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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Missing Peroxy Radical Sources Within a Rural Forest Canopy

Organic peroxy (RO2) and hydroperoxy (HO2) radicals are key intermediates in the photochemical processes that generate ozone, secondary organic aerosol and reactive nitrogen reservoirs throughout the troposphere. In regions with ample biogenic hydrocarbons, the richness and complexity of peroxy radical chemistry presents a significant challenge to current-generation models, especially given the scarcity of measurements in such environments. We present peroxy radical observations acquired within a Ponderosa pine forest during the summer 2010 Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen - Rocky Mountain Organic Carbon Study (BEACHON-ROCS). Total peroxy radical mixing ratios reach as high as 180 pptv and are among the highest yet recorded. Using the comprehensive measurement suite to constrain a near-explicit 0-D box model, we investigate the sources, sinks and distribution of peroxy radicals below the forest canopy. The base chemical mechanism underestimates total peroxy radicals by as much as a factor of 3. Since primary reaction partners for peroxy radicals are either measured (NO) or under-predicted (HO2 and RO2, i.e. self-reaction), missing sources are the most likely explanation for this result. A close comparison of model output with observations reveals at least two distinct source signatures. The first missing source, characterized by a sharp midday maximum and a strong dependence on solar radiation, is consistent with photolytic production of HO2. The diel profile of the second missing source peaks in the afternoon and suggests a process that generates RO2 independently of sun-driven photochemistry, such as ozonolysis of reactive hydrocarbons. The maximum magnitudes of these missing sources (approximately 120 and 50 pptv min−1, respectively) are consistent with previous observations alluding to unexpectedly intense oxidation within forests. We conclude that a similar mechanism may underlie many such observations.

atmosphere↗
DOE OSTI2024

Modeling Novel Aqueous Particle and Cloud Chemistry Processes of Biomass Burning Phenols and Their Potential to Form Secondary Organic Aerosols

Phenols emitted from biomass burning contribute significantly to secondary organic aerosol (SOA) formation through the partitioning of semivolatile products formed from gas-phase chemistry and multiphase chemistry in aerosol liquid water and clouds. The aqueous-phase SOA (aqSOA) formed via hydroxyl radical (˙OH), singlet molecular oxygen ( 1 O 2 *), and triplet excited states of organic compounds ( 3 C*), which oxidize dissolved phenols in the aqueous phase, might play a significant role in the evolution of organic aerosol (OA). However, a quantitative and predictive understanding of aqSOA has been challenging. Here, we develop a stand-alone box model to investigate the formation of SOA from gas-phase ˙OH chemistry and aqSOA formed by the dissolution of phenols followed by their aqueous-phase reactions with ˙OH, 1 O 2 *, and 3 C* in cloud droplets and aerosol liquid water. Here, we investigate four phenolic compounds, i.e., phenol, guaiacol, syringol, and guaiacyl acetone (GA), which represent some of the key potential sources of aqSOA from biomass burning in clouds. For the same initial precursor organic gas that dissolves in aerosol/cloud liquid water and subsequently reacts with aqueous phase oxidants, we predict that the aqSOA formation potential (defined as aqSOA formed per unit dissolved organic gas concentration) of these phenols is higher than that of isoprene-epoxydiol (IEPOX), a well-known aqSOA precursor. Cloud droplets can dissolve a broader range of soluble phenols compared to aqueous aerosols, since the liquid water contents of aerosols are orders of magnitude smaller than cloud droplets. Further, our simulations suggest that highly soluble and reactive multifunctional phenols like GA would predominantly undergo cloud chemistry within cloud layers, while gas-phase chemistry is likely to be more important for less soluble phenols. But in the absence of clouds, the condensation of low-volatility products from gas-phase oxidation followed by their reversible partitioning to organic aerosols dominates SOA formation, while the SOA formed through aqueous aerosol chemistry increases with relative humidity (RH), approaching 40% of the sum of gas and aqueous aerosol chemistry at 95% RH for GA. Our model developments of biomass-burning phenols and their aqueous chemistry can be readily implemented in regional and global atmospheric chemistry models to investigate the aqueous aerosol and cloud chemistry of biomass-burning organic gases in the atmosphere.

