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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Spatially resolved polarization swings in the supermassive binary black hole candidate OJ 287 with first Event Horizon Telescope observations

We present the first Event Horizon Telescope 1.3 mm observations of the supermassive binary black hole candidate OJ 287. The observations achieved an unprecedented angular resolution of 18 μas and reveal significant structural and polarization variability over just five days, marking the shortest timescale on which such changes have been directly imaged in this source. The inner jet exhibits a twisted ridgeline structure, with features displaying apparent superluminal motions up to about 22 c. The linear polarization maps reveal three main polarized features whose electric-vector position angles (EVPAs) change substantially over the time span of our observations, including a component with a radial polarization consistent with being produced by a recollimation shock. Most notably, we directly resolved two innermost jet components whose EVPAs rotate in opposite directions. The faster component, moving at 2.4 ± 0.9 μas/day (17.4 ± 6.5 c), exhibits counterclockwise EVPA swings of roughly 3.7° per day, while the slower component, with a proper motion of 1.4 ± 0.3 μas/day (10.2 ± 2.2 c), rotates clockwise at approximately 2.5° per day. Previous studies inferred helical magnetic fields in AGN jets from time-resolved or integrated polarization variability but lacked the angular resolution to directly image this effect. Our results provide spatially resolved evidence that a helical magnetic field threads the jet’s collimation and acceleration zone, ruling out models based on the superposition of unresolved components. Our analysis suggests that propagating shocks interact with a Kelvin–Helmholtz plasma instability, illuminating different phases of the helical magnetic field and producing the observed polarization spatial and temporal variability. Moreover, our model naturally accounts for the more rapid polarization rotation observed in the faster moving component. Our model predicts even more rapid swings in polarization, which could be tested with future observations featuring a more densely sampled time coverage.

OJ 287↗
DOE OSTI2025

Examining astrophysical gas cloud collapse using an optical depth-scaled, x-ray-irradiated, carbon-foam sphere

When stellar radiation interacts with a molecular cloud, the cloud's fate depends on the strength of the incident radiation and the radiation's mean-free-path within the cloud [F. Bertoldi, Astrophys. J. 346, 735–755 (1989)]. Under the right conditions, the radiation compresses the cloud and a star formation may occur. Where and when the stellar formation occurs in the cloud's collapse are open questions. Direct observation of the complete star–cloud lifecycle is nearly impossible due to the immense timescales and distances over which the interaction occurs. Laboratory astrophysics offers a way to investigate such a system by scaling the important astrophysical parameters to the laboratory. This work describes laboratory experiments to study the radiation-driven implosion of clouds, using x rays from a laser-irradiated, thin, gold foil as a surrogate star and a carbon-foam sphere as a surrogate cloud. An optically thick system, theoretically corresponding to a star-forming regime, was selected by choice of the foam density. Gold foil and sphere motions were imaged by x-ray radiography. Radiographic images show the formation of an interface between rarefied gold and carbon plasmas, a shock moving into the sphere, and a blunting of the initial sphere's shape. Measurements show that the shock moved linearly around 64 μm/ns into the sphere, and the gold–carbon interface formed by 2 ns at the sphere edge remained stationary. The deformation of the sphere was driven by the incident radiation and not by mechanical pressures applied by gold plasma. The blunting of the sphere was likely due to the geometric reduction of flux near the sphere's poles. Higher x-ray flux near the sphere's equator caused high compression and a faster shock, which flattened the sphere. We will discuss the results and implications of our observations.

VanDervort, R. W. [University of Michigan 1 , Ann ↗
DOE OSTI2024

Mesoscale simulation of granular materials under weak shock compaction–pore size distribution effects

This research established a systematic method to generate various pore-size distributions (PSDs) and studied the effect of PSDs on the shock compaction response of granular materials using two-dimensional mesoscale simulations under identical porosity. Simulations utilized various PSDs for three particle shapes (circle, ellipse, and square). Contacting particle configurations using three PSDs, characterized by spatially uniform distributed pores to heterogeneous distributed pores, and non-contacting particle configurations under a single case of PSD were tested. The PSD of generated particle sets was characterized using coordination number, mean diameter, and bimodality coefficient as statistical metrics. Mesoscale simulations showed that regardless of the conditions of pore distributions, shock compaction of granular materials consistently demonstrates a precursor, shock compaction front, and end. However, the shock compaction velocity of contacting particles was dependent on the PSDs despite the constant initial porosity. The compaction velocity was faster in particle configurations with relatively uniform pore distributions than in heterogeneous pore distributions, which our study demonstrated can be attributed to particle rearrangement during compaction. Circular-shaped particles had high sensitivity in shock compaction response to the various PSDs. Furthermore, a contacting particle configuration tended to propagate the shock compaction wave relatively faster than particles that were in a non-contact configuration. This study established the relative importance of considering PSD as a metric over the coordination number in studies of the shock compaction response of granular materials. Further, insights are provided on the evolving shock substructure to characterize the shock compaction response of granular materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The accuracy of multi-group models for nonlocal electron transport in magnetized plasmas

