Search NASA⌕ Search

SEARCH · Search NASA

Results for “Time parallelization”

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.

At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Decarburization and Carburization Behavior of Grade 91 Steel in Sodium Environments

Two heats of G91 steel have been investigated in sodium at 550-650°C to understand its corrosion behaviour, microstructural evolution, and tensile properties. Sodium exposure tests of subsized tensile and coupon specimens of G91 base metal and weldment were conducted in Argonne’s forced convection sodium materials testing loops. Maximum exposure times of ~39,000 h at 550°C, ~37,000 h at 600°C, and ~20,000 h at 650°C have been achieved, respectively. Thermal aging experiments of G91 steel were conducted in parallel, and the data of the sodium-exposed specimens were compared with the thermal aging data of the same heat to isolate the thermal aging effect from the effect of sodium exposure. It was found that the tensile strength of G91 steel can be reduced by more than 50% after sodium exposures at 650°C. The effect of a sodium environment on tensile properties is largely dependent on the carburization/decarburization behavior of G91 steel exposed to sodium. The carbon concentrations in sodium in Argonne’s SMT-1 and SMT-2 loops were determined by the equilibration method. The carbon activity in sodium was established by equilibrating high-purity nickel in sodium, analyzing the material for carbon, and using the reported data on the carbon solubility for this material. The carbon activity results were combined with the carbon solubility data in sodium to establish the carbon concentration in sodium. The carbon concentrations in the SMT-1 and SMT-2 loop were estimated to be 1.23 and 0.25 ppm, respectively. With this initial success, we will continue to monitor the carbon contents in the two loops to correlate the microstructural and mechanical property changes with the sodium purity. Future experiments will also consider sodium exposure tests in controlled carbon activities. Online monitoring and control of carbon contents in sodium in nuclear reactors would also be of interest. Thermodynamic analysis was conducted to understand the carburization-decarburization behavior of G91 steel exposed to sodium in the SMT-1 and SMT-2 loops. The equilibrium carbon activity in the alloy was evaluated by the thermodynamics of M 23 C 6 carbides. The calculated carburization-decarburization boundary showed that G91 would undergo decarburization at 650°C in the SMT-1 loop and carburization at 600°C in the SMT-2 loop, which is consistent with our findings from the sodium exposure tests. Future work will include thermodynamic analysis of the carbon concentration – activity relationship including the effect of carbide-forming elements, vanadium and niobium in G91 steel. Kinetics of carbon diffusion and the effect on microstructure and mechanical performance under various service conditions will also be investigated. The ultimate goal is to understand the mechanisms and the kinetics of carbon transfer in the sodium-steel system and to provide a basis for predicting the effect of long-term decarburization-carburization process on the integrity of reactor components.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Enhanced PDV waveform search and analysis method using parallel circular-convolution / cross-correlation for improved dynamic surface velocity extraction [Poster]

Previous work on exhaustive search methodologies for extracting best-match parameters pertaining to dynamic surface quantities from PDV was done by cross-correlating synthetically generated PDV waveforms with observed counterparts using the circular-convolution theorem. This work was further developed into an open-source PDV analysis toolkit called CCPDVANALYSIS which expands upon and enhances the previously tested methods by parallelizing serial algorithmic components and incorporating a comprehensive script library for different flavors of instantaneous frequency functions utilized in generating synthetic PDV waveforms. Results of these enhancements have been shown to markedly decrease execution times of exhaustive search and extraction algorithms and produce improved velocity recoveries for low-velocity and dynamically varying velocity signals. The CCPDVANALYSIS script library demonstrates an advanced method for extracting velocities from low-velocity and non-constant velocity signals further extending and improving the methods beyond capabilities of traditional frequency domain tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Multi-physics Preconditioning for Thermally Activated Batteries

Thermal batteries, also known as molten-salt batteries, are single-use reserve power systems activated by pyrotechnic heat generation, which transitions the solid electrolyte into a molten state. The simulation of these batteries relies on multiphysics modeling to evaluate performance and behavior under various conditions. This paper presents advancements in scalable preconditioning strategies for the Thermally Activated Battery Simulator (TABS) tool, enabling efficient solutions to the coupled electrochemical systems that dominate computational costs in thermal battery simulations. We propose a hierarchical block Gauss-Seidel preconditioner implemented through the Teko package in Trilinos, which effectively addresses the challenges posed by tightly coupled physics, including charge transport, porous flow, and species diffusion. The preconditioner leverages scalable subblock solvers, including smoothed aggregation algebraic multigrid (SA-AMG) methods and domain-decomposition techniques, to achieve robust convergence and parallel scalability. Strong and weak scaling studies demonstrate the solver’s ability to handle problem sizes up to 51.3 million degrees of freedom on 2048 processors, achieving near sub-second setup and solve times for the end-to-end electrochemical solve. These advancements significantly improve the computational efficiency and turnaround time of thermal battery simulations, paving the way for higher-resolution models and enabling the transition from 2D axisymmetric to full 3D simulations.

