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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Emission Factors and Evolution of SO2 Measured From Biomass Burning in Wildfires and Agricultural Fires

Fires emit sufficient sulfur to affect local and regional air quality and climate. This study analyzes SO2 emission factors and variability in smoke plumes from US wildfires and agricultural fires, as well as their relationship to sulfate and hydroxymethanesulfonate (HMS) formation. Observed SO2 emission factors for various fuel types show good agreement with the latest reviews of biomass burning emission factors, producing an emission factor range of 0.47–1.2 g SO2 kg^(−1) C. These emission factors vary with geographic location in a way that suggests that deposition of coal burning emissions and application of sulfur-containing fertilizers likely play a role in the larger observed values, which are primarily associated with agricultural burning. A 0-D box model generally reproduces the observed trends of SO2 and total sulfate (inorganic + organic) in aging wildfire plumes. In many cases, modeled HMS is consistent with the observed organosulfur concentrations. However, a comparison of observed organosulfur and modeled HMS suggests that multiple organosulfur compounds are likely responsible for the observations but that the chemistry of these compounds yields similar production and loss rates as that of HMS, resulting in good agreement with the modeled results. We provide suggestions for constraining the organosulfur compounds observed during these flights, and we show that the chemistry of HMS can allow organosulfur to act as an S(IV) reservoir under conditions of pH > 6 and liquid water content >10^(−7) g sm^(−3). This can facilitate long-range transport of sulfur emissions, resulting in increased SO2 and eventually sulfate in transported smoke.

Sulfur↗
NASA NTRS1991

Optical coronal emission lines from cooling flows in elliptical galaxies and galaxy clusters

Luminosities, surface brightnesses, and line profiles of the coronal lines predicted by standard models for cooling flows in clusters of galaxies and in individual early-type galaxies are presented. The coronal line luminosities of cooling flows depend primarily on the total rate at which gas is cooling. In homogeneous cooling flow models (HMs) in which all of the gas flows into the central regions, the coronal lines are very centrally concentrated, coming mainly from a limb-brightened shell lying just within the sonic radius of the inflow. For HMs, the central surface brightnesses of the stronger coronal lines from nearby clusters with large cooling rates are expected to be about 6 x 10 to the -17th ergs/sq cm/s/sq arcsec. For inhomogeneous models (IMs) in which gas cools out of the flow at large radii, the surface brightness profiles of the coronal lines are more extended, and the central surface brightness is much lower. The integrated line profiles of the HMs are very wide and flat and the central line profiles are double-peaked. The IMs have nearly Gaussian line profiles.

Sarazin, Craig L.↗
DOE OSTI2021

Quantifying the Uncertainty of the Future Hydrological Impacts of Climate Change: Comparative Analysis of an Advanced Hierarchical Sensitivity in Humid and Semiarid Basins

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. Further, the results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

Combining Hydrological Modeling and Remote Sensing Observations to Enable Data-Driven Decision Making for Devils Lake Flood Mitigation in a Changing Climate

This slide presentation reviews work to combine the hydrological models and remote sensing observations to monitor Devils Lake in North Dakota, to assist in flood damage mitigation. This reports on the use of a distributed rainfall-runoff model, HEC-HMS, to simulate the hydro-dynamics of the lake watershed, and used NASA's remote sensing data, including the TRMM Multi-Satellite Precipitation Analysis (TMPA) and AIRS surface air temperature, to drive the model.

