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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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cellular remains in a ~3.42-billion-year-old subseafloor hydrothermal environment

Subsurface habitats on Earth host an extensive extant biosphere and likely provided one of Earth’s earliest microbial habitats. Though the site of life’s emergence continues to be debated, evidence of early life provides insights into its early evolution1. Here we present our discovery of exceptionally well-preserved ~3.40-billion-years-old carbonaceous filamentous microfossils that inhabited a paleo-subseafloor hydrothermal vein system of the Barberton greenstone belt (BGB) in South Africa. The distribution of the microfossils in growth position on the walls of the initial conduits created by low- temperature hydrothermal fluid and their morphological and chemical characteristics as investigated over a range of scales, are consistent with an assemblage of methanogens that thrived in an ultramafic volcanic substrate.

Cavalazzi, Barbara↗
DOE OSTI2022

Protecting ice from melting under sunlight via radiative cooling

As ice plays a critical role in various aspects of life, from food preservation to ice sports and ecosystem, it is desirable to protect ice from melting, especially under sunlight. The fundamental reason for ice melt under sunlight is related to the imbalanced energy flows of the incoming sunlight and outgoing thermal radiation. Therefore, radiative cooling, which can balance the energy flows without energy consumption, offers a sustainable approach for ice protection. Here, we demonstrate that a hierarchically designed radiative cooling film based on abundant and eco-friendly cellulose acetate molecules versatilely provides effective and passive protection to various forms/scales of ice under sunlight. This work provides inspiration for developing an effective, scalable, and sustainable route for preserving ice and other critical elements of ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Redox stratification of an ancient lake in Gale crater, Mars

In 2012, NASA’s Curiosity rover landed on Mars to assess its potential as a habitat for past life and investigate the paleoclimate record preserved by sedimentary rocks inside the ~150-kilometer-diameter Gale impact crater. Geological reconstructions from Curiosity rover data have revealed an ancient, habitable lake environment fed by rivers draining into the crater. We synthesize geochemical and mineralogical data from lake-bed mudstones collected during the first 1300 martian solar days of rover operations in Gale. We present evidence for lake redox stratification, established by depth-dependent variations in atmospheric oxidant and dissolved-solute concentrations. Paleoclimate proxy data indicate that a transition from colder to warmer climate conditions is preserved in the stratigraphy. Lastly, a late phase of geochemical modification by saline fluids is recognized.

58 GEOSCIENCES↗
DOE OSTI2020

Microbial sulfate reduction and organic sulfur formation in sinking marine particles

Climate change is driving an expansion of marine oxygen-deficient zones, which may alter the global cycles of carbon, sulfur, nitrogen, and trace metals. Currently, however, we lack a full mechanistic understanding of how oxygen deficiency affects organic carbon cycling and burial. In this work, we show that cryptic microbial sulfate reduction occurs in sinking particles from the eastern tropical North Pacific oxygen-deficient zone and that some microbially produced sulfide reacts rapidly to form organic sulfur that is resistant to acid hydrolysis. Particle-hosted sulfurization could enhance carbon preservation in sediments underlying oxygen-deficient water columns and serve as a stabilizing feedback between expanding anoxic zones and atmospheric carbon dioxide. A similar mechanism may help explain more-extreme instances of organic carbon preservation associated with marine anoxia in Earth history.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Genetic diversity loss in the Anthropocene

Anthropogenic habitat loss and climate change are reducing species’ geographic ranges, increasing extinction risk and losses of species’ genetic diversity. Although preserving genetic diversity is key to maintaining species’ adaptability, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. We introduce a mathematical framework that bridges biodiversity theory and population genetics to understand the loss of naturally occurring DNA mutations with decreasing habitat. By analyzing genomic variation of 10,095 georeferenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a mutations-area relationship power law with geographic area, which can predict genetic diversity loss from local population extinctions. We estimate that more than 10% of genetic diversity may already be lost for many threatened and nonthreatened species, surpassing the United Nations’ post-2020 targets for genetic preservation.

Science & Technology - Other Topics↗
DOE OSTI2024

Quantum interference in atom-exchange reactions

Chemical reactions, in which bonds break and form, are highly dynamic quantum processes. A fundamental question is whether coherence can be preserved in chemical reactions and then harnessed to generate entangled products. Here we investigated this question by studying the 2KRb → K 2 + Rb 2 reaction at 500 nanokelvins, focusing on the nuclear spin degrees of freedom. We prepared the initial nuclear spins in KRb (potassium-rubidium) in an entangled state by lowering the magnetic field to where the spin-spin interaction dominates and characterized the preserved coherence in nuclear spin wave function after the reaction. We observed an interference pattern that is consistent with full coherence at the end of the reaction, suggesting that entanglement prepared within the reactants could be redistributed through the atom-exchange process.

