Search NASA⌕ Search

SEARCH · Search NASA

Results for “core optimization”

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 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Twin-core sunflower-type photonic quasicrystal fibers incorporated gold, silver, and copper microwire: an ultrashort and broad bandwidth polarization splitter

Polarization splitters are beneficial in the field of passive optical devices which are principally used in the coherent optical communication systems. An ideal polarization splitter must possess an efficient coupling with significant optical bandwidth. Hence, we proposed a novel metal-filled polarization splitter based on twin-core sunflower-type photonic quasicrystal fiber (PQF). The characteristics of the proposed splitter such as coupling length, extinction ratio, and available optical bandwidth were investigated by using the commercially available software, COMSOL Multiphysics. These aforementioned characteristics were determined by optimizing the geometrical parameters of proposed PQF—first by using gold-filled PQF (AuPQF). With optimized geometrical parameters, the splitter achieved an ultra-short coupling length of 132 µm and an ultra-broad bandwidth of 301 nm (wavelength range 1.36–1.68 µm) with an extinction ratio below than − 20 dB which comprised E + S+C + L+U band. Secondly, a comparative study to use silver- and copper-filled PQF (AgPQF and CuPQF) was explored. Both AgPQF and CuPQF retained a short length with adequate bandwidth and a significant extinction ratio below than − 20 dB. The AgPQF has a coupling length of 137 µm with a bandwidth of 136 nm which operated at 1.44 µm wavelength, while CuPQF with 134 µm lengths with a bandwidth of 149 nm and operated at 1.50 µm wavelengths.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Plasma Radiation Source on the Basis of the Gas Puff with Outer Plasma Shell in the Circuit of a Mega-Ampere Load Current Doubler

Characteristics of Z-pinch plasma radiation in the form of a double shell neon gas puff with outer plasma shell are investigated in the microsecond implosion mode. Experiments are performed using a GIT-12 mega-joule generator with load current doubler having a ferromagnetic core at implosion currents up to 5 MA. Conditions for matching of the nonlinear load with the mega-ampere current multiplier circuit are determined. The load parameters (plasma shell characteristics and mass and geometry of gas puff shells) are optimized on the energy supplied to the gas puff and n energy characteristics of radiation. It is established that the best modes of K-shell radiation in neon are realized for such radial distribution of the gas-puff material at which the compression velocity of the shell is close to a constant and amounts to 27–30 cm/μs. In these modes, up to 40% of energy supplied to the gas puff is converted into K-shell radiation. The reasons limiting the efficiency of the radiation source with increasing implosion current are analyzed. A modernized version of the energy supply from the current doubler to the Z-pinch is proposed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

Basic data of ions in He-air mixtures for fluid modeling of low temperature plasma jets

The basic ion data such as interaction potential parameters, elastic and inelastic collision cross sections, transport coefficients (reduced mobility and diffusion coefficients) and reaction coefficients have been analysed and determined for the case of He{sup +}, N{sub 2}{sup +}, and O{sub 2}{sup +} in He-dry air mixtures. The ion transport and reaction coefficients have been determined from an optimized Monte Carlo simulation using calculated elastic and experimentally fitted inelastic collision cross sections. The elastic momentum transfer cross sections have been calculated from a semi-classical JWKB (Jeffreys Wentzel Kramers Brillouin) approximation based on a (6-4) rigid core interaction potential model. The inelastic cross sections have been fitted using the measured reaction coefficients, such as, for instance, the non resonant charge transfer coefficients. The cross section sets involving elastic and inelastic processes were then validated using either the measured reduced mobility whenever available in the literature or the zero-field mobility calculated from Satoh's relation, and potential parameters available in the literature. From the sets of elastic and inelastic collision cross sections thus obtained for the first time for He{sup +}/N{sub 2}, He{sup +}/O{sub 2}, N{sub 2}{sup +}/He, and O{sub 2}{sup +}/He systems, the ion transport and reaction coefficients were calculated in the pure gases over a wide range of the density reduced electric field E/N. Then, from the present cross section and other literature sets, the ion mobility and the longitudinal and transverse diffusion coefficients were calculated for different concentrations of air in He in the case of He{sup +}, N{sub 2}{sup +}, O{sub 2}{sup +}, and also O{sup -} ions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