09 BIOMASS FUELS↗
DOE OSTI2021

Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0

Atmospheric aerosol microphysical processes are a significant source of uncertainty in predicting climate change. Specifically, aerosol nucleation, emissions, and growth rates, which are simulated in chemical transport models to predict the particle size distribution, are not understood well. However, long-term size distribution measurements made at several ground-based sites across Europe implicitly contain information about the processes that created those size distributions. This work aims to extract that information by developing and applying an inverse technique to constrain aerosol emissions as well as nucleation and growth rates based on hourly size distribution measurements. We developed an inverse method based upon process control theory into an online estimation technique to scale aerosol nucleation, emissions, and growth so that the model–measurement bias in three measured aerosol properties exponentially decays. The properties, which are calculated from the measured and predicted size distributions, used to constrain aerosol nucleation, emission, and growth rates are the number of particles with a diameter between 3 and 6 nm, the number with a diameter greater than 10 nm, and the total dry volume of aerosol ( N 3–6 , N 10 , V dry ), respectively. In this paper, we focus on developing and applying the estimation methodology in a zero-dimensional “box” model as a proof of concept before applying it to a three-dimensional simulation in subsequent work. The methodology is first tested on a dataset of synthetic and perfect measurements that span diverse environments in which the true particle emissions, growth, and nucleation rates are known. The inverse technique accurately estimates the aerosol microphysical process rates with an average and maximum error of 2 % and 13 %, respectively. Next, we investigate the effect that measurement noise has on the estimated rates. The method is robust to typical instrument noise in the aerosol properties as there is a negligible increase in the bias of the estimated process rates. Finally, the methodology is applied to long-term datasets of in situ size distribution measurements in western Europe from May 2006 through June 2007. At Melpitz, Germany, and Hyytiälä, Finland, the average diurnal profiles of estimated 3 nm particle formation rates are reasonable, having peaks near noon local time with average peak values of 1 and 0.15 cm –3 s –1 , respectively. The normalized absolute error in estimated N 3–6 , N 10 , and V dry at three European measurement sites is less than 15 %, showing that the estimation framework developed here has potential to decrease model–measurement bias while constraining uncertain aerosol microphysical processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

A Model-based Interpretation of Low-frequency Changes in the Carbon Cycle during the Last 120,000 years and its Implications for the Reconstruction of Atmospheric (delta) 14-C

A main caveat in the interpretation of observed changes in atmospheric (Delta)C-l4 during the last 50,000 years is the unknown variability of the carbon cycle, which together with changes in the C-14 production rates determines the C-14 dynamics. A plausible scenario explaining glacial/interglacial dynamics seen in atmospheric CO2 and (delta)C-13 was proposed recently (Kohler et al., 2005a). A similar approach that expands its interpretation to the C-14 cycle is an important step toward a deeper understanding of (Delta)C-14 variability. This approach is based on an ocean/atmosphere/biosphere box model of the global carbon cycle (BICYCLE) to reproduce low-frequency changes in atmospheric CO2 as seen in Antarctic ice cores. The model is forced forward in time by various paleoclimatic records derived from ice and sediment cores. The simulation results of our proposed scenario match a compiled CO2 record from various ice cores during the last 120,000 years with high accuracy (r(sup 2) = 0.89). We analyze scenarios with different C-14 production rates, which are either constant or based on Be-10 measured in Greenland ice cores or the recent high-resolution geomagnetic field reconstruction GLOPIS-75 and compare them with the available (Delta)C-14 data covering the last 50,000 years. Our results suggest that during the last glacial cycle in general less than 110%0o f the increased atmospheric (Delta)C-14 is based on variations in the carbon cycle, while the largest part (5/6) of the variations has to be explained by other factors. Glacial atmospheric (Delta)C-14 larger than 700% cannot not be explained within our framework, neither through carbon cycle-based changes nor through variable C-14 production. Superimposed on these general trends might lie positive anomalies in atmospheric (Delta)C-14 of approx. 50% caused by millennial-scale variability of the northern deep water production during Heinrich events and Dansgaard/Oeschger climate fluctuations. According to our model, the dominant processes that increase glacial (Delta)C-14 are a reduced glacial ocean circulation (+ approx.40%0), a restricted glacial gas exchange between the atmosphere and the surface ocean through sea ice coverage (+ approx. 20%), and the enrichment of dissolved inorganic carbon with C-14 in the surface waters through isotopic fractionation during higher glacial marine export production caused by iron fertilization (+ approx.10%).

