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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Probing the cosmic distance duality relation using time delay lenses

The construction of the cosmic distance-duality relation (CDDR) has been widely studied. However, its consistency with various new observables remains a topic of interest. We present a new way to constrain the CDDR η( z ) using different dynamic and geometric properties of strong gravitational lenses (SGL) along with SNe Ia observations. We use a sample of 102 SGL with the measurement of corresponding velocity dispersion σ{sub 0} and Einstein radius θ {sub E} . In addition, we also use a dataset of 12 two image lensing systems containing the measure of time delay Δ t between source images. Jointly these two datasets give us the angular diameter distance D {sub A} {sub ol} of the lens. Further, for luminosity distance, we use the 740 observations from JLA compilation of SNe Ia. To study the combined behavior of these datasets we use a model independent method, Gaussian Process (GP). We also check the efficiency of GP by applying it on simulated datasets, which are generated in a phenomenological way by using realistic cosmological error bars. Finally, we conclude that the combined bounds from the SGL and SNe Ia observation do not favor any deviation of CDDR and are in concordance with the standard value (η=1) within 2σ confidence region, which further strengthens the theoretical acceptance of CDDR.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Compositional and temporal division of labor modulates mixed sugar fermentation by an engineered yeast consortium

Abstract Synthetic microbial communities have emerged as an attractive route for chemical bioprocessing. They are argued to be superior to single strains through microbial division of labor (DOL), but the exact mechanism by which DOL confers advantages remains unclear. Here, we utilize a synthetic Saccharomyces cerevisiae consortium along with mathematical modeling to achieve tunable mixed sugar fermentation to overcome the limitations of single-strain fermentation. The consortium involves two strains with each specializing in glucose or xylose utilization for ethanol production. By controlling initial community composition, DOL allows fine tuning of fermentation dynamics and product generation. By altering inoculation delay, DOL provides additional programmability to parallelly regulate fermentation characteristics and product yield. Mathematical models capture observed experimental findings and further offer guidance for subsequent fermentation optimization. This study demonstrates the functional potential of DOL in bioprocessing and provides insight into the rational design of engineered ecosystems for various applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Dynamics of hot QCD matter 2024 — Bulk properties

The second Hot QCD Matter 2024 conference at IIT Mandi focused on various ongoing topics in high-energy heavy-ion collisions, encompassing theoretical and experimental perspectives. This proceedings volume includes 19 contributions that collectively explore diverse aspects of the bulk properties of hot QCD matter. The topics encompass the dynamics of electromagnetic fields, transport properties, hadronic matter, spin hydrodynamics, and the role of conserved charges in high-energy environments. These studies significantly enhance our understanding of the complex dynamics of hot QCD matter, the quark–gluon plasma (QGP) formed in high-energy nuclear collisions. Advances in theoretical frameworks, including hydrodynamics, spin dynamics and fluctuation studies, aim to improve theoretical calculations and refine our knowledge of the thermodynamic properties of strongly interacting matter. Experimental efforts, such as those conducted by the ALICE and STAR collaborations, play a vital role in validating these theoretical predictions and deepening our insight into the QCD phase diagram, collectivity in small systems, and the early-stage behavior of strongly interacting matter. In conclusion, combining theoretical models with experimental observations offers a comprehensive understanding of the extreme conditions encountered in relativistic heavy-ion and proton-proton collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Command of three-dimensional solitary waves via photopatterning

Multidimensional solitons are prevalent in numerous research fields. In orientationally ordered soft matter system, three-dimensional director solitons exemplify the localized distortion of molecular orientation. However, their precise manipulation remains challenging due to unpredictable and uncontrolled generation. Here, we utilize preimposed programmable photopatterning in nematics to control the kinetics of director solitons. This enables both unidirectional and bidirectional generation at specific locations and times, confinement within micron-scaled patterns of diverse shapes, and directed propagation along predefined trajectories. A focused dynamical model provides insight into the origins of these solitons and aligns closely with experimental observations, underscoring the pivotal role of anchoring conditions in soliton manipulation. Our findings pave the way for diverse fundamental research avenues and promising applications, including microcargo transportation and optical information processing.

