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At least 1,639 records · Page 91

Recent Progress on Numerical Modeling for Microgravity Electric Field Flames: Preprint

This paper presents the recent progress on the NASA Physical Science Informatics (PSI) project PeleLM CFD of Ion Driven Winds from Diffusion Flames in simulating the E-FIELD Flames microgravity results. The focus of this project is to comprehensively simulate the behavior of a small diffusion flame under the influence of an externally applied electric field in zero-gravity. To date, the capability of accurately simulating electric field flames has eluded researchers because the system exhibits dramatic ranges of coupled temporal and spatial scales. Moreover, in earth gravity the hot combustion products are subject to buoyancy effects that are difficult to isolate from those generated by the electric field. This work is an implementation in an existing powerful simulation framework (PeleLM) for this problem using PeleLMeX in order to validate the model and investigate the complex coupled system. The current effort includes establishing the domain of a diffusion coflow burner, examining the boundary conditions, flame geometry and ignition with gravitational forces and also with an electric field applied. A detailed model is used that includes the chemistry of charged ions and chemiluminescent flame intermediates to capture any feedback between ion-driven convection and combustion behavior, and to allow quantitative comparisons with experimental measurements.

coflow flames↗

Scale-Dependent Fracture-Matrix Interactions And Their Impact on Radionuclide Transport - Final Report

Matrix diffusion and adsorption within a rock matrix are widely regarded as important mechanisms for retarding the transport of radionuclides and other solutes in fractured rock (e.g., Neretnieks, 1980; Tang et al., 1981; Maloszewski and Zuber, 1985; Novakowski and Lapcevic, 1994; Jardine et al., 1999; Zhou and Xie, 2003; Reimus et al., 2003a,b). When remediation options are being evaluated for old sources of contamination, where a large fraction of contaminants reside within the rock matrix, slow diffusion out of the matrix greatly increases the difficulty and timeframe of remediation. Estimating the rates of solute exchange between fractures and the adjacent rock matrix is a critical factor in quantifying immobilization and/or remobilization of DOE-relevant contaminants within the subsurface. In principle, the most rigorous approach to modeling solute transport with fracture-matrix interaction would be based on local-scale coupled advection-diffusion/dispersion equations for the rock matrix and in discrete fractures that comprise the fracture network (Discrete Fracture Network and Matrix approach, hereinafter referred to as DFNM approach), fully resolving aperture variability in fractures and matrix property heterogeneity. However, such approaches are computationally demanding, and thus, many predictive models rely upon simplified models. These models typically idealize fracture rock masses as a single fracture or system of parallel fractures interacting with slabs of porous matrix or as a mobile-immobile or multi-rate mass transfer system. These idealizations provide tractable approaches for interpreting tracer tests and predicting contaminant mobility, but rely upon a fitted effective matrix diffusivity or mass-transfer coefficients. However, because these fitted parameters are based upon simplified conceptual models, their effectiveness at predicting long-term transport processes remains uncertain. Evidence of scale dependence of effective matrix diffusion coefficients obtained from tracer tests highlights this point and suggests that the underlying mechanisms and relationship between rock and fracture properties are not fully understood in large complex fracture networks. In this project, we developed a high-resolution DFN model of solute transport in fracture networks to explore and quantify the mechanisms that control transport in complex fracture networks and how these may give rise to observed scale-dependent matrix diffusion coefficients. Results demonstrate that small scale heterogeneity in the flow field caused by local aperture variability within individual fractures can lead to long-tailed breakthrough curves indicative of matrix diffusion, even in the absence of interactions with the fracture matrix. Furthermore, the temporal and spatial scale dependence of these processes highlights the inability of short-term tracer tests to estimate transport parameters that will control long-term fate and transport of contaminants in fractured aquifers.

58 GEOSCIENCES↗

Graph-Based Modeling and Decomposition of Hierarchical Optimization Problems

We present a graph-theoretic modeling approach for hierarchical optimization that leverages the OptiGraph abstraction implemented in the Julia package Plasmo.jl. We show that the abstraction is flexible and can effectively capture complex hierarchical connectivity that arises from decision-making over multiple spatial and temporal scales (e.g., integration of planning, scheduling, and operations in manufacturing and infrastructures). We also show that the graph abstraction facilitates the conceptualization and implementation of decomposition and approximation schemes. Specifically, we propose a graph-based Benders decomposition (gBD) framework that enables the exploitation of hierarchical (nested) structures and that uses graph aggregation/partitioning procedures to discover such structures. In addition, we provide a Julia implementation of gBD, which we call PlasmoBenders.jl. We illustrate the capabilities using examples arising in the context of energy and power systems.

