Light Propagation and Radiative Exciton Transport in Two-Dimensional Layered Perovskite Microwires
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Summary: Non-relativistic IMC/SN with Lagrangian hydrodynamics; IMC-DDMC momentum deposition; Post-ALE IMC particle remap and SN remap; collapse of multi-material state per zone.
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The highly energetic electron environment in the inner magnetosphere (GEO inward) has received a lot of research attention in resent years, as the dynamics of relativistic electron acceleration and transport are not yet fully understood. These electrons can cause deep dielectric charging in any space hardware in the MEO to GEO region. We use a new and novel approach to obtain a global representation of the inner magnetospheric energetic electron environment, which can reproduce the absolute environment (flux) for any spacecraft orbit in that region to within a factor of 2 for the energy range of 100 KeV to 5 MeV electrons, for any levels of magnetospheric activity. We combine the extensive set of inner magnetospheric energetic electron observations available at Los Alamos with the physics based Salammbo transport code, using the data assimilation technique of "nudging". This in effect input in-situ data into the code and allows the diffusion mechanisms in the code to interpolate the data into regions and times of no data availability. We present here details of the methods used, both in the data assimilation process and in the necessary inter-calibration of the input data used. We will present sample runs of the model/data code and compare the results to test spacecraft data not used in the data assimilation process.
The flight of a human phantom torso with head that containing active dosimeters at 5 organ sites and 1400 TLDs distributed in 34 1" thick sections is described. Experimental dose rates and quality factors are compared with calculations for shielding distributions at the sites using the Computerized Anatomical Male (CAM) model. The measurements were complemented with those obtained from other instruments. These results have provided the most comprehensive data set to map the dose distribution inside a human and to assess the accuracy of radiation transport models and astronaut radiation risk.
This study presents an experimental and numerical results of combustion dynamics of a Tier II certification fuel/iso-butanol mixtures and its surrogate(S5)/iso-butanol mixtures. The experiments were carried out where gas transport was determined entirely by fuel evaporation to promote spherical symmetry during the burning process. The droplet initial diameters were in the range 0.55 mm to 0.62 mm. The combustion characteristics such as droplet diameters, flame standoff ratios and soot shell standoff ratios of indolene/iso-butanol mixtures are compared with S5/iso-butanol mixtures and the implications of iso-butanol addition to indolene and S5 on combustion are discussed. The presence of iso-butanol is experimentally found to reduce the formation of soot as the iso-butanol concentration was increased and the iso-butanol addition insignificantly influence the burning rate. Simulations were carried out with the detailed kinetic chemistry (324 species and 17608 reactions) from POLIMI. The detailed numerical model included unsteady gas and liquid transport, soot chemistry and radiative transport. The impact of iso-butanol on the burning rate, flame structure and soot formation are presented. Droplet burn rates were well predicted while flame diameters showed more variability depending on how the flame was defined in the numerical simulation (peak gas temperature and OH concentration). Simulations also show the influence of iso-butanol concentration on flame temperature, greenhouse gas emissions and soot formations.
This presentation will describe results of an experimental and numerical study of combustion of a Tier II gasoline certification fuel and its surrogate blended with iso-butanol as a potential biofuel additive. The experiments were carried out under conditions where gas transport originated entirely by fuel evaporation with external convection (both forced and natural) were minimized. The resulting near spherically symmetric droplet flames were amenable to detailed numerical modeling. The droplet initial diameters were in the narrow range of 0.55 mm to 0.62 mm while and burning is performed under standard atmosphere. The combustion characteristics such as evolution of droplet, flame and soot shell diameters for indolene/iso-butanol and surrogate/iso-butanol mixtures are compared. The presence of iso-butanol is experimentally found to reduce the formation of soot as the iso-butanol concentration was increased. Simulations are also reported using a detailed kinetic chemistry comprised of 324 species and 17608 reactions using the CRECK POLIMI mechanism. The detailed numerical model included unsteady gas and liquid transport, soot chemistry and radiative transport. Droplet burn rates were well predicted while flame diameters showed more variability depending on how the flame was defined in the numerical simulation (peak gas temperature and OH concentration). Simulations also show the influence of iso-butanol concentration on flame temperature, greenhouse gas emissions and particulate emissions.
