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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Comprehensive Analysis of the Relative Dispersion of Droplet-Size Distributions and Their Relationships to Key Physical Fog Processes Under Different Aerosol Conditions and Evolutionary Stages

The relative dispersion of cloud and fog droplets has significant impacts on aerosol indirect effects, radiative transfer, and microphysical processes. However, previous studies have been mostly concerned with clouds, with limited studies on fog, particularly those that examine the combined influences of all key physical processes and their roles during fog evolution. As such, this study aims to conduct a comprehensive investigation by examining the relationships between relative dispersion and other microphysical variables, as well as the underlying microphysical and dynamic processes, based on field fog campaigns in polluted and clean conditions. In polluted fog, droplet concentrations are higher, leading to smaller droplets and increased dispersion. The correlation between dispersion and droplet volume-mean radius is positive in the polluted fog, but shifts to negative in clean fog. Here, we attribute the difference to various microphysical processes like aerosol activation, condensation, collision-coalescence, and entrainment-mixing. In polluted fog, high aerosol concentrations, low supersaturations, and strong turbulence (entrainment-mixing) provide suitable conditions for the simultaneous occurrence of droplet condensation and aerosol activation, resulting in a positive correlation between dispersion and volume-mean radius, especially during the fog formation stage. In contrast, during the mature stage in clean fog, condensation is dominant with weak aerosol activation leading to a negative correlation between relative dispersion and volume-mean radius. The collision-coalescence process is more active in the mature stage, increasing radii and leading to the negative correlation between dispersion and volume-mean radius. This result sheds new light on understanding the relative dispersion and mechanisms in fog under different aerosol backgrounds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Negative emissions and international climate goals—learning from and about mitigation scenarios

For aiming to keep global warming well-below 2 °C and pursue efforts to limit it to 1.5 °C, as set out in the Paris Agreement, a full-fledged assessment of negative emission technologies (NETs) that remove carbon dioxide from the atmosphere is crucial to inform science-based policy making. With the Paris Agreement in mind, we re-analyse available scenario evidence to understand the roles of NETs in 1.5 °C and 2 °C scenarios and, for the first time, link this to a systematic review of findings in the underlying literature. In line with previous research, we find that keeping warming below 1.5 °C requires a rapid large-scale deployment of NETs, while for 2 °C, we can still limit NET deployment substantially by ratcheting up near-term mitigation ambition. Most recent evidence stresses the importance of future socio-economic conditions in determining the flexibility of NET deployment and suggests opportunities for hedging technology risks by adopting portfolios of NETs. Importantly, our thematic review highlights that there is a much richer set of findings on NETs than commonly reflected upon both in scientific assessments and available reviews. In particular, beyond the common findings on NETs underpinned by dozens of studies around early scale-up, the changing shape of net emission pathways or greater flexibility in the timing of climate policies, there is a suite of “niche and emerging findings”, e.g. around innovation needs and rapid technological change, termination of NETs at the end of the twenty-first century or the impacts of climate change on the effectiveness of NETs that have not been widely appreciated. Future research needs to explore the role of climate damages on NET uptake, better understand the geophysical constraints of NET deployment (e.g. water, geological storage, climate feedbacks), and provide a more systematic assessment of NET portfolios in the context of sustainable development goals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Efficient Trainability of Linear Optical Modules in Quantum Optical Neural Networks

The existence of “barren plateau landscapes” for generic discrete-variable quantum neural networks, which obstructs efficient gradient-based optimization of cost functions defined by global measurements, would be surprising in the case of generic linear optical modules in quantum optical neural networks due to the tunability of the intensity of continuous variable states and the relevant unitary group having exponentially smaller dimension. We demonstrate that coherent light in m modes can be generically compiled efficiently if the total intensity scales sublinearly with m, and extend this result to cost functions based on homodyne, heterodyne, or photon detection measurement statistics, and to noisy cost functions in the presence of attenuation. Here, we further demonstrate efficient trainability of m mode linear optical quantum circuits for variational mean field energy estimation of positive quadratic Hamiltonians for input states that do not have energy exponentially vanishing with m.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Solving larger maximum clique problems using parallel quantum annealing

