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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Evaluation of Machine Learning Approaches to Estimate Aerosol Mixing State Metrics in Atmospheric Models

Aerosol mixing state describes how aerosol compositions are distributed among atmospheric aerosol particles in a population. Oversimplified assumptions of aerosol mixing state in atmospheric modeling can introduce errors in estimations of weather and climate-relevant aerosol microphysical properties. A more comprehensive representation of the aerosol mixing state can be achieved in principle with a Particle-resolved Monte Carlo (PartMC) model but at added computational cost that may be prohibitive for direct invocation in operational numerical weather prediction or multi-year climate simulations. The aim of our research is to explore the machine learning (ML) methodologies for estimating aerosol mixing state metrics, which we define here in three different ways: with respect to (a) hygroscopicity; (b) optical properties; and (c) chemical species abundance. We adopted a data-driven approach, leveraging deep learning and statistical learning techniques, to take advantage of massive PartMC model simulations. First, we performed particle-resolved simulations by PartMC to create a series of scenarios considering a range of global environmental conditions. Each scenario consists of aerosol populations and corresponding mixing state metrics. The gas concentration, aerosol mass concentration, environmental variables and mixing state metrics of each population constitute the datasets for machine learning implementations. We have adopted and evaluated various configurations of machine learning methodologies in this investigation, embracing deep learning, Extreme Gradient Boosting (XGBoost) algorithm, and the ensemble approaches. After a rigorous model selection process, we identified an appropriate model to derive estimates of aerosol mixing state metrics. We used the computational and data resources of the Oak Ridge Leadership Computing Facility (OLCF) and the ORNL Compute and Data Environment for Science (CADES). The NVIDIA DGX-1 hardware was used for the prototyping of the ML models. Our approach has allowed us to gain a new understanding of how machine learning methodologies can be applied to improve the representation of aerosol mixing state in atmospheric models and benefit the atmospheric research community. Next, we plan to extend our research and methodology to quantify some of the aerosol-related uncertainties in the E3SM Atmospheric Model (EAM) Version 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Spatial variability of aerosol iron mineralogy and oxidation states over the Arctic Ocean

The mineralogy and oxidation state of aerosol iron (Fe) play important roles in controlling aerosol Fe solubility and consequent bioavailability in seawater. In this study, the spatial variability of Fe mineralogy and oxidation states in aerosols collected during the US GEOTRACES Western Arctic cruise (GN01) were determined using synchrotronbased X-ray absorption near edge structure (XANES) spectroscopy. Both Fe(II) minerals (biotite, ilmenite) and Fe (III) minerals (ferrihydrite, hematite, Fe(III) phosphate) were found in these samples. However, aerosol Fe mineralogy and solubility observed during this cruise varied spatially and can be grouped into three clusters based on the air masses that affected aerosols collected in different regions: (1) biotite-enriched particles (87 % biotite, 13 % hematite) with the air masses passing over Alaska, showing relatively low Fe solubility (4.0 +/- 1.7 %); (2) ferrihydrite-enriched particles (82 % ferrihydrite, 18 % ilmenite) collected in the remote Arctic air, showing relatively high Fe solubility (9.6 +/- 3.3 %); (3) the fresh dust derived from North America and Siberia, primarily dominated by hematite (41 % hematite, 25 % Fe(III) phosphate, 20 % biotite, 13 % ferrihydrite), showing relatively low Fe solubility (5.1 +/- 3.5). A significant positive correlation was found between Fe oxidation state and Fe fractional solubility, suggesting that long-range transport could modify iron (hydr) oxide such as ferrihydrite through atmospheric processing, influencing aerosol Fe solubility and consequently Fe bioavailability in the remote Arctic Ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Support for Reactor Operators in Case of Cyber-Security Threats (NEUP Final Report)

