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At least 1,171 records · Page 65

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NASA NTRS2015

Relative Impacts of Mitigation, Temperature, and Precipitation on 21st-Century Megadrought Risk in the American Southwest

Megadroughts are comparable in severity to the worst droughts of the 20th century but are of much longer duration. A megadrought in the American Southwest would impose unprecedented stress on the limited water resources of the area, making it critical to evaluate future risks not only under different climate change mitigation scenarios but also for different aspects of regional hydroclimate. We find that changes in the mean hydroclimate state, rather than its variability, determine megadrought risk in the American Southwest. Estimates of megadrought probabilities based on precipitation alone tend to underestimate risk. Furthermore, business-as-usual emissions of greenhouse gases will drive regional warming and drying, regardless of large precipitation uncertainties. We find that regional temperature increases alone push megadrought risk above 70, 90, or 99% by the end of the century, even if precipitation increases moderately, does not change, or decreases, respectively. Although each possibility is supported by some climate model simulations, the latter is the most common outcome for the American Southwest in Coupled Model Intercomparison 5 generation models. An aggressive reduction in global greenhouse gas emissions cuts megadrought risks nearly in half.

risk↗

NASA NTRSDate not supplied

System Identification of Flexible Aircraft: Lessons Learned from the X-56A Phase 1 Flight Tests

The X-56A Multi-Utility Technology Testbed (MUTT) is a subscale airplane that was de-signed as an experimental flight research platform for improving aeroelastic modeling and control technologies. The Phase 1 flight tests, conducted from 2017 to 2019 at the NASA Arm-strong Flight Research Center (AFRC), included 39 flights and approximately 1000 research maneuvers, some of which demonstrated stable closed-loop flight beyond the open-loop flutter speed. This paper summarizes the system identification effort to extract nondimensional stability and control derivatives from the flight test data for constructing aeroelastic models of the flight dynamics. Topics discussed include instrumentation, experiment design, model postulation and reduction, parameter estimation, and others. Throughout the paper, unique challenges for the identification of flexible aircraft, practical aspects of the analysis, and lessons learned are presented.

Jared A Grauer↗

NASA NTRSDate not supplied

SWIPE: Spectral Water Inversion Processor and Emulator

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will be discussing the progress made developing SWIPE: Spectral Water Inversion Processor and Emulator. SWIPE is a platform for advanced modeling of coastal and inland aquatic habitats. The goal is create a comprehensive and cohesive system to leverage recent advancements in computation and machine learning to develop a synthetic training ground for sensitivity studies and algorithm development. The four principal facets of SWIPE include: 1. Advanced two-layer coated sphere bio-optical modeling and GPU radiative transfer modeling, 2. Big Data involving massive synthetic spectral libraries of optical properties of various global aquatic particles, surface reflectance, and top-of-atmosphere reflectance, all at hyperspectral resolution leveraging high-end computing systems at NASA Ames Research Center, 3. Deep Learning for algorithm development for water quality inversion of concentrations of common biogeophysical variables as well as optics, full uncertainty characterization by water type, and forward emulation, and lastly, 4. Image Processing for application of developed retrieval algorithms for both hyperspectral and multispectral sensors with experimental corrections for global adjacency, noise, sunglint, and benthic reflectance. This presentation will demonstrate the Equivalent Algal Populations (EAP) two-layer coated sphere scattering model which has been used develop spectral libraries of hyperspectral inherent optical properties of roughly 80 species of phytoplankton, covering 15 different classes and nine taxonomic functional types. The EAP model was also used to derive spectral properties of 10 different non-algal particle functional types. Examples of how the SMART-G (Speed-up Monte-carlo Advanced Radiative Transfer using GPU) radiative transfer code is used to model optically complex aquatic signals will be presented and discussed in the context of creating a massive synthetic database which can leverage the full power of next generation machine learning techniques and high end computing for water quality inversion. We will discuss our active investigation in things like appropriate model architectures, dimensionality reduction techniques such as PCA and autoencoders, uncertainty quantification and abstaining, and which variables actually benefit most from hyperspectral information versus multispectral resolution. We are also curious about questions relating to cost/benefit analysis in terms of computation resources, neural network complexity, and data volumes. Answers to these questions will hopefully elaborate on cost efficiency for potential future sensor design considerations.

