radiotherapy has also been revitalized in the U.S. Helium ions, with mass and charge between protons and carbon ions, have advantages of deep penetration distances with significant peak-to-entrance dose ratios and limited dose contributions from secondary neutrons and other secondary charged fragments. In consideration of these advantages, the U.S. ion beam therapy community has expressed interests in helium ions as a potential candidate for ion beam therapy, especially pediatric cases. As such, characterizing the secondary nuclear fragment field created by primary helium ion interactions along the beam delivery devices and in human tissue is important for dose assessment and patient protection. Up to now the experimental data with respect to helium ions over the therapeutic energy range, 100 - 230 MeV/amu, is scarce compared to protons and carbon ions. Kurosawa et al. measured stopping target yields and angular spectra of neutrons, protons, deuterons and tritons produced by various target materials bombarded by 100- and 180- MeV/amu helium ions; Heilbronn et al. and Cecil et al. also measured the stopping target neutron yields by 155-, 160- and 177.5-MeV/amu helium ion bombardments. We have proposed to measure angular distributions of secondary neutrons and light charged ions produced by 100- and 230-MeV/amu helium ions bombarding stopping water and iron targets. The experiment was conducted at the Heavy Ion Medical Accelerator in Chiba (HIMAC) of the National Institute of Radiological Sciences (NIRS) in Japan this June. During the literature survey for experimental setup, we found that the ratio of the ion beam range (R) to the target depth (d) varies from 0.5 - 0.98 in the stopping target measurements either for helium ions incident on various targets or carbon ions bombarding targets as tissue surrogates. The difference in the values of ratio R/d makes comparison of experimental results complicated and require accurate corrections. Hence, how the R/d ratio and cross sectional area of the targets influence the angular distributions of secondary particles is investigated by using a Monte Carlo method in this study. The influence of the depth and the cross sectional area of stopping water and iron targets on the yield and the spectra of secondary particles induced by 100- and 230-MeV/amu He ions was studied by a Monte Carlo simulation code, PHITS. The secondary charged particles and secondary neutrons who come from breakup of projectiles are highly forward-focused, while the secondary particles produced by target breakup are nearly isotropic. As the energy of {sup 4}He ions increases from 100 MeV/amu to 230 MeV/amu, the normalized intensities of charged particles decrease more gently with increasing angles, whereas the neutron angular distribution falls quickly at angles smaller than 30 deg. and starts to exceed the normalized profile of 100 MeV/amu He from 60 deg. When the target depth increases, the impact on attenuation of secondary particles is more significant for lighter target mass and higher-energy projectiles at forward angles. Also, with deeper targets, more interactions occur between the secondary particles and the target elements, which results in more target-like fragments at large and backward angles. In respect of the cross sectional area of the stopping targets, the forward angular distributions are similar to the system with smaller cross sectional area of the targets; however, no general rule was found for how the large-angle distributions are affected by the target cross sectional area. (authors)
Monte carlo method applied to nonisothermal nongray gas radiative heat transfer problem where absorption coefficient is allowed to vary with wavelength and temperature
In this paper, we rigorously investigate the reduced density matrix (RDM) associated to the ideal Bose gas in harmonic traps. We present a method based on a sum-decomposition of the RDM allowing to treat not only the isotropic trap, but also general anisotropic traps. When focusing on the isotropic trap, the method is analogous to the loop-gas approach developed by Mullin [“The loop-gas approach to Bose-Einstein condensation for trapped particles,” Am. J. Phys. 68(2), 120 (2000)]. Turning to the case of anisotropic traps, we examine the RDM for some anisotropic trap models corresponding to some quasi-1D and quasi-2D regimes. For such models, we bring out an additional contribution in the local density of particles which arises from the mesoscopic loops. The close connection with the occurrence of generalized-Bose-Einstein condensation is discussed. Our loop-gas-like approach provides relevant information which can help guide numerical investigations on highly anisotropic systems based on the Path Integral Monte Carlo method.
Resonant ultrasound spectroscopy is used to nondestructively measure the elastic resonances of small solids to elucidate the material's elastic properties or other qualities like size, shape, or composition. In this work, we introduce the software RUScal for the purpose of determining elastic properties by analyzing the eigenfrequencies of solid specimens with common shapes, such as rectangular parallelepipeds, cylinders (solid and hollow tube), ellipsoids, and octahedrons, as well as irregularly shaped ellipsoids that can be described analytically. All symmetry classes are supported, from isotropic to triclinic, along with the option to add or remove up to three orthogonal mirror planes as well as the ability to reorient the crystal axes with respect the sample edges via Euler angles. Additional features include tools to help find initial sets of elastic constants, including grid exploration and Monte Carlo methods, a tool to analyze frequencies as a function of sample length or crystal orientation, an error analysis tool to assess fit quality, and formatting of the input and output files for batch fitting, e.g., as a function of temperature. This software was validated with published resonant ultrasound spectroscopy data for various materials, shapes, and symmetries with noted improvements in calculation time compared to finite element methods.