Zhang, Xiaodong↗
DOE OSTI2024

PM 2.5 Is Insufficient to Explain Personal PAH Exposure

To understand how chemical exposure can impact health, researchers need tools that capture the complexities of personal chemical exposure. In practice, fine particulate matter (PM 2.5 ) air quality index (AQI) data from outdoor stationary monitors and Hazard Mapping System (HMS) smoke density data from satellites are often used as proxies for personal chemical exposure, but do not capture total chemical exposure. Silicone wristbands can quantify more individualized exposure data than stationary air monitors or smoke satellites. However, it is not understood how these proxy measurements compare to chemical data measured from wristbands. In this study, participants wore daily wristbands, carried a phone that recorded locations, and answered daily questionnaires for a 7-day period in multiple seasons. We gathered publicly available daily PM 2.5 AQI data and HMS data. We analyzed wristbands for 94 organic chemicals, including 53 polycyclic aromatic hydrocarbons. Wristband chemical detections and concentrations, behavioral variables (e.g., time spent indoors), and environmental conditions (e.g., PM 2.5 AQI) significantly differed between seasons. Machine learning models were fit to predict personal chemical exposure using PM 2.5 AQI only, HMS only, and a multivariate feature set including PM 2.5 AQI, HMS, and other environmental and behavioral information. On average, the multivariate models increased predictive accuracy by approximately 70% compared to either the AQI model or the HMS model for all chemicals modeled. This study provides evidence that PM 2.5 AQI data alone or HMS data alone is insufficient to explain personal chemical exposures. Our results identify additional key predictors of personal chemical exposure.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRSDate not supplied

Predictive Model for Workload in Remote Operators During sUAS Contingency Scenarios

The increase in automated capabilities of small Uncrewed Aerial Systems (sUAS) has enabled the human operators to manage larger numbers of vehicles simultaneously. As this happens, the operational paradigm shifts to an m:N configuration where multiple operators (m) are managing multiple vehicles (N) together. However, many questions about how operators will interact with each other and share interaction across the vehicle pool are yet unanswered. Therefore, stakeholders from government and industry have partnered to develop ground control station concepts for such operations. The work presented in this paper aims to identify factors that contribute to operator workload. A supervised machine learning-based method built using Support Vector Machines and K-fold cross-validation was used to create workload prediction models for various NASA TLX subscales by leveraging features related to interactions and their relative timings during m:N operations. Results show that the models yielded fairly high predictive accuracies ranging from ~60-75%.

workload prediction↗
DOE OSTI2023

Data to Accompany: PM2.5 is insufficient to explain personal PAH exposure

Fine particulate matter (PM2.5) air quality index (AQI) data from outdoor stationary monitors and Hazard Mapping System (HMS) smoke density data from satellites are often used as proxies for personal chemical exposure. Silicone wristbands can quantify more individualized exposure data than stationary air monitors or smoke satellites. However, it is not understood how these proxy measurements compare to chemical data measured from wristbands. We hypothesized that predictive models for personal chemical exposure would be significantly improved by expanding beyond stationary PM2.5 AQI data or satellite HMS data to also include environmental and behavioral information. In Eugene, Oregon, participants wore daily wristbands, carried a phone that recorded locations, and answered daily questionnaires for a seven-day period in multiple seasons. We gathered publicly available daily PM2.5 AQI data and HMS data. We analyzed wristbands for 94 organic chemicals, including 53 polycyclic aromatic hydrocarbons (PAHs). Wristband chemical detections and concentrations, behavioral variables (e.g., time spent indoors), and environmental conditions (e.g., PM2.5 AQI) significantly differed between seasons. Machine learning models were fit to predict personal chemical exposure using PM2.5 AQI only, HMS only, and a multivariate feature set including PM2.5 AQI, HMS, and other environmental and behavioral information. On average, the multivariate models increased predictive accuracy by approximately 70% compared to either the AQI model or the HMS model for all chemicals modeled. This study provides evidence that PM2.5 AQI data alone or HMS data alone is insufficient to explain personal chemical exposures. Our results identify additional key predictors of personal chemical exposure.

