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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

TPSAS-NF1676L-29139-DND

The Arctic has been warming at a rate outpacing globally-averaged warming by 2-3 times, a phenomenon known as "Arctic Amplification" (AA). AA is evident in observations of Arctic surface temperature over the last century as well as in model predictions. The accuracy of model predictions is crucial to determine our response to climate change, as Arctic changes have consequences for the atmospheric circulation, sea level rise, and the carbon cycle. Unfortunately, models show a wide range of possible futures and intermodel spread in projections of Arctic temperature is larger than for any other region: a 2°C global temperature increase- the limit set by the Paris Climate Agreement- would lead to an Arctic temperature change between 3-7°C according to Coupled Model Intercomparison 5 (CMIP5) models. This intermodel spread represents a key deficiency in the understanding of Arctic feedback processes and how changes in various physical parameters (e.g. sea ice cover, clouds, heat transport and surface fluxes) affect the temperature response. Using a process-oriented decomposition of the simulate Arctic surface energy budget, we attribute the largest contribution to the CMIP5 intermodel spead in projected Arctic warming to the Barents-Kara Sea region. Our results reveal that the increasing seasonal fluxing of ocean heat content is a significant part of the intermodel spread in AA and relates strongly to change in surface turbulent fluxes. Our results indicate that if we are to significantly reduce the intermodel spread in projected AA we must focus on understanding the behavior and physical processes that drive surface energy flows in the B-K sea region.

Patrick Taylor↗
NASA NTRS1978

Shear flow beneath oceanic plates - Local nonsimilarity boundary layers for olivine rheology

The principle of local similarity, which has been used to model the two-dimensional boundary layers in the oceanic upper mantle, permits calculation of the temperature, velocity, and stress fields with essentially analytic techniques. Finite difference numerical methods are hard pressed to resolve the detail required by the large variation of viscosity between the lithosphere and the asthenosphere. In this paper the local similarity approximation has been justified by quantitatively evaluating the effect of nonsimilarity due to viscous heating, nonlinear temperature- and pressure-dependent rheology, buoyancy, adiabatic cooling, etc. Nonsimilar effects produce only small modifications of the locally similar boundary layers; important geophysical observables such as surface heat flux and ocean floor topography are given to better than 10 percent by the locally similar solution. A posteriori evaluations of the terms neglected in the boundary layer simplification of the complete equations have been conducted on the locally similar temperature and velocity profiles close to the spreading ridge. The boundary layer models are valid to depths of 100 km at 3 m.y. and 10 km at 0.3 m.y.

Yuen, D. A.↗
DOE OSTI2018

Evaluation of the atmosphere–land–ocean–sea ice interface processes in the Regional Arctic System Model version 1 (RASM1) using local and globally gridded observations

The Regional Arctic System Model version 1 (RASM1) has been developed to provide high-resolution simulations of the Arctic atmosphere–ocean–sea ice–land system. Here, we provide a baseline for the capability of RASM to simulate interface processes by comparing retrospective simulations from RASM1 for 1990–2014 with the Community Earth System Model version 1 (CESM1) and the spread across three recent reanalyses. Evaluations of surface and 2 m air temperature, surface radiative and turbulent fluxes, precipitation, and snow depth in the various models and reanalyses are performed using global and regional datasets and a variety of in situ datasets, including flux towers over land, ship cruises over oceans, and a field experiment over sea ice. These evaluations reveal that RASM1 simulates precipitation that is similar to CESM1, reanalyses, and satellite gauge combined precipitation datasets over all river basins within the RASM domain. Snow depth in RASM is closer to upscaled surface observations over a flatter region than in more mountainous terrain in Alaska. The sea ice–atmosphere interface is well simulated in regards to radiation fluxes, which generally fall within observational uncertainty. RASM1 monthly mean surface temperature and radiation biases are shown to be due to biases in the simulated mean diurnal cycle. At some locations, a minimal monthly mean bias is shown to be due to the compensation of roughly equal but opposite biases between daytime and nighttime, whereas this is not the case at locations where the monthly mean bias is higher in magnitude. These biases are derived from errors in the diurnal cycle of the energy balance (radiative and turbulent flux) components. Therefore, the key to advancing the simulation of SAT and the surface energy budget would be to improve the representation of the diurnal cycle of radiative and turbulent fluxes. The development of RASM2 aims to address these biases. Still, an advantage of RASM1 is that it captures the interannual and interdecadal variability in the climate of the Arctic region, which global models like CESM cannot do.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Better Constraining Supercooled Clouds Could Reduce Projected Warming Spread

