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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Study of Molecular Line Parameters down to Very Low Temperature

The impact of precise spectroscopic measurements on the P(9) multiplet in the v3 band of 12CH4, obtained using a newly developed cold Herriott cell coupled with a tunable diode laser spectrometer, is examined in the context of remote sensing of methane in the atmosphere. More specifically, the influence of the air-broadening temperature dependence, of line narrowing and of line-mixing effects is assessed when fitting high resolution atmospheric spectra recorded from a balloon in the solar occultation mode. This is achieved by considering residuals and retrieved methane volume mixing ratio profiles. By combining data from previous studies together with new measurements, the expression for the temperature dependence of the broadening coefficient has been revisited. It is shown that a correcting factor applied to the usual law gives better results, when considering all the available measurements from room temperature down to about 10-20 K, as compared to the usual expression of the width versus temperature. These results clearly demonstrate that collision broadening is more sensitive to long-range attractive forces when the temperature is decreased.

Mondelain, D.↗
DOE OSTI2012

Toward understanding of differences in current cloud retrievals of ARM ground-based measurements

Accurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Experimental Studies of the Transport Parameters of Warm Dense Matter

There is a need to establish fundamental properties of matter and energy under extreme physical conditions. Although high energy density physics (HEDP) research spans a wide range of plasma conditions, there is one unifying regime that is of particular importance and complexity: that of warm dense matter, the transitional state between solid state condensed matter and energetic plasmas. Most laboratory experimental conditions, including inertial confinement implosion, fall into this regime. Because all aspects of laboratory-created high-energy-density plasmas transition through the warm dense matter regime, understanding the fundamental properties to determine how matter and energy interact in this regime is an important aspect of major research efforts in HEDP. Improved understanding of warm dense matter would have significant and wide-ranging impact on HEDP science, from helping to explain wire initiation studies on the Sandia Z machine to increasing the predictive power of inertial confinement fusion modeling. The central goal or objective of our proposed research is to experimentally determine the electrical resistivity, temperature, density, and average ionization state of a variety of materials in the warm dense matter regime, without the use of theoretical calculations. Since the lack of an accurate energy of state (EOS) model is primarily due to the lack of experimental data, we propose an experimental study of the transport coefficients of warm dense matter.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1996

Parallel Newton-Krylov-Schwarz algorithms for the transonic full potential equation

We study parallel two-level overlapping Schwarz algorithms for solving nonlinear finite element problems, in particular, for the full potential equation of aerodynamics discretized in two dimensions with bilinear elements. The overall algorithm, Newton-Krylov-Schwarz (NKS), employs an inexact finite-difference Newton method and a Krylov space iterative method, with a two-level overlapping Schwarz method as a preconditioner. We demonstrate that NKS, combined with a density upwinding continuation strategy for problems with weak shocks, is robust and, economical for this class of mixed elliptic-hyperbolic nonlinear partial differential equations, with proper specification of several parameters. We study upwinding parameters, inner convergence tolerance, coarse grid density, subdomain overlap, and the level of fill-in in the incomplete factorization, and report their effect on numerical convergence rate, overall execution time, and parallel efficiency on a distributed-memory parallel computer.

Cai, Xiao-Chuan↗
DOE OSTI2020

A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet

We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past climate forcings. Triggering such a threshold response through anthropogenic greenhouse gas emissions would drive drastic and potentially fast sea level rise with important implications for coastal flood risks. Previous studies have combined information from ice sheet models and observations to calibrate model parameters. These studies have broken important new ground but have either adopted simple ice sheet models or have limited the number of parameters to allow for the use of more complex models. These limitations are largely due to the computational challenges posed by calibration as models become more computationally intensive or when the number of parameters increases. Here, we propose a method to alleviate this problem: a fast sequential Monte Carlo method that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We use simulated examples to demonstrate how our sample-based approach provides accurate approximations to the posterior distributions of the calibrated parameters. The drastic reduction in computational times enables us to provide new insights into important scientific questions, for example, the impact of Pliocene era data and prior parameter information on sea level projections. These studies would be computationally prohibitive with other computational approaches for calibration such as Markov chain Monte Carlo or emulation-based methods. We also find considerable differences in the distributions of sea level projections when we account for a larger number of uncertain parameters. For example, based on the same ice sheet model and data set, the 99th percentile of the Antarctic ice sheet contribution to sea level rise in 2300 increases from 6.5 m to 13.1 m when we increase the number of calibrated parameters from three to 11. With previous calibration methods, it would be challenging to go beyond five parameters. Here, this work provides an important next step toward improving the uncertainty quantification of complex, computationally intensive and decision-relevant models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Ultrasonic Nakagami-parameter characterization of parotid-gland injury following head-and-neck radiotherapy: A feasibility study of late toxicity