Bramer, Lisa M↗
NASA NTRS1991

Reasoning about real-time systems with temporal interval logic constraints on multi-state automata

Models of real-time systems using a single paradigm often turn out to be inadequate, whether the paradigm is based on states, rules, event sequences, or logic. A model-based approach to reasoning about real-time systems is presented in which a temporal interval logic called TIL is employed to define constraints on a new type of high level automata. The combination, called hierarchical multi-state (HMS) machines, can be used to model formally a real-time system, a dynamic set of requirements, the environment, heuristic knowledge about planning-related problem solving, and the computational states of the reasoning mechanism. In this framework, mathematical techniques were developed for: (1) proving the correctness of a representation; (2) planning of concurrent tasks to achieve goals; and (3) scheduling of plans to satisfy complex temporal constraints. HMS machines allow reasoning about a real-time system from a model of how truth arises instead of merely depending of what is true in a system.

Gabrielian, Armen↗
NASA NTRS2017

Cross - Scale Intercomparison of Climate Change Impacts Simulated by Regional and Global Hydrological Models in Eleven Large River Basins

Ideally, the results from models operating at different scales should agree in trend direction and magnitude of impacts under climate change. However, this implies that the sensitivity to climate variability and climate change is comparable for impact models designed for either scale. In this study, we compare hydrological changes simulated by 9 global and 9 regional hydrological models (HM) for 11 large river basins in all continents under reference and scenario conditions. The foci are on model validation runs, sensitivity of annual discharge to climate variability in the reference period, and sensitivity of the long-term average monthly seasonal dynamics to climate change. One major result is that the global models, mostly not calibrated against observations, often show a considerable bias in mean monthly discharge, whereas regional models show a better reproduction of reference conditions. However, the sensitivity of the two HM ensembles to climate variability is in general similar. The simulated climate change impacts in terms of long-term average monthly dynamics evaluated for HM ensemble medians and spreads show that the medians are to a certain extent comparable in some cases, but have distinct differences in other cases, and the spreads related to global models are mostly notably larger. Summarizing, this implies that global HMs are useful tools when looking at large-scale impacts of climate change and variability. Whenever impacts for a specific river basin or region are of interest, e.g. for complex water management applications, the regional-scale models calibrated and validated against observed discharge should be used.

Hydrological models↗
DOE OSTI2020

Risk Assessment of Rare Events in Probabilistic Power Flow via Hybrid Multi-Surrogate Method

The risks associated with rare events threatening the security of power system operation are of paramount importance to power system planners and operators. To analyze the risks caused by high-impact, low-frequency rare events, an immensely large number of samples are typically required for the Monte-Carlo (MC) method on the high-fidelity power system model to achieve a sufficient accuracy, thereby rendering this approach computationally prohibitive. To handle this problem efficiently, it is desirable to construct a surrogate model for the power system response. However, the straightforward MC sampling of the low-fidelity surrogate can lead to biased results in the low-probability tail regions that are vital to risk assessment. Moreover, a single surrogate is unable to handle the topology uncertainties caused by random branch outages. To overcome these issues, we propose a hybrid multi-surrogate (HMS) method based on the polynomial chaos expansion (PCE) with low-probability tail events reevaluated by the high-fidelity model through a probabilistic analysis. This method improves the computational efficiency of the MC method for rare-event risk assessment by leveraging multi-fidelity models while retaining the desired accuracy. Simulations conducted in three test systems verify the excellent performances of the HMS method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2015