The increase of climate sensitivity to rising greenhouse gas concentrations in the coupled model intercomparison project phase 6 (CMIP6) Earth system models (ESMs) compared to CMIP5 ESMs is primarily attributed to a larger extratropical cloud response to climate change, referred to as cloud feedback. The ratio of supercooled liquid cloud water relative to all cloud water, termed liquid phase ratio (LPR), which has also notably increased in many recent ESMs, is thought to be a primary driver of the extratropical cloud feedback increase. Unlike the preponderance of previous studies that compare native model LPR directly with observations, here we evaluate LPR over three ESM generations against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) using an instrument simulator approach, which mimics instrument limitations and uses consistent cloud definitions and resolutions. We find that current coupled model intercomparison project (CMIP) ESMs collectively simulate greater LPR than previous CMIP generations and overestimate LPR compared to observations, contrary to previous findings, likely driven by past inconsistent comparisons of ESM outputs with CALIPSO observations. We further show that greater LPR in ESM present-day climate is unexpectedly correlated with a smaller extratropical cloud feedback, attributable to a decrease of cloud amount exceeding the increase of cloud optical depth. Finally, our results suggest that improving constraints on model LPR using our evaluation framework would likely reduce the spread in CMIP6 climate sensitivities, owing to its effect on extratropical cloud feedback from supercooled clouds.

greenhouse gas concentrations↗
DOE OSTI2012

THE BLANCO COSMOLOGY SURVEY: DATA ACQUISITION, PROCESSING, CALIBRATION, QUALITY DIAGNOSTICS, AND DATA RELEASE

The Blanco Cosmology Survey (BCS) is a 60 night imaging survey of {approx}80 deg{sup 2} of the southern sky located in two fields: ({alpha}, {delta}) = (5 hr, -55 Degree-Sign ) and (23 hr, -55 Degree-Sign ). The survey was carried out between 2005 and 2008 in griz bands with the Mosaic2 imager on the Blanco 4 m telescope. The primary aim of the BCS survey is to provide the data required to optically confirm and measure photometric redshifts for Sunyaev-Zel'dovich effect selected galaxy clusters from the South Pole Telescope and the Atacama Cosmology Telescope. We process and calibrate the BCS data, carrying out point-spread function-corrected model-fitting photometry for all detected objects. The median 10{sigma} galaxy (point-source) depths over the survey in griz are approximately 23.3 (23.9), 23.4 (24.0), 23.0 (23.6), and 21.3 (22.1), respectively. The astrometric accuracy relative to the USNO-B survey is {approx}45 mas. We calibrate our absolute photometry using the stellar locus in grizJ bands, and thus our absolute photometric scale derives from the Two Micron All Sky Survey, which has {approx}2% accuracy. The scatter of stars about the stellar locus indicates a systematic floor in the relative stellar photometric scatter in griz that is {approx}1.9%, {approx}2.2%, {approx}2.7%, and {approx}2.7%, respectively. A simple cut in the AstrOmatic star-galaxy classifier spread{sub m}odel produces a star sample with good spatial uniformity. We use the resulting photometric catalogs to calibrate photometric redshifts for the survey and demonstrate scatter {delta}z/(1 + z) = 0.054 with an outlier fraction {eta} < 5% to z {approx} 1. We highlight some selected science results to date and provide a full description of the released data products.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2022

Premature Deaths in Africa Due to Particulate Matter Under High and Low Warming Scenarios

Sustainable development and climate change mitigation can provide enormous public health benefits via improved air quality, especially in polluted areas. We use the latest state-of-the-art composition-climate model simulations to contrast human exposure to fine particulate matter in Africa under a “baseline” scenario with high material consumption, population growth, and warming to that projected under a sustainability scenario with lower consumption, population growth, and warming. Evaluating the mortality impacts of these exposures, we find that under the low warming scenario annual premature deaths due to PM2.5 are reduced by roughly 515,000 by 2050 relative to the high warming scenario (100,000, 175,000, 55,000, 140,000, and 45,000 in Northern, West, Central, East, and Southern Africa, respectively). This reduction rises to ∼800,000 by the 2090s, though by that time much of the difference is attributable to the projected differences in population. By contrast, during the first half of the century benefits are driven predominantly by emissions changes. Depending on the region, we find large intermodel spreads of ∼25%–50% in projected future exposures owing to different physics across the ensemble of 6 global models. The spread of projected deaths attributable to exposure to fine particulate matter, including uncertainty in the exposure-response function, are reduced in every region to ∼20%–35% by the non-linear exposure-response function. Differences between the scenarios have an even narrower spread of ∼5%–25% and are highly statistically significant in all regions for all models. These results provide valuable information for policy-makers to consider when working toward climate change mitigation and sustainable development goals.