Science & Technology - Other Topics↗
DOE OSTI2019

Climate impact on fluvial-lake system evolution, Eocene Green River Formation, Uinta Basin, Utah, USA

In light of a modern understanding of early Eocene greenhouse climate fluctuations and new highly seasonal fluvial system faces models, the role of climate in the evolution of one classically-cited continental, terminal lake system is re-examined. Detailed stratigraphic description and elemental abundance data from fifteen cores and seven outcrop regions of the Green River Formation were used to construct a ~150 km cross section across the Uinta Basin, Utah, USA. Lake Uinta in the Uinta Basin is divided into five lake phases: (1) post-Paleocene Eocene Thermal Maximum, (2) peak Eocene hyperthermal, (3) waning hyperthermal, Early Eocene Climatic Optimum (EECO), (4) post-hyperthermal, and (5) post-EECO regimes, based primarily on climatically driven changes in fluvial style in combination with sedimentary indicators of lacustrine carbonate deposition, organic matter preservation, salinity, and lake depth. Basinwide siliciclastic dominated intervals were deposited by highly seasonal fluvial systems and record negative organic carbon isotope excursions associated with early Eocene abrupt, transient global warming (hyperthermal) events. Carbonate dominated or organic rich intervals record stable, less seasonal climate periods between hyperthermals, with lower siliciclastic sediment supply allowing the development of carbonate and organic matter preservation. The stratigraphic progression from alternating organic rich and lean zones to the overlying organic rich Mahogany and R8 zones represents the global transition out of the pulsed early Eocene hyperthermal climate regime to a time of sediment starvation and lake stratification, sequestering sedimentary organic carbon. This study provides a novel approach to terrestrial paleoclimate reconstruction that relies largely on unique sedimentary indicators and secondarily on isotopic proxy records within the context of a large basin-wide sedimentologic and stratigraphic data set, thus setting the stage for future detailed geochemical terrestrial paleoclimate proxy development.

Geology↗
DOE OSTI2017

Two-center Coulomb problem with Calogero interaction

We show that the Calogero-type perturbation preserves the integrability and partial separation of variables for the Stark–Coulomb and two-center Coulomb problems, and present the explicit expressions of their constants of motion. We reveal that this perturbation preserves the spectra of initial systems, but leads to the change of degree of degeneracy.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Super-Heat Resistant Polymer Nanocomposites Based on Heterocyclic Networks: Structure and Properties

The heterocyclic polymer network nanocomposites obtained from bisphtalonitrile and various (from 0.03 to 5 wt %) modified silicate montmorillonite (MMT) nanolayers are studied. The nanostructure together with thermal, relaxation, and elastic properties of the composites are characterized with transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDXS), dynamic mechanical analysis (DMA), and thermogravimetric analysis (TGA). DMA and TGA measurements were performed in air and nitrogen mediums in the temperature range from 20 to 600–900°C. There was a various degree of exfoliation of MMT in the matrix to form single nanolayers, as well as thin and “thick” packs of MMT nanolayers, depending on its amount. We showed that there are strong constraining dynamics effects of the matrix by nanoparticles and sharp dynamic heterogeneity in the glass transition. We found that there are possibilities of complete suppression of the latter and of preservation of elastic characteristics of the composites unchanged at the temperatures from 20 to 600°C. The nanocomposites exhibit uniquely high thermal properties. A glass transition temperature of 570°C and satisfactory thermal stability are achieved with preservation of integrity of the material up to ~500°C in air and up to ∼900°C in nitrogen.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Algorithms for propagating uncertainty across heterogeneous domains

