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NASA NTRS1984

An Eulerian transport/transformation/removal model for SO2 and sulfate. I - Model development. II - Model calculation of SO(x) transport in the eastern United States

A three-dimensional, time dependent Eulerian atmospheric SO2 and sulfate transport/transformation/removal model is described and applied to the eastern U.S. The model was developed in anticipation of increased input to the atmospheric sulfur content by coal-burning power plants in the near future and is intended as an aid in identifying sources of SO2. The Eulerian transport model incorporates functions for chemical transformations, dry deposition, spatial topographical variations, spatial and temporal variations of mixing layer heights, the wind field, eddy diffusivities, deposition velocities, and temperature and water concentration profiles. Attention is also given to the SO2 photochemical oxidation mechanism and rates. Results from a 72-hr simulation of SO(x) transport over the eastern U.S., using actual 1974 meteorological data, illustrate the model's capability to depict interactions between emissions, transport, chemistry and removal. Concentration distributions are demonstrated to have significant spatial and temporal variations.

Carmichael, G. R.↗

NASA NTRSDate not supplied

Preliminary Dynamic Modeling of the Quarter-Scale Distributed Electric Propulsion Aircraft

This paper describes the early-stage modeling of a quarter-scale distributed electric propulsion aircraft, based on the SUbsonic Single Aft eNgine (SUSAN) Electrofan, a transformative concept aircraft for which a model exists. The full-scale 180 passenger SUSAN concept has a single turbofan engine in the tail that both produces thrust and provides electrical power to 16 electric fans distributed across the wings utilizing a series/parallel hybrid architecture. The quarter-scale version would have an internal combustion piston engine to provide power to 17 electric fans–16 on the wings, one in the tail–in a series hybrid configuration, and no vertical or horizontal stabilizers. It would also have a reduced flight envelope in terms of both altitude and speed. The initial modeling approach is to scale down the original airframe model to capture the dynamic behavior of a much smaller aircraft, albeit with an empennage. The original powertrain model is then replaced with one representing the physical components of that of the quarter-scale vehicle. This powertrain model is suitable for control design and analysis, and the fully integrated, although preliminary, aircraft model allows flight simulator testing and evaluation. Results from simulations are presented.

electrified aircraft propulsion↗

NASA NTRSDate not supplied

Preliminary Dynamic Modeling of the Quarter-Scale Distributed Electric Propulsion Aircraft

This presentation describes the early-stage modeling of a quarter-scale distributed electric propulsion aircraft, based on the SUbsonic Single Aft eNgine (SUSAN) Electrofan, a transformative concept aircraft for which a model exists. The full-scale 180 passenger SUSAN concept has a single turbofan engine in the tail that both produces thrust and provides electrical power to 16 electric fans distributed across the wings utilizing a series/parallel hybrid architecture. The quarter-scale version would have an internal combustion piston engine to provide power to 17 electric fans–16 on the wings, one in the tail–in a series hybrid configuration, and no vertical or horizontal stabilizers. It would also have a reduced flight envelope in terms of both altitude and speed. The initial modeling approach is to scale down the original airframe model to capture the dynamic behavior of a much smaller aircraft, albeit with an empennage. The original powertrain model is then replaced with one representing the physical components of that of the quarter-scale vehicle. This powertrain model is suitable for control design and analysis, and the fully integrated, although preliminary, aircraft model allows flight simulator testing and evaluation. Results from simulations are presented.

electrified aircraft propulsion↗

NASA NTRS2026

Preliminary Dynamic Modeling of the Quarter-Scale Distributed Electric Propulsion Aircraft

Electrified powertrain↗

DOE OSTI2021

Exact representations of many-body interactions with restricted-Boltzmann-machine neural networks

Restricted Boltzmann machines (RBMs) are simple statistical models defined on a bipartite graph which have been successfully used in studying more complicated many-body systems, both classical and quantum. In this work, we exploit the representation power of RBMs to provide an exact decomposition of many-body contact interactions into one-body operators coupled to discrete auxiliary fields. This construction generalizes the well known Hirsch's transform used for the Hubbard model to more complicated theories such as pionless effective field theory in nuclear physics, which we analyze in detail. Finally, we also discuss possible applications of our mapping for quantum annealing applications and conclude with some implications for RBM parameter optimization through machine learning.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2016

Hemispheric Differences in Tropical Lower Stratospheric Transport and Tracers Annual Cycle