PM2.5↗
NASA NTRS2001

Shock Formation of Slow Magnetosonic Waves in Coronal Plumes

We investigate the height of shock formation in coroner plumes for slow magnetosonic waves. The models take into account plume geometric spreading, heat conduction and radiative damping. The wave parameters as well as the spreading functions of the plumes and the base magnetic field strengths are given by empirical constraints mostly from Solar and Heliospheric Observatory/Ultraviolet Coronagraph Spectrometer (SOHO/UVCS). Our models show that shock formation occurs at low coronal heights, i.e., within 1.3 solar radius, depending on the model parameters. The shock formation is calculated using the well-established wave breaking condition given by the intersection of C+ characteristics in the space-time plane. Our models show that shock heating by slow magnetosonic waves is expected to be relevant at most heights in solar coronal plumes, although slow magnetosonic waves are most likely not a solely operating energy supply mechanism.

Cuntz, Manfred↗
NASA NTRS1995

Solid surface combustion experiment flame spread in a quiescent, microgravity environment implications of spread rate and flame structure

A unique environment in which flame spreading, a phenomenon of fundamental, scientific interest, has importance to fire safety is that of spacecraft in which the gravitational acceleration is low compared with that of the Earth, i.e., microgravity. Experiments aboard eight Space Shuttle missions between October 1990 and February 1995 were conducted using the Solid Surface Combustion Experiment (SSCE) payload apparatus in an effort to determine the mechanisms of gas-phase flame spread over solid fuel surfaces in the absence of any buoyancy induced or externally imposed oxidizer flow. The overall SSCE effort began in December of 1984. The SSCE apparatus consists of a sealed container, approximately 0.039 cu m, that is filled with a specified O2/N2 mixture at a prescribed pressure. Five of the experiments used a thin cellulosic fuel, ashless filter paper, 3 cm wide x 10 cm long, 0.00825 cm half-thickness, ignited in five different ambient conditions. Three of the experiments, the most recent, used thick polymethylmethacrylate (PMMA) samples 0.635 cm wide x 2 cm long, 0.32 cm half-thickness. Three experiments, STS 41, 40 and 43, were designed to evaluate the effect of ambient pressure on flame spread over the thin cellulosic fuel while flights STS 50 and 47 were at the same pressure as two of the earlier flights but at a lower oxygen concentration in order to evaluate the effect of ambient oxygen level on the flame spread process at microgravity. For the PMMA flights, two experiments, STS 54 and 63, were at the same pressure but different oxygen concentrations while STS 64 was at the same oxygen concentration as STS 63 but at a higher pressure. Two orthogonal views of the experiments were recorded on 16 mm cine-cameras operating at 24 frames/s. In addition to filmed images of the side view of the flames and surface view of the burning samples, solid- and gas-phase temperatures were recorded using thermocouples. The experiment is battery powered and follows an automated sequence upon activation by the Shuttle Crew. In this study we separate the SSCE data into two groups according to the fuel type: (1) thin cellulose; and (2) thick PMMA. The experimental spread rates are compared with prediction from a number of models in an effort to uncover the important physics that characterize microgravity flame spread. Both steady and unsteady solutions are employed to explore the flame evolution, especially for thick fuels. Finally, the flame structure in downward spread is compared with the microgravity flame structure and modeling results to delineate the difference between the two configurations and the influence of normal gravity.