HAM: Hotspot-Aware Manager for Improving Communications with 3D-Stacked Memory

merging High-Performance Computing (HPC) workloads, such as graph analytics, machine learning, and big data science, are data-intensive. Data-intensive workloads usually present fine-grained memory accesses with limited or no data locality, and thus incur frequent cache misses and low utilization of memory bandwidth. 3D-stacked memory devices such as Hybrid Memory Cube (HMC) and High Bandwidth Memory (HBM) can provide significantly higher bandwidth than conventional memory modules. However, the traditional interfaces and optimization methods for JEDEC DDR devices do not allow to fully exploit the potential performance of 3D-stacked memory with the massive amount of irregular memory accesses of data-intensive applications. In this paper, we propose a novel Hotspot-Aware Manager (HAM) infrastructure for 3D-stacked memory devices capable of optimizing memory access streams via request aggregation, hotspot detection, and in-memory prefetching. %and an associated hotspot-aware page policy. We present the HAM design and implementation, and simulate it on a system using RISC-V embedded cores with attached HMC devices. We extensively evaluate HAM with over 12 benchmarks and applications representing diverse irregular memory access patterns. The results show that, on average, HAM reduces redundant requests by 37.51\% and increases the prefetch buffer hit rate by 4.2 times, compared to a baseline streaming prefetcher. On the selected benchmark set, HAM provides performance gains of 21.81\% in average (up to 34.28\%) and power savings of 35.07\% over a standard 3D-stacked memory.

Wang, Xi↗
DOE OSTI2019

Scalable Deep Learning via I/O Analysis and Optimization

Scalable deep neural network training has been gaining prominence because of the increasing importance of deep learning in a multitude of scientific and commercial domains. Consequently, a number of researchers have investigated techniques to optimize deep learning systems. Much of the prior work has focused on runtime and algorithmic enhancements to optimize the computation and communication. Despite these enhancements, however, deep learning systems still suffer from scalability limitations, particularly with respect to data I/O. This situation is especially true for training models where the computation can be effectively parallelized, leaving I/O as the major bottleneck. In fact, our analysis shows that I/O can take up to 90% of the total training time. Thus, in this article, we first analyze LMDB, the most widely used I/O subsystem of deep learning frameworks, to understand the causes of this I/O inefficiency. Based on our analysis, we propose LMDBIO—an optimized I/O plugin for scalable deep learning. LMDBIO includes six novel optimizations that together address the various shortcomings in existing I/O for deep learning. Lastly, our experimental results show that LMDBIO significantly outperforms LMDB in all cases and improves overall application performance by up to 65-fold on a 9,216-core system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

JGI-Trichoderma v1.0

There is a series of Python and bash scripts to parse genomics datasets used to evaluate the coevolution of gene families and the feature importance of gene families using an SVM classifier. - Cover analysis: takes a list of single-copy genes in a set of genomes, aligns and builds the gene trees to determine if two gene families have a signature of covariation with one another. It parses the files to run phykit cover script described here: https://jlsteenwyk.com/PhyKIT/usage/index.html - SVM-classifier: This Python script is an SVM-based genomic classifier designed for biological data analysis. It combines machine learning with feature selection to identify important genomic markers and classify biological samples. Core Functionality: The script uses Support Vector Machines from scikit-learn to classify genomic data, incorporating SelectKBest for automated feature selection and leave-one-out cross-validation for performance assessment. It operates in multiple modes: feature ranking, optimal combination discovery, and sample prediction. Primary Applications: Genomic sample classification and biomarker discovery Feature importance analysis in high-dimensional biological datasets Prediction of sample categories based on genomic profiles Research applications requiring robust classification of biological data Key Advantages: High-dimensional handling: SVMs excel with genomic data's typical high feature-to-sample ratios Integrated feature selection: Reduces noise and computational overhead while identifying key markers Probability estimation: Provides confidence scores essential for biological interpretation Validation robustness: Leave-one-out cross-validation ensures reliable performance metrics Operational flexibility: Multiple analysis modes support different research phases from exploration to prediction

