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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.

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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A contextual sensor system for non-intrusive machine status and energy monitoring

Event-driven contexts in manufacturing occur pervasively as a result of interactions among involved entities such as machines, workers, materials, and environment. One of the primary tasks in smart manufacturing is to derive a context-aware system conveniently incorporating worker knowledge for generating timely actionable intelligence for workers on factory floor and supervisors to respond. In this paper, we propose to design a human-and-machine interaction recognition framework by using a causality concept to collect contextual data for classifications of normal and abnormal machine operations. The causes and effects are between workers and machines for this initial research. To apply the causality to recognize worker interactions, initially a reliable way to identify the states of machines is necessary. The proposed contextual sensor system, consisting of a power meter for measuring machine operation conditions, a visual camera for capturing worker and machine interactions via a finite state machine model, and an algorithm for determining power signatures of individual components via energy disaggregation is implemented on semiconductor fabrication machines (manual or PLC controlled) each with multiple components. The experiment results demonstrate its context extraction capability such as components states and their corresponding energy usage in real time as well as its ability to identify anomalous operation conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Mechanical failure of fresh nuclear grade iron–chromium–aluminum (FeCrAl) cladding under simulated hot zero power reactivity initiated accident conditions

The reactivity initiated accident (RIA) is a postulated accident in light water reactors initiated by a rapid increase in reactivity which causes an increase in fission rate and fuel temperature. One potential mode of fuel system failure during RIA is pellet cladding mechanical interaction (PCMI) due to the rapid thermal expansion of the fuel pellet. Simulated PCMI experiments were performed by rapidly pressurizing iron-chromium-aluminum (FeCrAl) cladding tube samples using a hydraulic modified burst test system, causing the specimens to burst under a biaxial loading path. Deformation and rupture of the specimens were tracked with a telecentric lens and high-speed camera system. Outer surface strains were calculated using digital image correlation (DIC) on speckle patterns painted on the specimens’ outer surfaces. Experiments were conducted at approximately 275 °C, representative of hot zero power RIA conditions. The failure hoop strain was DIC-calculated between approximately 1.8–3.4%, corresponding to quasi-static energy depositions of approximately 110–260 calories per gram UO 2 assuming initial pellet-cladding contact. These conditions are very similar to the proposed energy deposition limit of 150 calories per gram UO 2 for unirradiated zirconium-based cladding in US Nuclear Regulatory Commission (NRC) Draft Regulatory Guide 1327. A very small strain rate dependence was observed in the data, with the magnitude of the failure hoop strain decreasing slightly with increasing strain rate. This observed dependence may be relevant because the FeCrAl cladding strain rate will be approximately 20% higher than in zirconium-based cladding due to the harder neutron spectrum and resultant shorter neutron generation time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

BISON validation of FeCrAl cladding mechanical failure during simulated reactivity-initiated accident conditions*

Here, a reactivity-initiated accident (RIA) is a postulated design basis accident in light water reactors (LWRs) in which a rapid reactivity insertion induces a fission rate increase and a fuel pellet temperature rise. During RIA, the fuel pellet thermally expands and may cause pellet-cladding mechanical interaction (PCMI). Separate effects PCMI tests were performed on C26M FeCrAl cladding tube samples, introducing biaxial stress via a well understood modified burst test (MBT) system. A high-speed camera in the MBT system captured the projections, covering a 360° view of the cladding deformation and enabling a digital image correlation (DIC) method to quantify the surface strains with high fidelity. Representative hot zero-power RIA mechanical loading conditions were applied to the sample, and the test duration ranged from 20 to 500 ms at an average temperature of 573 K. BISON finite element–based fuel performance code modeling was performed against the high-fidelity DIC data produced from the MBTs. Validation calculations were conducted with 2D models and systematically compared with test data of the burst time, burst pressure, burst hoop strains, and hoop strain rates. Based on the behaviors from the separate effects test, BISON calculations satisfactorily predicted the cladding deformation behaviors. Sensitivity analysis was conducted to identify highly influential mechanical properties responsible for the cladding failure behavior during the MBT experiments. The results highlight the significance of the cladding's mechanical strength in governing cladding strain, followed by the significance of the mechanical interactions between the pellet and the cladding.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

In-situ determination of strain during transient burst testing and the temperature dependence of Zircaloy-4 claddings