Science & Technology - Other Topics↗
DOE OSTI2020

Using Machine Learning Tools to Predict Compressor Stall

Clean energy has become an increasingly important consideration in today’s power systems. As the push for clean energy continues, many coal-fired power plants are being decommissioned in favor of renewable power sources such as wind and solar. However, the intermittent nature of renewables means that dynamic load following traditional power systems is crucial to grid stability. With high flexibility and fast response at a wide range of operating conditions, gas turbine systems are poised to become the main load following component in the power grid. Yet, rapid changes in load can lead to fluid flow instabilities in gas turbine power systems. These instabilities often lead to compressor surge and stall, which are some of the most critical problems facing the safe and efficient operation of compressors in turbomachinery today. Although the topic of compressor surge and stall has been extensively researched, no methods for early prediction have been proven effective. This study explores the utilization of machine learning tools to predict compressor stall. The long short-term memory (LSTM) model, a form of recurrent neural network (RNN), was trained using real compressor stall datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. Two variations of the LSTM model, classification and regression, were tested to determine optimal performance. The regression scheme was determined to be the most accurate approach, and a tool for predicting compressor stall was developed using this configuration. Overall, results show that the tool is capable of predicting stalls 5–20 ms before they occur. With a high-speed controller capable of 5 ms time-steps, mitigating action could be taken to prevent compressor stall before it occurs.

42 ENGINEERING↗
DOE OSTI2019

Incorporating rhizosphere interactions and soil physical properties into a soil carbon degradation model through experimenting across ecotypes (Final Report)

Soil properties and biological communities contribute to carbon loss and gain, which vary significantly by ecosystem, but current carbon cycling models fail to incorporate interactions among these groups. We experimentally manipulated and modeled how mycorrhizal fungi and roots alter carbon dynamics in temperate, tropical, and boreal forests (see Jiang et al. 2017, Moore et al. 2015a, 2015b, in review, Zak et al. 2019, Rewcastle et al. in review). To date, we find that the influence of fungi on carbon dynamics varies by the nutrient constraints of that ecosystem. For example, in temperate forests mycorrhizal fungi compete with the free-living community to reduce carbon degradation - a pattern not seen in a boreal forest. We established a site in Costa Rica to explore the impact of fungi and roots on tropical forest carbon cycling and again found that the results were context dependent. Building on this, I held a workshop with soil carbon modelers and soil ecologists to explore how experiments and models predict carbon dynamics. We conducted a meta-analysis and ran model simulation with 5 soil carbon models. Surprisingly, we found that the spread in the models and experiments were similar - they predicted responses equally well (Sulman et al. 2018). However, the area we seemed to know little about and that produced the most spread in the models, and thus the underlying model assumptions, was the response of protected and unprotected carbon pools. These seem like exciting areas to follow-up on in the future. In addition, data from these experiments spurred numerous other studies across ecosystems and with a variety of modeling groups focusing on how plant-soil interactions alter carbon and nutrient cycling. For example, Classen wrote an Ecological Society of America invited centennial review paper on this topic in 2015. Our group hosted several workshops that resulted in the Sulman et al. (2018) paper in Biogeoscience and Classen recently attended a workshop at the University of Michigan on mycorrhizal influence on the carbon cycle that resulted in another thought piece (Zak et al. 2019) in New Phytologist and she has an invited review paper at Functional Ecology (Bennett & Classen in review) on this topic. Overall, there were >25 papers published on this topic over the grant period with 15 more papers in revision/review that were related to this project – we have at least two more papers in preparation. We trained >27 students and postdocs and participated in numerous talks, workshops, and community events promoting the importance of soils, soil diversity, and global change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Rapid Growth of Galactic Supermassive Black Holes through Accreting Giant Molecular Clouds during Major Mergers of Their Host Galaxies

