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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A method for achieving complete microbial genomes and improving bins from metagenomics data

Metagenomics facilitates the study of the genetic information from uncultured microbes and complex microbial communities. Assembling complete genomes from metagenomics data is difficult because most samples have high organismal complexity and strain diversity. Some studies have attempted to extract complete bacterial, archaeal, and viral genomes and often focus on species with circular genomes so they can help confirm completeness with circularity. However, less than 100 circularized bacterial and archaeal genomes have been assembled and published from metagenomics data despite the thousands of datasets that are available. Circularized genomes are important for (1) building a reference collection as scaffolds for future assemblies, (2) providing complete gene content of a genome, (3) confirming little or no contamination of a genome, (4) studying the genomic context and synteny of genes, and (5) linking protein coding genes to ribosomal RNA genes to aid metabolic inference in 16S rRNA gene sequencing studies. We developed a semi-automated method called Jorg to help circularize small bacterial, archaeal, and viral genomes using iterative assembly, binning, and read mapping. In addition, this method exposes potential misassemblies from k-mer based assemblies. We chose species of the Candidate Phyla Radiation (CPR) to focus our initial efforts because they have small genomes and are only known to have one ribosomal RNA operon. In addition to 34 circular CPR genomes, we present one circular Margulisbacteria genome, one circular Chloroflexi genome, and two circular megaphage genomes from 19 public and published datasets. We demonstrate findings that would likely be difficult without circularizing genomes, including that ribosomal genes are likely not operonic in the majority of CPR, and that some CPR harbor diverged forms of RNase P RNA. Code and a tutorial for this method is available at https://github.com/lmlui/Jorg and is available on the DOE Systems Biology KnowledgeBase as a beta app.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

TH-CD-207B-09: Task-Driven Fluence Field Modulation Design for Model-Based Iterative Reconstruction in CT

Purpose: There has been increasing interest in integrating fluence field modulation (FFM) devices with diagnostic CT scanners for dose reduction purposes. Conventional FFM strategies, however, are often either based on heuristics or the analysis of filtered-backprojection (FBP) performance. This work investigates a prospective task-driven optimization of FFM for model-based iterative reconstruction (MBIR) in order to improve imaging performance at the same total dose as conventional strategies. Methods: The task-driven optimization framework utilizes an ultra-low dose 3D scout as a patient-specific anatomical model and a mathematical formation of the imaging task. The MBIR method investigated is quadratically penalized-likelihood reconstruction. The FFM objective function uses detectability index, d’, computed as a function of the predicted spatial resolution and noise in the image. To optimize performance throughout the object, a maxi-min objective was adopted where the minimum d’ over multiple locations is maximized. To reduce the dimensionality of the problem, FFM is parameterized as a linear combination of 2D Gaussian basis functions over horizontal detector pixels and projection angles. The coefficients of these bases are found using the covariance matrix adaptation evolution strategy (CMA-ES) algorithm. The task-driven design was compared with three other strategies proposed for FBP reconstruction for a calcification cluster discrimination task in an abdomen phantom. Results: The task-driven optimization yielded FFM that was significantly different from those designed for FBP. Comparing all four strategies, the task-based design achieved the highest minimum d’ with an 8–48% improvement, consistent with the maxi-min objective. In addition, d’ was improved to a greater extent over a larger area within the entire phantom. Conclusion: Results from this investigation suggests the need to re-evaluate conventional FFM strategies for MBIR. The task-based optimization framework provides a promising approach that maximizes imaging performance under the same total dose constraint.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-EF-207A-01: Respiratory Motion Correction in 4D-Multi Pinhole Small Animal SPECT