Understanding fuel system behavior during postulated loss-of-coolant accidents is pertinent for continued safe and efficient operation of light water reactors, particularly as higher burnups are being pursued and safety margins re-evaluated. Conventional mechanical models for the incumbent Zr alloys typically rely on the assumption that steady-state creep is the dominant fuel cladding response during transient accident conditions. To investigate this assumption, simulated accident burst testing was performed on Zircaloy-4 claddings with balloon behavior measured in-situ. Here, two distinct loading conditions were utilized during burst testing: (1) constant-gas-inventory where pressure was allowed to increase with temperature and (2) constant pressure. In-situ strains and strain rates were measured via 2-dimensional digital image correlation techniques and synchronized with temperature to determine deformation dependencies. The temperature dependence of strain rate was characterized by a two segment Arrhenius relationship, with a distinct transition between the high and low temperature/strain regimes. The average activation energy of the lower temperature/strain regime was 328 ± 25 kJ/mol, in agreement with the ~320 kJ/mol used for conventional LOCA models. However, the higher temperature/strain segment, which encompassed most of ballooning, showed increased activation energies as well as a dependence on whether the burst region was in view. For tests that burst away from the camera view, the average high temperature/strain segment activation energy was 635 ± 150 kJ/mol. For samples where the rupture opening formed in view, the average activation energy was 1015 ± 179 kJ/mol. This observed shift in temperature dependence indicates a transition in deformation mechanism at the end of life, possibly to time independent failure mechanisms, which has not yet been visualized in the literature for Zr alloys. Parameters at the transition points were analyzed to determine thresholds for this change in behavior, which occurred at an average hoop strain of 6.9 ± 2.1 %.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Identifying common stored product insects using automated deep learning methods

Monitoring stored product insect pests is a common practice for post-harvest management of stored grain and grain-based commodities, which helps ensure product quality from harvest to final consumer. Current methods of sampling and monitoring can be time-consuming, labor-intensive, expensive and require expertise in insect identification. Therefore, this study aims to develop an image-based automated identification system for common stored product insect species using deep-learning methods. Top-down images of the common stored product adult insect species of Rhyzopertha dominica, Cryptolestes ferrugineus, Tribolium castaneum, Sitophilus oryzae, and Oryzaephilus surinamensis were acquired and analyzed. Deep learning-based, state-of-the-art Convolutional Neural Networks (CNN) models (ResNet-50, MobileNet-v2, DarkNet-53, and EfficientNet-b0) were fine-tuned with a transfer learning approach to classify the insect species. All models were able to correctly identify the insect species with at least 96% accuracy and with few misclassifications. One issue with trained CNNs is that they do not explain the reasoning for the classification and are often called a “black box”. Therefore, visualization methods called Gradient-weighted Class Activation Mapping (Grad-CAM) were implemented to explore the black box network. The Grad-CAM uses heat maps to highlight the major image features that the network focused on to make insect species predictions. The Grad-CAM verifies the network's prediction and also helps improve network performance. This study contributes to the overall goal of developing a camera-based system for monitoring stored grain insects. As a result, the developed system would empower warehouse, flour mills, and other food facilities with a tool to quickly and accurately identify insect species in stored product environments and could be implemented as part of a close to real-time monitoring system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing

Finding actionable trends in laser-based metal additive manufacturing process monitoring data is challenging owing to the diversity and complexity of the underlying physical interactions. A single monitoring solution that captures a particular process phenomenon, such as a photodiode that tracks melt pool intensity, is not alone capable of evaluating process stability or detecting flaw formation with sufficient precision for routine application in industry. In this work, to improve flaw detection performance, we adopted a data fusion approach that captures multiple process phenomena. To demonstrate this, we acquired data from laser powder bed fusion (LPBF) builds of cylindrical specimens produced with different laser spot sizes, emulating defocusing due to process faults such as thermal lensing. The resulting specimens had porosity of varying types and severity, quantified by post-build non-destructive X-ray computed tomography, Archimedes density measurements, and destructive metallographic characterization. During the build, the melt pool state was monitored with two coaxial high-speed video cameras and a temperature field imaging system. Physically intuitive low-level melt pool signatures, such as melt pool temperature, shape and size, and spatter intensity were extracted from this high-dimensional, image-based sensor data. These process signatures were subsequently used as input features in relatively simple machine learning models, such as a support vector machine, which were trained to detect laser defocusing, and in addition, predict porosity type and severity. The results show that the data fusion approach significantly enhanced system performance by reducing the overall false positive rate from ~ 0.1 to ~ 0.001 without sacrificing the true positive rate (~0.90). These results were at par with a black-box, deep machine learning approach (convolutional neural network).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fluorescence strobo-stereoscopy for specular reflection-suppressed full field of view imaging