Koehler, Peter↗
DOE OSTI2025

Leveraging interpolation models and error bounds for verifiable scientific machine learning

Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, but often rely on speculative assumptions about the data and methods involved. Error bounds for classical interpolation techniques can provide mathematically rigorous estimates of accuracy, but often are difficult or impractical to determine computationally. Here, in this work, we present a best-of-both-worlds approach to verifiable scientific machine learning by demonstrating that (1) multiple standard interpolation techniques have informative error bounds that can be computed or estimated efficiently; (2) comparative performance among distinct interpolants can aid in validation goals; (3) deploying interpolation methods on latent spaces generated by deep learning techniques enables some interpretability for black-box models. We present a detailed case study of our approach for predicting lift-drag ratios from airfoil images. Code developed for this work is available in a public Github repository.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Modeling of the chemistry in oxidation flow reactors with high initial NO

Oxidation flow reactors (OFRs) are increasingly employed in atmospheric chemistry research because of their high efficiency of OH radical production from low-pressure Hg lamp emissions at both 185 and 254 nm (OFR185) or 254 nm only (OFR254). OFRs have been thought to be limited to studying low-NO chemistry (in which peroxy radicals (RO 2 ) react preferentially with HO 2 ) because NO is very rapidly oxidized by the high concentrations of O 3 , HO 2 , and OH in OFRs. However, many groups are performing experiments by aging combustion exhaust with high NO levels or adding NO in the hopes of simulating high-NO chemistry (in which RO 2 + NO dominates). This work systematically explores the chemistry in OFRs with high initial NO. Using box modeling, we investigate the interconversion of N-containing species and the uncertainties due to kinetic parameters. Simple initial injection of NO in OFR185 can result in more RO 2 reacted with NO than with HO 2 and minor non-tropospheric photolysis, but only under a very narrow set of conditions (high water mixing ratio, low UV intensity, low external OH reactivity (OHR ext ), and initial NO concentration (NO in ) of tens to hundreds of ppb) that account for a very small fraction of the input parameter space. These conditions are generally far away from experimental conditions of published OFR studies with high initial NO. In particular, studies of aerosol formation from vehicle emissions in OFRs often used OHR ext and NO in several orders of magnitude higher. Due to extremely high OHR ext and NO in , some studies may have resulted in substantial non-tropospheric photolysis, strong delay to RO 2 chemistry due to peroxynitrate formation, VOC reactions with NO 3 dominating over those with OH, and faster reactions of OH–aromatic adducts with NO 2 than those with O 2 , all of which are irrelevant to ambient VOC photooxidation chemistry. Some of the negative effects are the worst for alkene and aromatic precursors. To avoid undesired chemistry, vehicle emissions generally need to be diluted by a factor of >100 before being injected into an OFR. However, sufficiently diluted vehicle emissions generally do not lead to high-NO chemistry in OFRs but are rather dominated by the low-NO RO 2 + HO 2 pathway. To ensure high-NO conditions without substantial atmospherically irrelevant chemistry in a more controlled fashion, new techniques are needed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics

Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical principles into the model. Most PiDL approaches regularize training by embedding governing equations into the loss function, yet this depends heavily on extensive hyperparameter tuning to weigh each loss term. To this end, we propose to leverage physics prior knowledge by “baking” the discretized governing equations into the neural network architecture via the connection between the partial differential equations (PDE) operators and network structures, resulting in a PDE-preserved neural network (PPNN). This method, embedding discretized PDEs through convolutional residual networks in a multi-resolution setting, largely improves the generalizability and long-term prediction accuracy, outperforming conventional black-box models. The effectiveness and merit of the proposed methods have been demonstrated across various spatiotemporal dynamical systems governed by spatiotemporal PDEs, including reaction-diffusion, Burgers’, and Navier-Stokes equations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