Quantum annealing has the potential to find low energy solutions of NP-hard problems that can be expressed as quadratic unconstrained binary optimization problems. However, the hardware of the quantum annealer manufactured by D-Wave Systems, which we consider in this work, is sparsely connected and moderately sized (on the order of thousands of qubits), thus necessitating a minor-embedding of a logical problem onto the physical qubit hardware. The combination of relatively small hardware sizes and the necessity of a minor-embedding can mean that solving large optimization problems is not possible on current quantum annealers. In this research, we show that a hybrid approach combining parallel quantum annealing with graph decomposition allows one to solve larger optimization problem accurately. We apply the approach to the Maximum Clique problem on graphs with up to 120 nodes and 6395 edges.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A bootstrapping approach to social media quantification

Abstract This work considers the use of classifiers in a downstream aggregation task estimating class proportions, such as estimating the percentage of reviews for a movie with positive sentiment. We derive the bias and variance of the class proportion estimator when taking classification error into account to determine how to best trade off different error types when tuning a classifier for these tasks. Additionally, we propose a method for constructing confidence intervals that correctly adjusts for classification error when estimating these statistics. We conduct experiments on four document classification tasks comparing our methods to prior approaches across classifier thresholds, sample sizes, and label distributions. Prior approaches have focused on providing the most accurate point estimate while this work focuses on the creation of correct confidence intervals that appropriately account for classifier error. Compared to the prior approaches, our methods provide lower error and more accurate confidence intervals.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Street context of various demographic groups in their daily mobility

Abstract We present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

Salgado, Ariel (ORCID:0000000177015372)↗
DOE OSTI2025

Generative AI models for learning flow maps of stochastic dynamical systems in bounded domains

Simulating stochastic differential equations (SDEs) in bounded domains, presents significant computational challenges due to particle exit phenomena, which requires accurate modeling of interior stochastic dynamics and boundary interactions. Despite the success of machine learning-based methods in learning SDEs, existing learning methods are not applicable to SDEs in bounded domains because they cannot accurately capture the particle exit dynamics. We present a unified hybrid data-driven approach that combines a conditional diffusion model with an exit prediction neural network to capture both interior stochastic dynamics and boundary exit phenomena. Our ML model consists of two major components: a neural network that learns exit probabilities using binary cross-entropy loss with rigorous convergence guarantees, and a training-free diffusion model that generates state transitions for non-exiting particles using closed-form score functions. The two components are integrated through a probabilistic sampling algorithm that determines particle exit at each time step and generates appropriate state transitions. Here, the performance of the proposed approach is demonstrated via three test cases: a one-dimensional simplified problem for theoretical verification, a two-dimensional advection-diffusion problem in a bounded domain, and a three-dimensional problem of interest to magnetically confined fusion plasmas.

Bounded domains↗
DOE OSTI2022

High-resolution laser-induced fluorescence spectroscopy of 28 Si 16 O + and 29 Si 16 O + in a cryogenic buffer-gas cell

Here, the electronic, laser-induced fluorescence spectrum of the B 2 Σ + ← X 2 Σ + transition in 28 Si 16 O + and 29 Si 16 O + has been recorded in a cryogenic buffer gas cell at K. Molecular constants are extracted for both 28 Si 16 O + and 29 Si 16 O + , including the Fermi contact hyperfine constant for both the B and X states of 29 Si 16 O + , and used in a discussion of the suitability of SiO in future quantum information experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Time-series learning of latent-space dynamics for reduced-order model closure