97 MATHEMATICS AND COMPUTING↗

A fully implicit, asymptotic-preserving, semi-Lagrangian algorithm for the time dependent anisotropic heat transport equation

In this paper, we extend the operator-split asymptotic-preserving, semi-Lagrangian algorithm for time dependent anisotropic heat transport equation proposed in Chacón et al. (2014) [18] to use a fully implicit time integration with backward differentiation formulas. The proposed implicit method can deal with arbitrary heat-transport anisotropy ratios $\mathcal{X}$∥ /$ \mathcal{X}$⟂ $\ggg$ 1 (with $\mathcal{X}$∥, $ \mathcal{X}$⟂ the parallel and perpendicular heat diffusivities, respectively) in complicated magnetic field topologies in an accurate and efficient manner. Further, the implicit algorithm is second-order accurate temporally and demonstrates an accurate treatment at boundary layers (e.g., island separatrices), which was not ensured by the operator-split implementation. The condition number of the resulting algebraic system is independent of the anisotropy ratio, and is inverted with preconditioned GMRES. We propose a simple preconditioner that renders the finite-dimensional linear operator compact, resulting in mesh-independent convergence rates for topologically simple magnetic fields, and convergence rates scaling as ~ (NΔt) 1/4 (with N the total mesh size and Δt the timestep) in topologically complex magnetic-field configurations. We demonstrate the accuracy and performance of the approach with test problems of varying complexity, including an analytically tractable boundary-layer problem in a straight magnetic field, and a topologically complex magnetic field featuring magnetic islands with extreme anisotropy ratios $\mathcal{X}$∥ /$ \mathcal{X}$⟂ = 10 10 ) .

97 MATHEMATICS AND COMPUTING↗

Implications of Model Structure and Detail for Utility Planning: Scenario Case Studies Using the Resource Planning Model

In this report, we analyze the impacts of model configuration and detail in capacity expansion models, computational tools used by utility planners looking to find the least cost option for planning the system and by researchers or policy makers attempting to understand the effects of various policy implementations. The present analysis focuses on the importance of model configurations — particularly those related to capacity credit, dispatch modeling, and transmission modeling — to the construction of scenario futures. Our analysis is primarily directed toward advanced tools used for utility planning and is focused on those impacts that are most relevant to decisions with respect to future renewable capacity deployment. To serve this purpose, we develop and employ the NREL Resource Planning Model to conduct a case study analysis that explores 12 separate capacity expansion scenarios of the Western Interconnection through 2030.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Fracture Mechanics—Theory, Modeling and Applications

The field of fracture mechanics was developed during the throes of World War II, and since then, it has been a very active area of research. This is a very energetic and vibrant field because fracture processes are relevant for a vast range of engineering applications spanning different temporal and length scales. Depending on the application, material fracture and fragmentation may be either a desirable feature (for example, for the purpose of hydraulic fracturing operations), or something that is to be avoided at all costs (for example, in key components of mechanical systems). In some other instances, fractures are already present and must be accommodated through novel design (for example, in the case of earthquake ruptures occurring in the Earth’s crust). Because of this wide spectra of motivation, there is a continuous need for the fracture mechanics research community to gain more insight on how fractures are created, evolve, and interact with themselves and with material microstructures. In addition, there are still many remaining questions about how fracture propagation, growth, and interaction impact overall material behavior, and how to develop accurate models to predict this behavior.

36 MATERIALS SCIENCE↗

Uncertainty quantification of graph convolution neural network models of evolving processes

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural networks have proved to be adept at modeling processes with spatial–temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial–temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar pushed forward uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance. As a result, projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape.