Increasing use of digital technology in nuclear power plants (NPPs) poses cyber-security as a crucial threat to public safety and to continuous energy production. Cyber-security risks are comprised of complex known and unknown interactions between various entities, system vulnerabilities, network protocols, human users and malicious attacks. There is little understanding or research geared towards plant operators’ response under cyber-security threats and operational procedures to cope with such threats. This is particularly critical when a cyber-security event masquerades as a safety incident or an evolving accident. Instead of leading operators to the remediation of the accident, the masked cyber event may lead them to circumvent the safety mechanisms of the plant. Given these challenges, the objective of this project is to develop a tool to characterize abnormal NPP events as “cyber” or “safety” incidents, and to further develop this tool as a real-time operator aid to assist in response to the incident. This NEUP Final Report summarizes the research efforts towards the above goals and the results in the following three aspects. First, a methodology for classifying abnormal NPP events as cyber-attacks or safety incidents was developed. This provides the basis for responding to potential cyber-attacks. In this methodology, the causal relationships between various variables (i.e., physical variables, network variables, and control variables) for a nuclear system under consideration are modeled using dynamic Bayesian networks. Based on observations of certain variables (i.e., the evidence), inference is performed to identify the state of hidden variables, which helps indicate the occurrence of cyber-attacks or safety incidents. The proposed methodology was applied to the full scope NPP simulator system at The Ohio State University as a case study. The result shows that the proposed methodology is capable of distinguishing between cyber-attacks and safety incidents. Second, a game-theoretic approach for responding to cyber-attacks on nuclear power plants was developed. This is the second major goal of the project, and the method is used to recommend optimal responses to reactor operators in the case of cyber-attacks. We consider two cases in this research. In the first case, the interaction between the defender (i.e., the reactor operator) and the attacker is modeled as a two-player, nonzero-sum, Markov game with an infinite time horizon. In the second case, we consider a semi-Markov game between the defender and the attacker within a finite time horizon. The model in the second case enables us to consider the time sensitive response of the defender. In each case, the method for deriving the Nash equilibrium is developed. The Nash Equilibrium of the game provides the valid prediction of both players’ actions because no single player can benefit from unilaterally deviating from the equilibrium policy if the other player adheres to his/hers, hence it provides the best response of the defender to cyber-attacks. The proposed method in each case is demonstrated through a simplified benchmark digital feedwater control system. Third, a prototype display is developed to integrate the above information. The prototype display depicts the communication information to the operator and incorporates the cyber-security tool developed by the team into the interface. It consists of the regular display in current control rooms, as well as a panel used to indicate the occurrence of cyber-attacks or safety events and a panel used to guide the operator’s response to detected cyber-attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2013

Attribution of Trends and Variability in Surface Ozone over the United States

Concentrations of tropospheric ozone, a greenhouse gas and air pollutant, are impacted by changes in precursor emissions as well meteorology and influx from the stratosphere. Observations show a decreasing trend in summertime surface ozone at rural stations in the eastern United States, while some western stations show increasing trends, particularly in springtime. We use the Global Modeling Initiative (GMI) global chemical transport model to investigate the roles of precursor emission changes, meteorological variability, and stratosphere-troposphere exchange (STE) in explaining observed trends in surface ozone from rural sites in the United States from 1991-2010. The model's interannual variability shows significant correlations with observations from many of the surface sites. We also compare the simulated ozone to ozonesonde data for several locations with sufficiently long records. We compare a simulation with time-dependent precursor emissions, including emission reductions over the United States and Europe and increases over Asia, to a simulation with fixed emissions to quantify the impact of changing emissions on the surface trends. The simulation with varying emissions reproduces much of the east-west difference in summertime ozone over the U.S., although it generally underestimates the negative trend in the East. In contrast, the fixed-emission simulation shows increasing ozone at both eastern and western sites. We will discuss possible causes of this behavior, including long-range transport and STE.

ozone↗
DOE OSTI2025

Investigating Particle Phase State Dependencies during a Historic Pacific Northwest Wildfire Event

The function and lifetime of an atmospheric particle are greatly dependent upon its phase state. Studying particle phase states from specific ambient source types is often challenging due to uncontrolled environmental conditions and multiple source contributions. However, a historic Pacific Northwest wildfire event occurred in September 2020, providing a rare opportunity to study a pseudo-single particle type ambient sample (i.e., wildfire) over time. Sampling was performed in Richland, Washington, capturing aged particles from shifting wildfire sources. In addition to source variability, temporal variation in wildfire particle phase state was observed from single particle analyses. Despite this, single particle elemental compositions were homogenous over time (=87% carbonaceous) with no significant temporal variation in organic functional group abundances within individual particles either (e.g., 4% relative standard deviation in alkene contributions). While particle composition is indeed known to affect phase state, water uptake appeared to be the most significant contributor to phase state variability amongst single particles here, as particle aspect ratios (related to phase state) linearly correlated with ambient relative humidity during sampling (R2 = 0.65). Therefore, while limited to a case study, meteorological considerations are found to convey greater comparative importance than particle composition for aged particle phase state predictions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Maximizing ozone signals among chemical, meteorological, and climatological variability