Bundy, Matthew↗
NASA NTRS2001

Modeling and Prediction of Wildfire Hazard in Southern California, Integration of Models with Imaging Spectrometry

Large urban wildfires throughout southern California have caused billions of dollars of damage and significant loss of life over the last few decades. Rapid urban growth along the wildland interface, high fuel loads and a potential increase in the frequency of large fires due to climatic change suggest that the problem will worsen in the future. Improved fire spread prediction and reduced uncertainty in assessing fire hazard would be significant, both economically and socially. Current problems in the modeling of fire spread include the role of plant community differences, spatial heterogeneity in fuels and spatio-temporal changes in fuels. In this research, we evaluated the potential of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data for providing improved maps of wildfire fuel properties. Analysis concentrated in two areas of Southern California, the Santa Monica Mountains and Santa Barbara Front Range. Wildfire fuel information can be divided into four basic categories: fuel type, fuel load (live green and woody biomass), fuel moisture and fuel condition (live vs senesced fuels). To map fuel type, AVIRIS data were used to map vegetation species using Multiple Endmember Spectral Mixture Analysis (MESMA) and Binary Decision Trees. Green live biomass and canopy moisture were mapped using AVIRIS through analysis of the 980 nm liquid water absorption feature and compared to alternate measures of moisture and field measurements. Woody biomass was mapped using L and P band cross polarimetric data acquired in 1998 and 1999. Fuel condition was mapped using spectral mixture analysis to map green vegetation (green leaves), nonphotosynthetic vegetation (NPV; stems, wood and litter), shade and soil. Summaries describing the potential of hyperspectral and SAR data for fuel mapping are provided by Roberts et al. and Dennison et al. To utilize remotely sensed data to assess fire hazard, fuel-type maps were translated into standard fuel models accessible to the FARSITE fire spread simulator. The FARSITE model and BEHAVE are considered industry standards for fire behavior analysis. Anderson level fuels map, generated using a binary decision tree classifier are available for multiple dates in the Santa Monica Mountains and at least one date for Santa Barbara. Fuel maps that will fill in the areas between Santa Barbara and the Santa Monica Mountains study sites are in progress, as part of a NASA Regional Earth Science Application Center, the Southern California Wildfire Hazard Center. Species-level maps, were supplied to fire managing agencies (Los Angeles County Fire, California Department of Forestry). Research results were published extensively in the refereed and non-refereed literature. Educational outreach included funding of several graduate students, undergraduate intern training and an article featured in the California Alliance for Minorities Program (CAMP) Quarterly Journal.

Roberts, Dar A.↗
NASA NTRS2002

Stagnation Flow in Streamer Boundaries

It is possible for flow tubes adjacent to closed magnetic field lines on the boundaries of streamers to have spreading factors that change rapidly according to height. Mathematical models in this thin layer are prone to uncertainties. In this paper we use an analytical model of magnetically closed and adjacent open regions to calculate the spreading factor close to the closed field lines and near the flow on the open field lines. We recovered the shape of the closed field region, or helmet, and show the evolution of the helmet under increasing temperature. We also show why and when stagnation flow can occur in the area of the cusp at the top of the helmet.

Suess, S. T.↗
DOE OSTI2023

A spatially regularized detector for emergent/re-emergent disease outbreaks

Early detection of outbreaks caused by emergent pathogens, using epidemiological surveillance data i.e., daily case counts, is difficult. This is because the data tend to be noisy during the early epoch of the outbreak. In contrast, the spread-rate of the disease tends to be well-behaved, as it depends only on the mixing patterns of the population and the characteristics of the pathogen, neither of which behave erratically in space-time. In this report, we explore whether the spread-rate can be used for epidemiological surveillance, conditional on case count data. Estimating the spread-rate from case count data allows us to exploit exogenous information, e.g., incubation period distributions etc., which can considerably smooth out any erratic temporal behavior. Further, epidemiological dynamics are spatially correlated, and if case counts are available for multiple areal units e.g., counties, these correlations could potentially be used to suppress noise in the early epoch data. These exogenous information and structure are not exploited by conventional syndromic surveillance detectors to extract a well-behaved latent variable for monitoring purposes. The technical challenge lies in the estimation of the spread-rate field jointly over a collection of areal units; further, the spread-rate varies over time. We develop a method based on mean-field variational inference to approximately estimate the spread-rate field, using a Gaussian Random Field Model for spatial regularization. The method is tested on the estimation of spread-rate in the thirty-three counties of New Mexico and detect the arrival of the Fall 2020 COVID-19 wave in September 2020. We find that the method is scalable, but underestimates the uncertainty in the estimated spread-rate field. We detect the arrival of the Fall 2020 wave a week ahead of conventional syndromic surveillance algorithms, but our simplistic detection algorithm, based on simple anomaly detection, suffers from a high false positive rate, similar to conventional detectors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

