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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A two-equation soot-in-flamelet modeling approach applied under Spray A conditions

Soot production (including formation and oxidation) is studied in the transient, high-pressure and turbulent n-dodecane Spray A flames from the Engine Combustion Network (ECN) using computational fluid dynamics (CFD) simulations. A two-equation soot-in-flamelet modeling approach is applied within the framework of the Unsteady Flamelet Progress Variable (UFPV) model and results are validated against experimental data. Equations for soot mass fraction and soot number density derived in the mixture fraction space are solved in the context of detailed flamelet calculations. Source terms for the different steps in the soot chemistry are tabulated and incorporated in the flamelet manifold. For the reference condition, the modeling approach based on the tabulated flamelet manifold reduces the computational cost of a CFD calculation by approximately 40 times compared to a non-tabulated well-mixed (WM) modeling approach. The soot-in-flamelet approach is then extended to study the effect of ambient oxygen concentration, ambient mixture composition and ambient temperature on soot production. Results show that the modeling approach is able to capture the experimental trends for the soot volume fraction (SVF) with good quantitative agreement, especially in the soot ramp-up region.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2022

Evaluating the Impact of Chemical Complexity and Horizontal Resolution on Tropospheric Ozone Over the Conterminous US With a Global Variable Resolution Chemistry Model

A new configuration of the Community Earth System Model (CESM)/Community Atmosphere Model with full chemistry (CAM-chem) supporting the capability of horizontal mesh refinement through the use of the spectral element (SE) dynamical core is developed and called CESM/CAM-chem-SE. Horizontal mesh refinement in CESM/CAM-chem-SE is unique and novel in that pollutants such as ozone are accurately represented at human exposure relevant scales while also directly including global feedbacks. CESM/CAM-chem-SE with mesh refinement down to ∼14 km over the conterminous US (CONUS) is the beginning of the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICAv0). Here MUSICAv0 is evaluated and used to better understand how horizontal resolution and chemical complexity impact ozone and ozone precursors over CONUS as compared to measurements from five aircraft campaigns, which occurred in 2013. This field campaign analysis demonstrates the importance of using finer horizontal resolution to accurately simulate ozone precursors such as nitrogen oxides and carbon monoxide. In general, the impact of using more complex chemistry on ozone and other oxidation products is more pronounced when using finer horizontal resolution where a larger number of chemical regimes are resolved. Large model biases for ozone near the surface remain in the Southeast US as compared to the aircraft observations even with updated chemistry and finer horizontal resolution. This suggests a need for adding the capability of replacing sections of global emission inventories with regional inventories, increasing the vertical resolution in the planetary boundary layer, and reducing model biases in meteorological variables such as temperature and clouds.

MUSICA↗
NASA NTRS1976

Technique for atmospheric rate chemistry calculations

The possibility that predictions of atmospheric photochemistry/transport models are sensitive to uncertainties in reaction rates and other inputs stresses the need for rapid numerical integration schemes in rate photochemistry problems. Reducing the computational burden has a major merit in facilitating sensitivity studies to assess the effect of uncertainties on predicted ozone diminutions from NOx (NO + NO2) in the exhaust plume of SST engines. The paper discusses the validity of an algorithmic approach to integration of rate chemistry problems in combustion, developed by Rubel and Baronti for an approximate calculation of the production rate of the i-th chemical species involved. An analysis of two projected SST engines confirms the validity of the proposed algorithm. Because of the relative arithmetical simplicity, it may be easier to treat diffusion rate chemistry calculations using the Rubel and Baronti approximation than would be possible by other approaches.