Purpose: Respiration causes lesion smearing, image blurring and quality degradation, affecting lesion contrast and the ability to define correct lesion size. The spatial resolution of current multi pinhole SPECT (MPHS) scanners is sub-millimeter. Therefore, the effect of motion is more noticeable in comparison to conventional SPECT scanner. Gated imaging aims to reduce motion artifacts. A major issue in gating is the lack of statistics and individual reconstructed frames are noisy. The increased noise in each frame, deteriorates the quantitative accuracy of the MPHS Images. The objective of this work, is to enhance the image quality in 4D-MPHS imaging, by 4D image reconstruction. Methods: The new algorithm requires deformation vector fields (DVFs) that are calculated by non-rigid Demons registration. The algorithm is based on the motion-incorporated version of ordered subset expectation maximization (OSEM) algorithm. This iterative algorithm is capable to make full use of all projections to reconstruct each individual frame. To evaluate the performance of the proposed algorithm a simulation study was conducted. A fast ray tracing method was used to generate MPHS projections of a 4D digital mouse phantom with a small tumor in liver in eight different respiratory phases. To evaluate the 4D-OSEM algorithm potential, tumor to liver activity ratio was compared with other image reconstruction methods including 3D-MPHS and post reconstruction registered with Demons-derived DVFs. Results: Image quality of 4D-MPHS is greatly improved by the 4D-OSEM algorithm. When all projections are used to reconstruct a 3D-MPHS, motion blurring artifacts are present, leading to overestimation of the tumor size and 24% tumor contrast underestimation. This error reduced to 16% and 10% for post reconstruction registration methods and 4D-OSEM respectively. Conclusion: 4D-OSEM method can be used for motion correction in 4D-MPHS. The statistics and quantification are improved since all projection data are combined together to update the image.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

A Proof of the Asymptotic Variance of Path Length Estimators for Single-Collision Monte Carlo Source Iteration in the Thick Diffusion Limit

Here, we prove a theorem relating the variance of path length estimators for single-collision Monte Carlo source iteration to a parameter that becomes infinitesimally small in an important physical regime arising in radiative transfer. In our usage, “single-collision Monte Carlo source iteration” refers to Monte Carlo Boltzmann transport methods in which each Monte Carlo particle history includes no more than a single collision, and the physics of multiple scattering is modeled by lagging the scattering source term and iterating until this term converges. Our theorem can be used to construct variance reduction techniques which improve the order of the estimator variance. This enables calculations that would otherwise require impractically large sample sizes to achieve practical estimator uncertainties. We believe this is the first postulation of a theorem relating estimator variance to a limiting case parameter for single-collision Monte Carlo source iteration, and the first proof of such a theorem. We illustrate the theorem’s value with an example in which the authors of a transport method used the theorem to design a variance reduction technique that improved the uncertainty of their solution by a factor of about 500 for a proxy problem from radiative transfer that contains both optically-thick and optically-thin material.

Mathematics and Computing↗
DOE OSTI2023

Accelerating self-consistent field iterations in Kohn-Sham density functional theory using a low-rank approximation of the dielectric matrix

We present an efficient preconditioning technique for accelerating the fixed-point iteration in real-space Kohn-Sham density functional theory (DFT) calculations. The preconditioner uses a low-rank approximation of the dielectric matrix (LRDM) based on Gâteaux derivatives of the residual of fixed-point iteration along appropriately chosen direction functions. We develop a computationally efficient method to evaluate these Gâteaux derivatives in conjunction with the Chebyshev filtered subspace iteration procedure, an approach widely used in large-scale Kohn-Sham DFT calculations. Further, we propose a variant of LRDM preconditioner based on adaptive accumulation of low-rank approximations from previous self-consistent field iterations, and also extend the LRDM preconditioner to spin-polarized Kohn-Sham DFT calculations. We demonstrate the robustness and efficiency of the LRDM preconditioner against other widely used preconditioners on a range of benchmark systems with sizes ranging from ~100 to 1100 atoms (~500–20,000 electrons). The benchmark systems include various combinations of metal-insulating-semiconducting heterogeneous material systems, nanoparticles with localized d orbitals near the Fermi energy, nanofilm with metal dopants, and magnetic systems. In all benchmark systems, the LRDM preconditioner converges robustly within 20–30 iterations. In contrast, other widely used preconditioners show slow convergence in many cases, as well as divergence of the fixed-point iteration in some cases. Lastly, we demonstrate the computational efficiency afforded by the LRDM method, with up to 3.4-fold reduction in computational cost for the total ground-state calculation compared to other preconditioners.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Solving high-dimensional inverse problems using amortized likelihood-free inference with noisy and incomplete data