Stecca Steindorff, Andrei [Lawrence Berkeley Natio↗
DOE OSTI2013

PoPLAR: Portal for Petascale Lifescience Applications and Research

Background: We are focusing specifically on fast data analysis and retrieval in bioinformatics that will have a direct impact on the quality of human health and the environment. The exponential growth of data generated in biology research, from small atoms to big ecosystems, necessitates an increasingly large computational component to perform analyses. Novel DNA sequencing technologies and complementary high-throughput approaches–such as proteomics, genomics, metabolomics, and meta-genomics–drive data-intensive bioinformatics. While individual research centers or universities could once provide for these applications, this is no longer the case. Today, only specialized national centers can deliver the level of computing resources required to meet the challenges posed by rapid data growth and the resulting computational demand. Consequently, we are developing massively parallel applications to analyze the growing flood of biological data and contribute to the rapid discovery of novel knowledge. Methods: The efforts of previous National Science Foundation (NSF) projects provided for the generation of parallel modules for widely used bioinformatics applications on the Kraken supercomputer. We have profiled and optimized the code of some of the scientific community’s most widely used desktop and small-cluster-based applications, including BLAST from the National Center for Biotechnology Information (NCBI), HMMER, and MUSCLE; scaled them to tens of thousands of cores on high-performance computing (HPC) architectures; made them robust and portable to next-generation architectures; and incorporated these parallel applications in science gateways with a web-based portal. Results: This paper will discuss the various developmental stages, challenges, and solutions involved in taking bioinformatics applications from the desktop to petascale with a front-end portal for very-large-scale data analysis in the life sciences. Conclusions: This research will help to bridge the gap between the rate of data generation and the speed at which scientists can study this data. The ability to rapidly analyze data at such a large scale is having a significant, direct impact on science achieved by collaborators who are currently using these tools on supercomputers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Comparative study of spectral broadening and few-cycle compression of Yb:KGW laser pulses in gas-filled hollow-core fibers

While industrial-grade Yb-based amplifiers have become very prevalent, their limited gain bandwidth has created a large demand for robust spectral broadening techniques that allow for few-cycle pulse compression. In this work, we perform a comparative study between several atomic and molecular gases as media for spectral broadening in a hollow-core fiber geometry. Exploiting nonlinearities such as self-phase modulation, self-steepening, and stimulated Raman scattering, we explore the extent of spectral broadening and its dependence on gas pressure, the critical power for self-focusing, and the optimal regime for few-cycle pulse compression. Using a 3-mJ, 200-fs input laser pulses, we achieve 17 fs, few-cycle pulses with 80% fiber energy transmission efficiency. The optimal parameters can be scaled for higher or lower input pulse energies with appropriate gas parameters and fiber geometry.

Shalaby, Islam (ORCID:0000000332846636)↗
DOE OSTI2020

Mirostructure Characterization of Friction Consolidated Copper-Nickel using a Machine Learning Approach: Developing Process to Microstructure Associations

Friction consolidation (FC) is a solid phase processing approach where discrete material forms such as powders, chips, nuggets, etc. are densified via shear deformation. The precursors are placed in a billet container and brought in contact with a rotating tool that applying the desirable amount of normal force. Under the combined action of the rotation and normal pressure, the discrete precursor is consolidated through porosity reduction and shear deformation. FC is increasingly being studied as an attractive approach to manufacturing fully dense parts from powder forms owing to its ability to mix, alloy and consolidate difficult-to-process precursors in minimal number of process steps. Material consolidation and deformation in shear consolidation processes have been studied extensively previously for different material combinations previously. However, despite the extensive research in this area, understanding of the mechanistic processes in pore consolidation, deformation-induced mixing and material solubility during FC is still evolving. Material development using solid phase processing approaches such as FC is often performed based on research experience/education, which can be biased. Conventional analysis and simulation tools in this area tend to be successful only when material thermodynamic pathways and microstructural evolution sequences resulting from processing are clearly defined or known. They are not as effective for emerging advanced manufacturing technologies where material evolution pathways are not well established. The ability to predict optimal process parameters based on material chemistry and bulk properties is essential to accelerate materials design and processing, as are an understanding of the relevant structure-processing-property relationships. These structure-processing-property-performance relationships are at the core of materials science research. Microstructure characterization provides the link to these four core areas, often through visualizing material microstructure using imaging techniques. However, linking microstructure image data (i.e., micrographs) to variables of interest (e.g., processing parameters, material chemistry) in a reproducible, generalizable, and quantitative manner is a significant challenge. Typically, quantitatively linking image data to processing history relies on significant domain knowledge and manual or subject matter expert (SME)-heuristic based image analysis. Such an approach to image analysis has the potential to be biased, inefficient, and difficult to replicate.