Transport of long-lived tracers (such as O, CO, and N O) in the lower stratosphere largely determines the composition of the entire stratosphere. Stratospheric transport includes the mean residual circulation (with air rising in the tropics and sinking in the polar and middle latitudes), plus two-way isentropic (quasi-horizontal) mixing by eddies. However, the relative importance of two transport components remains uncertain. Previous studies quantified the relative role of these processes based on tropics-wide average characteristics under common assumption of well-mixed tropics. However, multiple instruments provide us with evidence that show significant differences in the seasonal cycle of ozone between the Northern (0-20N) and Southern (0-20S) tropical (NT and ST respectively) lower stratosphere. In this study we investigate these differences in tracer seasonality and quantify transport processes affecting tracers annual cycle amplitude using simulations from Goddard Earth Observing System Chemistry Climate Model (GEOSCCM) and Whole Atmosphere Community Climate Model (WACCM) and compare them to observations from the Microwave Limb Sounder (MLS) on the Aura satellite. We detect the observed contrast between the ST and NT in GEOSCCM and WACCM: annual cycle in ozone and other chemical tracers is larger in the NT than in the ST but opposite is true for the annual cycle in vertical advection. Ozone budgets in the models, analyzed based on the Transformed Eulerian Mean (TEM) framework, demonstrate a major role of quasi-horizontal mixing vertical advection in determining the NTST ozone distribution and behavior. Analysis of zonal variations in the NT and ST ozone annual cycles further suggests important role of North American and Asian Summer Monsoons (associated with strong isentropic mixing) on the lower stratospheric ozone in the NT. Furthermore, multi model comparison shows that most CCMs reproduce the observed characteristic of ozone annual cycle quite well. Thus, latitudinal variations within the tropics have to be considered in order to understand the balance between upwelling and quasi- horizontal mixing in the tropical lower stratosphere and the paradigm of well mixed tropics has to be reconsidered.

Chemistry-Climate Models↗

NASA NTRS1999

Impact of Track Structure Effects on Shielding and Dosimetry

Galactic cosmic rays (GCR) consisting of nuclei of all the known elements with kinetic energies extending from tens to millions of MeV pose a significant health hazard to future deep space operations. Even half of the radiation exposures expected in ISS will result from GCR components. The biological actions of these radiations are known to depend on the details of the energy deposition (not just linear energy transfer, LET, but the lateral dispersion of energy deposition about the particle track). Energy deposits in tissues are dominated by the transfer of tens to hundreds of eV to the tissue's atomic electrons. In the case of low LET radiations, the collisions are separated by large dimensions compared to the size of important biomolecular structures. If such events are also separated in time, then the radiation adds little to the background of radicals occurring from ordinary metabolic processes and causes little or no biological injury. Hence, dose rate is a strong determinant of the action of low LET exposures. The GCR exposures are dominated by ions of high charge and energy (HZE) characterized by many collisions with atomic electrons over biomolecular dimensions, resulting in high radical- density events associated with a few isolated ion paths through the cell and minimal dose rate dependence at ordinary exposure levels. The HZE energy deposit declines quickly laterally and merges with the background radical density in the track periphery for which the exact lateral distribution of the energy deposit is the determinant of the biological injury. Although little data exists on human exposures from HZE radiations, limited studies in mice and mammalian cell cultures allow evaluation of the effects of track structure on shield attenuation properties and evaluation of implications for dosimetry. The most complete mammalian cell HZE exposure data sets have been modeled including the C3H10T1/2 survival and transformation data of Yang et al., the V79 survival and mutation data of various groups, and the Harderian gland tumor data of Alpen et al. Model results for the Harderian gland tumor data in comparison with data from Alpen et al. The Harderian target cell initiation cross section compares closely with the transformation cross section found for the C3H10T1/2 cell transformation data of Yang et al. The most notable feature of the cross sections are the multivalued cross sections for a given LET which implies the corresponding relative biological effectiveness (RBE) is dependent not only on the LET but also the ion type. This fact is at variance with the latest ICRP recommended quality factor which is a defined function of only the LET.