Here, we present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observations into a fixed-size vector of summary features, while the inference network generates samples of the approximate posterior distribution of the model parameters based on these summary features. The posterior samples are produced in a deep generative fashion by sampling from a latent Gaussian distribution and passing these samples through an invertible transformation. We construct this invertible transformation by sequentially alternating conditional invertible neural network and conditional neural spline flow layers. The summary and inference networks are trained simultaneously. We apply the proposed method to an inversion problem in groundwater hydrology to estimate the posterior distribution of the log-conductivity field conditioned on spatially sparse time-series observations of the system’s hydraulic head responses. The conductivity field is represented with 706 degrees of freedom in the considered problem. Comparison with the likelihood-based iterative ensemble smoother PEST-IES method demonstrates that the proposed method accurately estimates the parameter posterior distribution and the observations’ predictive posterior distribution at a fraction of the inference time of PEST-IES.

conditional invertible neural network↗
DOE OSTI2016

Recursive Factorization of the Inverse Overlap Matrix in Linear Scaling Quantum Molecular Dynamics Simulations

We present a reduced complexity algorithm to compute the inverse overlap factors required to solve the generalized eigenvalue problem in a quantum-based molecular dynamics (MD) simulation. Our method is based on the recursive iterative re nement of an initial guess Z of the inverse overlap matrix S. The initial guess of Z is obtained beforehand either by using an approximate divide and conquer technique or dynamically, propagated within an extended Lagrangian dynamics from previous MD time steps. With this formulation, we achieve long-term stability and energy conservation even under incomplete approximate iterative re nement of Z. Linear scaling performance is obtained using numerically thresholded sparse matrix algebra based on the ELLPACK-R sparse matrix data format, which also enables e cient shared memory parallelization. As we show in this article using selfconsistent density functional based tight-binding MD, our approach is faster than conventional methods based on the direct diagonalization of the overlap matrix S for systems as small as a few hundred atoms, substantially accelerating quantum-based simulations even for molecular structures of intermediate size. For a 4,158 atom water-solvated polyalanine system we nd an average speedup factor of 122 for the computation of Z in each MD step.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Comparing deflection measurements of a magnetically steerable catheter using optical imaging and MRI

Purpose: Magnetic resonance imaging (MRI) is an emerging modality for interventional radiology, giving clinicians another tool for minimally invasive image-guided interventional procedures. Difficulties associated with endovascular catheter navigation using MRI guidance led to the development of a magnetically steerable catheter. The focus of this study was to mechanically characterize deflections of two different prototypes of the magnetically steerable catheterin vitro to better understand their efficacy. Methods: A mathematical model for deflection of the magnetically steerable catheter is formulated based on the principle that at equilibrium the mechanical and magnetic torques are equal to each other. Furthermore, two different image based methods for empirically measuring the catheter deflection angle are presented. The first, referred to as the absolute tip method, measures the angle of the line that is tangential to the catheter tip. The second, referred to the base to tip method, is an approximation that is used when it is not possible to measure the angle of the tangent line. Optical images of the catheter deflection are analyzed using the absolute tip method to quantitatively validate the predicted deflections from the mathematical model. Optical images of the catheter deflection are also analyzed using the base to tip method to quantitatively determine the differences between the absolute tip and base to tip methods. Finally, the optical images are compared to MR images using the base to tip method to determine the accuracy of measuring the catheter deflection using MR. Results: The optical catheter deflection angles measured for both catheter prototypes using the absolute tip method fit very well to the mathematical model (R{sup 2} = 0.91 and 0.86 for each prototype, respectively). It was found that the angles measured using the base to tip method were consistently smaller than those measured using the absolute tip method. The deflection angles measured using optical data did not demonstrate a significant difference from the angles measured using MR image data when compared using the base to tip method. Conclusions: This study validates the theoretical description of the magnetically steerable catheter, while also giving insight into different methods and modalities for measuring the deflection angles of the prototype catheters. These results can be used to mechanically model future iterations of the design. Quantifying the difference between the different methods for measuring catheter deflection will be important when making deflection measurements in future studies. Finally, MR images can be used to reliably measure deflection angles since there is no significant difference between the MR and optical measurements.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