Purpose: The study aims to investigate whether Nakagami parameters—estimated from the statistical distribution of the backscattered ultrasound radio-frequency (RF) signals—could provide a means for quantitative characterization of parotid-gland injury resulting from head-and-neck radiotherapy. Methods: A preliminary clinical study was conducted with 12 postradiotherapy patients and 12 healthy volunteers. Each participant underwent one ultrasound study in which ultrasound scans were performed in the longitudinal, i.e., vertical orientation on the bilateral parotids. For the 12 patients, the mean radiation dose to the parotid glands was 37.7 ± 9.5 Gy, and the mean follow-up time was 16.3 ± 4.8 months. All enrolled patients experienced grade 1 or 2 late salivary-gland toxicity (RTOG/EORTC morbidity scale). The normal parotid glands served as the control group. The Nakagami-scaling and Nakagami-shape parameters were computed from the RF data to quantify radiation-induced parotid-gland changes. Results: Significant differences in Nakagami parameters were observed between the normal and postradiotherapy parotid glands. Compared with the control group, the Nakagami-scaling parameter of the postradiotherapy group decreased by 25.8% (p < 0.001), and the Nakagami-shape parameter decreased by 31.3% (p < 0.001). The area under the receiver operating characteristic curve was 0.85 for the Nakagami-scaling parameter and was 0.95 for the Nakagami-shape parameter, which further demonstrated the diagnostic efficiency of the Nakagami parameters. Conclusions: Nakagami parameters could be used to quantitatively measure parotid-gland injury following head-and-neck radiotherapy. Moreover, the clinical feasibility was demonstrated and this study provides meaningful preliminary data for future clinical investigation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2010

Study of the transport parameters of cloud lightning plasmas

Three spectra of cloud lightning have been acquired in Tibet (China) using a slitless grating spectrograph. The electrical conductivity, the electron thermal conductivity, and the electron thermal diffusivity of the cloud lightning, for the first time, are calculated by applying the transport theory of air plasma. In addition, we investigate the change behaviors of parameters (the temperature, the electron density, the electrical conductivity, the electron thermal conductivity, and the electron thermal diffusivity) in one of the cloud lightning channels. The result shows that these parameters decrease slightly along developing direction of the cloud lightning channel. Moreover, they represent similar sudden change behavior in tortuous positions and the branch of the cloud lightning channel.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Electro-optical and dielectric properties of CdSe quantum dots and 6CHBT liquid crystals composites

We have prepared the composites of a room temperature nematic liquid crystal namely 4-(trans-4-n-hexylcyclohexyl) isothiocyanatobenzoate (6CHBT) and Cadmium Selenide Quantum Dots (CdSe-QDs) and investigated their electro-optical and dielectric properties. Effect of dispersion of CdSe-QDs on various electro-optical and display parameters of host liquid crystalline material have been studied. Physical parameters, such as switching threshold voltage and splay elastic constant have been altered drastically for composites. Dispersion of QDs in a liquid crystals medium destabilizes nematic ordering of the host and decreases the nematic-to-isotropic transition temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Study of Shock Formation Parameters With Drive Conditions in Magnetically Accelerated Plasma Flows

We present experimental data regarding the formation of high-energy-density shocks in magnetically accelerated plasma flows using pulsed power drivers. We quantify the flow velocity and temperature of the ablated plasma using optical Thomson scattering and gated emission imaging across two different generators. We show that, regardless of the drive parameters, the plasma flows show continuous acceleration over centimeter spatial scales, in line with trends in published simulation work. When stationary targets are placed in these supersonic flows, bow-shock formation is observed at all drive parameters in a range of materials. In the higher density flow generated on the 1-MA COBRA generator at Cornell University, heating of the upstream flow ahead of the shock is observed and quantified, which is not observed at the lower density flow on the 0.2-MA Bertha driver at UC San Diego. Here, when combined with previous work on the XP generator at Cornell, we can show that these three experimental setups allow control of the effect of radiation loss and upstream absorption on the formation of the bow shock.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