36 MATERIALS SCIENCE↗

Robust Spectral Unmixing of Sparse Multispectral Lidar Waveforms using Gamma Markov Random Fields

Here, this paper presents a new Bayesian spectral un-mixing algorithm to analyse remote scenes sensed via sparse multispectral Lidar measurements. To a first approximation, in the presence of a target, each Lidar waveform consists of a main peak, whose position depends on the target distance and whose amplitude depends on the wavelength of the laser source considered (i.e, on the target reflectivity). Besides, these temporal responses are usually assumed to be corrupted by Poisson noise in the low photon count regime. When considering multiple wavelengths, it becomes possible to use spectral information in order to identify and quantify the main materials in the scene, in addition to estimation of the Lidar-based range profiles. Due to its anomaly detection capability, the proposed hierarchical Bayesian model, coupled with an efficient Markov chain Monte Carlo algorithm, allows robust estimation of depth images together with abundance and outlier maps associated with the observed 3D scene. The proposed methodology is illustrated via experiments conducted with real multispectral Lidar data acquired in a controlled environment. The results demonstrate the possibility to unmix spectral responses constructed from extremely sparse photon counts (less than 10 photons per pixel and band).

97 MATHEMATICS AND COMPUTING↗

gPhoton: THE GALEX PHOTON DATA ARCHIVE

gPhoton is a new database product and software package that enables analysis of GALEX ultraviolet data at the photon level. The project’s stand-alone, pure-Python calibration pipeline reproduces the functionality of the original mission pipeline to reduce raw spacecraft data to lists of time-tagged, sky-projected photons, which are then hosted in a publicly available database by the Mikulski Archive at Space Telescope. This database contains approximately 130 terabytes of data describing approximately 1.1 trillion sky-projected events with a timestamp resolution of five milliseconds. A handful of Python and command-line modules serve as a front end to interact with the database and to generate calibrated light curves and images from the photon-level data at user-defined temporal and spatial scales. The gPhoton software and source code are in active development and publicly available under a permissive license. We describe the motivation, design, and implementation of the calibration pipeline, database, and tools, with emphasis on divergence from prior work, as well as challenges created by the large data volume. We summarize the astrometric and photometric performance of gPhoton relative to the original mission pipeline. For a brief example of short time-domain science capabilities enabled by gPhoton, we show new flares from the known M-dwarf flare star CR Draconis. The gPhoton software has permanent object identifiers with the ASCL (ascl:1603.004) and DOI (doi:10.17909/T9CC7G). This paper describes the software as of version v1.27.2.

79 ASTRONOMY AND ASTROPHYSICS↗

A Fast Solver for Implicit Integration of the Vlasov--Poisson System in the Eulerian Framework

In this paper, we present a domain decomposition algorithm to accelerate the solution of Eulerian-type discretizations of the linear, steady-state Vlasov equation. The steady-state solver then forms a key component in the implementation of fully implicit or nearly fully implicit temporal integrators for the nonlinear Vlasov--Poisson system. The solver relies on a particular decomposition of phase space that enables the use of sweeping techniques commonly used in radiation transport applications. The original linear system for the phase space unknowns is then replaced by a smaller linear system involving only unknowns on the boundary between subdomains, which can then be solved efficiently with Krylov methods such as GMRES. Steady-state solves are combined to form an implicit Runge--Kutta time integrator, and the Vlasov equation is coupled self-consistently to the Poisson equation via a linearized procedure or a nonlinear fixed-point method for the electric field. Finally, numerical results for standard test problems demonstrate the efficiency of the domain decomposition approach when compared to the direct application of an iterative solver to the original linear system.

97 MATHEMATICS AND COMPUTING↗

Quantification of sauter mean diameter in diesel sprays using scattering-absorption extinction measurements