Line-by-line calculations of the radiative transport for a condition near peak heating for entry of the Galileo probe into the Jovian atmosphere are described. The discussion includes a thorough specification of the atomic and molecular input data used in the calculations that could be useful to others working in the field. The results show that the use of spectrally averaged cross sections for diatomic absorbers such as CO and C2 in the boundary layer can lead to an underestimation (by as much as 29%) of the spectral flux at the stagnation point. On the other hand, for the turbulent region near the cone frustum on the probe, the flow tends to be optically thin, and the spectrally averaged results commonly used in coupled radiative transport-flow field calculations are in good agreement with the present line-by-line results. It is recommended that these results be taken into account in sizing the final thickness of the Galileo's heat shield.
One of the common methods for solving the radiation transport equation is to use a polynomial expansion for the angle variable(s). Recent research that reduces the oscillations and improves the positivity of the gray transport equation solutions is here applied to the multigroup transport equations. Constant scale factors that stretch the time axis and constant scattering opacities that filter the solution greatly increase the accuracy of the solution with no added nonlinearities. No new solution techniques are required. Test problems are presented in one and two dimensions.
The time-dependent, gray, linear radiation transport equation is discretized using the meshless local Petrov-Galerkin method with reproducing kernels. The integration is performed using a Voronoi tessellation, which creates a partition of unity that only depends on the position and extent of the kernels. The resolution of the integration automatically follows the particles and requires no manual adjustment. The discretization includes streamline-upwind Petrov-Galerkin stabilization to prevent oscillations and improve numerical conditioning. The angular quadrature is selectively refineable to increase angular resolution in chosen directions. The time discretization is done using backward Euler. The transport solve for each direction and the solve for the scattering source are both done using Krylov iterative methods. The results indicate first-order convergence in time and second-order convergence in space for linear reproducing kernels.
Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high- delity simulations of radiation transport within an urban domain. The model is constructed to employ a source location (x, y, z) as input and return the estimated count rate for a set of speci ed detector locations. Because MCNP simulations are computationally expensive, we develop e cient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes (GP) and neural networks (NN) that we train and verify using the MCNP simulations. The trained surrogate models provide an e cient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo (MCMC) algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source's location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%.
A computationally efficient radiative transport model is presented that predicts a camera measurement and accounts for the light reflected and blocked by an object in a scattering medium. The model is in good agreement with experimental data acquired at the Sandia National Laboratory Fog Chamber Facility (SNLFC). The model is applicable in computational imaging to detect, localize, and image objects hidden in scattering media. Here, a statistical approach was implemented to study object detection limits in fog.
The transport of infrared radiation in a single cuboidal cloud using a vertical two steam approximation was modeled. The emittance of the top face of the model cloud is always less than that for a plane parallel cloud of the same optical depth. The hemisphere flux escaping from the cloud top has a gradient from the center to the edges which brighten when the cloud is over warmer ground. Cooling rate calculations in the 8 to 13.6 micrometer region show that there is cooling from the sides of the cloud at all levels even when there is heating of the core from the ground below. The radiances exiting from model cuboidal clouds were computed by path integration over the source function obtained with the two stream approximation. It is suggested that the brightness temperature measured from finite clouds will overestimate the cloud top temperature.
The transport of infrared radiation in a single cuboidal cloud is modeled using a variable azimuth two-stream approximation. Computations are made at 10 microns for a Deirmendjian (1969) C-1 water cloud where the single scattering albedo is equal to 0.638 and the asymmetry parameter is 0.865. The results indicate that the emittance of the top face of the model cloud is always less than that for a plane parallel cloud of the same optical depth. The hemispheric flux escaping from the cloud top possesses a gradient from the center to the edges which are warmer when the cloud is over warmer ground. Cooling rate calculations in the 8-13.6 micron region demonstrate that there is cooling out of the sides of the cloud at all levels even when there is heating of the core from the ground below. The radiances exiting from model cuboidal clouds are computed by path integration over the source function obtained with the two-stream approximation. Results indicate that the brightness temperature measured from finite clouds will overestimate the cloud-top temperature.