SWIPE↗

NASA NTRSDate not supplied

James Webb Space Telescope Fuel Slosh Estimation

The mitigation of fuel slosh in microgravity environments is a pressing matter for the control of both manned and unmanned spacecraft. This paper investigates the attitude perturbations caused by fuel slosh aboard the James Webb Space Telescope (JWST). We develop a dynamical model to parse these perturbations from slew telemetry data. This model uses special Euclidean group SE(3) as well as a novel, N-body variational integrator. The enhanced ability to validate fuel slosh models offers the reduction of settling time after slew operations and, accordingly, the maximization of available science time.

GN&C↗

DOE OSTI2025

Brillouin Sensing with PCA, and PCA-Based Neural Networks for Efficient Temperature Monitoring

This work explores peak estimation techniques in Brillouin Optical Time Domain Analysis (BOTDA), emphasizing both accuracy and efficiency. Euclidean distance measurement method is applied to principal components derived from Brillouin Gain Spectrum data. It offers a major speed advantage being 180 170 times faster than traditional curve fitting methods such as Lorentzian curve fitting, while maintaining similar accuracy. Additionally, a PCA- based neural network model shows significant reduction of peak estimation time compared to Lorentzian fitting. Results show Brillouin frequency shift errors lie under 0.75 MHz in both Euclidean distance-based and neural network-based methods, both of which utilize PCA components. For large data sets and long length fibers, PCA- assisted neural network for peak estimation would be an efficient solution.

Distributed optical fiber sensing↗

DOE OSTI2026

A Generalized Grain-Scale Model for the Non-Plasma and Plasma-Assisted Hydrogen Direct Reduction of Iron Ore

Direct Reduction of Iron ore using hydrogen (H-DRI) is a promising pathway towards efficient steelmaking and accurate predictive models are a necessity for scale-up and optimization of this technology. However, accurate models of this process remain limited because existing models oversimplify grain-scale phenomena, such as nonlinearity inside grain, self-sufficient porosity, surface reactions, and the role of plasma species. These phenomena are important for flash steelmaking and plasma-assisted H-DRI processes. To address this need, we present a phenomenological model for simulating H-DRI at the scale of a single micron-sized grain of the iron ore. We call this the Transient Reactive Grain Model (TRGM). TRGM incorporates key physical process: gas species transport, a chemical kinetics of material conversion, nanopore structural evolution and, adsorption-desorption surface kinetics at the reactive nanopore surface. The important contribution of this work is that the model provides a dependence on different reductant species, specifically hydrogen atoms versus molecules, so that role of hydrogen plasma reduction can be clarified compared to the use of pure hydrogen gas reduction. TRGM predictions agree well with experimental data for both molecular H2 reduction of Fe2O3 and plasma hydrogen reduction of Fe3O4. Results reveal species concentration gradients with a diffuse reaction zone, and enhanced hydrogen diffusion at the grain outer surface due to evolving porosity. These findings challenge common assumptions in existing models, including sharp reaction fronts, quasi-steady diffusion and kinetics, and the neglect of surface chemistry. As a generalized grain-scale model for H-DRI processes, TRGM has practical applications in flash steelmaking and in-flight reduction using both molecular and plasma hydrogen.

08 HYDROGEN↗

DOE OSTI2017

Chromium (VI) reduction in acetate- and molasses-amended natural media: empirical model development