Matloff, G. L.↗
NASA NTRS2010

Modeling of Alkane Oxidation Using Constituents and Species

It is currently not possible to perform simulations of turbulent reactive flows due in particular to complex chemistry, which may contain thousands of reactions and hundreds of species. This complex chemistry results in additional differential equations, making the numerical solution of the equation set computationally prohibitive. Reducing the chemical kinetics mathematical description is one of several important goals in turbulent reactive flow modeling. A chemical kinetics reduction model is proposed for alkane oxidation in air that is based on a parallel methodology to that used in turbulence modeling in the context of the Large Eddy Simulation. The objective of kinetic modeling is to predict the heat release and temperature evolution. This kinetic mechanism is valid over a pressure range from atmospheric to 60 bar, temperatures from 600 K to 2,500 K, and equivalence ratios from 0.125 to 8. This range encompasses diesel, HCCI, and gas-turbine engines, including cold ignition. A computationally efficient kinetic reduction has been proposed for alkanes that has been illustrated for n-heptane using the LLNL heptane mechanism. This model is consistent with turbulence modeling in that scales were first categorized into either those modeled or those computed as progress variables. Species were identified as being either light or heavy. The heavy species were decomposed into defined 13 constituents, and their total molar density was shown to evolve in a quasi-steady manner. The light species behave either in a quasi-steady or unsteady manner. The modeled scales are the total constituent molar density, Nc, and the molar density of the quasi-steady light species. The progress variables are the total constituent molar density rate evolution and the molar densities of the unsteady light species. The unsteady equations for the light species contain contributions of the type gain/loss rates from the heavy species that are modeled consistent with the developed mathematical forms for the total constituent molar density rate evolution; indeed, examination of these gain/loss rates shows that they also have a good quasi-steady behavior with a functional form resembling that of the constituent rate. This finding highlights the fact that the fitting technique provides a methodology that can be repeatedly used to obtain an accurate representation of full or skeletal kinetic models. Assuming success with the modified reduced model, the advantage of the modeling approach is clear. Because this model is based on the Nc rate rather than on that of individual heavy species, even if the number of species increases with increased carbon number in the alkane group, providing that the quasi-steady rate aspect persists, then extension of this model to higher alkanes should be conceptually straightforward, although it remains to be seen if the functional fits would remain valid or would require reconstruction.

Bellan, Jasette↗
NASA NTRS2016

The Effects of Consistent Chemical Kinetics Calculations on the Pressure-Temperature Profiles and Emission Spectra of Hot Jupiters

In this work we investigate the impact of calculating non-equilibrium chemical abundances consistently with the temperature structure for the atmospheres of highly-irradiated, close-in gas giant exoplanets. Chemical kinetics models have been widely used in the literature to investigate the chemical compositions of hot Jupiter atmospheres which are expected to be driven away from chemical equilibrium via processes such as vertical mixing and photochemistry. All of these models have so far used pressure-temperature (P-T) profiles as fixed model input. This results in a decoupling of the chemistry from the radiative and thermal properties of the atmosphere, despite the fact that in nature they are intricately linked. We use a one-dimensional radiative-convective equilibrium model, ATMO, which includes a sophisticated chemistry scheme to calculate P-T profiles which are fully consistent with non-equilibrium chemical abundances, including vertical mixing and photochemistry. Our primary conclusion is that, in cases of strong chemical disequilibrium, consistent calculations can lead to differences in the P-T profile of up to 100 K compared to the P-T profile derived assuming chemical equilibrium. This temperature change can, in turn, have important consequences for the chemical abundances themselves as well as for the simulated emission spectra. In particular, we find that performing the chemical kinetics calculation consistently can reduce the overall impact of non-equilibrium chemistry on the observable emission spectrum of hot Jupiters. Simulated observations derived from non-consistent models could thus yield the wrong interpretation. We show that this behaviour is due to the non-consistent models violating the energy budget balance of the atmosphere.