In this work, we study the performance of long short-term memory networks (LSTMs) and neural ordinary differential equations (NODEs) in learning latent-space representations of dynamical equations for an advection-dominated problem given by the viscous Burgers equation. Our formulation is devised in a nonintrusive manner with an equation-free evolution of dynamics in a reduced space with the latter being obtained through a proper orthogonal decomposition. In addition, we leverage the sequential nature of learning for both LSTMs and NODEs to demonstrate their capability for closure in systems that are not completely resolved in the reduced space. We assess our hypothesis for two advection-dominated problems given by the viscous Burgers equation. We observe that both LSTMs and NODEs are able to reproduce the effects of the absent scales for our test cases more effectively than does intrusive dynamics evolution through a Galerkin projection. This result empirically suggests that time-series learning techniques implicitly leverage a memory kernel for coarse-grained system closure as is suggested through the Mori–Zwanzig formalism.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Stability in integrable nonlocal nonlinear equations

Recently a variety of nonlocal integrable systems has been introduced that besides fields located at particular space-time points simultaneously also contain fields that are located at different, but symmetrically related, points. Here we investigate different types of soliton solutions with regard to their stability against linear perturbations obtained for the nonlocal version of the Hirota/nonlinear Schrödinger equation and the so-called Alice and Bob versions of the Korteweg-de Vries and Bousinesq equations. In this work, we encounter different types of scenarios: Solition solutions that are linearly stable or unstable and also solutions that change their stability properties depending on the parameter regime they are in.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Quantum entropy as a harbinger of factorizability

Deeply inelastic scattering (DIS) is a powerful probe for investigating the QCD structure of hadronic matter and testing the standard model (SM). DIS can be described through QCD factorization theorems which separate contributions to the scattering interaction arising from disparate scales — e.g ., with nonperturbative matrix elements associated with long distances and a perturbative hard scattering kernel applying to short-distance parton-level interactions. The fundamental underpinnings of factorization may be recast in the quantum-theoretic terms of entanglement, (de)coherence, and system localization in a fashion which sheds complementary light on the dynamics at work in DIS from QCD bound states. In this Letter, we propose and quantitatively test such a quantum-information theoretic approach for dissecting factorization in DIS and its domain of validity; we employ metrics associated with quantum entanglement such as a differential quantum entropy and associated Kullback-Leibler (KL) divergences in numerical tests. We deploy these methods on an archetypal quark-spectator model of the proton, for which we monitor quantum decoherence in DIS as underlying model parameters are varied. On this basis, we demonstrate quantitatively how factorization-breaking effects may be imprinted on quantum entropies in a kinematic regime where leading-twist factorization increasingly receives large corrections from finite- Q 2 effects; our findings suggest potential applications of quantum simulation to QCD systems and their interactions.

Deep inelastic scattering↗
DOE OSTI2024

Confirming X-ray Parametric Down Conversion by Time-Energy Correlation

We present measurements of X-ray Parametric Down Conversion at the Advanced Photon Source synchrotron facility. Using an incoming pump beam at 22 keV, we observe the simultaneous, elastic emission of down-converted photon pairs generated in a diamond crystal. The pairs are detected using high count rate silicon drift detectors with low noise. Production by down-conversion is confirmed by measuring time-energy correlations in the detector signal, where photon pairs within an energy window ranging from 10 to 12 keV are only observed at short time differences. By systematically varying the crystal misalignment and detector positions, we obtain results that are consistent with the constant total of the down-converted signal. Our maximum rate of observed pairs was 130 /hour, corresponding to a conversion efficiency for the down-conversion process of 5.3±0.5×10 -13 .