Wintertime Overnight NOx Removal in a Southeastern United States Coal-Fired Power Plant Plume: A Model for Understanding Winter NOx Processing and Its Implications

Nitric oxide (NO) is emitted in large quantities from coal-burning power plants. During the day, the plumes from these sources are efficiently mixed into the boundary layer, while at night, they may remain concentrated due to limited vertical mixing during which they undergo horizontal fanning. At night, the degree to which NO is converted to HNO3 and therefore unable to participate in next-day ozone (O3) formation depends on the mixing rate of the plume, the composition of power plant emissions, and the composition of the background atmosphere. In this study, we use observed plume intercepts from the Wintertime INvestigation of Transport, Emissions and Reactivity (WINTER) campaign to test sensitivity of overnight NOx removal to the N2O5 loss rate constant, plume mixing rate, background O3, and background levels of volatile organic compounds using a 2-D box model of power plant plume transport and chemistry. The factor that exerted the greatest control over NOx removal was the loss rate constant of N2O5. At the lowest observed N2O5 loss rate constant, no other combination of conditions converts more than 10 percent of the initial NOx to HNO3. The other factors did not influence NOx removal to the same degree.

air quality↗
DOE OSTI2019

Hybridizing Classifiers and Collection Systems to Maximize Intelligence and Minimize Uncertainty in National Security Data Analytics Applications

There are numerous applications that combine data collected from sensors with machine-learning based classification models to predict the type of event or objects observed. Both the collection of the data itself and the classification models can be tuned for optimal performance, but we hypothesize that additional gains can be realized by jointly assessing both factors together. Through this research, we used a seismic event dataset and two neural network classification models that issued probabilistic predictions on each event to determine whether it was an earthquake or a quarry blast. Real world applications will have constraints on data collection, perhaps in terms of a budget for the number of sensors or on where, when, or how data can be collected. We mimicked such constraints by creating subnetworks of sensors with both size and locational constraints. We compare different methods of determining the set of sensors in each subnetwork in terms of their predictive accuracy and the number of events that they observe overall. Additionally, we take the classifiers into account, treating them both as black-box models and testing out various ways of combining predictions among models and among the set of sensors that observe any given event. We find that comparable overall performance can be seen with less than half the number of sensors in the full network. Additionally, a voting scheme that uses the average confidence across the sensors for a given event shows improved predictive accuracy across nearly all subnetworks. Lastly, locational constraints matter, but sometimes in unintuitive ways, as better-performing sensors may be chosen instead of the ones excluded based on location. This being a short-term research effort, we offer a lengthy discussion on interesting next-steps and ties to other ongoing research efforts that we did not have time to pursue. These include a detailed analysis of the subnetwork performance broken down by event type, specific location, and model confidence. This project also included a Campus Executive research partnership with Texas A&M University. Through this, we worked with a professor and student to study information gain for UAV routing. This was an alternative way of looking at the similar problem space that includes sensor operation for data collection and the resulting benefit to be gained from it. This work is described in an Appendix.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Global perturbation of the marine calcium cycle during the Permian-Triassic transition