planets and satellites↗
NASA NTRS2022

Multidecadal Trends in Ozone Chemistry in the Baltimore-Washington Region

Over the past four decades, policy-led reductions in anthropogenic emissions have improved air quality over the Baltimore-Washington region (BWR). Most of the improvements in meeting the ozone air quality metrics (NAAQS) did not occur until the early 2000s despite large reductions in ozone precursors (NOx, CO, and volatile organic compounds (VOCs)) in the prior decades. We use observations of ozone and ozone precursors from satellites, ground-based sites, and the 2011 DISCOVER-AQ aircraft campaign in Maryland to illustrate how ozone chemistry in the BWR evolved between 1972 and 2019. Analysis of weekday vs weekend probability of ozone exceedance indicates the BWR transitioned to the NOx-limited regime by 2000–2003. A data-constrained box model agrees with this transition period and illustrates the key roles of reduced emissions of formaldehyde (HCHO), aromatics, and other VOCs since 1996, which reduced the peak of ozone production at the time of the transition and likely prevented the BWR from experiencing worsening surface air quality as the region transitioned to NOx-limited chemistry. Analysis of satellite observations of tropospheric column HCHO to NO2 analyzed using a new approach for evaluation of chemical regimes derived from DISCOVER-AQ data also provide a consistent depiction of the timing of the transition period that we infer from ground-based observations and the box model. Finally, despite significant improvements in air quality over the past two decades, the BWR still has not met the EPA standard for surface ozone. With predominantly NOx-limited ozone chemistry over the BWR, continued decreases in emission of NOx will slow the rate of ozone production and help improve air quality. We highlight emissions of NO2 from the diesel truck fleet as a worthwhile focus for future policy because emissions from this source appear to influence day-of-week variations in observed NO2, with an accompanying effect on ozone.

Ozone↗
NASA NTRS2012

Development of a Climate Record of Tropospheric and Stratospheric Column Ozone from Satellite Remote Sensing: Evidence of an Early Recovery of Global Stratospheric Ozone

Ozone data beginning October 2004 from the Aura Ozone Monitoring Instrument (OMI) and Aura Microwave Limb Sounder (MLS) are used to evaluate the accuracy of the Cloud Slicing technique in effort to develop long data records of tropospheric and stratospheric ozone and for studying their long-term changes. Using this technique, we have produced a 32-yr (1979-2010) long record of tropospheric and stratospheric column ozone from the combined Total Ozone Mapping Spectrometer (TOMS) and OMI. Analyses of these time series suggest that the quasi-biennial oscillation (QBO) is the dominant source of inter-annual variability of stratospheric ozone and is clearest in the Southern Hemisphere during the Aura time record with related inter-annual changes of 30- 40 Dobson Units. Tropospheric ozone for the long record also indicates a QBO signal in the tropics with peak-to-peak changes varying from 2 to 7 DU. The most important result from our study is that global stratospheric ozone indicates signature of a recovery occurring with ozone abundance now approaching the levels of year 1980 and earlier. The negative trends in stratospheric ozone in both hemispheres during the first 15 yr of the record are now positive over the last 15 yr and with nearly equal magnitudes. This turnaround in stratospheric ozone loss is occurring about 20 yr earlier than predicted by many chemistry climate models. This suggests that the Montreal Protocol which was first signed in 1987 as an international agreement to reduce ozone destroying substances is working well and perhaps better than anticipated.

ozone↗
DOE OSTI2021

Evaluation of the interactive stratospheric ozone (O3v2) module in the E3SM version 1 Earth system model

Stratospheric ozone affects climate directly as the predominant heat source in the stratosphere and indirectly through chemical reactions controlling other greenhouse gases. The U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1) implemented a new ozone chemistry module that improves the simulation of the sharp tropopause gradients, replacing a version based partly on long-term average climatologies that poorly represented heating rates in the lowermost stratosphere. The new O3v2 module extends seamlessly into the troposphere and preserves the naturally sharp cross-tropopause gradient, with 20 %–40 % less ozone in this region. Additionally, O3v2 enables the diagnosis of stratosphere–troposphere exchange flux of ozone, a key budget term lacking in E3SMv1. Here, we evaluate key features in ozone abundance and other closely related quantities in atmosphere-only E3SMv1 simulations driven by observed sea surface temperatures (SSTs, years 1990–2014), comparing them with satellite observations of ozone and also with the University of California, Irvine chemistry transport model (UCI CTM) using the same stratospheric chemistry scheme but driven by European Centre forecast fields for the same period. In terms of stratospheric column ozone, O3v2 shows reduced mean bias and improved northern midlatitude variability, but it is not quite as good as the UCI CTM. As expected, SST-forced E3SMv1 simulations cannot synchronize with observed quasi-biennial oscillations (QBOs), but they do show the typical QBO pattern seen in column ozone. This new O3v2 E3SMv1 model mostly retains the same climate state and climate sensitivity as the previous version, and we recommend its use for other climate models that still use ozone climatologies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