36 MATERIALS SCIENCE↗
DOE OSTI2010

Selective catalysts for the hydrogen oxidation and oxygen reduction reactions by patterning of platinum with calix[4]arene molecules.

The design of new catalysts for polymer electrolyte membrane fuel cells must be guided by two equally important fundamental principles: optimization of their catalytic behaviour as well as the long-term stability of the metal catalysts and supports in hostile electrochemical environments. The methods used to improve catalytic activity are diverse, ranging from the alloying and de-alloying of platinum to the synthesis of platinum core-shell catalysts. However, methods to improve the stability of the carbon supports and catalyst nanoparticles are limited, especially during shutdown (when hydrogen is purged from the anode by air) and startup (when air is purged from the anode by hydrogen) conditions when the cathode potential can be pushed up to 1.5 V. Under the latter conditions, stability of the cathode materials is strongly affected (carbon oxidation reaction) by the undesired oxygen reduction reaction (ORR) on the anode side. This emphasizes the importance of designing selective anode catalysts that can efficiently suppress the ORR while fully preserving the Pt-like activity for the hydrogen oxidation reaction. Here, we demonstrate that chemically modified platinum with a self-assembled monolayer of calix[4]arene molecules meets this challenging requirement.

08 HYDROGEN↗
DOE OSTI2024

SEAS Communication Engine: An Extensible, Flexible Wrapper for Co-Simulation Agents

When modeling and analyzing the power grid and other large scale systems, researchers often express scenarios as optimization problems and feed them into advanced software solvers. In order to allow multiple solvers to communicate with each other and share data from different domains, the National Renewable Energy Laboratory (NREL) and associated Department of Energy (DOE) labs have developed a software framework called the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS allows cosimulation via a collection of client libraries for different languages that can be called from the appropriate optimization software. However, these client libraries do not provide a higher level of abstraction beyond reading and writing data off of the shared HELICS bus. In this paper, we describe a new software library called the SEAS Communication Engine that exposes a higher-level API for running cosimulation problems. The SEAS Engine provides a class-based abstraction on top of the Python HELICS client, in order to allow users to implement their domain-specific cosimulations without needing to interact with core HELICS primitives. This will make adoption of HELICS and cosimulation in general easier, by exposing a simpler API. In the second part of the paper, we validate our library on a collection of different simulation examples, including the canonical IEEE 13 Bus Feeder. Lastly, we demonstrate using the SEAS Engine to directly call domain-specific code written in the Julia programming language. Our hope is that this will serve as a template for easily calling software in different programming languages via the SEAS Engine, thereby avoiding code duplication and complexity.

co-simulation↗
DOE OSTI2013

Benchmark of the No-Core Monte Carlo Shell Model in Light Nuclei

We report the benchmark comparison of the properties in light nuclei among the Monte Carlo Shell Model, Full Configuration Interaction and No Core Full Configuration approaches. For this benchmark process, we compare results for the energies of 9 states from helium-4 to carbon-12 that includes 7 ground states and 2 excited states. The JISP16 NN interaction is adopted. The Coulomb and all other interactions are neglected. The contributions of spurious center-of-mass excitation are not discussed here. The basis space is truncated by the cutoff of the single-particle basis space, N shell . We select the optimal $\hbar$ω that minimizes the energy for that state and basis space cutoff. The MCSM results are obtained by exploiting the recent development in the computation of two-body matrix elements between non-orthogonal Slater determinants and technique of the energy-variance extrapolation. All results are found to be consistent with each other to within quoted uncertainties when they could be quantified. As an exploratory attempt, we also demonstrate how to draw the intrinsic density from no-core MCSM wave functions by taking the 8 Be ground state as an example.