A negative shift in the calcium isotopic composition of marine carbonate rocks spanning the end-Permian extinction horizon in South China has been used to argue for an ocean acidification event coincident with mass extinction. This interpretation has proven controversial, both because the excursion has not been demonstrated across multiple, widely separated localities, and because modeling results of coupled carbon and calcium isotope records illustrate that calcium cycle imbalances alone cannot account for the full magnitude of the isotope excursion. Here, we further test potential controls on the Permian-Triassic calcium isotope record by measuring calcium isotope ratios from shallow-marine carbonate successions spanning the Permian-Triassic boundary in Turkey, Italy, and Oman. All measured sections display negative shifts in δ 44/40 Ca of up to 0.6‰. Consistency in the direction, magnitude, and timing of the calcium isotope excursion across these widely separated localities implies a primary and global δ 44/40 Ca signature. Based on the results of a coupled box model of the geological carbon and calcium cycles, we interpret the excursion to reflect a series of consequences arising from volcanic CO 2 release, including a temporary decrease in seawater δ 44/40 Ca due to short-lived ocean acidification and a more protracted increase in calcium isotope fractionation associated with a shift toward more primary aragonite in the sediment and, potentially, subsequently elevated carbonate saturation states caused by the persistence of elevated CO 2 delivery from volcanism. Locally, changing balances between aragonite and calcite production are sufficient to account for the calcium isotope excursions, but this effect alone does not explain the globally observed negative excursion in the δ 13 C values of carbonate sediments and organic matter as well. Only a carbon release event and related geochemical consequences are consistent both with calcium and carbon isotope data. Furthermore, the carbon release scenario can also account for oxygen isotope evidence for dramatic and protracted global warming as well as paleontological evidence for the preferential extinction of marine animals most susceptible to acidification, warming, and anoxia.

58 GEOSCIENCES↗
NASA NTRS1983

The application of forest classification from Landsat data as a basis for natural hydrocarbon emission estimation and photochemical oxidant model simulations in southeastern Virginia

The possible contribution by natural hydrocarbon emissions to the total ozone budget recorded in the Tidewater region of southeastern Virginia during the height of the summer period was examined. Natural sources investigated were limited to the primary HC emitters and most prevalent natural vegetation, the forests. Three types and their areal coverage were determined for Region VI of the Virginia State Air Pollution Control Board using remotely sensed data from Landsat, a NASA experimental earth resources satellite. Emission factors appropriate to the specific types (coniferous 0.24 x 10 to the 13th, mixed 0.63 x 10 to the 13th, deciduous 1.92 x 10 to the 13th, microgram/h), derived from contemporary procedures, were applied to produce an overall regional emission rate of 2.79 x 10 to the 13th microgram/h for natural non-methane hydrocarbons (NMHC). This rate was used with estimates of the anthropogenic NO(x) and NMHC loading, as input into a photochemical box model. Additional HC loading on the order of that estimated to be produced by the natural forest communities was required in order to reach certain measured summer peak ozone levels as the computer simulation was unable to account for the measured episodic levels on the basis of the anthropogenic inventory alone.

Salop, J.↗
DOE OSTI2020

Anthropogenic Impacts on Atmospheric Carbonyl Sulfide Since the 19th Century Inferred From Polar Firn Air and Ice Core Measurements

Carbonyl sulfide (COS) was measured in firn air collected during seven different field campaigns carried out at four different sites in Greenland and Antarctica between 2001 and 2015. A Bayesian probabilistic statistical model is used to conduct multisite inversions and to reconstruct separate atmospheric histories for Greenland and Antarctica. The firn air inversions cover most of the 20th century over Greenland and extend back to the 19th century over Antarctica. The derived atmospheric histories are consistent with independent surface air time series data from the corresponding sites and the Antarctic ice core COS records during periods of overlap. Atmospheric COS levels began to increase over preindustrial levels starting in the 19th century, and the increase continued for much of the 20th century. Atmospheric COS peaked at higher than present-day levels around 1975 CE over Greenland and around 1987 CE over Antarctica. An atmosphere/surface ocean box model is used to investigate the possible causes of observed variability. The results suggest that changes in the magnitude and location of anthropogenic sources have had a strong influence on the observed atmospheric COS variability.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

New aspects of heavy cosmic rays from calcium to nickel (Z = 20 to 28)

Over the two year course of this grant a study was conducted to explore the implications of composition measurements of heavy cosmic rays made by the Third High Energy Astronomy Observatory (HEAO-3). To interpret these and other measurements this study combined for the first time new laboratory measurements of the fragmentation cross sections of heavy nuclei, a new semi-empirical cross section formula, and the latest in cosmic ray propagation and solar modulation models. These models were used to interpret abundance measurements from six recent satellite experiments, including, in particular, two from HEAO-3. The principal results of the study were: (1) an improved interpretation of the Mn-54 clock in cosmic rays, including predictions of the isotopic abundances of Mn for comparison with future isotope measurements; (2) the first realization of the effect of Mn-54 decay on studies of the source abundances of Fe isotopes; (3) improved source abundances of the elements Ar, Ca, Cr, Mn, Fe, and Ni in the cosmic ray source material; (4) an improved fit to the abundances of Fe secondaries in cosmic rays; and (5) additional evidence that supports the validity of the leaky-box model of cosmic ray propagation in the galaxy. This final report summarizes these new results, the new tools that were developed to obtain them, and presents a bibliography of talks and publications that resulted from this work.