COMPUTATIONAL MODELING OF IGNITION AND PREMIXED FLAME PROPAGATION INITIATED BY A PRE-CHAMBER TURBULENT JET

Addressing the pressing need for reduced carbon emissions, Turbulent Jet Ignition (TJI) emerges as a promising technology for ultra-lean combustion, offering enhanced thermal efficiencies and minimized cyclic variability in spark-ignited engines. To facilitate rapid testing and integration of this technology, a robust computational modeling framework is crucial. This study delves into the predictive capabilities of computational models for main-chamber ignition and premixed flame propagation using a single-cycle TJI rig measured by Biswas et al. (Applied Thermal Engineering, vol 106, 2016). Employing an open-source compressible flow simulation solver with Large Eddy Simulation (LES) for turbulence modeling, the investigation integrates the conventional Laminar Finite Rate Chemistry (LFRC) model alongside the transported Probability Density Method (PDF) for turbulence-chemistry interaction. A fully-consistent Eulerian Monte-Carlo Fields (EMCF) method is utilized to approximate the transported PDF, while Interaction by Exchange with Mean is employed to close micro-mixing terms in stochastic differential equations. A reduced chemical reaction mechanism with 21 species and 84 reactions (DRM-19) is used for solving chemical kinetics, and a double Gaussian energy deposition model is used to approximate the spark ignition in the pre-chamber. An unstructured O-grid mesh with 0.3 million cells in the prechamber and 1 million cells in the main chamber is employed. Results are divided into two phases: pre-chamber initialization and full TJI simulations. Validation of the predicted pre-chamber flame propagation and the lean ignition in the main-chamber is carried out by using available experimental data. Under quiescent conditions, both the LFRC and transported PDF methods largely underestimate the flame speed and subsequent pressure growth in the pre-chamber. A linear momentum forcing technique is applied to investigate the impact of initial turbulence in the pre-chamber, demonstrating a notable influence on flame propagation. Fine-tuning of the forcing coefficient reproduces the sudden pressure growth observed in the experiment. The experimentally validated pre-chamber simulation serves as the initial condition for the full TJI simulations. It is found that the LFRC model fails to predict lean-ignition in the main-chamber, resulting in a misfiring event. Incorporation of turbulence-chemistry interaction using the transported PDF method substantially improves the prediction of the ignition event in the main-chamber, achieving fair qualitative agreement and quantitative validation of combustion parameters within ±10% of the reported experimental data. The rich simulation results consisting of a full set of statistical description of the thermo-chemical states enable us to gain deep insights into the ignition mechanisms in the main chamber, which is limited when done experimentally. A novel dual ignition phenomenon is revealed in the TJI rig for the first time. Initially, a primary ignition kernel is formed at a downstream location which eventually detaches from the main jet. As the jet momentum decreases, a secondary ignition event follows, this time at a more upstream location which eventually combines with the primary ignition kernel to form a single connected flame front. Investigation of these ignition sequences in chemical composition space reveal distinct differences between the two. The primary ignition event in the main-chamber is followed by a large concentration of active radicals from the pre-chamber jet, accelerating the chain-branching steps, characterizing what has been referred to as flame ignition. In contrast, the secondary ignition occurs in the absence of active radicals in the pre-chamber jet, hence characterized as jet ignition. Further analysis of the effect of pre-chamber jet characteristics on lean ignition in the main-chamber is conducted by setting up cases with different initial pressure ratios (por) between the two chambers, a non-dimensional parameter, ranging from 1.2 to 3.2. As the initial pressure ratio increases, jet momentum increases, with dual ignition observed in cases above por= 2.2. Case with por= 3.2 lead to misfiring. The effect of ignition sequence on global combustion characteristics of TJI is analyzed. Dual ignition events lead to non-monotonicity in combustion characteristics such as global reaction progress variable, flame penetration, and global heat release rate. In dual ignition events, although the rate of fuel consumption and global heat release rate is initially lower, the secondary ignition leads to a sudden increase in flame surface area, resulting in a sudden jump and promoting the overall performance of the TJI system.