Quantitative measurements of the primary breakup process in diesel sprays are lacking due to a range of experimental and diagnostic challenges, including: high droplet number density environments, very small characteristic drop size scales (~1-10 μm), and high characteristic velocities in the primary breakup region (~600 m/s). Due to these challenges, existing measurement techniques have failed to resolve a sufficient range of the temporal and spatial scales involved and much remains unknown about the primary atomization process in practical diesel sprays. To gain a better insight into this process, we have developed a joint visible and x-ray extinction measurement technique to quantify axial and radial distributions of the path-integrated Sauter Mean Diameter (SMD) and Liquid Volume Fraction (LVF) for diesel-like sprays. This technique enables measurement of the SMD in regions of moderate droplet number density, enabling construction of the temporal history of drop size development within practical diesel sprays. The experimental campaign was conducted jointly at the Georgia Institute of Technology and Argonne National Laboratory using the Engine Combustion Network “Spray D” injector. X-ray radiography liquid absorption measurements, conducted at the Advanced Photon Source at Argonne, quantify the liquid-fuel mass and volume distribution in the spray. Diffused back-illumination liquid scattering measurements were conducted at Georgia Tech to quantify the optical thickness throughout the spray. By application of Mie-scatter equations, the ratio of the absorption and scattering extinction measurements is demonstrated to yield solutions for the SMD. This work introduces the newly developed scattering-absorption measurement technique and highlights the important considerations that must be taken into account when jointly processing these measurements to extract the SMD. These considerations include co-alignment of measurements taken at different institutions, identification of viable regions where the measurement ratio can be accurately interpreted, and uncertainty analysis in the measurement ratio and resulting SMD. Because the measurement technique provides the spatial history of the SMD development, it is expected to be especially informative to the diesel spray modeling community. Results from this work will aid in understanding the effect of ambient densities and injection pressures on primary breakup and help assess the appropriateness of spray submodels for engine computational fluid dynamics codes.

33 ADVANCED PROPULSION SYSTEMS↗

Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach

Abstract. Atmospheric aerosols have important impacts on air quality and the Earth–atmospheric energy balance. However, as computing power is limited, Earth system models generally use coarse spatial grids and parameterize finer-scale atmospheric processes. These parameterizations and the simulation of atmospheric aerosols are often sensitive to model horizontal resolutions. Understanding the sensitivities is necessary for the development of Earth system models at higher resolutions with the deployment of more powerful supercomputers. Using the Energy Exascale Earth System Model (E3SM) version 1, this study investigates the impact of horizontal grid spacing on the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols (ERFaer) over the contiguous United States. We examine the resolution sensitivity by comparing the nudged simulation results for 2016 from the low-resolution model (LR) and the regional refinement model (RRM). As expected, the simulated emissions of natural dust, sea salt, and marine organic matter are substantially higher in the RRM than in the LR. In addition, RRM simulates stronger aqueous-phase production of sulfate through the enhanced oxidation of sulfur dioxide by hydrogen peroxide due to increased cloud liquid water content. In contrast, the gas-phase chemical production of sulfate is slightly suppressed. The RRM resolves more large-scale precipitation and produces less convective precipitation than the LR, leading to increased (decreased) aerosol wet scavenging by large-scale (convective) precipitation. Regarding aerosol effects on clouds, RRM produces larger temporal variabilities in the large-scale liquid cloud fractions than LR, resulting in increased microphysical cloud processing of aerosols (more interstitial aerosols are converted to cloud-borne aerosols via aerosol activation) in RRM. Water vapor condensation is also enhanced in RRM compared to LR. Consequently, the RRM simulation produces more cloud droplets, a larger cloud droplet radius, a higher liquid water path, and a larger cloud optical depth than the LR simulation. A comparison of the present-day and pre-industrial simulations indicates that, for this contiguous United States domain, the higher-resolution increases ERFaer at the top of the model by about 12 %, which is mainly attributed to the strengthened indirect effect associated with aerosol–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗

DONKEY: A Flexible and Accurate Algorithm for Clustering

We propose an accurate clustering algorithm suitable for the varied and multidimensional data sets that correspond to temporal snapshots from on-the-fly nonadiabatic trajectory-based simulations of photoexcited dynamics. The algorithm approximates the underlying probability density function using variable kernel density estimation, with local maxima corresponding to cluster centers. Each data point is then assigned to one of the maxima by employing a maximization procedure. Finally, clusters artificially separated by minor fluctuations in the probability density are merged. The algorithm does not require parameter tuning, which ensures flexibility and reduces the risk of bias. It is tested on several synthetic data sets, where it consistently outperforms conventional clustering algorithms. As a final example, the algorithm is applied to the excited dynamics of the norbornadiene ⇌ quadricyclane (C 7 H 8 ) molecular photoswitch, demonstrating how distinct reaction pathways can be identified.