Stimulating indigenous microbes to reduce heavy metals from highly toxic oxidized species to more benign reduced species is a promising groundwater remediation technique that has already seen successful field applications. Designing such a bio-remediation scheme requires a model incorporating the kinetics of nonlinear bio-geochemical interactions between multiple species. With this motivation, we performed a set of microcosm experiments in natural sediments and their indigenous pore water and microbes, generating simultaneous time series for concentrations of Cr(VI), an electron donor (both molasses and acetate were considered), and biomass. Molasses was found to undergo a rapid direct abiotic reaction which eliminated all Cr(VI) before any biomass had time to grow. This was not found in the acetate microcosms, and a distinct zero-order bio-reduction process was observed. Existing models were found inappropriate and a new set of three coupled governing equations representing these process dynamics were developed and their parameters calibrated against the time series from the acetate-amended microcosms. Cell suspension batch experiments were also performed to calibrate bio-reduction rates in the absence of electron donor and sediment. The donor used to initially grow the cells (molasses or acetate) was found not to impact the reduction rate constants in suspension, which were orders of magnitude larger than those explaining the natural media microcosm experiments. This suggests the limited utility of kinetics determined in suspension for remedial design. Scoping studies on the natural media microcosms were also performed, suggesting limited impact of foreign abiotic material and minimal effect of diffusion limitation in the vertical dimension. These analyses may be of independent value to future researchers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Numerical Simulation of Multiphase Magnetohydrodynamic Flow and Deformation of Electrolyte–Metal Interface in Aluminum Electrolysis Cells

A computational fluid dynamics based multiphase magnetohydrodynamic (MHD) flow model for simulating the melt flow and bath–metal interface deformation in realistic aluminum reduction cells is presented. The model accounts for the complex physics of the MHD problem in aluminum reduction cells by coupling two immiscible fluids, electromagnetic field, Lorentz force, flow turbulence, and complex cell geometry with large length scale. Especially, the deformation of bath–metal interface is tracked directly in the simulation, and the condition of constant anode–cathode distance (ACD) is maintained by moving anode bottom dynamically with the deforming bath–metal interface. The metal pad deformation and melt flow predicted by the current model are compared to the predictions using a simplified model where the bath–metal interface is assumed flat. The effects of the induced electric current due to fluid flow and the magnetic field due to the interior cell current on the metal pad deformation and melt flow are investigated. The presented model extends the conventional simplified box model by including detailed cell geometry such as the ledge profile and all channels (side, central, and cross-channels). The simulations show the model sensitivity to different side ledge profiles and the cross-channel width by comparing the predicted melt flow and metal pad heaving. In addition, the model dependencies upon the reduction cell operation conditions such as ACD, current distribution on cathode surface and open/closed channel top, are discussed.

42 ENGINEERING↗

NASA NTRS2018

ST7 Disturbance Reduction System (DRS) Colloid Micronewton Thruster Performance and Control Algorithm Model Simulation Validation in Flight

Colloid Micronewton Thrusters (CMNT) were flight demonstrated for the first time on the ST7Disturbance Reduction System (DRS) payload on the European Space Agency (ESA) Laser Interferometer Space Antenna (LISA) Pathfinder spacecraft for attitude and drag-free spacecraft control. LISA Pathfinder was a technology demonstration mission for ESA’s LISA gravitational wave observatory, currently in Phase A with a launch date of 2034. The DRS included the Integrated Avionics Unit (IAU), eight Colloid Micronewton Thrusters (CMNT), Dynamic Control Software (DCS) and Flight Software (FSW). The CMNT technology met performance requirements operating at 5-30 µN of thrust with ≤0.1 µN resolution and ≤0.1 µN/Hz thrust noise to deliver the required nanometer-level precision spacecraft control measured by the gravitational reference sensor (GRS) in the ESA LISA Technology Package (LTP). The performance of seven of the CMNT in flight was consistent with ground test results. The colloid thruster performance model of thrust and thrust noise as a function of operational parameters (i.e. beam current, voltage, temperature, etc.) was validated in flight over a wide range of conditions. A model and simulation of the thruster control algorithm was developed and validated with flight data to predict thrust noise. This capability is important because it is considered to be a significant source of position noise on the spacecraft and, therefore, the acceleration noise on the test masses, which provide the gravity wave measurements. The CMNT thruster model data and validation with LISA Pathfinder/ST7-DRS flight experiments are discussed in this paper.