SU-E-T-478: IMRT Delivery Parameter Dependence of Dose-Mass Optimization

Purpose: To compare DMH and DVH optimization sensitivity to changes in IMRT delivery parameters. Methods: Two lung and two head and neck (HN) cases were retrospectively optimized using DVH and DMH optimization. For both optimization approaches, changes to two parameters were studied: number of IMRT segments (5 and 10 per beam) and the minimum segment area (2 and 6 cm2). The number of beams, beam angles, and minimum MUs per segment were the same for both optimizations approaches for each patient. During optimization, doses to the organs at risk (OARs) were iteratively lowered until the standard deviation across the PTV was above ∼3.0%. For each patient DVH and DMH plans were normalized such that 95% of the PTV received the same dose. Plan quality was evaluated by dose indices (DIs), which represent the dose delivered to a certain anatomical structure volume. For the lung cases, DIs assessed included: 1% cord, 33% heart, both lungs 20% and 30%, and 50% esophagus. In the HN cases: 1% cord, 1% brainstem, left/right parotids 50%, 50% larynx, and 50% esophagus. Results: When increasing the number of segments, while keeping a small segment area (2cm2), the average percent change of all DIs for DVH/DMH optimizations for each patient were: −4.66/4.71, 3.21/3.46, −9.62/21.69 and −3.28/−7.62. For a large segment area (6cm2): −0.26/−1.46, −5.04/−1.92, −5.23/−2.19 and 4.12/19.63. Results from increasing segment area while keeping a small number of segments (5segments/beam) were: 1.41/7.90, 8.17/11.66, 0.09/33.58 and −4.83/−11.60 for each case. For large number of segments (10 segments/beam): 8.35/1.30, −0.91/5.77, 6.29/7.08 and 2.62/5.16. Conclusion: This preliminary study showed case dependent results. Changes in IMRT parameters did not show consistent DI changes for either optimization approach. A larger population of patients is warranted for such comparison.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Phase-field modeling for restructuring in the dark zone of high burnup UO 2

This report summarizes the mesoscale modeling work performed in fiscal year 2024 under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to capture the microstructural evolution and restructuring observed in the dark regions of high burnup UO 2 nuclear fuel. This is the first attempt to realistically simulate the restructuring behavior observed in different region of a high burnup fuel. We employ a grand-potential based phase-field model to concurrently evaluate the formation of subgrains and growth of fission bubbles within the fuel. A energy-based subgrain formation criteria is introduced to simulate the restructuring process. Effect of different initial conditions and different modeling parameters are studies systematically to capture how each of these parameters influence the characteristics of the restructured fuel. It is observed that the subgrain formation begins around existing fission gas bubbles and then proceeds towards triple junctions, grain boundaries and grain interiors. It is demonstrated that restructuring is influenced by a combination of initial dislocation densities, subgrain formation rate, and temperature. Rate of restructuring increases with increase in fuel temperature. A restructuring bias is observed within the microstructure due to variation in defect accumulation among different grains. Furthermore, bubble sizes and distribution does not have a significant effect on rate of restructuring. The predicted microstructures resembles the characteristics of the restructured regions as observed in experiments. Finally, a correlation is presented that demonstrates the evolution of the restructuring volume fraction as a function of local effective burnup. This work provides a first of its kind restructuring model for darkzone that can be used by BISON for performance prediction of high burnup UO 2 fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

FUN-BioCROP model with litter decomposition parameters derived from the LIDET dataset