We address an important research area in stochastic multi-scale modeling, namely the propagation of uncertainty across heterogeneous domains characterized by partially correlated processes with vastly different correlation lengths. This class of problems arise very often when computing stochastic PDEs and particle models with stochastic/stochastic domain interaction but also with stochastic/deterministic coupling. The domains may be fully embedded, adjacent or partially overlapping. The fundamental open question we address is the construction of proper transmission boundary conditions that preserve global statistical properties of the solution across different subdomains. Often, the codes that model different parts of the domains are black-box and hence a domain decomposition technique is required. No rigorous theory or even effective empirical algorithms have yet been developed for this purpose, although interfaces defined in terms of functionals of random fields (e.g., multi-point cumulants) can overcome the computationally prohibitive problem of preserving sample-path continuity across domains. The key idea of the different methods we propose relies on combining local reduced-order representations of random fields with multi-level domain decomposition. Specifically, we propose two new algorithms: The first one enforces the continuity of the conditional mean and variance of the solution across adjacent subdomains by using Schwarz iterations. The second algorithm is based on PDE-constrained multi-objective optimization, and it allows us to set more general interface conditions. The effectiveness of these new algorithms is demonstrated in numerical examples involving elliptic problems with random diffusion coefficients, stochastically advected scalar fields, and nonlinear advection-reaction problems with random reaction rates.

Cho, Heyrim↗
DOE OSTI2019

Multilevel Techniques for Compression and Reduction of Scientific Data-Quantitative Control of Accuracy in Derived Quantities

Although many compression algorithms are focused on preserving pointwise values of the data, application scientists are generally more concerned with derived quantities. Equally well, the user may even be willing to accept a high level of lossiness in the compression provided that the compressed data respect certain invariants, such as mass conservation. In the current work, we develop a mathematical framework and techniques that enable data to be adaptively compressed while maintaining a specified tolerance on a class of user-prescribed quantities. Here, the algorithm is used to augment the functionality of the data reduction package MGARD developed in previous work and the functionality is illustrated by a range of application including data from computational simulation of autocatalytic reaction simulation, turbulent combustion simulation, experimental data obtained from magnetic confinement fusion experiment, and simulation of turbulent flow along a rectangular channel. In each case, we consider one or more relevant quantities of interest and reduce the data so as to preserve these quantities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Conservative High-Order Time Integration for Lagrangian Hydrodynamics

In tis study, we develop novel time integration methods for the compressible Euler equations in the Lagrangian frame that are of arbitrary high order and exactly preserve the mass, momentum, and total energy of the system. The equations are considered in nonconservative form, that is, common for staggered grid hydrodynamics (SGH) methods; namely, the evolved quantities are mass, momentum, and internal energy. A general family of time integration schemes is formulated, and practical pairs for orders three and four are derived. Numerical results on standard hydrodynamics benchmarks confirm the high-order convergence on smooth problems and the exact numerical preservation of all physically conserved quantities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Analysis of the SiMPL Method for Density-Based Topology Optimization

We present a rigorous convergence analysis of a new method for density-based topology optimization that provides pointwise bound-preserving design updates and faster convergence than other popular first-order topology optimization methods. Due to its strong bound preservation, the method is exceptionally robust, as demonstrated in numerous examples here and in the companion article [D. Kim et al., Struct. Multidiscip. Optim., 68 (2025), 74]. Furthermore, it is easy to implement with clear structure and analytical expressions for the updates. Our analysis covers two versions of the method, characterized by the employed line search strategies. We consider a modified Armijo backtracking line search and a Bregman backtracking line search. For both line search algorithms, our algorithm delivers a strict monotone decrease in the objective function and further intuitive convergence properties, e.g., strong and pointwise convergence of the density variables on the active sets, norm convergence to zero of the increments, convergence of the Lagrange multipliers, and more. In addition, the numerical experiments demonstrate apparent mesh-independent convergence of the algorithm. Here, we refer to the new algorithm as the SiMPL method (pronounced “simple”), which stands for Sigmoidal Mirror descent with a Projected Latent variable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Hierarchical Gaussian Random Field Sampling for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and the Karhunen–Loève Decomposition

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRFs) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equations (SPDEs), often reduce the dimensionality of the sample space at the cost of accuracy of inference. Other approaches, such that those based on Karhunen-Loève (KL) expansions, offer sample space dimensionality reduction but sacrifice GRF representation accuracy and ergodicity of the Markov chain Monte Carlo (MCMC) sampler and are computationally expensive for high-dimensional problems. The proposed method integrates the dimensionality reduction capabilities of KL expansions with the scalability of SPDE-based sampling, thereby providing a robust, unified framework for high-dimensional uncertainty quantification (UQ) that is scalable and accurate, preserves ergodicity, and offers dimensionality reduction of the sample space. The hierarchy in our multilevel algorithm is derived from the geometric multigrid hierarchy. By constructing a hierarchical decomposition that maintains the covariance structure across the levels in the hierarchy, the approach enables efficient coarse-to-fine sampling while ensuring that all samples are drawn from the desired distribution. The effectiveness of the proposed method is demonstrated on a benchmark subsurface flow problem, demonstrating its effectiveness in improving computational efficiency and statistical accuracy. Furthermore, our proposed technique is more efficient and accurate and displays better convergence properties than existing methods for high-dimensional Bayesian inference problems.