The detection of meteorological, chemical, or other signals in modeled or observed air quality data – such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season – is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical, meteorological, and climatological variabilities (and their interactions) that exist both in space and in time, and which include variability in emissions and surface processes. This can present difficulties for both policymakers and researchers as they attempt to identify the influence or signal of climate trends (e.g., any pauses in warming trends), the impact of enacted emission reductions policies (e.g., United States NO x State Implementation Plans), or an estimate of the mean state of highly variable data (e.g., summertime ozone over the northeastern United States). Here we examine the scale dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the continental US. For signals that are large compared to the meteorological variability (e.g., strong emissions reductions), shorter averaging periods and smaller spatial averaging regions may be sufficient, but for many signals that are smaller than or comparable in magnitude to the underlying meteorological variability, we recommend temporal averaging of 10–15 years combined with some level of spatial averaging (up to several hundred kilometers). If this level of averaging is not practical (e.g., the signal being examined is at a local scale), we recommend some exploration of the spatial and temporal variability to provide context and confidence in the robustness of the result. These results are consistent between simulated and observed data, as well as within a single model with different sets of parameters. The strategies selected in this study are not limited to surface ozone data and could potentially maximize signal detection capabilities within a broad array of climate and chemical observations or model output.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Parametric Sensitivity Analysis for the Asian Summer Monsoon Precipitation Simulation in the Beijing Climate Center AGCM Version 2.1

In this study, we apply an efficient sampling approach and conduct a large number of simulations to explore the sensitivity of the simulated Asian summer monsoon (ASM) precipitation, including the climatological state and interannual variability, to eight parameters related to the cloud and precipitation processes in the Beijing Climate Center AGCM version 2.1 (BCC_AGCM2.1). Our results show that BCC_AGCM2.1 has large biases in simulating the ASM precipitation. The precipitation efficiency and evaporation coefficient for deep convection are the most sensitive parameters in simulating the ASM precipitation. With optimal parameter values, the simulated precipitation climatology could be remarkably improved, e.g. increased precipitation over the equator Indian Ocean, suppressed precipitation over the Philippine Sea, and more realistic Meiyu distribution over Eastern China. The ASM precipitation interannual variability is further analyzed, with a focus on the ENSO impacts. It shows the simulations with better ASM precipitation climatology can also produce more realistic precipitation anomalies during El Niño decaying summer. In the low-skill experiments for precipitation climatology, the ENSO-induced precipitation anomalies are most significant over continents (vs. over ocean in observation) in the South Asian monsoon region. More realistic results are derived from the higher-skill experiments with stronger anomalies over the Indian Ocean and weaker anomalies over India and the western Pacific, favoring more evident easterly anomalies forced by the tropical Indian Ocean warming and stronger Indian Ocean-western Pacific tele-connection as observed. Our model results reveal a strong connection between the simulated ASM precipitation climatological state and interannual variability in BCC_AGCM2.1 when key parameters are perturbed.

Asian summer monsoon precipitation↗
NASA NTRS2002

A Numerical Scheme for Ordinary Differential Equations Having Time Varying and Nonlinear Coefficients Based on the State Transition Matrix

A variable order method of integrating initial value ordinary differential equations that is based on the state transition matrix has been developed. The method has been evaluated for linear time variant and nonlinear systems of equations. While it is more complex than most other methods, it produces exact solutions at arbitrary time step size when the time variation of the system can be modeled exactly by a polynomial. Solutions to several nonlinear problems exhibiting chaotic behavior have been computed. Accuracy of the method has been demonstrated by comparison with an exact solution and with solutions obtained by established methods.