Nuclear data is commonly used in applications such as nuclear nonproliferation, safeguards, and criticality safety. More specifically, nuclear data is used in predictive simulation codes like the Monte-Carlo N-Particle (MCNP ® ) transport code, Serpent, and similar radiation transport codes. The improvement of nuclear data enables more precise and accurate simulations, which result in higher fidelity designs and reduced operational/procedural costs. Therefore, the improvement of nuclear data is of paramount importance across the nuclear community. Nuclear data is improved and validated through integral benchmark experiments. The design of benchmark experiments is an extensive process; therefore, these experiments are often optimized on multiple characteristics, including sensitivity to the nuclear data, during the design process. Sensitivity is a measure of how much a quantity changes due to changes in independent variables such as experimental configuration. An experimental design that has a larger sensitivity to the nuclear data of interest will have a larger impact on the accuracy and precision of the validated data. Past integral benchmark experiments have primarily used the effective multiplication factor ($k_{eff}$) as the predominant measured quantity; however, experiments designed with other quantities in mind would be able to optimize on validating different areas of the nuclear data. A primary goal of the EUCLID project is to design, constrain, and reduce compensating errors in experiments focused on quantities other than $k_{eff}$ to better validate nuclear data across the board. Currently, there is a capability in MCNP to easily calculate the sensitivity of $k_{eff}$ to specific nuclear data of numerous reactions types and isotopes (KSEN card); however, the sensitivity of other quantities must be estimated in more strenuous manners. For example, the perturbation feature (PERT card) of MCNP can be used to estimate first-order sensitivities of some response in fixed source simulations. A recent announcement revealed that the first- and second-order perturbation features in previous releases of MCNP contained a bug. It was identified that particles were being scored into the wrong energy bin. The bug is in the most recent public release (MCNP6.2); however, a patch has been added to the most up to date version (MCNP6.2.2) that has not been released publicly. A direct comparison of the PERT card results for an F4 (neutron flux averaged over a cell) tally before and after the patch are shown in figure 1. All simulations used in the sensitivity estimates in this report were performed with MCNP6.2.2. This work verifies the patched MCNP perturbation tool by comparing first order sensitivities made using the PERT card to estimates made using manual perturbation of the compact ENDF (ACE) files.
Spontaneously emitted radiation from excited atoms can be of principle importance in certain modes of electrical breakdown, especially positive streamers and some regimes of Townsend breakdown. The electrostatic particle-in-cell code Aleph utilizes the direct simulation Monte Carlo method to compute radiation transport. When there is strong radiation trapping, this approach is limited in that it must resolve the timescale associated with self-absorption. This renders many cases computationally intractable as sub-femtosecond time steps can be required to compute solutions for phenomena that occur over nanoseconds or microseconds. For two specific cases which exhibit strong radiation trapping, we find that spontaneous emissions having a frequency near the line center are inactive in the breakdown process and can be neglected. This enables larger time steps and a computational speedup of up to two orders of magnitude is observed. Some considerations for determining the validity of making such an approximation for Townsend breakdown problems and positive ionization wave problems are presented.
Monte Carlo codes are essential components of many reactor physics simulation workflows as high-fidelity continuous-energy neutron transport solvers. Among Monte Carlo radiation transport codes, MCNP is particularly notable due to its diverse simulation capabilities, large user base, and long validation history. Despite being a powerful simulation tool, MCNP provides limited capabilities to allow automated execution, model transformation, or support for user-defined logic and abstractions that limit its compatibility with modern workflows. Here, to better integrate MCNP into a modern scientific workflow, we have developed an intuitive yet full-featured MCNP Application Program Interface (API) in Python, named MCNPy, which provides a specialized set of classes for MCNP input development. Moreover, to guarantee that our reading, writing, and modeling capabilities remain self-consistent (and to render the huge scope of the MCNP API manageable), we have adopted a strategy of model-driven software development in which a generalized model of the MCNP input format has been created. From this generalized model, or “metamodel,” problem-specific implementations such as an engine for input validation or a codebase for programmatic operations may be automatically generated. Since MCNPy primarily acts as a Python front-end to the underlying Java API that directly interfaces with the metamodel, it is intrinsically linked to the metamodel and thus remains maintainable. With MCNPy, users can programmatically read, write, and modify any syntactically valid MCNP input file regardless of its origin. These capabilities allow users to automate complicated tasks like design optimization and model translation for nuclear systems. As examples, this work demonstrates the use of MCNPy to find the critical radius of a plutonium sphere and to translate a 9000+ line MCNP input file into a corresponding OpenMC model.