Understanding the formation of the supermassive black holes (SMBHs) present in the centers of galaxies is a crucial topic in modern astrophysics. Observations have detected SMBHs with masses of 10 9 M ⊙ in the high-redshift galaxies with z ~ 7. However, how SMBHs grew to such huge masses within the first billion years after the Big Bang remains elusive. One possible explanation is that SMBHs grow quickly through the frequent mergers of galaxies, which provides sustainable gas to maintain rapid growth. This study presents the hydrodynamics simulations of the SMBHs’ growth with their host galaxies using the GIZMO code. In contrast to previous simulations, we have developed a giant molecular cloud (GMC) model by separating molecular gas particles from atomic gas particles and then evolving them independently. During major mergers, we show that the more massive molecular gas particles’ cloud bears stronger dynamical friction. Consequently, GMCs are substantially accreted onto the galactic centers that grow SMBHs from ~10 7 to ~10 9 M ⊙ within 300 Myr, which explains the rapid growth of SMBHs, and this accretion also triggers a violent starburst at the galactic center. Furthermore, we examine the impact of minor mergers on the bulge of a Milky Way–like galaxy and find that the size and mass of the bulge can increase from 0.92 to 1.9 kpc and from 4.7 × 10 10 to 7 × 10 10 M ⊙ .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk

Abstract Historically, fire has been essential in Southwestern US forests. However, a century of fire-exclusion and changing climate created forests which are more susceptible to uncharacteristically severe wildfires. Forest managers use a combination of thinning and prescribed burning to reduce forest density to help mitigate the risk of high-severity fires. These treatments are laborious and expensive, therefore optimizing their impact is crucial. Landscape simulation models can be useful in identifying high risk areas and assessing treatment effects, but uncertainties in these models can limit their utility in decision making. In this study we examined underlying uncertainties in the initial vegetation layer by leveraging a previous study from the Santa Fe fireshed and using new inventory plots from 111 stands to interpolate the initial forest conditions. We found that more inventory plots resulted in a different geographic distribution and wider range of the modelled biomass. This changed the location of areas with high probability of high-severity fires, shifting the optimal location for management. The increased range of biomass variability from using a larger number of plots to interpolate the initial vegetation layer also influenced ecosystem carbon dynamics, resulting in simulated forest conditions that had higher rates of carbon uptake. We conclude that the initial forest layer significantly affects fire and carbon dynamics and is dependent on both number of plots, and sufficient representation of the range of forest types and biomass density.

Science & Technology - Other Topics↗
DOE OSTI2025

CACNA1D is a circadian gene and causes familial advanced sleep phase

Familial advanced sleep phase (FASP) is a heritable human sleep trait characterized by early sleep onset and offset times. We have identified five variants in five different families in the human voltage-gated calcium channel subunit alpha1 D (CACNA1D) that cosegregate with FASP. The variants inCACNA1Dlead to altered channel dynamics in vitro. A mouse model of the E427K variant has a normal circadian period under constant darkness but displays altered phase shifts in response to light in the subjective night at circadian time (CT) 16 and CT22. Overall, these experiments establishCACNA1Das an FASP gene with altered entrainment, highlighting the ability of human genetics to uncover novel aspects of human circadian regulation.

Science & Technology - Other Topics↗
DOE OSTI2023

A superconducting quantum simulator based on a photonic-bandgap metamaterial

Synthesizing many-body quantum systems with various ranges of interactions facilitates the study of quantum chaotic dynamics. Such extended interaction range can be enabled by using nonlocal degrees of freedom such as photonic modes in an otherwise locally connected structure. Here, we present a superconducting quantum simulator in which qubits are connected through an extensible photonic-bandgap metamaterial, thus realizing a one-dimensional Bose-Hubbard model with tunable hopping range and on-site interaction. Using individual site control and readout, we characterize the statistics of measurement outcomes from many-body quench dynamics, which enables in situ Hamiltonian learning. Further, the outcome statistics reveal the effect of increased hopping range, showing the predicted crossover from integrability to ergodicity. Our work enables the study of emergent randomness from chaotic many-body evolution and, more broadly, expands the accessible Hamiltonians for quantum simulation using superconducting circuits.