algorithms↗

Multidimensional Simulations of Core Convection

The cores of main sequence intermediate- and high-mass stars are convective. Mixing at the radiative–convective boundary, waves excited by the convection, and magnetic fields generated by convective dynamos all influence the main sequence and post-main sequence evolution of these stars. These effects must be understood to accurately model the structure and evolution of intermediate- and high-mass stars. Unfortunately, there are many challenges in simulating core convection due to the wide range of temporal and spatial scales, as well as many important physics effects. In this review, we describe the latest numerical strategies to address these challenges. We then describe the latest state-of-the-art simulations of core convection, summarizing their main findings. These simulations have led to important insights into many of the processes associated with core convection. Two outstanding problems with multidimensional simulations are, 1. it is not always straightforward to extrapolate from simulation parameters to the parameters of real stars; and 2. simulations using different methods sometimes appear to arrive at contradictory results. To address these issues, next generation simulations of core convection must address how their results depend on stellar luminosity, dimensionality, and turbulence intensity. Furthermore, code comparison projects will be essential to establish robust parameterizations that will become the new standard in stellar modeling.

79 ASTRONOMY AND ASTROPHYSICS↗

In-operando synchronous time-multiplexed O K-edge x-ray absorption spectromicroscopy of functioning tantalum oxide memristors

Memristors are receiving keen interest because of their potential varied applications and promising large-scale information storage capabilities. Tantalum oxide is a memristive material that has shown promise for high-performance nonvolatile computer memory. The microphysics has been elusive because of the small scale and subtle physical changes that accompany conductance switching. In this study, we probed the atomic composition, local chemistry, and electronic structure of functioning tantalum oxide memristors through spatially mapped O K-edge x-ray absorption. We developed a time-multiplexed spectromicroscopy technique to enhance the weak and possibly localized oxide modifications with spatial and spectral resolutions of <30 nm and 70 meV, respectively. During the initial stages of conductance switching of a micrometer sized crosspoint device, the spectral changes were uniform within the spatial resolution of our technique. When the device was further driven with millions of high voltage-pulse cycles, we observed lateral motion and separation of ∼100 nm-scale agglomerates of both oxygen interstitials and vacancies. We also demonstrate a unique capability of this technique by identifying the relaxation behavior in the material during electrical stimuli by identifying electric field driven changes with varying pulse widths. In addition, we show that changes to the material can be localized to a spatial region by modifying its topography or uniformity, as against spatially uniform changes observed here during memristive switching. The goal of this report is to introduce the capability of time-multiplexed x-ray spectromicroscopy in studying weak-signal transitions in inhomogeneous media through the example of the operation and temporal evolution of a memristor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Greenhouse Gas Source Attribution: Measurements Modeling and Uncertainty Quantification

In this project we have developed atmospheric measurement capabilities and a suite of atmospheric modeling and analysis tools that are well suited for verifying emissions of green- house gases (GHGs) on an urban-through-regional scale. We have for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate atmospheric CO 2 . This will allow for the examination of regional-scale transport and distribution of CO 2 along with air pollutants traditionally studied using CMAQ at relatively high spatial and temporal resolution with the goal of leveraging emissions verification efforts for both air quality and climate. We have developed a bias-enhanced Bayesian inference approach that can remedy the well-known problem of transport model errors in atmospheric CO 2 inversions. We have tested the approach using data and model outputs from the TransCom3 global CO 2 inversion comparison project. We have also performed two prototyping studies on inversion approaches in the generalized convection-diffusion context. One of these studies employed Polynomial Chaos Expansion to accelerate the evaluation of a regional transport model and enable efficient Markov Chain Monte Carlo sampling of the posterior for Bayesian inference. The other approach uses de- terministic inversion of a convection-diffusion-reaction system in the presence of uncertainty. These approaches should, in principle, be applicable to realistic atmospheric problems with moderate adaptation. We outline a regional greenhouse gas source inference system that integrates (1) two ap- proaches of atmospheric dispersion simulation and (2) a class of Bayesian inference and un- certainty quantification algorithms. We use two different and complementary approaches to simulate atmospheric dispersion. Specifically, we use a Eulerian chemical transport model CMAQ and a Lagrangian Particle Dispersion Model - FLEXPART-WRF. These two models share the same WRF assimilated meteorology fields, making it possible to perform a hybrid simulation, in which the Eulerian model (CMAQ) can be used to compute the initial condi- tion needed by the Lagrangian model, while the source-receptor relationships for a large state vector can be efficiently computed using the Lagrangian model in its backward mode. In ad- dition, CMAQ has a complete treatment of atmospheric chemistry of a suite of traditional air pollutants, many of which could help attribute GHGs from different sources. The inference of emissions sources using atmospheric observations is cast as a Bayesian model calibration problem, which is solved using a variety of Bayesian techniques, such as the bias-enhanced Bayesian inference algorithm, which accounts for the intrinsic model deficiency, Polynomial Chaos Expansion to accelerate model evaluation and Markov Chain Monte Carlo sampling, and Karhunen-Lo %60 eve (KL) Expansion to reduce the dimensionality of the state space. We have established an atmospheric measurement site in Livermore, CA and are collect- ing continuous measurements of CO 2 , CH 4 and other species that are typically co-emitted with these GHGs. Measurements of co-emitted species can assist in attributing the GHGs to different emissions sectors. Automatic calibrations using traceable standards are performed routinely for the gas-phase measurements. We are also collecting standard meteorological data at the Livermore site as well as planetary boundary height measurements using a ceilometer. The location of the measurement site is well suited to sample air transported between the San Francisco Bay area and the California Central Valley.