Hruby, Vlad↗

DOE OSTI2024

Wetlands Geoprocessor (v 1.0)

Wetlands have often been described as the “Earth’s kidneys” because wetlands have the ability filter pollutants and purify water. Based upon wetland storage capacity, average daily flow, and inflow nutrient loading this model estimates potential load reductions of nitrate and orthophosphate) from a constructed surface flow wetland (CW). The model relies upon equations, coefficients, and models provided by Cheng et al. 2020 and Cheng & Basu 2017. Nitrogen (N) and phosphorus are important nutrients to ecosystems, but high concentrations can lead to water quality issues. Major sources of these nutrients come from agricultural areas where fertilizer is applied in large quantitates and/or animal waste is present. Excess nitrate and phosphorus may result in overgrowth of algae, which can decrease the dissolved oxygen in water, thereby harming and killing fish and other aquatic species. This model evaluates water purification results and estimates cost to install a surface flow constructed wetlands.

Davis, Cleve↗

DOE OSTI2015

Periodic orbit analysis of a system with continuous symmetry—A tutorial

Dynamical systems with translational or rotational symmetry arise frequently in studies of spatially extended physical systems, such as Navier-Stokes flows on periodic domains. In these cases, it is natural to express the state of the fluid in terms of a Fourier series truncated to a finite number of modes. Here, we study a 4-dimensional model with chaotic dynamics and SO(2) symmetry similar to those that appear in fluid dynamics problems. A crucial step in the analysis of such a system is symmetry reduction. We use the model to illustrate different symmetry-reduction techniques. The system's relative equilibria are conveniently determined by rewriting the dynamics in terms of a symmetry-invariant polynomial basis. However, for the analysis of its chaotic dynamics, the “method of slices,” which is applicable to very high-dimensional problems, is preferable. We show that a Poincaré section taken on the 'slice' can be used to further reduce this flow to what is for all practical purposes a unimodal map. This enables us to systematically determine all relative periodic orbits and their symbolic dynamics up to any desired period. We then present cycle averaging formulas adequate for systems with continuous symmetry and use them to compute dynamical averages using relative periodic orbits. The convergence of such computations is discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Molecular-Level Insights into Oxygen Reduction Catalysis by Graphite-Conjugated Active Sites

With a combination of experimental and computational investigations, we design a consistent mechanistic model for the oxygen reduction reaction (ORR) at molecularly well-defined graphite-conjugated catalyst ( GCC ) active sites featuring aryl-pyridinium moieties ( N+-GCC ). ORR catalysis at glassy carbon surfaces modified with N+-GCC fragments displays near-first-order dependence in O 2 partial pressure and near-zero-order dependence on electrolyte pH. Tafel analysis suggests an equilibrium one-electron transfer process followed by a rate-limiting chemical step at modest overpotentials that transitions to a rate-limiting electron transfer sequence at higher overpotentials. Finite-cluster computational modeling of the N+-GCC active site reveals preferential O 2 adsorption at electrophilic carbons alpha to the pyridinium moiety. Together, the experimental and computational data indicate that ORR proceeds via a proton-decoupled O 2 activation sequence involving either concerted or stepwise electron transfer and adsorption of O 2 , which is then followed by a series of electron/proton transfer steps to generate water and turn over the catalytic cycle. The introduced mechanistic model serves as a roadmap for the bottom-up synthesis of highly active N-doped carbon ORR catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Inversion Reduction Method for Real and Complex Distribution Feeder Models

We report the proliferation of distributed generation on distribution feeders triggers a large number of integration and planning studies. Further, the complexity of distribution feeder models, short simulation time steps, and long simulation horizons rapidly render studies computational burdensome. To mend this issue, we propose a methodology for reducing the number of nodes, loads, generators, line, and transformers of p-phase distribution feeders with unbalanced loads and generation, non-symmetric wire impedance, mutual coupling, shunt capacitance, and changes in voltage and phase. The methodology is derived on a constant power load assumption and employs a Gaussian elimination inversion technique to design the reduced feeder. Compared to previous work by the authors, the inversion reduction takes half the time and voltage errors after reduction are reduced by an order of magnitude. Using a snapshot simulation the reduction is tested on six additional publicly available feeders with a maximum voltage error 0.0075 p.u. regardless of feeder size or complexity, and typical errors on the order of 1 × 10 -4 p.u. Lastly, for a day long quasi-static time series simulation on the UCSD A feeder, errors are shown to increase with changes in loading when a large number of buses removed, but shows less variation for less than 85% of buses removed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Synthesis, Properties, and Electrochemical Proton Reduction of a Homoleptic Tetrathiolato Ni-Site Model of [NiFe]-Hydrogenase