V-SUIT Model Validation Using PLSS 1.0 Test Results

The dynamic portable life support system (PLSS) simulation software Virtual Space Suit (V-SUIT) has been under development at the Technische Universitat Munchen since 2011 as a spin-off from the Virtual Habitat (V-HAB) project. The MATLAB(trademark)-based V-SUIT simulates space suit portable life support systems and their interaction with a detailed and also dynamic human model, as well as the dynamic external environment of a space suit moving on a planetary surface. To demonstrate the feasibility of a large, system level simulation like V-SUIT, a model of NASA's PLSS 1.0 prototype was created. This prototype was run through an extensive series of tests in 2011. Since the test setup was heavily instrumented, it produced a wealth of data making it ideal for model validation. The implemented model includes all components of the PLSS in both the ventilation and thermal loops. The major components are modeled in greater detail, while smaller and ancillary components are low fidelity black box models. The major components include the Rapid Cycle Amine (RCA) CO2 removal system, the Primary and Secondary Oxygen Assembly (POS/SOA), the Pressure Garment System Volume Simulator (PGSVS), the Human Metabolic Simulator (HMS), the heat exchanger between the ventilation and thermal loops, the Space Suit Water Membrane Evaporator (SWME) and finally the Liquid Cooling Garment Simulator (LCGS). Using the created model, dynamic simulations were performed using same test points also used during PLSS 1.0 testing. The results of the simulation were then compared to the test data with special focus on absolute values during the steady state phases and dynamic behavior during the transition between test points. Quantified simulation results are presented that demonstrate which areas of the V-SUIT model are in need of further refinement and those that are sufficiently close to the test results. Finally, lessons learned from the modelling and validation process are given in combination with implications for the future development of other PLSS models in V-SUIT.

Olthoff, Claas↗
DOE OSTI2020

Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach

Exploring water fluxes between hydrological model (HM) components is essential to assess and improve model realism. Many classical metrics for HM diagnosis rely solely on streamflow and hence provide limited insights into model performance across processes. This study applies an information theory measure known as “transfer entropy” (TE) to systematically quantify the transfer of information among major HM components. To test and demonstrate the benefits of TE, we use the Framework for Understanding Structural Errors (FUSE) model to mimic and compare four commonly used HM structures, VIC, PRMS, SACRAMENTO, and TOPMODEL, across 671 catchments spanning a variety of hydrologic regimes in the conterminous United States. We explore connections between HM components and catchment landscape characteristics (e.g., climate, topography, soil, and vegetation) and characterize their nonlinear associations using distance correlation and Spearman correlation coefficients. Our results indicate that while the information transferred from precipitation to runoff is similar across model structures (likely as a result of calibration), the information transferred among other components can vary significantly from a FUSE structure to another. We find that aridity, precipitation duration and frequency, snow fraction, mean elevation, forest area, and leaf area index are often significantly associated with TE between the main HM components. Here, we propose that the presence of meaningful nonlinear associations can be used to diagnose process representation in HMs. Our results highlight the necessity to enhance the conventional streamflow-only calibration approach for a more realistic representation of water dynamics in the models.

large-sample hydrology↗
NASA NTRS2024

A Computational Framework for Making Early Design Decisions in Deep Space Habitats

The dynamics of systems of systems often involve complex interactions among the individual systems, making the implications of design choices challenging to predict. Design features in such systems may trigger unexpected behaviors or result in large variations in safety, performance or resilience. To provide a means of simulating such systems for aiding in these decisions, we have developed a prototype tool, the control-oriented dynamic computational modeling tool (CDCM). The CDCM provides rapid simulation capabilities to perform trade studies in systems of systems. The general class of systems of systems that we aim to examine involve multiple hazards, damage, cascading consequences, repair and recovery. We especially focus on systems-of-systems that incorporate a health management system (HMS) that can monitor the state of the habitat and make decisions about actions to take. In this paper we describe the features of the CDCM, the architecture we devised for simulation of systems-of-systems, the unique functionalities of this tool, and we provide a demonstration of the capabilities by performing two illustrative examples. We articulate the use of this tool for making early design decisions and demonstrate its use for trade studies that consider a model of a deep space habitat. We also share some experiences and lessons that may be useful for others seeking to address similar problems.