An interface reconstruction method based on an analytical formula for 3D arbitrary convex cells

In this study, we are interested in an interface reconstruction method for 3D arbitrary convex cells that could be used in multi-material flow simulations for instance. We assume that the interface is represented by a plane whose normal vector is known and we focus on the volume-matching step that consists in finding the plane constant so that it splits the cell according to a given volume fraction. We follow the same approach as in the recent authors' publication for 2D arbitrary convex cells in planar and axisymmetrical geometries, namely we derive an analytical formula for the volume of the specific prismatoids obtained when decomposing the cell using the planes that are parallel to the interface and passing through all the cell nodes. This formula is used to bracket the interface plane constant such that the volume-matching problem is rewritten in a single prismatoid in which the same formula is used to find the final solution. Finally, the proposed method is tested against an important number of reproducible configurations and shown to be at least five times faster.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Improving nuclear mass predictions through the Garvey-Kelson relations

We describe a procedure to improve the nuclear mass predictions obtained by means of the Garvey-Kelson iterative process. This is achieved by reducing the degrees of freedom involved in the process, expressing the Garvey-Kelson relations in terms of one-neutron and one-proton separation energies, and using a theoretical estimation for them. This approach has the effect that the intrinsic error associated with the iterative process grows significantly more slowly. This leads to a better quality of nuclear mass predictions and therefore extends the range for which the predictions are accurate. We test these ideas using different mass models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

SU-E-J-83: Ion Imaging to Better Estimate In-Vivo Relative Stopping Powers Using X-Ray CT Prior-Knowledge Information

Purpose: To reduce uncertainties in relative stopping power (RSP) estimates for ions (alpha and carbon) by using Ion radiographic-imaging and X-ray CT prior-knowledge. Methods: A 36×36 phantom matrix composed of 9 materials with different thicknesses and randomly placed is generated. Theoretical RSPs are calculated using stopping power (SP) data from three references (Janni, ICRU49 and Bischel). We introduced an artificial systematic error (1.5%, 2.5% or 3.5%) and a random error (<0.5%) to the SP to simulated patient ion-range errors present in clinic environment. Carbon/alpha final energy for each RSPs set (theoretical and from CT images) is obtained with a ray-tracing algorithm. A gradient descent (GD) method is used to minimize the difference in exit particle energy, between theory and X-ray CT RSP maps, by iteratively correcting the RSP map from X-ray CT. Once a new set of RSPs is obtained for a direction a new optimization is done over other direction using the RSPs from the previous optimization. Theoretical RSPs are compared with experimental RSPs obtained with Gammex Phantom. Results: Preliminary results show that optimized RSP values can be obtained with smaller uncertainties (<1%) than clinical RSPs (1.5% to 3.5%). Theoretical values from three different references show uncertainties, up to 3% from experimental values. Further investigation will consider prior-knowledge from RSP obtained with CT images and ion radiographies from Monte Carlo Simulations. Conclusion: GD and ray-tracing methods have been implemented to reduce RSP uncertainties from values obtained for clinical treatment. Experimental RSPs will be obtained using carbon/alpha beams to consider the existence of material dependent systematic errors. Based on the results, it is hoped to show that using ray-tracing optimization with ion radiography and prior knowledge on RPSs, treatment planning accuracy and cost-effectiveness can be improved.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

WE-G-207-06: 3D Fluoroscopic Image Generation From Patient-Specific 4DCBCT-Based Motion Models Derived From Physical Phantom and Clinical Patient Images