Purpose: To establish a simple relation of backscatter dose enhancement around a high-Z dental alloy in head and neck radiation therapy to its average atomic number based on Monte Carlo calculations. Methods: The PHITS Monte Carlo code was used to calculate dose enhancement, which is quantified by the backscatter dose factor (BSDF). The accuracy of the beam modeling with PHITS was verified by comparing with basic measured data namely PDDs and dose profiles. In the simulation, a high-Z alloy of 1 cm cube was embedded into a tough water phantom irradiated by a 6-MV (nominal) X-ray beam of 10 cm × 10 cm field size of Novalis TX (Brainlab). The ten different materials of high-Z alloys (Al, Ti, Cu, Ag, Au-Pd-Ag, I, Ba, W, Au, Pb) were considered. The accuracy of calculated BSDF was verified by comparing with measured data by Gafchromic EBT3 films placed at from 0 to 10 mm away from a high-Z alloy (Au-Pd-Ag). We derived an approximate equation to determine the relation of BSDF and range of backscatter to average atomic number of high-Z alloy. Results: The calculated BSDF showed excellent agreement with measured one by Gafchromic EBT3 films at from 0 to 10 mm away from the high-Z alloy. We found the simple linear relation of BSDF and range of backscatter to average atomic number of dental alloys. The latter relation was proven by the fact that energy spectrum of backscatter electrons strongly depend on average atomic number. Conclusion: We found a simple relation of backscatter dose enhancement around high-Z alloys to its average atomic number based on Monte Carlo calculations. This work provides a simple and useful method to estimate backscatter dose enhancement from dental alloys and corresponding optimal thickness of dental spacer to prevent mucositis effectively.
Multi-model Monte Carlo methods have been demonstrated to be an efficient and accurate alternative to standard Monte Carlo (MC) in the model-based propagation of uncertainty in entry, descent, and landing (EDL) applications. These multi-model MC methods fuse predictions from low-fidelity models with the high-fidelity EDL model of interest to produce unbiased statistics with a fraction of the computational cost. The accuracy and efficiency of the multi-model MC methods are dependent upon the magnitude of correlations of the low-fidelity models with the high-fidelity model, but also upon the correlation among the low-fidelity models, and their relative computational cost. Because of this layer of complexity, the question of how to optimally select the set of low-fidelity models has remained open. In this work, methods for optimal model construction and tuning are investigated as a means to increase the speed and precision of trajectory simulation for EDL. Specifically, the focus is on the inclusion of low-fidelity model tuning within the sample allocation optimization that accompanies multi-model MC methods. Preliminary results indicate that low-fidelity model tuning can significantly improve efficiency and precision of trajectory simulations and provide an increased edge to multi-model MC methods when compared to standard MC. The challenges and potential benefits to exploring a fully iterative and comprehensive optimization strategy in future work are highlighted.
RX J1713.7−3946 is a prototype in the γ-ray-bright supernova remnants (SNRs) and is in continuing debates on its hadronic versus leptonic origin of the γ-ray emission. We explore the role played by the diffusive relativistic protons that escape from the SNR shock wave in the γ-ray emission, apart from the high-energy particles’ emission from the inside of the SNR. In the scenario that the SNR shock propagates in a clumpy molecular cavity, we consider that the γ-ray emission from the inside of the SNR may arise either from the inverse Compton scattering or from the interaction between the trapped energetic protons and the shocked clumps. The dominant origin between them depends on the electron-to-proton number ratio. The diffusive protons that escaped from the shock wave during the expansion history can provide an outer hadronic γ-ray component by bombarding the surrounding dense matter. The broadband spectrum can be well explained by this two-zone model, in which the γ-ray emission from the inside governs the TeV band, while the outer emission component substantially contributes to the GeV γ-rays. The two-zone model can also explain the TeV γ-ray radial brightness profile that significantly stretches beyond the nonthermal X-ray-emitting region. In the calculation, we present a simplified algorithm for Li and Chen's “accumulative diffusion” model for escaping protons and apply the Markov Chain Monte Carlo method to constrain the physical parameters.
The evolution of the particle weight in a δf particle-in-cell simulation depends on the marker distribution that can evolve in a turbulent field due to turbulent diffusion. When Monte Carlo methods are used to implement the test particle collision operator, or when the particle motion is not strictly Hamiltonian in a collisionless simulation, the marker distribution will evolve along the particle trajectory and, in general, cannot be known exactly. A two-dimensional numerical marker distribution is proposed as an approximation. It is shown to be advantageous over other common methods for evaluating the marker distribution in long-time turbulence simulations. A generalized two-weight δf-method is proposed to mitigate the marker evolution problem.