42 ENGINEERING↗
NASA NTRS2016

The Viability of Trajectory Analysis for Diagnosing Dynamical and Chemical Influences on Ozone Concentrations in the UTLS

The viability of trajectory analysis for diagnosing the interplay between chemistry and dynamics is investigated by comparing ozone mixing ratios modelled using air-parcel pathways to values observed along flight tracks during ATTREX (Airborne Tropical TRopopause EXperiment). Trajectories are initiated at the locations of ozone observations and tracked backward in time to their sources at termini of backward trajectories. The modelled values of ozone utilize 3-dimensional analysis fields from WACCM (Whole Atmosphere Community Climate Model) (a chemical-climate model with dynamical fields nudged towards MERRA (Modern-Era Retrospective Analysis and Research Applications) reanalysis) and ERA-interim (product of ECMWF - the European Centre for Medium-Range Weather Forecasts) to determine source mixing ratios with chemical production and loss terms derived from the ozone chemistry used in WACCM. A statistical base of modelled ozone is constructed with 6 trajectory platforms (adiabatic, diabatic, and kinematic forced by ERA-interim and MERRA), two chemical models (WACCM chemistry and no chemistry), and 4 trajectory lengths (5, 10, 20, and 30 days). Linear regression is employed to separate systematic errors from random errors and to characterize the impact of source mixing ratios, path length, vertical motion, and chemistry on modelled ozone errors. Errors in the analysis ozone fields are large, if not dominant, contributors to model error. Random errors are particularly large for point-by-point comparisons, however averaging over 800 km (75 minutes) flight segments substantially reduces random error and exposes systematic errors. Of the two analysis ozone data sets, WACCM, which incorporates detailed chemistry, provides the smaller systematic errors while ERA-interim, which has crude chemistry but assimilates observational data, has the smaller random errors. Of the different trajectory platforms, adiabatic calculations produce the smaller random errors (irrespective of the use of chemistry) but both vertical motion and chemistry are required to optimally reduce systematic errors. These results suggest that meaningful analysis of dynamical and chemical interactions that control ozone mixing ratios are viable on spatial scales larger than a few reanalysis grid spaces, that errors in the analyzed ozone data sets are large but not prohibitively so, and that vertical velocities and heating rates from reanalysis data, while problematic, contain useful information [on the ozone concentrations in the UTLS (Upper Troposphere/Lower Stratosphere)].

ozone↗
DOE OSTI2021

Pulse Detonation Engine for Advanced Oxy-Combustion of Coal-Based Fuel for Direct Power Extraction Applications

Pressure gain combustion and magnetohydrodynamic (MHD) systems have the potential to provide a step increase in the efficiency of combined-cycle power plants. Specific advantages include a net pressure increase to the system instead of a pressure drop, the high temperature of the detonation waves can increase the efficiency of power extraction compared to other processes, significant thermal energy can be released in a compact region, and the high velocities of the flow increase extraction of electrical power. In summary, a pressure gain combustor coupled with a MHD has the potential to be transformative. Despite the potential advantages, relatively little research has been conducted considering coupled pressure gain combustion systems with MHD systems. With this background and motivation, the overall goal of this effort was to advance the knowledge, technology, and computational tools associated with coupled detonation and MHD systems. A joint experimental and computational approach was used while seeking to accomplish the goals of this work. Specifically, two pulse detonation engines were developed and used for the experiments to produce detonations. Detonation speeds were measured for a variety of flow and fuel conditions (e.g., methane, propane, with coal particles). Preliminary electrical conductivity measurements were collected. An extensive amount of research was performed to identify sensitivities of detonation behaviors to the presence of combustion products. Computationally, a twofold approach was used in this work. First, a solver was developed for solving the governing equations for a reactive flow with coupled detonation and ionization chemistry. The solver was applied to study the impacts of seed material ionization on detonation. Second, a conservation element-solution element (CE-SE) based numerical solver for detonation studies with a reduced reaction mechanism for oxy-methane combustion was developed and verified on standard test cases. Key findings and contributions from this work are as follows. A system was developed for injecting powderized coal, or other seeding material, into a pulse-detonation system. The influence of a combustion product (i.e., CO 2 ) on detonation behavior was identified. Knowledge gained from this work is applicable to devices such as rotating detonation engines, where combustion products mix with fresh reactants. A system for measuring the electrical conductivity of the exhaust from a pulse-detonation engine was developed. The open-source solver, Clawpack, was extended to solve the reactive Euler equations for simulating detonations. A coupled combustion and ionization chemistry was developed in a single chemical kinetic model for methane oxidation. This model can be used to solve coupled MHD and detonation simulations. It was found that parasitic interactions from ionization chemistry with the magnetic field can reduce the detonation velocity by up to 8%, with a potential impact on power extraction of 15%. It is recommended that interactions between the detonation front and MHD field be considered. Higher gas temperatures and velocities were achieved owing to oxy-fuel detonations. Use of radical dissociation reactions in the reduced reaction mechanism, was found to be critical in predicting detonation temperature and velocity accurately.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2018