This report introduces fluorescence strobo-stereoscopy (FSS) to suppress strong specular reflection and enable the full field of view (FFOV) 3D surface imaging while the part is rotating. Specular reflection off the target surface significantly degrades the image quality and becomes critical for highly reflective surface measurements. In FSS, the fluorescent dye-doped fluid applied on the machined surface is excited upon incident ultra-violet light and becomes a new light source by Stokes' Law. Thus, specular reflection off of smooth surface can be suppressed by separating the fluorescent light from the excitation light. The developed FSS comprises a pair of imaging cameras, spatial filters, and an excitation light source. As a result, FSS effectively rejected the specular reflection and improved the FFOV 3D surface image quality of the machined part by enhancing contrast in the rotating target surface. Such enhancements in 3D imaging allowed to identify manufacturing tolerance of the part and to detect the surface features. The axial and lateral accuracy errors of FSS were 2.3% and 1.4% with the target size of 4.07 mm and 0.215 mm, respectively. A whole view reconstruction of the cylindrical target sample was performed, and the corresponding cylindricity and diameter deviation were assessed. The fluid media effect and the target surface quality effect were discussed.

3D surface imaging↗
DOE OSTI2021

Initial tests of large format sensors for the ATLAS ITk strip tracker

For the construction of the Inner Tracker (ITk) as part of the phase-II upgrade programme of the ATLAS detector for the High-Luminosity (HL) LHC, batches of Long Strip (LS) and Short Strip (SS) n + -in-p type micro-strip sensors have been produced by Hamamatsu Photonics and Infineon. The full size sensors measure approximately 98 × 98 mm 2 and are designed and engineered for tolerance against the 9.7 × 10 14 1 MeV n eq /cm 2 fluence expected at the HL-LHC, including a safety factor of 1.5. Each sensor has 2 or 4 columns of 1280 individual channels arranged at 75.5 μ m horizontal pitch. To ensure the sensors comply with their specifications, a Quality Control (QC) procedure has been implemented, comprising measurements on every individual sensor as well as on a sample basis. Every sensor is subjected to an initial visual inspection, after which the full surface of the sensor is captured with very high resolution by an automated camera setup. Non-contact metrology is performed to obtain the sensor surface profile. Electrical measurements establishing the reverse bias leakage current and depletion voltage are then conducted automatically. Sample sensors from every batch are subjected to 40 h of leakage stability checks in controlled atmosphere, and tests on every channel measuring leakage current, coupling capacitance and bias resistance are done. The recorded results are uploaded to a production database following data quality checks. In this paper, QC test validation data and the compiled results for the first batches of production grade sensors are presented. The QC protocol was validated, and the first production sensors were confirmed to be within specification. The results are compared to those from the previous generation of prototype sensors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Characterization of a collimated neutron imager for low-rate fast neutron imaging

Spatial localization of special nuclear materials (SNM) via their neutron signatures amidst background requires knowledge of the background neutron environment or a means of separating a source from background based on low amounts of information. This requirement has created the need for characterizing the spatial distribution of the cosmogenic neutron background. Neutron scatter cameras have been developed and optimized for rapid detection of high activity sources, but have low imaging efficiency, making it difficult to use them to characterize low rate diffuse sources, such as the neutron background. The Low Intensity Neutron Imaging System (LINIS) is a collimated neutron imager that has been designed and optimized for imaging diffuse cosmogenic neutron background in the energy range of 0.5–15 MeV. LINIS operates using 16 liquid scintillation detectors shielded by ultra-high molecular weight polyethylene cylindrical collimators in a staggered orientation and rotates to 7 discrete positions, giving it roughly 2π sensitivity. Finally, LINIS has been characterized using (α, n) and fission neutron sources using two imaging techniques for neutron source localization, simple backprojection and Maximum Likelihood Expectation Maximization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Reconstruction of fast neutron direction in segmented organic detectors using deep learning

A method for reconstructing the direction of a fast neutron source using a segmented organic scintillator-based detector and deep learning model is proposed and analyzed. Here, the model is based on recurrent neural network, which can be trained by a sequence of data obtained from an event recorded in the detector and suitably pre-processed. The performance of deep learning-based model is compared with the conventional double-scatter detection algorithm in reconstructing the direction of a fast neutron source. With the deep learning model, the uncertainty in source direction of 0.301 rad is achieved with 100 neutron detection events in a segmented cubic organic scintillator detector with a side length of 46 mm. To reconstruct the source direction with the same angular resolution as the double-scatter algorithm, the deep learning method requires 75% fewer events. Application of this method could augment the operation of segmented detectors operated in the neutron scatter camera configuration for applications such as special nuclear material detection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Contextually aware roadside radiation measurement testbed