A study of grout flow pattern analysis

A new disposal unit, designated as Salt Disposal Unit no. 6 (SDU6), is being designed for support of site accelerated closure goals and salt nuclear waste projections identified in the new Liquid Waste System plan. The unit is cylindrical disposal vault of 380 ft diameter and 43 ft in height, and it has about 30 million gallons of capacity. Primary objective was to develop the computational model and to perform the evaluations for the flow patterns of grout material in SDU6 as function of elevation of grout discharge port, and slurry rheology. A Bingham plastic model was basically used to represent the grout flow behavior. A two-phase modeling approach was taken to achieve the objective. This approach assumes that the air-grout interface determines the shape of the accumulation mound. The results of this study were used to develop the design guidelines for the discharge ports of the Saltstone feed materials in the SDU6 facility. The focusing areas of the modeling study are to estimate the domain size of the grout materials radially spread on the facility floor under the baseline modeling conditions, to perform the sensitivity analysis with respect to the baseline design and operating conditions such as elevation of discharge port, discharge pipe diameter, and grout properties, and to determine the changes in grout density as it is related to grout drop height. An axi-symmetric two-phase modeling method was used for computational efficiency. Based on the nominal design and operating conditions, a transient computational approach was taken to compute flow fields mainly driven by pumping inertia and natural gravity. Detailed solution methodology and analysis results are discussed here.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Investigating Powder Spreading Dynamics in Additive Manufacturing Processes by In-situ High-speed X-ray Imaging

The quality of the powder bed is known to be one of the main factors that influence the quality of the part being manufactured by powderbed- based additive manufacturing processes. For example, in the laser powder bed fusion process, it has been demonstrated that improving the powder bed density and homogeneity improves the final quality of the part. Understanding powder spreading dynamics in the powder-bed-based additive manufacturing processes is critical for designing spreading structures and spreading parameters, identifying defect formation mechanisms in the powder bed, and guiding the design of optimized feedstock powders. Extensive efforts have been devoted to understand the fundamental mechanism of the powder flow and spreading process. Various powder flow testers were used to study the flow behavior of powders used in additive manufacturing. However, the powder flow conditions in flow meters are different from those in real additive manufacturing conditions. Models have also been developed to simulate and study the spreading process. The discrete element method (DEM) is currently the main tool for computationally simulating the spreading process. However, these models are based on many physical assumptions. Visible light cameras have been used recently to conduct in-situ study of powder spreading in additive manufacturing. Due to the limited resolution and penetration depth of visible light, it is extremely difficulty to study the particle-scale powder spreading dynamics. Here, to overcome the challenges of studying powder spreading dynamics under real additive manufacturing conditions, we developed an experimental approach to study the particle-scale powder spreading process with high spatial and temporal resolution using the high-speed high-energy X-ray imaging facility at beamline 32-ID-B of the APS. Figure 1 shows an overview of the research setup. It is composed of a vacuum chamber (building environment), an automatic powder spreading system, a laser system, an X-ray source (X-ray outlet), and an X-ray detection/imaging system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Epidemic forecasting is messier than weather forecasting: The role of human behavior and internet data streams in epidemic forecast

Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease. While weather models rely on physical systems, data from satellites, and weather stations, epidemic models rely on human interactions, multiple data sources such as clinical surveillance and Internet data, and environmental or biological factors that can change the pathogen dynamics. We describe some of similarities and differences between these 2 fields and how the epidemic modeling community is rising to the challenges posed by forecasting to help anticipate and guide the mitigation of epidemics. Here, we conclude that some of the fundamental differences between these 2 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Computational Investigation of Melt Pool Process Dynamics and Pore Formation in Laser Powder Bed Fusion

In the laser powder bed fusion additive manufacturing process, the presence of porosity may result in cracks and significantly affects the part performance. A comprehensive understanding of the melt pool process dynamics and porosity evolution can help to improve build quality. In this study, a novel multi-physics computational fluid dynamics (CFD) model has been applied to investigate the fluid dynamics in melt pools and resultant pore defects. To accurately capture the melting and solidification process, major process physics, such as the surface tension, evaporation as well as laser multi-reflection, have been considered in the model. A discrete element method is utilized to model the generation of powder spreading upon build plate by additional numerical simulations. Multiple single track experiments have been performed to obtain the melt pool shape and cross-sectional dimension information. The predicted melt pool dimensions were found to have a reasonable agreement with experimental measurements, e.g., the errors are in the range of 1.3 to 10.6% for melt pool width, while they are between 1.4 and 15.9% for melt depth. Pores are captured by both CFD simulation and x-ray computed tomography measurement for the case with a laser power of 350 W and laser speed of 100 mm/s. The formation of keyholes maybe related to the melt pool front wall angle, and it is found that the front wall angle increases with the increase in laser line energy density. In addition, a larger laser power or smaller scanning speed can help to generate keyhole-induced pores; they also contribute to produce larger sized pores.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A review of the behavior of radioiodine in the subsurface at two DOE sites