25 ENERGY STORAGE↗
DOE OSTI2023

Observation of parallel intersystem crossing and charge transfer-state dynamics in [Fe(bpy) 3 ] 2+ from ultrafast 2D electronic spectroscopy

Transition metal-based charge-transfer complexes represent a broad class of inorganic compounds with diverse photochemical applications. Charge-transfer complexes based on earth-abundant elements have been of increasing interest, particularly the canonical [Fe(bpy) 3 ] 2+ . Photoexcitation into the singlet metal–ligand charge transfer ( 1 MLCT) state is followed by relaxation first to the ligand-field manifold and then to the ground state. While these dynamics have been well-studied, processes within the MLCT manifold that facilitate and/or compete with relaxation have been more elusive. We applied ultrafast two-dimensional electronic spectroscopy (2DES) to disentangle the dynamics immediately following MLCT excitation of this compound. First, dynamics ascribed to relaxation out of the initially formed 1 MLCT state was found to correlate with the inertial response time of the solvent. Second, the additional dimension of the 2D spectra revealed a peak consistent with a ~20 fs 1 MLCT → 3 MLCT intersystem crossing process. These two observations indicate that the complex simultaneously undergoes intersystem crossing and direct conversion to ligand-field state(s). Resolution of these parallel pathways in this prototypical earth-abundant complex highlights the ability of 2DES to deconvolve the otherwise obscured excited-state dynamics of charge-transfer complexes.

2-dimensional electronic spectroscopy↗
DOE OSTI2020

Automated pipeline framework for processing of large-scale building energy time series data

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for “virtual” energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings’ time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building’s daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Fault Diagnosis of Hybrid Computing Systems Using Chaotic-Map Method

Computing systems are becoming increasingly complex with nodes consisting of a combination of multi-core central processing units (CPUs), many integrated core (MIC) and graphics processing unit (GPU) accelerators. These computing units and their interconnections are subject to different classes of hardware and software faults, which should be detected to support mitigation measures. We present the chaotic-map method that uses the exponential divergence and wide Fourier properties of the trajectories, combined with memory allocations and assignments to diagnose component-level faults in these hybrid computing systems. We propose lightweight codes that utilize highly parallel chaotic-map computations tailored to isolate faults in arithmetic units, memory elements and interconnects. The diagnosis module on a node utilizes pthreads to place chaotic-map threads on CPU and MIC cores, and CUDA C and OpenCL kernels on GPU blocks. We present experimental diagnosis results on five multi-core CPUs; one MIC; and, seven GPUs with typical diagnosis run-times under a minute.

Rao, Nageswara↗
DOE OSTI2021

Deep Learning Approaches to Surrogates for Solving the Diffusion Equation for Mechanistic Real-World Simulations

In many mechanistic medical, biological, physical, and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs), especially for diffusion, fluid flow and mechanical relaxation, can make simulations impractically slow. Biological models of tissues and organs often require the simultaneous calculation of the spatial variation of concentration of dozens of diffusing chemical species. One clinical example where rapid calculation of a diffusing field is of use is the estimation of oxygen gradients in the retina, based on imaging of the retinal vasculature, to guide surgical interventions in diabetic retinopathy. Furthermore, the ability to predict blood perfusion and oxygenation may one day guide clinical interventions in diverse settings, i.e., from stent placement in treating heart disease to BOLD fMRI interpretation in evaluating cognitive function (Xie et al., 2019; Lee et al., 2020). Since the quasi-steady-state solutions required for fast-diffusing chemical species like oxygen are particularly computationally costly, we consider the use of a neural network to provide an approximate solution to the steady-state diffusion equation. Machine learning surrogates, neural networks trained to provide approximate solutions to such complicated numerical problems, can often provide speed-ups of several orders of magnitude compared to direct calculation. Surrogates of PDEs could enable use of larger and more detailed models than are possible with direct calculation and can make including such simulations in real-time or near-real time workflows practical. Creating a surrogate requires running the direct calculation tens of thousands of times to generate training data and then training the neural network, both of which are computationally expensive. Often the practical applications of such models require thousands to millions of replica simulations, for example for parameter identification and uncertainty quantification, each of which gains speed from surrogate use and rapidly recovers the up-front costs of surrogate generation. We use a Convolutional Neural Network to approximate the stationary solution to the diffusion equation in the case of two equal-diameter, circular, constant-value sources located at random positions in a two-dimensional square domain with absorbing boundary conditions. Such a configuration caricatures the chemical concentration field of a fast-diffusing species like oxygen in a tissue with two parallel blood vessels in a cross section perpendicular to the two blood vessels. To improve convergence during training, we apply a training approach that uses roll-back to reject stochastic changes to the network that increase the loss function. The trained neural network approximation is about 1000 times faster than the direct calculation for individual replicas. Because different applications will have different criteria for acceptable approximation accuracy, we discuss a variety of loss functions and accuracy estimators that can help select the best network for a particular application. We briefly discuss some of the issues we encountered with overfitting, mismapping of the field values and the geometrical conditions that lead to large absolute and relative errors in the approximate solution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A memory-distributed quasi-Newton solver for nonlinear programming problems with a small number of general constraints