54 ENVIRONMENTAL SCIENCES↗

Extreme Scale Plasma Turbulence Simulations on Top Supercomputers Worldwide

The goal of the extreme scale plasma turbulence studies described in this paper is to expedite the delivery of reliable predictions on confinement physics in large magnetic fusion systems by using world-class supercomputers to carry out simulations with unprecedented resolution and temporal duration. This has involved architecture-dependent optimizations of performance scaling and addressing code portability and energy issues, with the metrics for multi-platform comparisons being 'time-to-solution' and 'energy-to-solution'. Realistic results addressing how confinement losses caused by plasma turbulence scale from present-day devices to the much larger $25 billion international ITER fusion facility have been enabled by innovative advances in the GTC-P code including (i) implementation of one-sided communication from MPI 3.0 standard; (ii) creative optimization techniques on Xeon Phi processors; and (iii) development of a novel performance model for the key kernels of the PIC code. Our results show that modeling data movement is sufficient to predict performance on modern supercomputer platforms.

97 MATHEMATICS AND COMPUTING↗

SU-E-T-300: Spatial Variations of Multiple Off-Axial Targets for a Single Isocenter SRS Treatment Plan in ExacTrac 6D Robotic Couch System

Purpose: To evaluate the spatial variations of multiple off-axial targets for a single isocenter stereotactic radiosurgery (SRS) treatment plan in ExacTrac 6D robotic couch system (BrainLab AG). Methods: Five metallic ball bearing (BB) markers were placed sparsely in 3D off-axial locations (non-coplanar) inside a skull phantom as the representatives of multiple targets mimicking multiple brain metastases. The locations of the BB markers were carefully chosen to minimize overlapping of each other in a port imaging detector plane. The skull phantom was immobilized by a frameless mask and CT scanned with a BrainLab Head and Neck Localizer using a GE Optima MDCT scanner. The CT images were exported to iPlan software (BrainLab AG) and a multiple target PTV was drawn by combining all the contours of the BBs. The margin of the MLC opening was selected as 3 mm expansion outward. Two coplanar arc beams were placed to generate a single isocenter SRS plan to treat the PTV. The arc beams were delivered using Novalis Tx system with portal imaging acquisition mode per 10% temporal resolution. The locations of the BBs were visualized and analyzed with respect to the MLC aperture in the treatment plan similar to the Winston-Lutz test. Results: All the BBs were clearly identified inside the MLC openings. The positional errors for the BBs were overall less than 1 mm along the rotational path of the two arcs. Conclusion: This study verified that the spatial deviations of multiple off-axial targets for a single isocenter SRS treatment plan is within sub-millimeter range in ExacTrac 6D robotic couch system. Accompanied with the Winston-Lutz test, this test will quality-assure the spatial accuracies of the isocenter as well as the positions of multiple off-axial targets for the SRS treatment using a single isocenter multiple target treatment plan.

60 APPLIED LIFE SCIENCES↗