Cloud Impacts on Photochemistry: Building a Climatology of Photolysis Rates from the Atmospheric Tomography Mission

Measurements from actinic flux spectroradiometers on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission provide an extensive set of statistics on how clouds alter photolysis rates (J values) throughout the remote Pacific and Atlantic Ocean basins. J values control tropospheric ozone and methane abundances, and thus clouds have been included for more than three decades in tropospheric chemistry modeling. ATom made four profiling circumnavigations of the troposphere capturing each of the seasons during 2016–2018. This work examines J values from the Pacific Ocean flights of the first deployment, but publishes the complete Atom-1 data set (29 July to 23 August 2016). We compare the observed J values (every 3 s along flight track) with those calculated by nine global chemistry– climate/transport models (globally gridded, hourly, for a mid-August day). To compare these disparate data sets, we build a commensurate statistical picture of the impact of clouds on J values using the ratio of J -cloudy (standard, sometimes cloudy conditions) to J -clear (artificially cleared of clouds). The range of modeled cloud effects is inconsistently large but they fall into two distinct classes: (1) models with large cloud effects showing mostly enhanced J values aloft and or diminished at the surface and (2) models with small effects having nearly clear-sky J values much of the time. The ATom-1 measurements generally favor large cloud effects but are not precise or robust enough to point out the best cloud-modeling approach. The models here have resolutions of 50–200 km and thus reduce the occurrence of clear sky when averaging over grid cells. In situ measurements also average scattered sunlight over a mixed cloud field, but only out to scales of tens of kilometers. A primary uncertainty remains in the role of clouds in chemistry, in particular, how models average over cloud fields, and how such averages can simulate measurements.

actinic flux spectroradiometers↗
DOE OSTI2024

Development of Transformational Solvents for CO2 Separations

The primary objective of this CRADA activity is to use a combined molecular modeling and experimental validation approach to refine and develop transformational solvents for carbon capture. PNNL’s role on this project is currently funded by the Department of Energy’s (DOE) Office of Fossil Energy (OFE). PNNL is developing advanced molecular modeling based on their CO2BOLs solvent platform as a demonstration solvent for the activity; the model was developed and compared against measured data for CO2BOL derivatives. Here, a CRADA with PNNL and GE will leverage their current molecular models and apply them to solvent classes that operate on carbamate chemistry, specifically GE’s aminosilicone solvent class. The molecular models will be used to predict physical and thermodynamic properties, such as viscosity, as a means to predict advanced formulations with reduced viscosity compared to current aminosilicone derivatives, enabling optimized thermodynamic and kinetic metrics for economical carbon capture for this class of materials. Together, PNNL and GE will develop a comprehensive means of linking molecular modeling parameters to intermediate physical properties as a means to improve solvent performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning

The ability to perform ab initio molecular dynamics simulations using potential energy surfaces provided by quantum computers would open the door to virtually exact dynamics for a variety of chemical and biochemical systems, with impacts on catalysis and biophysics. Nonetheless, performing molecular dynamics on surfaces produced by quantum hardware has been hampered by the noisy energies typically produced by quantum computers and challenges associated with computing gradients and scaling to large systems interest. A recent set of advances in machine learning, known as transfer learning, provides a new path forward for molecular dynamics simulations on quantum hardware. Transfer learning offers a workaround, where one first trains models on larger, less accurate classical datasets and then refines them on smaller, more accurate quantum datasets. We explore this approach by training machine learning models to predict a molecule's potential energy based on its geometric structure using Behler-Parrinello neural networks. When successfully trained, the model enables energy gradient predictions necessary for dynamic simulations. To reduce the quantum resources needed, the model is initially trained with data derived from classical density functional theory and subsequently refined with a smaller dataset obtained from a variational quantum eigensolver optimization of the unitary coupled cluster ansatz. We show that this approach significantly reduces the size of the needed quantum training dataset while capturing the high accuracies needed within quantum chemistry simulations. The success of this two-step training method opens more opportunities to apply machine learning models on quantum data, a significant stride towards efficient quantum-classical hybrid computational models.

quantum computing↗
NASA NTRS1998

Stratospheric Sulfuric Acid and Black Carbon Aerosol Measured During POLARIS and its Role in Ozone Chemistry

Stratospheric aerosol can affect the environment in three ways. Sulfuric acid aerosol have been shown to act as sites for the reduction of reactive nitrogen and chlorine and as condensation sites to form Polar Stratospheric Clouds, under very cold conditions, which facilitate ozone depletion. Recently, modeling studies have suggested a link between BCA (Black Carbon Aerosol) and ozone chemistry. These studies suggest that HNO3, NO2, and O3 may be reduced heterogeneously on BCA particles. The ozone reaction converts ozone to oxygen molecules, while HNO3 and NO2 react to form NOx. Finally, a buildup of BCA could reduce the single-scatter albedo of aerosol below a value of 0.98, a critical value that has been postulated to change the effect of stratospheric aerosol from cooling to warming. Correlations between measured BCA amounts and aircraft usage have been reported. Attempts to link BCA to ozone chemistry and other stratospheric processes have been hindered by questions concerning the amount of BCA that exists in the stratosphere, the magnitude of reaction probabilities, and the scarcity of BCA measurements. The Ames Wire Impactors (AWI) participated in POLARIS as part of the complement of experiments on the NASA ER-2. One of our main objectives was to determine the amount of aerosol surface area, particularly BCA, available for reaction with stratospheric constituents and assess if possible, the importance of these reactions. The AWI collects aerosol and BCA particles on thin Palladium wires that are exposed to the ambient air in a controlled manner. The samples are returned to the laboratory for subsequent analysis. The product of the AWI analysis is the size, surface area, and volume distributions, morphology and elemental composition of aerosol and BCA. This paper presents results from our experiments during POLARIS and puts these measurements in the context of POLARIS and other missions in which we have participated. It describes modifications to the AWI data analysis procedures in which the collection of BCA is modeled as a fractal aggregate. The new analysis results in an increase in BCA surface area of approximately 24 and an increase in mass of 7-10 from the previous method. For the current study, BCA surface area is used in computer models that attempt to predict measured NOx/NOy ratios and O3 depletion rates. Inclusion of the HNO3 reaction with BCA in one model tends to improve the agreement of calculated to measured NOx/NOy ratio. However, it was found that these trends are viable only if the reactions are catalytic.

Strawa, Anthony W.↗
DOE OSTI2022

Uncertainty-aware mixed-variable machine learning for materials design

Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Mapping Interfacial Solution Forces that Drive Fluorescent Nanoparticle Incorporation into Crystals

Interfacial solution structure governs processes ranging from catalytic and electrochemical reactions to particle aggregation and crystallization. Here, we design bio-inspired nanocomposites by mapping interfacial forces and controlling nanoparticle incorporation during crystal growth. Our analytical model contains measured kinetic barriers for surface approaches and equilibrium binding constants with the crystal surface. We validate this model using fluorescent silica nanoparticles and calcite. Our results show that surface chemistry dictates incorporation pathways: methoxy groups are least favorable, hydroxyls reduce kinetic barriers by interacting favorably with hydration layers, carboxylates bind strongly but must overcome a kinetic barrier, and amines outperform all kinetically and thermodynamically. This framework resolves the interplay between interfacial solution structures, particle forces and dynamics, and growth kinetics, enabling predicative control of nanocomposite formation and multiplexed mixed-particle systems.