We approach the problem of parallelizing state-of-the-art nonlinear programming optimization algorithms.Specifically, we focus on parallelizing quasi-Newton interior-point methods that use limited-memory secant Hessian approximations. Such interior-point methods are known to have better convergence properties and to be more effective on large-scale problems than gradient-based and derivative-free optimization algorithms. We target nonlinear and potentially nonconvex optimization problems with an arbitrary number of bound constraints and a small number of general equality and inequality constraints on the optimization variables. These problems occur for example in the form of optimal control, optimal design, and inverse problems governed by ordinary or partial differential equations, whenever they are expressed in a “reduced-space” optimization approach. We introduce and analyze the time and space complexity of a decomposition method for solving the quasi-Newton linear systems that leverages the fact that the quasi-Newton Hessian matrix has a small number of dense blocks that border a low-rank update of a diagonal matrix. This enables an efficient parallelization on memory-distributed computers of the iterations of the optimization algorithm, a state-of-the-art filter line-search interior-point algorithm by Wächter et. al. We illustrate the efficiency of the proposed method by solving structural topology optimization problems on up to 4608 cores on a parallel machine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Strong reconnection electric fields in shock-driven turbulence

Turbulent magnetic reconnection in a quasi-parallel shock under parameters relevant to the Earth's bow shock is investigated by means of a two-dimensional particle-in-cell simulation. The addressed aspects include the reconnection electric field, the reconnection rate, and the electron and the ion outflow speeds. In the shock transition region, many current sheets are generated in shock-driven turbulence, and electron-only reconnection and reconnection where both ions and electrons are involved can occur in those current sheets. The electron outflow speed in electron-only reconnection shows a positive correlation with the theoretical speed, which is close to the local electron Alfvén speed, and a strong convection electric field is generated by the large electron outflow. As a result, the reconnection electric field becomes much larger than those in the standard magnetopause or magnetotail reconnection. In shock-driven reconnection that involves ion dynamics, both electron outflows and ion outflows can reach of the order of 10 times the Alfvén speed in the X-line rest frame, leading to a reconnection electric field the same order as that in electron-only reconnection. An electron-only reconnection event observed by the magnetospheric multiscale mission downstream of a quasi-parallel shock is qualitatively similar to those in the simulation and shows that the outflow speed reaches approximately half the local electron Alfvén speed, supporting the simulation prediction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A description of structured waves in shock compressed particulate composites

Dynamic compression of composite materials is of scientific interest because the mechanical mismatch between internal phases challenges continuum theories. Typical assumptions about steady wave propagation and quasi-instantaneous state changes require reexamination along with the need for time-dependent models. To that end, data and models are presented here for the shock compression of an idealized particulate composite. To serve as a generic representative of this material class, a polymer matrix was filled with tungsten particles, ranging from 1 to 50 vol. %. This creates a simple microstructure containing randomly scattered particles with an extreme impedance mismatch to the binding matrix. These materials were parallel plate impact loaded by Al flyers traveling at 1.8–5.0 km/s. Velocimetry provided records of the equilibrium state and the compression wave structure for each case with trends quantified by an empirical fit. The same quantities were also studied as a function of the wave's propagation distance. A homogenized viscoelastic model then made it possible to progress from cataloging trends to making predictions. Starting from a Mie–Gruneisen equation of state, additional time varying terms were added to capture the transient response. After calibration, accurate predictions of the steady wave structure were possible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Modeling Dynamic Helium Release as a Tracer of Rock Deformation