The 129 I isotope has a half-life of nearly 16 million years, creating a potential risk to receptors when 129I is present in groundwater and effective remedies are needed to mitigate its potential risk in the environment. This paper not only provides an in-depth review of various processes and reactions that control the fate and transport of iodine, but also describes how this information can be used for in situ management of iodine. The majority of the iodine fate and transport studies have been conducted at the US Department of Energy (DOE) Hanford and Savannah River sites, where radioisotopes of iodine were inadvertently released into the environment during weapons production. Site-specific remediation technologies are currently being evaluated as part of the DOE cleanup mission. The sites are markedly different in terms of climate (arid vs. sub-tropical), major geochemical parameters (pH ~ 7.5 vs. 4) and mineralogy (carbonate vs. Fe/Al oxide dominated), but they share the same network of processes and reactions that control iodine fate and transport. These include adsorption to minerals and sediments, interactions with organic matter, electron transfer reactions with minerals and microbes, and precipitation. In conclusion, this process-based network serves as basis for regional scale model calculations to predict plume spreading and/or dissipation, design efficient remediation techniques and conduct contamination management activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Gyro-fluid and two-fluid theory and simulations of edge-localized-modes

This paper reports on the theoretical and simulation results of a gyro-Landau-fluid extension of the BOUT++ code, which contributes to increasing the physics understanding of edge-localized-modes (ELMs). Large ELMs with low-to-intermediate-n peeling-ballooning (P-B) modes are significantly suppressed due to finite Larmor radius (FLR) effects when the ion temperature increases. For type-I ELMs, it is found from linear simulations that retaining complete first order FLR corrections as resulting from the incomplete “gyroviscous cancellation” in Braginskii's two-fluid model is necessary to obtain good agreement with gyro-fluid results for high ion temperature cases (T{sub i}≽3 keV) when the ion density has a strong radial variation, which goes beyond the simple local model of ion diamagnetic stabilization of ideal ballooning modes. The maximum growth rate is inversely proportional to T{sub i} because the FLR effect is proportional to T{sub i}. The FLR effect is also proportional to toroidal mode number n, so for high n cases, the P-B mode is stabilized by FLR effects. Nonlinear gyro-fluid simulations show results that are similar to those from the two-fluid model, namely that the P-B modes trigger magnetic reconnection, which drives the collapse of the pedestal pressure. Due to the additional FLR-corrected nonlinear E × B convection of the ion gyro-center density, for a ballooning-dominated equilibrium the gyro-fluid model further limits the radial spreading of ELMs. In six-field two fluid simulations, the parallel thermal diffusivity is found to prevent the ELM encroachment further into core plasmas and therefore leads to steady state L-mode profiles. The simulation results show that most energy is lost via ion channel during an ELM event, followed by particle loss and electron energy loss. Because edge plasmas have significant spatial inhomogeneities and complicated boundary conditions, we have developed a fast non-Fourier method for the computation of Landau-fluid closure terms based on an accurate and tunable approximation. The accuracy and the fast computational scaling of the method have been demonstrated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Spatio-temporal evolution of the H → L back transition

Since ITER will operate close to threshold and with limited control, the H → L back transition is a topic important for machine operations as well as physics. Using a reduced mesoscale model [Miki et al., Phys. Plasmas 19, 092306 (2012)], we investigate ELM-free H → L back transition dynamics in order to isolate transport physics effects. Model studies indicate that turbulence spreading is the key process which triggers the back transition. The transition involves a feedback loop linking turbulence and profiles. The I-phase appears during the back transition following a slow power ramp down, while fast ramp-downs reveal a single burst of zonal flow during the back transition. The I-phase nucleates at the pedestal shoulder, as this is the site of the residual turbulence in H-mode. Hysteresis in the profile gradient scale length is characterized by the Nusselt number, where Nu=χ{sub i,turb}/χ{sub i,neo}. Relative hysteresis of temperature gradient vs density gradient is sensitive to the pedestal Prandtl number, where Pr{sub ped}=D{sub ped}/χ{sub i,neo}. We expect the H-mode to be somewhat more resilient in density than in temperature.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