Zhang, Mingyi [Pacific Northwest National Laborato↗
DOE OSTI2023

Assessing the Role of Interfacial and Metal Sites in Pt/TiO2-Catalyzed Acetic Acid Hydrodeoxygenation

Thermochemical conversion of biomass to produce drop-in quality biofuels typically involves hydrodeoxygenation (HDO) steps following catalytic fast pyrolysis (CFP) to remove excess oxygen and create a more-stable bio-oil product. HDO reactions are performed by co-feeding the CFP vapor-phase product and H2 gas over a bi-functional catalyst. Noble-metal catalysts supported on reducible metal oxides (e.g., Pt/TiO2) are active and selective toward these HDO reactions. Griffin and co-workers showed that Pt/TiO2 catalysts promote the desired deoxygenation steps for m-cresol HDO while mitigating undesired C-C bond-breaking steps that reduce the overall value/energy density of the biofuel. Such model-compound studies to inform the design of improved catalysts for HDO chemistry are necessary to improve the overall process economics/efficiencies for biofuels production. One important class of bio-derived compounds that has not been studied extensively with respect to HDO chemistry, particularly at the atomic level, is carboxylic acids. Past experimental work indicates that Pt/C and Pt/TiO2 catalysts are selective toward C-C and C-O bond-dissociation products for acetic acid HDO, respectively. To understand the role of Pt/TiO2 active sites in this observed change in selectivity and guide catalyst development, the work in this presentation focuses on modeling the role of Pt-metal and Pt-TiO2-interfacial sites in promoting key C-C bond-breaking, C-O bond-breaking, and (de)hydrogenation steps in acetic acid HDO. Density functional theory (DFT) calculations were performed using the Vienna Ab initio Simulation Package (VASP). The exchange-correlation functional was approximated by the Perdew-Burke-Ernzerhof functional. Dispersion interactions were captured using the D3 method. Projector augmented-wave potentials described electron-ion interactions, and electron wavefunctions were expanded via a planewave basis with an energy cutoff of 500 eV. Activation barriers were calculated using the climbing image nudged elastic band method. Results and Discussion: To discern the role of Pt-metal and Pt-TiO2 interface sites in promoting acetic acid HDO chemistry, Pt(111) slab and anatase TiO2(101)-supported Pt-nanowire (PtNW/OH-TiO2) surface models were constructed, respectively. Because H2 is co-fed in HDO reactions, the anatase support was terminated with OH groups. Interfacial vacancies have been shown to facilitate Ru/TiO2-catalyzed phenol HDO; thus, an interfacial model with an OH vacancy was also considered (PtNW/OHv-TiO2). Pt-TiO2-interface sites stabilize adsorption of all studied acetic acid HDO surface intermediates relative to terrace Pt-metal sites, particularly when an interfacial-OH vacancy is present. Oxygenated species prefer to bind at the OH vacancy through the O atom, suggesting a preference for C-O over C-C bond cleavage at these sites. This hypothesis is supported by net-negative and net-positive average shifts in the reaction energy and activation energy barriers for C-O and C-C bond-breaking steps, respectively, at Pt-TiO2-interface sites relative to Pt-metal sites. Using the calculated energetics, the predicted minimum-energy pathway was determined for each surface. Pt(111) is predicted to follow decarboxylation, producing undesired methane and carbon dioxide. Conversely, PtNW/OH-TiO2 and PtNW/OHv-TiO2 are both predicted to produce desired acetaldehyde and ethane. The interfacial vacancy may also play a key role in facilitating the first C-O bond-breaking step in acetic acid HDO, lowering the barrier by 0.6 eV relative to the defect-free interface model. These results demonstrate the critical role of the Pt-TiO2 interface in the shift in acetic acid HDO selectivity experimentally observed on Pt/C and Pt/TiO2 catalysts. The results herein demonstrate the important role of the Pt-TiO2 interface and interfacial oxygen vacancies in improving the carbon efficiency for HDO reactions in CFP upgrading.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