[NiFe]-hydrogenase enzymes process H 2 at a nonplanar tetracysteinato-Ni site, the sole participator in proton binding/redox chemistry during turnover. With the objective of assessing whether a simple tetrahedral/ tetrathiolato-Ni 2+ core could promote H 2 evolution reaction (HER), we synthesized (Et 4 N) 2 [Ni(S-p-CF 3 −Ph) 4 ] (1) employing para-trifluoromethylbenzenethiolate ( − S-p-CF 3 −Ph) as a Ni-site analog of [NiFe]-hydrogenase. Spectroscopic measurements and X-ray crystallography confirm the distorted tetrahedral geometry of 1. Dissolution of 1 results in partial thiolate dissociation and formation of S,S-bridged complexes such as (Et 4 N) 2 [Ni 2 (S-p-CF 3 −Ph) 6 ] (3) among other ill-defined species. Dissociation is further accelerated in the presence of Brønsted acids, complicating the assessment of 1 for proton reduction. However, this dissociation/proton instability is suppressed in the presence of additional thiolate ligand to ensure tetrahedral/tetrathiolato 1 persists in solution. Electrochemical HER activity was evaluated by monitoring the current response of an MeCN solution of 1/ excess thiolate after sequential titration with a weak Brønsted acid (acetic acid). The results suggest that 1 is a modest electrocatalyst for the HER with a turnover frequency of 14.5 ± 3.6 s −1 and an overpotential of 0.72 ± 0.02 V. Control experiments and supplementary DFT computations indicate that 1, or a species derived from 1, is responsible for the HER and suggest an ECCE-type mechanism.

Evolution reactions↗

DOE OSTI2016

Global Climate Impacts of Fixing the Southern Ocean Shortwave Radiation Bias in the Community Earth System Model (CESM)

Here, a large, long-standing, and pervasive climate model bias is excessive absorbed shortwave radiation (ASR) over the midlatitude oceans, especially the Southern Ocean. This study investigates both the underlying mechanisms for and climate impacts of this bias within the Community Earth System Model, version 1, with the Community Atmosphere Model, version 5 [CESM1(CAM5)]. Excessive Southern Ocean ASR in CESM1(CAM5) results in part because low-level clouds contain insufficient amounts of supercooled liquid. In a present-day atmosphere-only run, an observationally motivated modification to the shallow convection detrainment increases supercooled cloud liquid, brightens low-level clouds, and substantially reduces the Southern Ocean ASR bias. Tuning to maintain global energy balance enables reduction of a compensating tropical ASR bias. In the resulting preindustrial fully coupled run with a brighter Southern Ocean and dimmer tropics, the Southern Ocean cools and the tropics warm. As a result of the enhanced meridional temperature gradient, poleward heat transport increases in both hemispheres (especially the Southern Hemisphere), and the Southern Hemisphere atmospheric jet strengthens. Because northward cross-equatorial heat transport reductions occur primarily in the ocean (80%), not the atmosphere (20%), a proposed atmospheric teleconnection linking Southern Ocean ASR bias reduction and cooling with northward shifts in tropical precipitation has little impact. In summary, observationally motivated supercooled liquid water increases in shallow convective clouds enable large reductions in long-standing climate model shortwave radiation biases. Of relevance to both model bias reduction and climate dynamics, quantifying the influence of Southern Ocean cooling on tropical precipitation requires a model with dynamic ocean heat transport.