Here we demonstrate a contextually aware multimodal roadside radiation measurement detection testbed for traffic monitoring applications in nuclear nonproliferation. Many variables in traffic such as vehicle or cargo size, mass, speed, shape, and distance of closest approach can have significant impacts on the radiation measured from a vehicle-transported radiation source. These factors can lead to uncertainties in the analysis of the radiation source, especially for lower-strength radiation sources of interest. Our testbed, known as the Multimodal Measurement System (MMS) uses non-radiation sensors including magnetometers, geophones, radiofrequency receivers, cameras, and LiDAR to extract contextual information about vehicles passing by the system. These contextual data can then be fused with data from radiation measurements to increase the system’s sensitivity and accuracy in nuclear threat detection applications. This work describes the instrumentation of the MMS and its data acquisition pipeline. Furthermore, we describe the pre-analysis performed on the raw multimodal data streams for data fusion, and the high-level machine learning analyses for detection and characterization. The variety of sensors within the MMS provides a valuable testbed that can be used to identify the combinations of contextual sensors that provide the greatest improvements to radiation source detection and characterization within the restrictions for various proliferation detection applications. The MMS is also modular so that additional combinations of sensors can be explored in the future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Measurement of charge state distributions using a scintillation screen

Absolute cross sections measured using electromagnetic devices to separate and detect heavy recoiling ions need to be corrected for charge state fractions. Accurate prediction of charge state distributions using theoretical models is not always a possibility, especially in energy and mass regions where data is sparse. As such, it is often necessary to measure charge state fractions directly. In this paper we present a novel method of using a scintillation screen along with a CMOS camera to image the charge dispersed beam after a set of magnetic dipoles. A measurement of the charge state distribution for 88 Sr passing through a natural carbon foil is performed. Using a Bayesian model to extract statistically meaningful uncertainties from these images, we find agreement between the new method and a more traditional method using Faraday cups. Additional future work is need to better understand systematic uncertainties. Our technique offers a viable method to measure charge state distributions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Innovative approaches to neutron beam optimization: A case study of FP14 at LANSCE

This paper describes an experimental method to acquire high resolution energy- and spatially- resolved neutron beam spots using the time-gated neutron imaging system with Teledyne Pi-MAX4 camera. These experimental data offer a unique opportunity for benchmarking beam spot simulations. High-quality simulations depend significantly on a high-fidelity geometry model, which can be challenging for legacy facilities. We informed our MCNPX geometry model by latest metrology survey employing Leica laser tracker ATS600. It gave us a high fidelity description of our facility geometry. Such a robust integration of novel tools and methods yields a previously unattainable level of accuracy in both predicting and capturing neutron beam spots, marking a notable advancement over traditional methods reliant on static image plates. Here, to demonstrate the practical application of these tools, we are showing a non-uniform beam spot challenge at our Flight Path 14 (FP14) at the Los Alamos Neutron Science Center (LANSCE). Our precise MCNPX prediction of beam spot shifting as function of neutron energy was confirmed by experimental beam spot measured with extremely high level of detail. Results of this research demonstrate a significant leap in neutron beam optimization at LANSCE and set a new benchmark in beam spot characterization. The advanced methods presented here have potential for adoption at similar research facilities worldwide, aiming at substantial improvement in neutron beam delivery for experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Informing solar blind radioluminescence imaging through a calibrated spectrum