In this paper, we introduce SuperLite, an open-source Monte Carlo radiation transport code designed to produce synthetic spectra for astrophysical transient phenomena affected by circumstellar interaction. SuperLite utilizes Monte Carlo methods for semi-implicit, semirelativistic radiation transport in high-velocity shocked outflows, employing multigroup structured opacity calculations. The code enables rapid post-processing of hydrodynamic profiles to generate high-quality spectra that can be compared with observations of transient events, including superluminous supernovae, pulsational pair-instability supernovae, and other peculiar transients. We present the methods employed in SuperLite and compare the code's performance to that of other radiative transport codes, such as SuperNu and CMFGEN. We show that SuperLite has successfully passed standard Monte Carlo radiation transport tests and can reproduce spectra of typical supernovae of Type Ia, Type IIP, and Type IIn.
We examine the hypothesis that the red near-infrared colors of some L dwarfs could be explained by a “dust haze” of small particles in their upper atmospheres. This dust haze would exist in conjunction with the clouds found in dwarfs with more typical colors. We developed a model that uses Mie theory and the Hansen particle size distributions to reproduce the extinction due to the proposed dust haze. We apply our method to 23 young L dwarfs and 23 red field L dwarfs. We constrain the properties of the dust haze including particle size distribution and column density using Markov Chain Monte Carlo methods. We find that sub-micron-range silicate grains reproduce the observed reddening. Current brown dwarf atmosphere models include large-grain (1–100 μ m) dust clouds but not sub-micron dust grains. Our results provide a strong proof of concept and motivate a combination of large and small dust grains in brown dwarf atmosphere models.
The following topics are addressed: the model and the method; equation of state of neutron matter, role of three-neutron force; symmetry energy; Λ-hypernuclei; Λ-neutron matter; and neutron star structure. In summary, quantum Monte Carlo methods are useful to study nuclear systems in a coherent framework; the three-neutron force is the bridge between E sym and neutron star structure; and neutron star observations are becoming competitive with experiments. Λ-nucleon data are very limited, but ΛNN is very important. The role of Λ in neutron stars is far from understood; more ΛN data are needed. The author's conclusion: We cannot conclude anything with present models.
A scheme is proposed to classify the basins for attractors of dynamical systems in arbitrary dimensions. There are four basic classes depending on their size and extent, and each class can be further quantified to facilitate comparisons. The calculation uses a Monte Carlo method and is applied to numerous common dissipative chaotic maps and flows in various dimensions.
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can also be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
Ferrofluids are often favored for their ability to be remotely positioned via external magnetic fields. The behavior of particles in ferromagnetic clusters under uniformly applied magnetic fields has been computationally simulated using the Brownian dynamics, Stokesian dynamics, and Monte Carlo methods. However, few methods have been established that effectively handle the basic principles of magnetic materials, namely, Maxwell’s equations. An iterative constraint method was developed to satisfy Maxwell’s equations when a uniform magnetic field is imposed on ferrofluids in a heterogeneous Brownian dynamics simulation that examines the impact of ferromagnetic clusters in a mesoscale particle collection. This was accomplished by allowing a particulate system in a simple shear flow to advance by a time step under a uniformly applied magnetic field, then adjusting the ferroparticles via an iterative constraint method applied over sub-volume length scales until Maxwell’s equations were satisfied. The resultant ferrofluid model with constraints demonstrates that the magnetoviscosity contribution is not as substantial when compared to homogeneous simulations that assume the material’s magnetism is a direct response to the external magnetic field. This was detected across varying intensities of particle-particle interaction, Brownian motion, and shear flow. Ferroparticle aggregation was still extensively present but less so than typically observed.
A benchmark calculation has been performed for a startup core physics test of Japan's High Temperature Engineering Test Reactor (HTTR). The calculation is carried out by the McCARD code, which adopts the Monte Carlo method. The cross section library is ENDF-B/VII.0. The fuel cell is modeled by the reactivity-equivalent physical transform (RPT) method. Effective multiplication factors with different numbers of fuel columns have been analyzed. The calculation shows that the HTTR becomes critical with 19 fuel columns with an excess reactivity of 0.84% Δk/k. The discrepancies between the measurements and Monte Carlo calculations are 2.2 and 1.4 % Δk/k for 24 and 30 columns, respectively. The reasons for the discrepancy are thought to be the current version of cross section library and the impurity in the graphite which is represented by the boron concentration. In the future, the depletion results will be proposed for further benchmark calculations. (authors)