Science & Technology - Other Topics↗
DOE OSTI2023

Impact of crowding on the diversity of expanding populations

Crowding effects critically impact the self-organization of densely packed cellular assemblies, such as biofilms, solid tumors, and developing tissues. When cells grow and divide, they push each other apart, remodeling the structure and extent of the population’s range. Recent work has shown that crowding has a strong impact on the strength of natural selection. However, the impact of crowding on neutral processes, which controls the fate of new variants as long as they are rare, remains unclear. Here, we quantify the genetic diversity of expanding microbial colonies and uncover signatures of crowding in the site frequency spectrum. By combining Luria–Delbrück fluctuation tests, lineage tracing in a novel microfluidic incubator, cell-based simulations, and theoretical modeling, we find that the majority of mutations arise behind the expanding frontier, giving rise to clones that are mechanically “pushed out” of the growing region by the proliferating cells in front. These excluded-volume interactions result in a clone-size distribution that solely depends on where the mutation first arose relative to the front and is characterized by a simple power law for low-frequency clones. Our model predicts that the distribution depends on a single parameter—the characteristic growth layer thickness—and hence allows estimation of the mutation rate in a variety of crowded cellular populations. Combined with previous studies on high-frequency mutations, our finding provides a unified picture of the genetic diversity in expanding populations over the whole frequency range and suggests a practical method to assess growth dynamics by sequencing populations across spatial scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Rayleigh-Taylor Instability: A Status Review of Experimental Designs and Measurement Diagnostics

The focus of experiments and the sophistication of diagnostics employed in Rayleigh-Taylor instability (RTI) induced mixing studies have evolved considerably over the past seven decades. The first theoretical analysis by Taylor and the two-dimensional experimental results by Lewis on RTI in 1950 examined single-mode RTI using conventional imaging techniques. Over the next 70 years, several experimental designs have been used to creating an RTI unstable interface between two materials of different densities. These early experiments though innovative, were arduous to diagnose and provided little information on the internal, turbulent structure and initial conditions of the RT mixing layer. Coupled with the availability of high-fidelity diagnostics, the experiments designed and developed in the last three decades allow detailed measurements of various turbulence statistics that have allowed broadly to validate and verify late-time nonlinear models and mix-models for buoyancy-driven flows. Besides, they have provided valuable insights to solve several long-standing disagreements in the field. Herein, this review serves as an opportunity to discuss the understanding of the RTI problem and highlight valuable insights gained into the RTI driven mixing process with a focus on low to high Atwood number (> 0.1) experiments.

42 ENGINEERING↗
DOE OSTI2026

A stress-based fracture model for reacting metal ejecta

The evolution of reacting metal ejecta continues to be a topic of interest at the forefront of metals in reactive and extreme environments. Ejecta are small particles formed when the surface of a metal undergoes Richtmyer–Meshkov instability from a strong shock. Experiments have shown that in the case where ejecta are in ambient conditions that induce a reaction, the ejecta behave irregularly. The ejecta temperature rises and then plateaus, and the acceleration profile shows unexpected jumps. These variations are assumed to be related to the exothermic heat release and particle mass loss caused by the reaction. To explain this phenomenon, efforts to model this in simulations have increased. While current models can capture many of these physical processes, they currently assign a constant reaction shell thickness with little physical reasoning. This work remedies this problem by assigning a dynamic physically informed shell thickness to the reacting particles, using solid analysis. The shell thickness of the particles impacts the rate of change of reacted mass in the system, as well as the rate at which the particles react. The model is based on a simple stress–strain relationship and gives a dynamic assignment for when the reacting particle should begin to fracture. We compare our model to the previous computational and simulation data to analyze the effects of different model parameters.