While direct radiation detection methods offer great insight into the origin of ionizing particles and photons, their use to locate contaminated areas or concealed radioactive sources can lead to undue exposure of personnel and equipment to ionizing radiation or the potential for contamination. These same sources induce ultraviolet (UV) optical photon fluorescence in air – a process referred to as radioluminescence – that may be imaged from low dose regions over larger attenuation lengths than ionizing radiation. However, most optical detection methods are limited to low lighting conditions to image the more abundant ultraviolet-A (UV-A) photons. To extend this capability to room light or daytime conditions, the solar blind region (Ultraviolet-C (UV-C), <280 nm) can be tapped. Though the emission yield of UV-C photons is roughly two orders of magnitude lower than that in the UV-A regime, the UV-C offers dramatic improvements in signal-to-noise ratios under bright lighting conditions due to decreased background interferences. The yield of specific UV-C lines, if present in the literature at all, varies widely, which has a large impact in modeling and analyzing standoff UV-C measurement scenarios. Thus, we have captured improved radioluminescence spectra over 250–400 nm and identified observed emission peaks in ambient air. Many of the UV-C photons produced by ionizing radiation excitation result from high-energy, molecular nitrogen Gaydon-Herman transitions which have had limited study to date for this application. Relating these findings to published UV-A yields, we estimate emissions between 0.15–0.19 photons/MeV over 250–280 nm. Additionally, we also use a commercial corona-discharge imaging camera to demonstrate outdoor UV-C radiation mapping of alpha and gamma emitters from 50 and 75 m standoffs, respectively. The imaged “counts” are compared to optically modelled values and show the same trend over distance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Portable Distortion Free Large Solid Angle Coverage Detector

Neutron single crystal diffractometers require large solid angle coverage for optimum performance. This can be achieved by tiling flat detectors in a cylindrical or spherical geometry, but results in large gaps in detector coverage and parallax distortion. The detector edges exhibit degraded resolution, distortion, and gamma rejection. Since the detector edge regions are a significant fraction of the detector active area, they must be removed from the experimental data set, requiring extra beam time to collect enough analysis data. A spherical detector with a continuous surface would effectively address this issue while eliminating most boundary ‘dead’ areas. Here we report on the development of a novel hemispherical shaped neutron detector using seamlessly tiled readout modules to form the desired shape. The heart of the detector is a specially developed curved neutron scintillator coupled to high resolution silicon photomultiplier (SiPM) Anger cameras via custom made fiber optic tapers (FOTs). The detector has been assembled and initial tests have been conducted at the High Flux Isotope Reactor (HFIR) beamlines at Oak Ridge National Laboratory (ORNL). Here, in this work, we describe details of the scintillator design, fabrication and characterization, evaluation of individual detector modules, the details of the detector design implementation, and evaluation of the assembled detector at ORNL beamlines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Full-torus impurity transport simulation in boron powder injection experiments in the Large Helical Device

The toroidal distribution of boron deposition on plasma-facing components (PFCs) in boron powder injection using an impurity power dropper (IPD) was investigated by full-torus simulation and observations in a systematic plasma density-scan experiment. The images of the ablation of dropped boron powders observed with a visible CCD camera were consistently explained by the simulations of the ablation positions of the boron powders considering the size distribution. Simulations assuming full-torus boron deposition on the PFCs did not reproduce the observed intensity profile of boron emission lines for higher plasma densities. It indicated that the density of boron deposited on PFCs installed toroidally far from the IPD was low for higher plasma densities due to the change in the ablation positions of the boron powders toward the outboard side. The experimental results verified the previous full-torus simulation of the toroidal distribution of the boron deposition in both lower and higher plasma densities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Particle control via cryopumping and its impact on the edge plasma profiles of Alcator C-Mod

At the high n e proposed for high-field fusion reactors, it is uncertain whether ionization, as opposed to plasma transport, will be most influential in determining n e at the pedestal and separatrix. A database of Alcator C-Mod discharges is analyzed to evaluate the impact of source modification via cryopumping. The database contains similarly-shaped H-modes at fixed I P = 0.8 MA and B t = 5.4 T, spanning a large range in P net and ionization. Measurements from an edge Thomson scattering system are combined with those from a midplane-viewing Ly α camera to evaluate changes to n e and T e in response to changes in ionization rates, S ion ∙ $n^{sep}_e$ and $T^{ped}_e$ are found to be most sensitive to changes to $S^{sep}_{ion}$, as opposed to $n^{ped}_e$ and $T^{sep}_e$. Dimensionless quantities, namely α MHD and v*, are found to regulate attainable pedestal values. Select discharges at different values of P net and in different pumping configurations are analyzed further using SOLPS-ITER. It is determined that changes to plasma transport coefficients are required to self-consistently model both plasma and neutral edge dynamics. Pumping is found to modify the poloidal distribution of atomic neutral density, n 0 , along the separatrix, increasing n 0 at the active X-point. Opaqueness to neutrals from high n e in the divertor is found to play a role in mediating neutral penetration lengths and hence, the poloidal distribution of neutrals along the separatrix. Pumped discharges thus require a larger particle diffusion coefficient than that inferred purely from 1D experimental profiles at the outer midplane.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