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Estimation of intensity, footprint, and capacity of surface urban heat islands using a direction-enhanced adaptive synchronous extraction (DEASE) method

Here, the surface urban heat island (SUHI) effect, assessed through remotely sensed land surface temperature (LST), remains a focal point in urban climate research. Conventional indicators like SUHI intensity (SUHII) and footprint (SUHIF) capture peak values and spatial extent but fail to account for the cumulative thermal load—a critical dimension reflecting the total heat exposure imposed by spatially continuous warming, which directly limits a holistic assessment of ecological and societal impacts of the SUHI effect. Therefore, this study introduces an indicator termed SUHI capacity (SUHIC), designed to quantify the aggregated SUHI effect by integrating the magnitude of the warming signal across all affected areas, thereby enabling a more comprehensive evaluation of urban thermal environments. Furthermore, a direction-enhanced adaptive synchronous extraction (DEASE) method is proposed for the quantification of SUHIC. This method can dynamically identify the optimal background reference area based on the urban-rural LST gradients in various directions within the city, without relying on predefined mathematical models as previously. The results from 102 European cities first confirm that the directional variations in urban-rural LST gradients, and the DEASE method can effectively capture these distinctions for the simultaneous estimation of SUHII, SUHIF, and SUHIC. Secondly, the spatial patterns of absolute SUHIC values show strong associations with those of SUHIF (R2>0.86), while its relative values (normalized by the area of urban) align more closely with SUHII (R2 > 0.64). More importantly, SUHIC can serve as a crucial reference for assessing the urban thermal signal when SUHII and SUHIF diverge. The proposed method and framework contribute to standardizing the quantification of the SUHI effect.

Indicator↗
DOE OSTI2019

Environmental Mercury Chemistry – In Silico

Mercury (Hg) is a global environmental contaminant. Major anthropogenic sources of Hg emission include gold mining and the burning of fossil fuels. Once deposited in aquatic environments, Hg can undergo redox reactions, form complexes with ligands, and adsorb onto particles. It can also be methylated by microorganisms. Mercury, especially its methylated form methylmercury, can be taken up by organisms, where it bioaccumulates and biomagnifies in the food chain, leading to detrimental effects on ecosystem and human health. In support of the recently enforced Minamata Convention on Mercury, a legally binding international convention aimed at reducing the anthropogenic emission of—and human exposure to—Hg, its global biogeochemical cycle must be understood. Thus, a detailed understanding of the molecular-level interactions of Hg is crucial.The ongoing rapid development of hardware and methods has brought computational chemistry to a point that it can usefully inform environmental science. This is particularly true for Hg, which is difficult to handle experimentally due to its ultratrace concentrations in the environment and its toxicity. The current account provides a synopsis of the application of computational chemistry to filling several major knowledge gaps in environmental Hg chemistry that have not been adequately addressed experimentally.Environmental Hg chemistry requires defining the factors that determine the relative affinities of different ligands for Hg species, as they are critical for understanding its speciation, transformation and bioaccumulation in the environment. Formation constants and the nature of bonding have been determined computationally for environmentally relevant Hg(II) complexes such as chlorides, hydroxides, sulfides and selenides, in various physical phases. Quantum chemistry has been used to determine the driving forces behind the speciation of Hg with hydrochalcogenide and halide ligands. Of particular importance is the detailed characterization of solvation effects. Indeed, the aqueous phase reverses trends in affinities found computationally in the gas phase. Computation has also been used to investigate complexes of methylmercury with (seleno)amino acids, providing a molecular-level understanding of the toxicological antagonism between Hg and selenium (Se). Furthermore, evidence is emerging that ice surfaces play an important role in Hg transport and transformation in polar and alpine regions. Therefore, the diffusion of Hg and its ions through an idealized ice surface has been characterized.Microorganisms are major players in environmental mercury cycling. Some methylate inorganic Hg species, whereas others demethylate methylmercury. Quantum chemistry has been used to investigate catalytic mechanisms of enzymatic Hg methylation and demethylation. The complex interplay between the myriad chemical reactions and transport properties both in and outside microbial cells determines net biogeochemical cycling. In conclusion, prospects for scaling up molecular work to obtain a mechanistic understanding of Hg cycling with comprehensive multiscale biogeochemical modeling are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