Wilson, J. W.↗

NASA NTRS1998

Heavy-ion radiobiology: new approaches to delineate mechanisms underlying enhanced biological effectiveness

Shortly after the discovery of polonium and radium by Marie Curie and her husband and colleague, Pierre Curie, it was learned that exposure to these alpha-particle emitters produced deleterious biological effects. The mechanisms underlying the increased biological effectiveness of densely ionizing radiations, including alpha particles, neutrons and highly energetic heavy charged particles, remain an active area of investigation. In this paper, we review recent advances in several areas of the radiobiology of these densely ionizing radiations, also known as heavy ions. Advances are described in the areas of DNA damage and repair, chromosome aberrations, mutagenesis, neoplastic transformation in vitro, genomic instability, normal tissue radiobiology and carcinogenesis in vivo. We focus on technical innovations, including novel applications of pulsed-field gel electrophoresis, fluorescence in situ hybridization (FISH), linkage analysis, and studies of gene expression and protein expression. We also highlight the use of new cellular and animal systems, including those with defined DNA repair deficiencies, as well as epithelial cell model systems to assess neoplastic transformation both in vitro and in vivo. The studies reviewed herein have had a substantial impact on our understanding of the genotoxic effects of heavy ions as well as their distinct effects on tissue homeostasis. The use of these radiations in cancer therapy is also discussed. The use of both heavy-ion and proton therapy is on the upswing in several centers around the world, due to their unique energy deposition characteristics that enhance the therapeutic effect and help reduce damage to normal tissue.

Review, Academic↗

DOE OSTI2023

HLA-Clus: HLA class I clustering based on 3D structure

In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository (https://github.com/yshen25/HLA-Clus).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2022

Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Laura Duncanson↗

NASA NTRS2018

Permeability of a New Parachute Fabric – Measurements, Modeling, and Application

In response to a NASA request, Heathcoat Fabrics Limited has woven a new parachute fabric (Custom Design 1 G-60315-1800-Q01). This fabric was tested to obtain its permeability in air (i.e., flow-through volume of air per area per time) over a range of differential pressures from 0.146 to 25 psf (7 to 1197 Pa). The fabric met its specification permeability of 60 to 100 ft3/ft2/min (30.5 to 50.8 cm3/cm2/s) at the U.S. standard differential pressure of 0.5 inch of water (2.60 psf, 124 Pa). The permeability results were transformed into an effective porosity model for use in calculations related to the total porosity of parachutes. The tested fabric is being considered for use in parachutes for future missions to Mars. Calculations of drag coefficient were performed for two geometrically identical parachutes using either the new fabric or fabric woven to Parachute Industry Specification PIA-C-7020D Type I (Mars Science Laboratory Disk-Gap-Band parachute operating on Mars at a Mach number of 0.41). These calculations indicate essentially no difference in the drag coefficient between the two parachutes.

Cruz, Juan R.↗

DOE OSTI2020

Relationships between Vehicle Pricing and Features: Data Driven Analysis of the Chinese Vehicle Market

A full-scale understanding of the dynamics of the Chinese vehicle market can benefit stakeholders with respect to rational decision-making and effective long-term investment. This study attempts to discover the common vehicle pricing patterns in the Chinese market by quantifying statistical correlations among critical vehicle features from intrinsic powertrain systems to extrinsic market positioning. The data samples involve almost all passenger vehicle models sold in 2013 to 2019. After comparing multiple statistical methodologies, a log-transformation variant of the multinomial linear regression model was found to be the best one, and the goodness of fit shows that this model can offer stable estimates, which were validated using 2019 market data. The insights achieved are: (1) The price and major performance features of SUVs/crossovers are similar to those of sedans; (2) If all other explicit features remain the same, the price of a Japanese midsize sedan is 62% higher than that of a Chinese midsize sedan, and European midsize vehicles have the highest prices overall. (3) The incremental price of fuel consumption varies by vehicle class and fuel economy. For example, from 30 to 50 MPG, the vehicle price increases by $119 for a Chinese brand sedan vehicle, by $69 for a Chinese brand SUV.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Building Stock Models for Embodied Carbon Emissions—A Review of a Nascent Field

Building stock modeling emerges as a critical tool in the strategic reduction of embodied carbon emissions, which is pivotal in reshaping the evolving construction sector. This review provides an overall view of modern methodologies in building stock modeling, homing in on the nuances of embodied carbon analysis in construction. Examining 23 seminal papers, our study delineates two primary modeling paradigms—top-down and bottom-up—each further compartmentalized into five innovative methods. This study points out the challenges of data scarcity and computational demands, advocating for methodological advancements that promise to refine the precision of building stock models. A groundbreaking trend in recent research is the incorporation of machine learning algorithms, which have demonstrated remarkable capacity, improving stock classification accuracy by 25% and urban material quantification by 40%. Furthermore, the application of remote sensing has revolutionized data acquisition, enhancing data richness by a factor of five. This review offers a critical examination of current practices and charts a course toward an environmentally prudent future. It underscores the transformative impact of building stock modeling in driving ecological stewardship in the construction industry, positioning it as a cornerstone in the quest for sustainability and its significant contribution toward the grand vision of an eco-efficient built environment.