Gaussian random fields↗
DOE OSTI2017

When Labels Fall Short: Property Graph Simulation via Blending of Network Structure and Vertex Attributes

Property graphs can be used to represent heterogeneous networks with attributed vertices and edges. Given one property graph, simulating another graph with same or greater size with identical statistical properties with respect to the attributes and connectivity is critical for privacy preservation and benchmarking purposes. In this work we tackle the problem of capturing the statistical dependence of the edge connectivity on the vertex labels and using the same distribution to regenerate property graphs of the same or expanded size in a scalable manner. However, accurate simulation becomes a challenge when the attributes do not completely explain the network structure. We propose the Property Graph Model (PGM) approach that uses an attribute (or label) augmentation strategy to mitigate the problem and preserve the graph connectivity as measured via degree distribution, vertex label distributions and edge connectivity. Our proposed algorithm is scalable with a linear complexity in the number of edges in the target graph. We illustrate the efficacy of the PGM approach in regenerating and expanding the datasets by leveraging two distinct illustrations.

Visweswara Sathanur, Arun↗
DOE OSTI2020

COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibility

Demand flexibility is increasingly important for power grids. Careful coordination of thermostatically controlled loads (TCLs) can modulate energy demand, decrease operating costs, and increase grid resiliency. We propose a novel distributed control framework for the Coordination Of HeterOgeneous Residential Thermostatically controlled loads (COHORT). COHORT is a practical, scalable, and versatile solution that coordinates a population of TCLs to jointly optimize a grid-level objective, while satisfying each TCL’s end-use requirements and operational constraints. To achieve that, we decompose the grid-scale problem into subproblems and coordi- nate their solutions to find the global optimum using the alternating direction method of multipliers (ADMM). The TCLs’ local problems are distributed to and computed in parallel at each TCL, making COHORT highly scalable and privacy-preserving. While each TCL poses combinatorial and non-convex constraints, we characterize these constraints as a convex set through relaxation, thereby making COHORT computationally viable over long planning horizons. After coordination, each TCL is responsible for its own control and tracks the agreed-upon power trajectory with its preferred strategy. In this work, we translate continuous power back to discrete on/off actuation, using pulse width modulation. COHORT is generalizable to a wide range of grid objectives, which we demonstrate through three distinct use cases: generation following, minimizing ramping, and peak load curtailment. In a notable experiment, we validated our approach through a hardware-in-the-loop simulation, including a real-world air conditioner (AC) controlled via a smart thermostat, and simulated instances of ACs modeled after real-world data traces. During the 15-day experimental period, COHORT reduced daily peak loads by an average of 12.5% and maintained comfortable temperatures.

demand response↗
DOE OSTI2025

Securing Federated Learning Against Active Reconstruction Attacks

Federated Learning (FL) has amassed notable attention for its ability to preserve user privacy while emphasizing the retainment of model training efficiency. Due to this potential, FL has been integrated in many domains, such as healthcare, finance, law, and industrial engineering, where data cannot be easily exchanged due to sensitive information and strict privacy laws. However, current research has indicated that FL protocols are easily compromised by active data reconstruction attacks employed by actively dishonest servers. The malicious modification of global model parameters allows an actively dishonest server to obtain a direct copy of users’ private data via gradient inversion. Here, this class of attacks is highly underexplored and continues to be a major challenge due to the intense threat model. In this paper, we propose OASIS as a scalable and modality-agnostic defense based on data augmentation that counteracts active data reconstruction attacks while preserving model performance. To generalize our defense, we uncover the intuition behind gradient inversion that enables these attacks and theoretically establish the conditions by which the defense can be considered robust regardless of attack design. From this, we formulate our defense with data augmentation that illustrates its ability to undermine the attack principle. We evaluate OASIS on five real-world datasets–two image-based (ImageNet and CIFAR100) and three text-based (Wikitext, Stack Overflow, and Shakespeare)–which span diverse uses cases such as vision tasks and language modeling. Comprehensive evaluations on these datasets exhibit the efficacy of OASIS and highlight its feasibility as a solution.

97 MATHEMATICS AND COMPUTING↗