Bartels, Robert E.↗
DOE OSTI2016

THE MIRA–TITAN UNIVERSE: PRECISION PREDICTIONS FOR DARK ENERGY SURVEYS

Large-scale simulations of cosmic structure formation play an important role in interpreting cosmological observations at high precision. The simulations must cover a parameter range beyond the standard six cosmological parameters and need to be run at high mass and force resolution. A key simulation-based task is the generation of accurate theoretical predictions for observables using a finite number of simulation runs, via the method of emulation. Using a new sampling technique, we explore an eight-dimensional parameter space including massive neutrinos and a variable equation of state of dark energy. We construct trial emulators using two surrogate models (the linear power spectrum and an approximate halo mass function). The new sampling method allows us to build precision emulators from just 26 cosmological models and to systematically increase the emulator accuracy by adding new sets of simulations in a prescribed way. Emulator fidelity can now be continuously improved as new observational data sets become available and higher accuracy is required. Finally, using one ΛCDM cosmology as an example, we study the demands imposed on a simulation campaign to achieve the required statistics and accuracy when building emulators for investigations of dark energy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Radical–Radical Reactions in Molecular Weight Growth: The Phenyl + Propargyl Reaction

The mechanism for hydrocarbon ring growth in sooting environments is still the subject of considerable debate. The reaction of phenyl radical (C 6 H 5 ) with propargyl radical (H 2 CCCH) provides an important prototype for radical–radical ring-growth pathways. We studied this reaction experimentally over the temperature range of 300–1000 K and pressure range of 4–10 Torr using time-resolved multiplexed photoionization mass spectrometry. We detect both the C 9 H 8 and C 9 H 7 + H product channels and report experimental isomer-resolved product branching fractions for the C 9 H 8 product. We compare these experiments to theoretical kinetics predictions from a recently published study augmented by new calculations. Here, these ab initio transition state theory-based master equation calculations employ high-quality potential energy surfaces, conventional transition state theory for the tight transition states, and direct CASPT2-based variable reaction coordinate transition state theory (VRC-TST) for the barrierless channels. At 300 K only the direct adducts from radical–radical addition are observed, with good agreement between experimental and theoretical branching fractions, supporting the VRC-TST calculations of the barrierless entrance channel. As the temperature is increased to 1000 K we observe two additional isomers, including indene, a two-ring polycyclic aromatic hydrocarbon, and a small amount of bimolecular products C 9 H 7 + H. Our calculated branching fractions for the phenyl + propargyl reaction predict significantly less indene than observed experimentally. We present further calculations and experimental evidence that the most likely cause of this discrepancy is the contribution of H atom reactions, both H + indenyl (C 9 H 7 ) recombination to indene and H-assisted isomerization that converts less stable C 9 H 8 isomers into indene. Especially at low pressures typical of laboratory investigations, H-atom-assisted isomerization needs to be considered. Regardless, the experimental observation of indene demonstrates that the title reaction leads, either directly or indirectly, to the formation of the second ring in polycyclic aromatic hydrocarbons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Clar’s Goblet on Graphene: Field-Modulated Charge Transfer in a Hydrocarbon Heterostructure

In certain configurations, the aromatic properties of benzene ring structured molecules allow for unpaired, reactive valence electrons (known as radicals). Clar’s goblets are such molecules. With an even number of unpaired radicals, these nanographenes are topologically frustrated hydrocarbons in which π-bonding network and topology of edges give rise to the magnetism. Clar’s goblets are therefore valued as prospective qubits provided they can be modulated between magnetic states. Here, using first-principles DFT, we demonstrate the effects of adsorption on both molecule and substrate in a graphene–Clar’s goblet heterostructure. We look at the energy difference bewteen FM and AFM states of the system and discuss underlying physical and chemical mechanisms in reference to the highest occupied molecular orbital (HOMO) and second HOMO (HOMO–1). We find that the HOMO of the molecule in the FM state is right at the Fermi surface, which leads to the hybridization between molecular state and the graphene state near the Dirac point. Furthermore, we investigate qualitative changes in charge realignment and magnetic state under a variable electric field. Transitions from FM to AFM and back to FM states are observed.

Bruce, Adam V.↗
NASA NTRS1972

Application of numerical methods to extend capabilities for optimal rocket guidance: report on reentry guidance of shuttle orbiter

Analytical models are presented for optimal trajectories and reentry guidance of the space shuttle orbiter. Major emphasis is placed on the development of a "footprint', which refers to a set of reachable destination positions attainable by the shuttle at a specified terminal altitude. An unconstrained reentry footprint was calculated for a shuttle vehicle which enters the earth's atmosphere at 93 km initial altitude after a deboost from a near earth orbit. The method of computation is briefly described, and graphs are presented which illustrate the footprint and the variation of state and control variables along it. The effects of constraints and of variations in initial state upon the footprint are discussed.