Mewaldt, Richard A.↗
NASA NTRS2007

F-15B Quiet Spike(TradeMark) Aeroservoelastic Flight-Test Data Analysis

System identification is utilized in the aerospace community for development of simulation models for robust control law design. These models are often described as linear, time-invariant processes and assumed to be uniform throughout the flight envelope. Nevertheless, it is well known that the underlying process is inherently nonlinear. Over the past several decades the controls and biomedical communities have made great advances in developing tools for the identification of nonlin ear systems. In this report, we show the application of one such nonlinear system identification technique, structure detection, for the an alysis of Quiet Spike(TradeMark)(Gulfstream Aerospace Corporation, Savannah, Georgia) aeroservoelastic flight-test data. Structure detectio n is concerned with the selection of a subset of candidate terms that best describe the observed output. Structure computation as a tool fo r black-box modeling may be of critical importance for the development of robust, parsimonious models for the flight-test community. The ob jectives of this study are to demonstrate via analysis of Quiet Spike(TradeMark) aeroservoelastic flight-test data for several flight conditions that: linear models are inefficient for modelling aeroservoelast ic data, nonlinear identification provides a parsimonious model description whilst providing a high percent fit for cross-validated data an d the model structure and parameters vary as the flight condition is altered.

Kukreja, Sunil L.↗
NASA NTRS2005

Atmospheric Hydroperoxides in West Antarctica: Links to Stratospheric Ozone and Atmospheric Oxidation Capacity

The troposphere above the West Antarctic Ice Sheet (WAIS) was sampled for hydroperoxides at 21 locations during 2-month-long summer traverses from 2000 to 2002, as part of US ITASE (International Transantarctic Scientific Expedition). First time quantitative measurements using an HPLC method showed that methylhydroperoxide (MHP) is the only important organic hydroperoxide occurring in the Antarctic troposphere, and that it is found at levels ten times those previously predicted by photochemical models. During three field seasons, means and standard deviations for hydrogen peroxide (H2O2) were 321+/-158 pptv, 650+/-176 pptv and 330+/-147 pptv. While MHP was detected, but not quantified in December 2000, levels in summer 2001 and 2002 were 317+128 pptv and 304+/-172.2 pptv. Results from firn air experiments and diurnal variability of the two species showed that atmospheric H2O2 is significantly impacted by a physical snow pack source between 76 and 90degS, whereas MHP is not. We show strong evidence of a positive feedback between stratospheric ozone and H2O2 at the surface. Between November-27 and December-12 in 2001, when ozone column densities dropped below 220 DU (means in 2000 and 2001 were 318 DU and 334 DU, respectively), H2O2 was 1.7 times that observed in the same period in 2000 and 2002, while MHP was only 80% of the levels encountered in 2002. Photochemical box model runs suggest that NO and OH levels on WAIS are closer to coastal values, while Antarctic Plateau levels are higher, confirming that region to be a highly oxidizing environment. The modeled sensitivity of H2O2 and particularly MHP to NO offers the potential to use atmospheric hydroperoxides to constrain the NO background and thus estimate the past oxidation capacity of the remote atmosphere. Index Terms: 0365 Atmospheric Composition and Structure: Troposphere: composition and chemistry; 0322 Atmospheric Composition and Structure: Constituent sources and sinks; 1610 Global Change: Atmosphere (03 15,0325); 0736 Cryosphere: Snow (1827, 1863); 0724 Cryosphere: Ice Cores (4932) Keywords: hydrogen peroxide, methylhydroperoxide, Antarctica, air-snow exchange, stratospheric ozone, atmospheric oxidation capacity