Physics↗
DOE OSTI2015

SU-E-T-500: Initial Implementation of GPU-Based Particle Swarm Optimization for 4D IMRT Planning in Lung SBRT

Purpose 4D-IMRT planning, combined with dynamic MLC tracking delivery, utilizes the temporal dimension as an additional degree of freedom to achieve improved OAR-sparing. The computational complexity for such optimization increases exponentially with increase in dimensionality. In order to accomplish this task in a clinically-feasible time frame, we present an initial implementation of GPU-based 4D-IMRT planning based on particle swarm optimization (PSO). Methods The target and normal structures were manually contoured on ten phases of a 4DCT scan of a NSCLC patient with a 54cm3 right-lower-lobe tumor (1.5cm motion). Corresponding ten 3D-IMRT plans were created in the Eclipse treatment planning system (Ver-13.6). A vendor-provided scripting interface was used to export 3D-dose matrices corresponding to each control point (10 phases × 9 beams × 166 control points = 14,940), which served as input to PSO. The optimization task was to iteratively adjust the weights of each control point and scale the corresponding dose matrices. In order to handle the large amount of data in GPU memory, dose matrices were sparsified and placed in contiguous memory blocks with the 14,940 weight-variables. PSO was implemented on CPU (dual-Xeon, 3.1GHz) and GPU (dual-K20 Tesla, 2496 cores, 3.52Tflops, each) platforms. NiftyReg, an open-source deformable image registration package, was used to calculate the summed dose. Results The 4D-PSO plan yielded PTV coverage comparable to the clinical ITV-based plan and significantly higher OAR-sparing, as follows: lung Dmean=33%; lung V20=27%; spinal cord Dmax=26%; esophagus Dmax=42%; heart Dmax=0%; heart Dmean=47%. The GPU-PSO processing time for 14940 variables and 7 PSO-particles was 41% that of CPU-PSO (199 vs. 488 minutes). Conclusion Truly 4D-IMRT planning can yield significant OAR dose-sparing while preserving PTV coverage. The corresponding optimization problem is large-scale, non-convex and computationally rigorous. Our initial results indicate that GPU-based PSO with further software optimization can make such planning clinically feasible. This work was supported through funding from the National Institutes of Health and Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Optimizing a Multiple Right-Hand Side Dslash Kernel for Intel Knights Corner

Lattice Quantum Chromo-dynamics (LQCD) stands unique as the only computationally tractable, non-perturbative, and model-independent quantum field theory of the strong nuclear force. LQCD achieves tractability by discretizing our familiar 4-dimensional space-time onto a hypercubic lattice. The core component of the linear solver used in LQCD is the Wilson-Dirac operator whose finite difference component is called the Dslash operator. Dslash is a nearest neighbor stencil operator summing matrix-vector multiplications over lattice points. The arithmetic intensity of a regular Dslash operator acting on a single vector in single precision is between 0.88-2.29 (depending on the tricks employed) making it memory bandwidth bound on most current architectures, including Xeon Phi. Maximizing the performance of Dslash on Xeon Phi requires deliberate exploitation of its architectural features. One key feature of Xeon Phi is its powerful vector processing unit with lengthy vector registers. There is a class of LQCD computations which aims to solve the Dirac equation for the same lattice gauge fields multiple times. In this case one can gain additional reuse of the gauge field, and potentially simplified vectorization, through applying Dslash to several vectors at the same time; often referred to as Multiple Right Hand Side (MRHS) Dslash. Previous work in this area focused on applying this approach to the Highly Improved Staggered operator, and in cases to the multi-Grid algorithm for Wilson clover fermions. Here, we present two approaches to MRHS Dslash: a vector register blocking approach and one using the software package QPhiX with a custom Code Generator for low-level intrinsics. We explore the Knights Corner instruction set, memory hierarchy, and threading capabilities. For standard lattice sizes used in testing, we observed significant speedups, with sustained performance of over 700 GFLOPS (single precision) in one instance. We achieved up to 27% of theoretical peak performance compared to a maximum of 12% obtained by the previous single RHS method in QPhiX.

Joó, Bálint↗
DOE OSTI2022

MEITNER Resource Team Modeling and Simulation Support to Holos-Quad Reactor Development (Final CRADA Report)