58 GEOSCIENCES↗

DOE OSTI2024

Non-Electricity Based Renewable Fuels: Theory and Computation for Solar Thermochemical Hydrogen

Dominated by photovoltaics and wind, current renewable energy sources generate mostly electricity, but 80% of the global final energy consumption occurs in form of fuels. Therefore, direct solar fuel generation would be a major breakthrough for the energy transition. Solar thermochemical hydrogen (STCH) is one of the very few potential routes towards scalable renewable fuels, but currently suffers from lack of an oxide working material that could optimally perform energy conversion within the thermodynamic boundary conditions. Theory and computation can contribute in two distinct ways, through materials search and discovery, but also by providing detailed mechanistic models for specific systems so to advance our understanding of possible design strategies. To enable high-throughput materials screening, we developed a defect graph neural network (dGNN) machine learning approach,[1] which accelerates the prediction of defect formation energies by replacing the tedious density functional theory (DFT) supercell calculations for all possible defect sites. This approach enables high-throughput database screening of oxides, which was integrated with thermodynamic modeling to extract the reduction entropies as additional selection criterion for STCH. Once potential candidate materials are identified, detailed models can guide materials design by predicting performance characteristics. One challenge is to quantitatively predict thermochemical equilibria at high concentrations when the redox active defects start to interact with each other, thereby impeding the formation of additional defects. Introducing a model for the free energy of defect interaction, parametrized on the basis of DFT data, we simulated the complete STCH redox cycle for (Sr,Ce)MnO3 alloys, achieving near-quantitative agreement with experimental data.[2] The analysis of these simulations reveals how defect interactions diminish the reduction entropy and H2 yield, suggesting to include these interactions in design considerations. Finally, we revisit the popular van't Hoff method for analyzing reduction enthalpies and entropies. This method is not ideal, as it involves a temperature-dependent convolution of gas-phase and solid-state entropies, causing uncertainties in the same order of magnitude as the physical quantities of interest. To avoid this problem, we suggest a simple alternative approach which can be applied to experimental and simulated data alike.

first-principles calculations↗

DOE OSTI2021

A Detailed Vehicle Modeling & Simulation Study Quantifying Energy Consumption and Cost Reduction of Advanced Vehicle Technologies Through 2050

The U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) aim to develop sustainable, affordable, and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate vehicle-level fuel savings potential is critical for understanding DOE’s impact. In this work, we simulated technologies funded by VTO and HFTO for light duty vehicles. The simulations were performed across: (1) Multiple powertrain configurations (conventional, power-split, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles); (2) Vehicle classes (compact car, midsize car, small sport utility vehicle [SUV], midsize SUV, and pickup trucks); and (3) Fuels (gasoline, diesel, natural gas, hydrogen, and battery electricity). These various technologies are assessed for six different timeframes: laboratory years 2015 (reference), 2020, 2025, 2030, and 2045. A delay of five years is assumed between laboratory year and model year (the year the technology is introduced into production).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

The role of methane in future climate strategies: mitigation potentials and climate impacts

This study examines model-specific assumptions and projections of methane (CH 4 ) emissions in deep mitigation scenarios generated by integrated assessment models (IAMs). For this, scenarios of nine models are compared in terms of sectoral and regional CH 4 emission reduction strategies, as well as resulting climate impacts. The models’ projected reduction potentials are compared to sector and technology-specific reduction potentials found in literature. Significant cost-effective and non-climate policy related reductions are projected in the reference case (10–36% compared to a “frozen emission factor” scenario in 2100). Still, compared to 2010, CH 4 emissions are expected to rise steadily by 9–72% (up to 412 to 654 Mt CH 4 /year). Ambitious CO 2 reduction measures could by themselves lead to a reduction of CH 4 emissions due to a reduction of fossil fuels (22–48% compared to the reference case in 2100). However, direct CH 4 mitigation is crucial and more effective in bringing down CH 4 (50–74% compared to the reference case). Given the limited reduction potential, agriculture CH 4 emissions are projected to constitute an increasingly larger share of total anthropogenic CH 4 emissions in mitigation scenarios. Enteric fermentation in ruminants is in that respect by far the largest mitigation bottleneck later in the century with a projected 40–78% of total remaining CH 4 emissions in 2100 in a strong (2 °C) climate policy case.

54 ENVIRONMENTAL SCIENCES↗