Here, we use helium released during mechanical deformation of shales as a signal to explore the effects of deformation and failure on material transport properties. A dynamic dual-permeability model with evolving pore and fracture networks is used to simulate gases released from shale during deformation and failure. Changes in material properties required to reproduce experimentally observed gas signals are explored. We model two different experiments of 4He flow rate measured from shale undergoing mechanical deformation, a core parallel to bedding and a core perpendicular to bedding. We also found that the helium signal is sensitive to fracture development and evolution as well as changes in the matrix transport properties. We constrain the timing and effective fracture aperture, as well as the increase in matrix porosity and permeability. Increases in matrix permeability are required to explain gas flow prior to macroscopic failure, and the short-term gas flow postfailure. Increased matrix porosity is required to match the long-term, postfailure gas flow. This model provides the first quantitative interpretation of helium release as a result of mechanical deformation. The sensitivity of this model to changes in the fracture network, as well as to matrix properties during deformation, indicates that helium release can be used as a quantitative tool to evaluate the state of stress and strain in earth materials.

58 GEOSCIENCES↗
DOE OSTI2021

Asynchronous distributed-memory task-parallel algorithm for compressible flows on unstructured 3D Eulerian grids

Here, we discuss the implementation of a finite element method, used to numerically solve the Euler equations of compressible flows, using an asynchronous runtime system (RTS). The algorithm is implemented for distributed-memory machines, using stationary unstructured 3D meshes, combining data-, and task-parallelism on top of the Charm++ RTS. Charm++’s execution model is asynchronous by default, allowing arbitrary overlap of computation and communication. Task-parallelism allows scheduling parts of an algorithm independently of, or dependent on, each other. Built-in automatic load balancing enables continuous redistribution of computational load by migration of work units based on real-time CPU load measurement. The RTS also features automatic checkpointing, fault tolerance, resilience against hardware failure, and supports power-, and energy-aware computation. We demonstrate scalability up to 25 x 10 9 cells at $\mathscr{O}$10 4 compute cores and the benefits of automatic load balancing for irregular workloads. The full source code with documentation is available at https://quinoacomputing.org.

42 ENGINEERING↗
DOE OSTI2014

A Longitudinal Evaluation of Partial Lung Irradiation in Mice by Using a Dedicated Image-Guided Small Animal Irradiator

Purpose: In lung cancer radiation therapy, the dose constraints are determined mostly by healthy lung toxicity. Preclinical microirradiators are a new tool to evaluate treatment strategies closer to clinical irradiation devices. In this study, we quantified local changes in lung density symptomatic of radiation-induced lung fibrosis (RILF) after partial lung irradiation in mice by using a precision image-guided small animal irradiator integrated with micro-computed tomography (CT) imaging. Methods and Materials: C57BL/6 adult male mice (n=76) were divided into 6 groups: a control group (0 Gy) and groups irradiated with a single fraction of 4, 8, 12, 16, or 20 Gy using 5-mm circular parallel-opposed fields targeting the upper right lung. A Monte Carlo model of the small animal irradiator was used for dose calculations. Following irradiation, all mice were imaged at regular intervals over 39 weeks (10 time points total). Nonrigid deformation was used to register the initial micro-CT scan to all subsequent scans. Results: Significant differences could be observed between the 3 highest (>10 Gy) and 3 lowest irradiation (<10 Gy) dose levels. A mean difference of 120 ± 10 HU between the 0- and 20-Gy groups was observed at week 39. RILF was found to be spatially limited to the irradiated portion of the lung. Conclusions: The data suggest that the severity of RILF in partial lung irradiation compared to large field irradiation in mice for the same dose is reduced, and therefore higher doses can be tolerated.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Flow-Through Porous Silicon Membranes for Real-Time Label-Free Biosensing

A flow-through sensing platform based on open-ended porous silicon (PSi) microcavity membranes that are compatible with integration in on-chip sensor arrays is demonstrated. Because of the high aspect ratio of PSi nanopores, the performance of closed-ended PSi sensors is limited by infiltration challenges and slow sensor responses when detecting large molecules such as proteins and nucleic acids. In order to improve molecule transport efficiency and reduce sensor response time, open-ended PSi nanopore membranes were used in a flow-through sensing scheme, allowing analyte solutions to pass through the nanopores. The molecular binding kinetics in these PSi membranes were compared through experiments and simulation with those from closed-ended PSi films of comparable thickness in a conventional flow-over sensing scheme. The flow-through PSi membrane resulted in a 6-fold improvement in sensor response time when detecting a high molecular weight analyte (streptavidin) versus in the flow-over PSi approach. As a result, this work demonstrates the possibility of integrating multiple flow-through PSi sensor membranes within parallel microarrays for rapid and multiplexed label-free biosensing.