Source record↗
NASA NTRS1978

Procedures for generation and reduction of linear models of a turbofan engine

A real time hybrid simulation of the Pratt & Whitney F100-PW-F100 turbofan engine was used for linear-model generation. The linear models were used to analyze the effect of disturbances about an operating point on the dynamic performance of the engine. A procedure that disturbs, samples, and records the state and control variables was developed. For large systems, such as the F100 engine, the state vector is large and may contain high-frequency information not required for control. This, reducing the full-state to a reduced-order model may be a practicable approach to simplifying the control design. A reduction technique was developed to generate reduced-order models. Selected linear and nonlinear output responses to exhaust-nozzle area and main-burner fuel flow disturbances are presented for comparison.

Seldner, K.↗
NASA NTRS1994

Canonical-variables multigrid method for steady-state Euler equations

A novel approach for the solution of inviscid flow problems for subsonic compressible flows is described. The approach is based on canonical forms of the equations, in which subsystems governed by hyperbolic operators are separated from those governed by elliptic ones. The discretizations used as well as the iterative techniques for the different subsystems are inherently different. Hyperbolic parts, which describe, in general, propagation phenomena, are discretized using upwind schemes and are solved by marching techniques. Elliptic parts, which are directionally unbiased, are discretized using h-elliptic central discretizations, and are solved by pointwise relaxations together with coarse grid acceleration. The resulting discretization schemes introduce artificial viscosity only for the hyperbolic parts of the system; thus a smaller total artificial viscosity is used, while the multigrid solvers used are much more efficient. Solutions of the subsonic compressible Euler equations are achieved at the same efficiency as the full potential equation.

Taasan, Shlomo↗
DOE OSTI2016

Thermodynamics of the variable modified Chaplygin gas

A cosmological model with a new variant of Chaplygin gas obeying an equation of state (EoS), P = A ρ − B /ρ{sup α} where B = B {sub 0} a {sup n} is investigated in the context of its thermodynamical behaviour. Here B {sub 0} and n are constants and a is the scale factor. We show that the equation of state of this 'Variable Modified Chaplygin gas' (VMCG) can describe the current accelerated expansion of the universe. Following standard thermodynamical criteria we mainly discuss the classical thermodynamical stability of the model and find that the new parameter, n introduced in VMCG plays a crucial role in determining the stability considerations and should always be negative. We further observe that although the earlier model of Lu explains many of the current observational findings of different probes it fails the desirable tests of thermodynamical stability. We also note that for 0 n < our model points to a phantom type of expansion which, however, is found to be compatible with current SNe Ia observations and CMB anisotropy measurements. Further the third law of thermodynamics is obeyed in our case. Our model is very general in the sense that many of earlier works in this field may be obtained as a special case of our solution. An interesting point to note is that the model also apparently suggests a smooth transition from the big bang to the big rip in its whole evaluation process.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Automation & Control Systems↗
DOE OSTI2025

Weather and climate extremes in a changing Arctic

Weather and climate extremes are increasingly occurring in the Arctic. Here, in this Review, we evaluate historical and projected changes in rare Arctic extremes across the atmosphere, cryosphere and ocean and elucidate their driving mechanisms. Clear shifts occur in mean and extreme distributions after ~2000. For instance, pre-2000 to post-2000 observational probabilities of 1.5 standard deviation events increase by 20% for atmospheric heat waves, 76.7% for Atlantic layer warm events, 83.5% for Arctic sea ice loss and 62.9% for Greenland Ice Sheet melt extent — in many cases, low probability, rare extreme events in the early period become the norm in the latter period. These observed changes can be explained using a ‘pushing and triggering’ concept, representing interplay between external forcing and internal variability: long-term warming destabilizes the climate system and ‘pushes’ it to a new state, allowing subsequent variability associated with large-scale atmosphere–ocean–ice interactions and synoptic systems to ‘trigger’ extreme events over different timescales. Ongoing anthropogenic warming is expected to further increase the frequency and magnitude of extremes, such that simulated probabilities of 1.5 standard deviation events increase by 72.6% for atmospheric heat waves, 68.7% for Atlantic layer warm events and 93.3% for Greenland Ice Sheet melt rate between historic (1984–2014) and future (2069–2099) periods under a very high emission scenario. Future research should prioritize the development of physically based metrics, enhance high-resolution observation and modelling capabilities and improve understanding of multiscale Arctic climate drivers.

atmospheric dynamics↗