Hu, Ming (ORCID:0000000325831161)↗

DOE OSTI2021

Use of Traveling Wave Signatures in Medium-Voltage Distribution Systems for Fault Detection and Location

Protection systems are a critical feature for the proper operation of a power grid. Protection systems should be able to quickly detect, identify, locate, and isolate the faults within a time frame that prevents damage to grid components and reduces customer outages. As more inverter-based photovoltaics (PV) are integrated into the grid, phasor domain based protection schemes become more inaccurate due to low levels of fault current from inverters or respond too slowly to accurately identify fault conditions. This points to the need to identify and develop novel protection approaches for distribution systems with high penetration of inverter based PV. The main objective of the project is to develop new time domain-based protection schemes to enable more accurate and faster detection, identification and location of faults in distribution systems and/or microgrids with high penetration of inverter-based PV. Our approach aims to exploit the traveling waves that are generated during a fault or other disturbances in the grid.

14 SOLAR ENERGY↗

DOE OSTI2023

Ultra-low concentration terbium (Tb) adsorption on garlic peels biosorbent and its application for $\mathrm{Nd}$-$\mathrm{Fe}$-$\mathrm{B}$ scraps recovery

Motivated by the strategic value of middle-heavy rare earth elements (MHREEs), we proposed a bio-adsorption method to recover ultra-low concentration terbium (Tb(Ⅲ)) from industrial wastewater that originated from the Nd-Fe-B scraps recovery process. It could be found that Tb(Ⅲ) ions as low as 5.34 ng·mL -1 (ppb) could be adsorbed onto Ca(Ⅱ)-modified garlic peels (Ca-GP) within 10 min with the adsorption efficiency of 99.2% at pH 3.5. The adsorption behavior of Tb(Ⅲ) onto Ca-GP conformed the Langmuir isotherm model and pseudo-second-order kinetic model. Attenuated total reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR), X-ray photoelectron spectroscopy (XPS) and density-functional theory (DFT) calculations results showed that Tb(Ⅲ) ions could be ion-exchanged with cations in Ca-GP such as Ca(Ⅱ) and -COOH. In application, column experiment results showed that the maximum bed adsorption capacity for Tb, praseodymium (Pr), neodymium (Nd), dysprosium (Dy), and Total rare earth elements (REEs) ions calculated by the Thomas model was 0.06, 3.50, 9.65, 7.34, and 27.37 µg·g -1 , respectively, with the initial concentration of 0.37 ng·mL -1 Tb, 20 ng·mL -1 Pr, 44 ng·mL -1 Nd, 67 ng·mL -1 Dy and 170 ng·mL -1 total REEs ions of actual solutions (below the solubility of rare-earth hydroxide). Here this manuscript thus provided a novel cost-effective and efficient approach to the enrichment and recovery of MHREEs from ultra-low concentration REEs ions-containing solutions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Circular Economy Modeling and Analysis

NREL is working to transform the "linear economy" - where materials are simply used and disposed - into a circular one. The linear economy is not only unsustainable and inefficienct, but will ultimately bring us face-to-face with resource limitations. NREL is dedicated to developing a circular economy for energy materials, with recycling, repairing, and reusing in mind. This factsheet describes NREL's core competencies and capabilities, and opportunities for partnership.

analysis↗

NASA NTRS1987

Quaternions as astrometric plate constants

A new method for solving problems in relative astrometry is proposed. In it, the relationship between the measured quantities and the components of the position vector of a star is modeled using quaternions, in effect replacing the plate constants of a standard four-plate-constant solution with the four components of a quaternion. The method allows a direct solution for the position vectors of the stars, and hence for the equatorial coordinates. Distortions, magnitude, and color effects are readily incorporated into the formalism, and the method is directly applicable to overlapping-plate problems. The advantages of the method include the simplicity of the resulting equations, their freedom from singularities, and the fact that trigonometric functions and tangential point transformations are not needed to model the plate material. A global solution over the entire sky is possible.

Jefferys, William H.↗