36 MATERIALS SCIENCE↗
DOE OSTI2015

In situ study of the endotaxial growth of hexagonal CoSi{sub 2} nanoplatelets in Si(001)

This investigation aims at studying–by in situ grazing-incidence small-angle x-ray scattering–the process of growth of hexagonal CoSi{sub 2} nanoplatelets endotaxially buried in a Si(001) wafer. The early formation of spherical Co nanoparticles with bimodal size distribution in the deposited silica thin film during a pretreatment at 500 °C and their subsequent growth at 700 °C were also characterized. Isothermal annealing at 700 °C promotes a drastic reduction in the number of the smallest Co nanoparticles and a continuous decrease in their volume fraction in the silica thin film. At the same time, Co atoms diffuse across the SiO{sub 2}/Si(001) interface into the silicon wafer, react with Si, and build up thin hexagonal CoSi{sub 2} nanoplatelets, all of them with their main surfaces parallel to Si(111) crystallographic planes. The observed progressive growths in thickness and lateral size of the hexagonal CoSi{sub 2} nanoplatelets occur at the expense of the dissolution of the small Co nanoparticles that are formed during the pretreatment at 500 °C and become unstable at the annealing temperature (700 °C). The kinetics of growth of the volume fraction of hexagonal platelets is well described by the classical Avrami equation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Chebyshev polynomial filtered subspace iteration in the discontinuous Galerkin method for large-scale electronic structure calculations

The Discontinuous Galerkin (DG) electronic structure method employs an adaptive local basis (ALB) set to solve the Kohn-Sham equations of density functional theory in a discontinuous Galerkin framework. The adaptive local basis is generated on-the-fly to capture the local material physics and can systematically attain chemical accuracy with only a few tens of degrees of freedom per atom. A central issue for large-scale calculations, however, is the computation of the electron density (and subsequently, ground state properties) from the discretized Hamiltonian in an efficient and scalable manner. We show in this work how Chebyshev polynomial filtered subspace iteration (CheFSI) can be used to address this issue and push the envelope in large-scale materials simulations in a discontinuous Galerkin framework. We describe how the subspace filtering steps can be performed in an efficient and scalable manner using a two-dimensional parallelization scheme, thanks to the orthogonality of the DG basis set and block-sparse structure of the DG Hamiltonian matrix. The on-the-fly nature of the ALB functions requires additional care in carrying out the subspace iterations. We demonstrate the parallel scalability of the DG-CheFSI approach in calculations of large-scale twodimensional graphene sheets and bulk three-dimensional lithium-ion electrolyte systems. In conclusion, employing 55 296 computational cores, the time per self-consistent field iteration for a sample of the bulk 3D electrolyte containing 8586 atoms is 90 s, and the time for a graphene sheet containing 11 520 atoms is 75 s.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Fast magnetic reconnection and the ideal evolution of a magnetic field

Regardless of how small non-ideal effects may be, phenomena associated with changes in magnetic field line connections are frequently observed to occur on an Alfvénic time scale. Since it is mathematically impossible for magnetic field line connections to change when non-ideal effects are identically zero, an ideal evolution must naturally lead to states of unbound sensitivity to non-ideal effects. That such an evolution is natural is demonstrated by the use of Lagrangian coordinates based on the flow velocity of the magnetic field lines. The Lagrangian representation of an evolving magnetic field is highly constrained when neither the magnetic field strength nor the forces exerted by the magnetic field increase exponentially with time. The development of a state of fast reconnection consistent with these constraints (1) requires a three-dimensional evolution, (2) has an exponentially increasing sensitivity to non-ideal effects, and (3) has a parallel current density, which lies in exponentially thinning but exponentially widening ribbons, with a magnitude that is limited to a slow growth. The implication is that exponential growth in sensitivity is the cause of fast magnetic reconnection when non-ideal effects are sufficiently small. The growth of the non-ideal effect of the resistivity multiplied by the parallel current density is far too slow to be competitive.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Resonant x-ray absorption of strong-field-ionized CF 3 Br

We report on an experimental and theoretical study of strong-field laser ionization of CF 3 Br followed by resonant x-ray absorption at the Br K-edge. Distinct 1s -> 4p, 5p Rydberg transitions of Br q+ (q = 1-4) atomic ions are observed and identified with Hartree-Fock-Slater and relativistic configuration interaction calculations. Time-dependent density functional theory and ab initio molecular dynamics calculations were performed to simulate the dissociative ionization process and the molecular orbitals for the q = 1-4 charge states. Measurements were made with both parallel and perpendicular linear polarizations of the laser and x-rays, but dichroism was not observed, indicating negligible alignment by the laser ionization process. This result is explained by calculations on atomic Br and the molecular simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