The main objective of this CRADA is to provide Argonne National Laboratory (ANL)’s modeling and simulation capabilities via ARPA-E's MEITNER (Modeling-Enhanced Innovations Trailblazing Nuclear Energy Reinvigoration) Resource Team (RT) arrangement to support the demonstration of the viability of HolosGen’s Holos-Quad reactor design. The Holos-Quad reactor design is an advanced reactor concept that incorporates many new design features, such as the neutron-coupled Subcritical Power Modules (SPMs) in its core design, elimination of balance of plant (BOP) by direct integration of a helium Brayton cycle power conversion system with each SPM, among many other innovative features. The demonstration of the viability of such an innovative reactor design warrants iterations of modeling and simulation and testing. The purpose of this project is to utilize ANL’s modeling and simulation capabilities in nuclear reactor analysis and power conversion system analysis to inform HolosGen and the Design Team (DT) in the design and optimization of the Holos-Quad concept. The major work scopes of this project include: to investigate conceptual designs and materials for radiation shielding to protect personnel and internal components such as turbomachinery; Assess the the helium Brayton cycle power conversion system performance in both nominal and load following conditions; Investigate power conversion components and overall system performance; Identify control strategies to enable load following; Perform simulations of HolosGen’s subscale simulator and using available test data for code validation/benchmark purpose; Perform core thermal-hydraulics, safety analysis, and structural analysis.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

StoreRush: An Application-Level Approach to Harvesting Idle Storage in a Best Effort Environment

For a production HPC system where storage devices are shared between multiple applications and managed in a best effort manner, contention is often a major problem leading to some storage devices being more loaded than others and causing a significant reduction in I/O throughput. In this paper, we describe our latest efforts StoreRush to resolve this practical issue at the application level without requiring modification to the file and storage system. The proposed scheme uses a two-level messaging system to harvest idle storage via re-routing I/O requests to a less congested storage location so that write performance is improved while limiting the impact on read by throttling re-routing if deemed too much. An analytical model is derived to guide the setup of optimal throttling factor. The proposed scheme is verified against production applications Pixie3D, XGC1 and QMCPack during production windows, which very well demonstrated the effectiveness (e.g., up to 1.8x improvement in write) and scalability of our approach (up to 131,072 cores).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

AlGaN/GaN Core-Shell Nanofin Diodes on Si Substrates for Vertical GaN Electronics

We demonstrate plasma-assisted MBE selective-area growth of GaN/AlGaN core-shell structures on Si(111) as a path to vertical GaN devices. The selective-area growth enables the full vertical and uniform core-shell fin structures to be realized in a continuous growth without breaking vacuum. TEM images and EDS mapping of the core-shell structures show well-aligned crystal structure and sharp heterointerfaces. Dislocation filtering was observed in STEM imaging of the fin structure. P-N junction diodes fabricated with GaN/AlGaN core-shell structures reveal ideality factors as low as 1.5, and the reverse-bias leakage is consistent with trap-assisted space-charge-limited conduction. While practical challenges exist, including material-related leakage, growth-related challenges and optimization, this demonstration of p-n junction by this method may provide a path to vertical Superjunction device concepts in GaN and related materials.

Xiong, Juncheng [University of Notre Dame, IN (Uni↗
DOE OSTI2017

Impurity screening behavior of the high-field side scrape-off layer in near-double-null configurations: prospect for mitigating plasma–material interactions on RF actuators and first-wall components

The impurity screening response of the high-field side (HFS) scrape-off layer (SOL) to localized nitrogen injection is investigated on Alcator C-Mod for magnetic equilibria spanning lower-single-null, double-null and upper-single-null configurations under otherwise identical plasma conditions. L-mode, EDA H-mode and I-mode discharges are investigated. HFS impurity screening is found to depend on magnetic flux balance and the direction of B × $\nabla$B relative to the most active divertor. Impurity 'plume' emission patterns indicate that both parallel and perpendicular (E × B) flows in the SOL contribute to the 'flushing' of impurities towards the active divertor, thereby affecting the overall impurity screening behavior. Despite the fact that the HFS SOL is extremely narrow in near-double-null configurations, this SOL is able to screen locally injected nitrogen at least as effectively as the low-field side (LFS) SOL—up to a factor of 10 more effective, depending on specific plasma conditions and whether the magnetic geometry produces parallel flows that work with or against E × B flows. For situations in which the E × B drift of the impurity ions opposes parallel flow toward the primary divertor, HFS impurity screening is found to be least effective. When E × B drifts assist parallel flow toward the primary divertor, HFS impurity screening is found to be very effective. These data support the idea of placing RF actuators and close-fitting wall components on the HFS of the tokamak. Finally, with this configuration, near-double-null magnetic topologies may be used for active control of plasma parameters at the antenna/plasma interface for optimal RF coupling, mitigate the generation of local impurities from plasma–material interactions and, taking advantage of favorable plasma flows and good screening properties of the HFS SOL, further minimize the impact of wall-born impurity sources on the plasma core.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