Purpose: Respiratory-correlated cone-beam CT (4DCBCT) images acquired immediately prior to treatment have the potential to represent patient motion patterns and anatomy during treatment, including both intra- and inter-fractional changes. We develop a method to generate patient-specific motion models based on 4DCBCT images acquired with existing clinical equipment and used to generate time varying volumetric images (3D fluoroscopic images) representing motion during treatment delivery. Methods: Motion models are derived by deformably registering each 4DCBCT phase to a reference phase, and performing principal component analysis (PCA) on the resulting displacement vector fields. 3D fluoroscopic images are estimated by optimizing the resulting PCA coefficients iteratively through comparison of the cone-beam projections simulating kV treatment imaging and digitally reconstructed radiographs generated from the motion model. Patient and physical phantom datasets are used to evaluate the method in terms of tumor localization error compared to manually defined ground truth positions. Results: 4DCBCT-based motion models were derived and used to generate 3D fluoroscopic images at treatment time. For the patient datasets, the average tumor localization error and the 95th percentile were 1.57 and 3.13 respectively in subsets of four patient datasets. For the physical phantom datasets, the average tumor localization error and the 95th percentile were 1.14 and 2.78 respectively in two datasets. 4DCBCT motion models are shown to perform well in the context of generating 3D fluoroscopic images due to their ability to reproduce anatomical changes at treatment time. Conclusion: This study showed the feasibility of deriving 4DCBCT-based motion models and using them to generate 3D fluoroscopic images at treatment time in real clinical settings. 4DCBCT-based motion models were found to account for the 3D non-rigid motion of the patient anatomy during treatment and have the potential to localize tumor and other patient anatomical structures at treatment time even when inter-fractional changes occur. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc., Palo Alto, CA. The project was also supported, in part, by Award Number R21CA156068 from the National Cancer Institute.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Beam hardening correction using iterative total variation (ITV)-based algorithm in CBCT reconstruction

Recently, beam hardening reduction is required to produce high-quality reconstructions of X-ray cone-beam computed tomography (CBCT) system for medical applications. This paper introduces the iterative total variation (ITV) for filtered-backprojection suffering from the serious beam hardening problems. Feldkamp, Davis, and Kress (FDK) reconstruction algorithm for CBCT system is widely used reconstruction technique. FDK reconstruction algorithm could be realized by generating the weighted projection data, filtering the projection images, and back-projecting the filtered projection data into the volume. However, FDK algorithm suffers from the beam hardening artifacts by X-ray attenuation coefficients. Recently, total variation (TV) method for compressed sensing (CS) has been particularly useful in exploiting the prior knowledge of minimal variation in the X-ray attenuation characteristics across object or human body. But a practical implementation of this method still remains a challenge. The main problem is the iterative nature of solving the TV-based CS formulation, which generally requires multiple iterations of forward and backward projections of a large dataset in clinically or industrially feasible time frame. In this paper, we propose ITV method after FDK reconstruction for reducing the beam hardening artifacts. The beam hardening problems are reduced by the ITV method to promote sparsity inherent in the X-ray attenuation characteristics. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Uncertainty propagation and sensitivity analysis for constrained optimization of nuclear waste vitrification

Abstract The vitrification of high‐level waste (HLW) by heating a mixture of glass‐forming chemicals (GFCs) with the waste can be improved using a constrained optimization problem. This study explores how different uncertainty propagation (UP) methods implemented with the optimization process can affect the glass formulation of nuclear waste glasses. UP is the effort of propagating uncertain inputs through a system to understand and quantify output distributions. Uncertainty intervals are crafted from output distributions to inform the optimization algorithm. UP is often implemented with Monte Carlo (MC) sampling for large nonlinear systems, which can be difficult to implement within a constrained optimization algorithm that requires derivative information. Other UP methods often used for optimization under uncertainty (OUU) can be designed to work within an established constrained optimization framework. Methods of UP are evaluated in this study including iterative sampling approaches, first‐order approximations, and surrogate modeling with machine learning (ML). A method of dimensional reduction based on global sensitivity analysis is introduced to support the UP methods for the large dimensionality of the problem. Analytical UP methods able to achieve similar optimums 10 times faster than the baseline MC approach, and produce 93.9% similar output distributions are reported.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

A 2D/1D coupling neutron transport method based on the matrix MOC and NEM methods