Frey, Markus M.↗
DOE OSTI2016

Quantification of black carbon mixing state from traffic: Implications for aerosol optical properties

The climatic impacts of black carbon (BC) aerosol, an important absorber of solar radiation in the atmosphere, remain poorly constrained and are intimately related to its particle-scale physical and chemical properties. Using particle-resolved modelling informed by quantitative measurements from a soot-particle aerosol mass spectrometer, we confirm that the mixing state (the distribution of co-emitted aerosol amongst fresh BC-containing particles) at the time of emission significantly affects BC-aerosol optical properties even after a day of atmospheric processing. Both single particle and ensemble aerosol mass spectrometry observations indicate that BC near the point of emission co-exists with hydrocarbon-like organic aerosol (HOA) in two distinct particle types: HOA-rich and BC-rich particles. The average mass fraction of black carbon in HOA-rich and BC-rich particle classes was < 0.1 and 0.8, respectively. Notably, approximately 90 % of BC mass resides in BC-rich particles. This new measurement capability provides quantitative insight into the physical and chemical nature of BC-containing particles and is used to drive a particle-resolved aerosol box model. Lastly, significant differences in calculated single scattering albedo (an increase of 0.1) arise from accurate treatment of initial particle mixing state as compared to the assumption of uniform aerosol composition at the point of BC injection into the atmosphere.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

Structure Computation of Quiet Spike[Trademark] Flight-Test Data During Envelope Expansion

System identification or mathematical modeling is used in the aerospace community for development of simulation models for robust control law design. These models are often described as linear time-invariant processes. Nevertheless, it is well known that the underlying process is often nonlinear. The reason for using a linear approach has been due to the lack of a proper set of tools for the identification of nonlinear systems. Over the past several decades, the controls and biomedical communities have made great advances in developing tools for the identification of nonlinear systems. These approaches are robust and readily applicable to aerospace systems. In this paper, we show the application of one such nonlinear system identification technique, structure detection, for the analysis of F-15B Quiet Spike(TradeMark) aeroservoelastic flight-test data. Structure detection is concerned with the selection of a subset of candidate terms that best describe the observed output. This is a necessary procedure to compute an efficient system description that may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modeling may be of critical importance for the development of robust parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion, which may save significant development time and costs. The objectives of this study are to demonstrate via analysis of F-15B Quiet Spike aeroservoelastic flight-test data for several flight conditions that 1) linear models are inefficient for modeling aeroservoelastic data, 2) nonlinear identification provides a parsimonious model description while providing a high percent fit for cross-validated data, and 3) the model structure and parameters vary as the flight condition is altered.

Kukreja, Sunil L.↗
NASA NTRS2007

F-15B QuietSpike(TradeMark) Aeroservoelastic Flight Test Data Analysis

System identification or mathematical modelling is utilised in the aerospace community for the development of simulation models for robust control law design. These models are often described as linear, time-invariant processes and assumed to be uniform throughout the flight envelope. Nevertheless, it is well known that the underlying process is inherently nonlinear. The reason for utilising a linear approach has been due to the lack of a proper set of tools for the identification of nonlinear systems. Over the past several decades the controls and biomedical communities have made great advances in developing tools for the identification of nonlinear systems. These approaches are robust and readily applicable to aerospace systems. In this paper, we show the application of one such nonlinear system identification technique, structure detection, for the analysis of F-15B QuietSpike(TradeMark) aeroservoelastic flight test data. Structure detection is concerned with the selection of a subset of candidate terms that best describe the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modelling may be of critical importance for the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion which may save significant development time and costs. The objectives of this study are to demonstrate via analysis of F-15B QuietSpike(TradeMark) aeroservoelastic flight test data for several flight conditions (Mach number) that (i) linear models are inefficient for modelling aeroservoelastic data, (ii) nonlinear identification provides a parsimonious model description whilst providing a high percent fit for cross-validated data and (iii) the model structure and parameters vary as the flight condition is altered.

Kukreja, Sunil L.↗