This repository contains the code and data necessary to run the FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms, and Protection) model with litter decomposition parameters derived from a modified Monte Carlo simulation that used the Long-term Intersite Decomposition Experiment Team dataset. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of Files: FUNBioCROP_LIDET Study.Rmd R code with FUN-BioCROP model that can be run with 10 different sets of parameters for litter decomposition (Baseline, LIDET, or eight other best parameter sets identified in the modified Monte Carlo simulation. CORPSE Functions_Bioenergy_V2.R Code with CORPSE model functions, called by FUNBioCROP_LIDET Study.Rmd Model Input Data: bulk.csv, bulk_till.csv, rhizo.csv, rhizo_till.csv, litter.csv Initial C and N (kg C or N/m2) pool values for each soil compartment, final values from spin up. All five files have the same columns: (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 Model Input Data: FluxTower_AvgSoilT.csv: Average daily soil temperature (oC) at 10 cm depth at University of Illinois Urbana-Champaign (UIUC) Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: FluxTower_AvgSoilVWC.csv: Average daily soil volumetric water content (VWC) at 10 cm depth at UIUC Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: input_CCS_LIDET Study.csv: This file has daily data to run FUN-BioCROP (Column - Description - Units): yr - calendar year - year doy - day of year (1 to 365) (no leap year) - day anpp - aboveground NPP (DayCent) - kg C/m2/day bnpp - belowground NPP (DayCent) - kg C/m2/day aglivc - live aboveground biomass carbon (DayCent) - kg C/m2 bglivcj - live juvenile fine root biomass carbon (DayCent) - kg C/m2 bglivcm - live mature fine root biomass carbon (DayCent) - kg C/m2 aglivn - live aboveground biomass nitrogen (DayCent) - kg N/m2 bglivnj - live juvenile fine root biomass nitrogen (DayCent) - kg N/m2 bglivnm - live mature fine root biomass nitrogen (DayCent) - kg N/m2 nyr - simulation year - year cult - indicates a cultivation event (0 or 1) crop - indicates a new crop (0 or 1) fert - indicates a fertilizer event (0 or 1) frst - indicates the first day of the growing season (0 or 1) harv - indicates a harvest event (0 or 1) last - indicates the end of the growing season (0 or 1) croptype - crop type (0=none; 1=alfalfa; 2=corn; 3=grass clover pasture; 4=soybean; 5=wheat) cropsrl - crop specific root length - mm/g root cultrhizmix - fraction of rhizosphere mixed with bulk soil during cultivation (0.0-1.0) - fraction cultlitmix - fraction of litter mixed with bulk soil during cultivation (0.0-1.0) - fraction harvremov - fraction of above ground biomass removed during harvest (0.0-1.0) - fraction fertamt - fertilization amount - g N/m2 lifehist - plant life history (0 = annual, 1 = perennial) froot_turnover_c - amount of C in fine root turnover - kg C/m2 froot_turnover_n - amount of N in fine root turnover - kg N/m2 agrd_turnover_c - amount of C in aboveground biomass turnover - kg C/m2 agrd_turnover_n - amount of N in aboveground biomass turnover - kg N/m2 leaf_litter_fastfrac - Fast decomposing fraction of leaf litter (0.0-1.0) - fraction root_litter_fastfrac - Fast decomposing fraction of root litter (0.0-1.0) - fraction root_diameter - root diameter - mm root_length - root length - mm root/m2 rhizo_frac - fraction of total soil volume that is rhizosphere (0.0 - 1.0) - fraction date - date in format YYYY-MM-DD Instructions: Save the model code ("FUN-BioCROP_LIDET Study.Rmd") and accompanything files (data streams and CORPSE function code) in the same folder. In model code "Chunk 3: Load CORPSE Data Streams" set the working directory (setwd) to the folder with the files saved in step #1. In "Chunk 5: Define LIDET parameter sets" select the litter decomposition parameter set to be used in the run, and comment out all other sets. If changing any parameter values, edit them in "Chunk 6: Load parameters." Run all chunks up to and including "Chunk 10: Prepare Data for Export." In "Chunk 11: Export Output Data" edit data frames for export and filenames, as necessary. "Chunk 12: Graph Total Soil C" makes a figure of C remaining over the model run period. Description of each model chunk (in file FUN-BioCROP_LIDET Study.Rmd): Chunk 1: Remove all functions, clear memory. Removes all functions from R environment, clears the memory. Chunk 2: Load Packages. Loads packages necessary to run the code. Chunk 3: Load CORPSE Data Streams. Sets the working directory and loads the data files necessary to run CORPSE. Chunk 4: Load CORPSE Functions. Loads the R script with CORPSE functions from the working directory, "CORPSE Functions_Bioenergy_V2.R". Chunk 5: Define LIDET parameter sets. Has ten different parameter sets for litter decomposition tested in this study: Baseline parameters, LIDET parameters, and the other 8 best performing parameter sets identified in the modified Monte Carlo. To run the model, all but one parameter set must be commented out. Chunk 6: Load Parameters. Loads all fixed parameters to run the model. Data frame with definitions of parameters is in the CORPSE function script "CORPSE Functions_Bioenergy_V2.R" Chunk 7: Prepare Data Streams. Takes data streams loaded in Chunk 3 and puts them in the format necessary to run the model. The model is coded to run at least two sites at a time, so if only one site is being run it must be run in duplicate. Individual data tables of daily values are created in this chunk from the input data file. Chunk 8: Set Initial Conditions. Creates data tables of soil C and N pools for each soil compartment (rhizo_till, rhizo, bulk_till, bulk, litter) and loads initial values into the data tables. Creates lists for each soil compartment to hold model output. Chunk 9: Load FUN Data and Set Up Matrices. Uses DayCent data to calculate FUN input data: root and leaf N demand, total N demand, plant CN, leaf N available for retranslocation, and litter production. Creates matrices for FUN model outputs. Chunk 10: Run Model. Runs the model. Chunk 11: Prepare Data for Export. Combines data from each day saved as lists into data frames for each soil compartment. Adds values from all soil compartments together to calculate total soil values, creates separate data frames for each soil C and N pool (e.g., protected slow C) for the total soil value. Adds different C and N pools together to calculate total soil C and N for all layers. Creates data frame of ratio of protected to unprotected SOC. Organizes FUN data for export. Chunk 12: Export Results. Exports CSV files of model results to the working directory. Chunk 13: Graph Total Soil C. Makes figure of C remaining over time. Related Links: Original FUN-BioCROP model: https://github.com/BrzostekEcologyLab/FUN-BioCROP LIDET dataset: https://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023

Juice, Stephanie↗