42 ENGINEERING↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

We must know. We will know

The after-dinner talk has by now become a tradition of this Conference series on Quark Confinement and the Hadron Spectrum. On this occasion, I have tried to combine a free-style and (hopefully) amusing presentation with deep questions of physics especially connected with the dynamics of strong interaction. To this end some masterpieces of classical music (by Beethoven, Mozart, Dvorak, Stravinsky ...) and pop music (by Bob Dylan, Eric Clapton) were employed to illustrate certain aspects of physics. By no means was this presentation (neither this paper) intended as a comprehensive review of the different topics examined during the Conference, but rather as a call for further thinking on the sinergy of different branches of physics and the excitement of foreseen discoveries in a not too distant future.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Dynamic gating of infrared radiation in a textile

The human body absorbs and loses heat largely through infrared radiation centering around a wavelength of 10 micrometers. However, neither our skin nor the textiles that make up clothing are capable of dynamically controlling this optical channel for thermal management. By coating triacetate-cellulose bimorph fibers with a thin layer of carbon nanotubes, we effectively modulated the infrared radiation by more than 35% as the relative humidity of the underlying skin changed. Both experiments and modeling suggest that this dynamic infrared gating effect mainly arises from distance-dependent electromagnetic coupling between neighboring coated fibers in the textile yarns. This effect opens a pathway for developing wearable localized thermal management systems that are autonomous and self-powered, as well as expanding our ability to adapt to demanding environments.

Science & Technology - Other Topics↗
DOE OSTI2024

Changes in dislocation punching behavior due to hydrogen-seeded helium bubble growth in tungsten

The accumulation of gas atoms in tungsten is a topic of long-standing interest to the plasma-facing materials community due the metal's use as a divertor material in some tokamak fusion reactors. The nucleation and growth of He/H gas bubbles (along with their isotopes) can result from impinging fluxes of these gases which give rise to damage at the W divertor surface. The inclusion of He or H in W has been studied extensively by the community, finding that He bubbles modify the surface through periodic dislocation punching and bursting mechanisms while H bubbles impact the metal through plastic-strain induced material failure. However, the mechanisms which are present during the combined flux of both He and H is not well-studied atomistically. Motivated by this, an atomistic modeling study is conducted using molecular dynamics to assess the behavior of mixed concentration He:H bubbles in W. Here we find that the introduction of H into a growing He bubble results in a dramatic change in the nature and presence of dislocation loops which are typically generated via dislocation punching in over-pressurized He bubbles. Most notably, at high H concentrations, there is a switchover in energetic favorability from glissile 1/2<111> dislocations to sessile <100> dislocations. This thermodynamic crossover could imply a significant reduction in W surface morphology changes than with pure He bubbles and, additionally, we show this to have implications on the trapping of H in the bubbles and their associated dislocations.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Supercluster-coupled crystal growth in metallic glass forming liquids

While common growth models assume a structure-less liquid composed of atomic flow units, structural ordering has been shown in liquid metals. Here, we conduct in situ transmission electron microscopy crystallization experiments on metallic glass nanorods, and show that structural ordering strongly affects crystal growth and is controlled by nanorod thermal history. Direct visualization reveals structural ordering as densely populated small clusters in a nanorod heated from the glass state, and similar behavior is found in molecular dynamics simulations of model metallic glasses. At the same growth temperature, the asymmetry in growth rate for rods that are heated versus cooled decreases with nanorod diameter and vanishes for very small rods. We hypothesize that structural ordering enhances crystal growth, in contrast to assumptions from common growth models. The asymmetric growth rate is attributed to the difference in the degree of the structural ordering, which is pronounced in the heated glass but sparse in the cooled liquid.

36 MATERIALS SCIENCE↗