A new 2D/1D coupling method based on the matrix MOC method (MMOC) and nodal expansion method (NEM) is proposed for solving the three-dimensional heterogeneous neutron transport problem. The MMOC method, used for radial two-dimensional calculation, constructs a response matrix between source and flux with only one sweep and then solves the linear system by using the restarted GMRES algorithm instead of the traditional trajectory sweeping process during within-group iteration for angular flux update. Long characteristics are generated by using the customization of commercial software AutoCAD. A one-dimensional diffusion calculation is carried out in the axial direction by employing the NEM method. The 2D and ID solutions are coupled through the transverse leakage items. The 3D CMFD method is used to ensure the global neutron balance and adjust the different convergence properties of the radial and axial solvers. A computational code is developed based on these theories. Two benchmarks are calculated to verify the coupling method and the code. It is observed that the corresponding numerical results agree well with references, which indicates that the new method is capable of solving the 3D heterogeneous neutron transport problem directly. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Well-Posedness of the Iterative Boltzmann Inversion

The iterative Boltzmann inversion is a fixed point iteration to determine an effective pair potential for an ensemble of identical particles in thermal equilibrium from the corresponding radial distribution function. Although the method is reported to work reasonably well in practice, it still lacks a rigorous convergence analysis. In this paper we provide some first steps towards such an analysis, and we show under quite general assumptions that the associated fixed point operator is Lipschitz continuous (in fact, differentiable) in a suitable neighborhood of the true pair potential, assuming that such a potential exists. In other words, the iterative Boltzmann inversion is well-defined in the sense that if the kth iterate of the scheme is sufficiently close to the true pair potential then the k+1st iterate is an admissible pair potential, which again belongs to the domain of the fixed point operator. On our way we establish important properties of the cavity distribution function and provide a proof of a statement formulated by Groeneveld concerning the rate of decay at infinity of the Ursell function associated with a Lennard-Jones type potential.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Causality-respecting adaptive refinement for PINNs: enabling precise interface evolution in phase field modeling

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving physical systems described by partial differential equations (PDEs). However, their accuracy in dynamical systems, particularly those involving sharp moving boundaries with complex initial morphologies, remains a challenge. Here, this study introduces an approach combining residual-based adaptive refinement (RBAR) with causality-informed training to enhance the performance of PINNs in solving spatio-temporal PDEs. Our method employs a three-step iterative process: initial causality-based training, RBAR-guided domain refinement, and subsequent causality training on the refined mesh. Applied to the Allen-Cahn equation, a widely-used model in phase field simulations, our approach demonstrates significant improvements in solution accuracy and computational efficiency over traditional PINNs. Notably, we observe an ‘overshoot and relocate’ phenomenon in dynamic cases with complex morphologies, showcasing the method’s adaptive error correction capabilities. This synergistic interaction between RBAR and causality training enables accurate capture of interface evolution, even in challenging scenarios where traditional PINNs fail. Our framework not only resolves the limitations of uniform refinement strategies but also provides a generalizable methodology for solving a broad range of spatio-temporal PDEs. The enhanced performance of the RBAR–causality combined framework demonstrates its strong potential for advancing PINN-based modeling of physical systems characterized by complex, evolving interfaces.

Allen-Cahn equations↗
DOE OSTI2015

High-performance finite-difference time-domain simulations of C-Mod and ITER RF antennas

Finite-difference time-domain methods have, in recent years, developed powerful capabilities for modeling realistic ICRF behavior in fusion plasmas [1, 2, 3, 4]. When coupled with the power of modern high-performance computing platforms, such techniques allow the behavior of antenna near and far fields, and the flow of RF power, to be studied in realistic experimental scenarios at previously inaccessible levels of resolution. In this talk, we present results and 3D animations from high-performance FDTD simulations on the Titan Cray XK7 supercomputer, modeling both Alcator C-Mod’s field-aligned ICRF antenna and the ITER antenna module. Much of this work focuses on scans over edge density, and tailored edge density profiles, to study dispersion and the physics of slow wave excitation in the immediate vicinity of the antenna hardware and SOL. An understanding of the role of the lower-hybrid resonance in low-density scenarios is emerging, and possible implications of this for the NSTX launcher and power balance are also discussed. In addition, we discuss ongoing work centered on using these simulations to estimate sputtering and impurity production, as driven by the self-consistent sheath potentials at antenna surfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