Amir Behjat↗
DOE OSTI2023

Precision Measurement of the Neutron Asymmetry An 1 at Large Bjorken x at 12GeV Jefferson Lab

The virtual photon asymmetry A1 is one of the fundamental quantities that provide information on the spin structure of the nucleon. The value of A1 at high xBj is of particular interest because valence quarks dominate in this region, which makes it a relatively clean region to study the nucleon structure. Several theoretical calculations, including naive SU(6) quark model, relativistic constituent quark model (RCQM), perturbative QCD (pQCD), predict the behavior for A1 and the quark polarization in the high xBj valence quark region. The An 1 experiment during the 6 GeV JLab era showed that An 1 turns positive at x ? 0.5, while up to the highest measured x value of 0.61 the down quark polarization ?d/d remains negative, in contrast to the pQCD prediction. Subsequent theoretical studies following the 6 GeV results claimed that quark orbital angular momentum could delay the upward turn of ?d/d to higher xBj or the non-perturbative nature of the strong interaction could keep it negative all the way to xBj = 1 as predicted in a Schwinger-Dyson approach with di-quark model assumptions. With the 12 GeV upgrade of JLab, a new experiment on An1 (E12-06-110)1 was carried out using a 10.4 GeV beam, a polarized 3He target, and the HMS and the Super-HMS (spectrometers) in Hall C. This measurement reached a deeper valence quark region, reaching up to x ? 0.75. This thesis reports on the 3He asymmetries A?, A?, A3He 1 , and A3He 2 without radiative corrections.

Chen, Mingyu↗
DOE OSTI2023

Measuring the Neutron Spin Asymmetry A1n in the Valence Quark Region in Hall C at Jefferson Lab

The quest to understand how the nucleon spin is decomposed into its constituent quark and gluon spin and orbital angular momentum (OAM) components has been at the forefront of nuclear physics for decades. Due to the non-perturbative nature of Quantum Chromodynamics (QCD) - the theory describing how quarks and gluons bind together to form protons and neutrons - making absolute predictions of nucleon spin structure is generally difficult, especially as a function of its quark and gluon longitudinal momentum fraction x. Measurements involving nucleon spin structure serve as a sensitive test for QCD, including ab-initio lattice QCD calculations due to the advent of the quasi-PDF formalism, and various predictions that diverge at large-x. The neutron spin asymmetry An 1 at high?x is a key observable for probing nucleon spin structure. In the valence domain (x > 0.5), sea effects are expected to be negligible, and so the total nucleon spin is considered to be carried by the valence quarks. The valence region can therefore enable us to study the role of quark OAM and other non-perturbative effects of the strong force. An 1 was measured in the deep inelastic scattering region of 0.40 < x < 0.75 and 6 < Q2 < 10 GeV2 in Hall C at Jefferson Lab using a 10.4 GeV longitudinally polarized electron beam, upgraded polarized 3He target, and the High Momentum Spectrometer (HMS) and Super High Momentum Spectrometer (SHMS). E12- 06-110 provides the first precision data in the valence quark region above x = 0.60, and its preliminary results proved consistent with earlier data disqualifying a pQCD model that excluded quark OAM. Combined with previous world proton data, the ratio of the polarized-to-unpolarized up quark momentum distribution (?u + ?u)/(u + u) remained positive at large-x, and the down quark (?d + ?d)/(d + d) remained negative.

Rehfuss, Melanie↗
DOE OSTI2024

Pion electroproduction measurements in the nucleon resonance region

Here, we report new pion electroproduction measurements in the $\Delta (1232)$ resonance, utilizing the SHMS - HMS magnetic spectrometers of Hall C at Jefferson Lab. The data focus on a region that exhibits a strong and rapidly changing interplay of the mesonic cloud and quark-gluon dynamics in the nucleon. The results are in reasonable agreement with models that employ pion cloud effects and chiral effective field theory calculations, but at the same time they suggest that an improvement is required to the theoretical calculations and provide valuable input that will allow their refinements. The data illustrate the potential of the magnetic spectrometers setup in Hall C towards the study the $\Delta (1232)$ resonance. These first reported results will be followed by a series of measurements in Hall C, that will expand the studies of the $\Delta (1232)$ resonance offering a high precision insight within a wide kinematic range from low to high momentum transfers.

13.60.Fz Transition Form Factors↗