In the extreme conditions of inertial confinement fusion experiments, heat flow plays a vital role, but local diffusive models frequently break down and overestimate the heat flow. The situation becomes more complicated again in the significant magnetic fields generated during laser–plasma interactions or in magnetized fusion schemes. Accurate non-local and magnetized heat flow computations can be carried out using Vlasov–Fokker–Planck (VFP) simulations, but these are computationally expensive. There is, therefore, significant interest in using faster multi-group models to accurately calculate the non-local heat flow in magnetized plasmas. We benchmark two such multi-group models for calculating the heat flow, M1 and hybrid-AWBS-BGK, against diffusive models and full VFP simulations, before applying the models to realistic example test cases, both magnetized and unmagnetized. We find that the multi-group models generally perform very well for moderate non-localities up to kλmfp∼0.01, but the computational cost increases dramatically. hybrid-AWBS-BGK performs more effectively than M1 at high non-localities, up to kλmfp∼1, due to its adaptive solver and robust P1 closure, but tends to fail in very strong magnetic fields. Both codes are much faster than VFP simulations but are still slow in steep temperature gradients.

Arran, C. (ORCID:0000000286448118)↗
DOE OSTI2025

Longitudinal position dependence of dark line defect growth rate in high-power diode lasers at 790 nm

Using a nondestructive, in situ near-infrared defect imaging technique, we obtained time-resolved images of dark line defect (DLD) growth in the cavities of several high-power 790-nm diode lasers and calculated individual DLD growth rates. A clear longitudinal spatial dependence in the DLD growth rate was observed, in which the DLD growth rate was faster near the anti-reflective (AR) facet. Longitudinal spatial hole burning simulation results indicate that the total photon density increases toward the AR facet, while the carrier density decreases toward the AR facet. Here, these results suggest that photon absorption plays a key role in enhancing DLD growth beyond the contribution of the traditional carrier capture mechanism. Asymmetric DLD growth that was preferentially directed toward the high-reflectivity facet was also observed, providing further support for the role of photon absorption in the growth process. To account for these findings, we propose a photon-accelerated mechanism for the enhancement of DLD growth in which the slower carrier capture process in the traditional recombination-enhanced dislocation glide mechanism is replaced by a faster photon absorption process.

Engineering - Electronic and electrical engineerin↗
DOE OSTI2025

Codon bias, nucleotide selection, and genome size predict in situ bacterial growth rate and transcription in rewetted soil

In soils, the first rain after a prolonged dry period represents a major pulse event impacting soil microbial community function, yet we lack a full understanding of the genomic traits associated with the microbial response to rewetting. Genomic traits such as codon usage bias and genome size have been linked to bacterial growth in soils—however, often through measurements in culture. Here, we used metagenome-assembled genomes (MAGs) with 18 O-water stable isotope probing and metatranscriptomics to track genomic traits associated with growth and transcription of soil microorganisms over one week following rewetting of a grassland soil. We found that codon bias in ribosomal protein genes was the strongest predictor of growth rate. We also found higher growth rates in bacteria with smaller genomes, suggesting that reduced genome size enables a faster response to pulses in soil bacteria. Faster transcriptional upregulation of ribosomal protein genes was associated with high codon bias and increased nucleotide skew. We found that several of these relationships existed within phyla, indicating that these associations between genomic traits and activity could be generalized characteristics of soil bacteria. Finally, we used publicly available metagenomes to assess the distribution of codon bias across a pH gradient and found that microbial communities in higher pH soils—which are often more water limited and pulse driven—have higher codon usage bias in their ribosomal protein genes. Together, these results provide evidence that genomic characteristics affect soil microbial activity during rewetting and pose a potential fitness advantage for soil bacteria where water and nutrient availability are episodic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Physics-informed neural networks for heterogeneous poroelastic media

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs (I-PINNs) framework (Sarma et al., Comput. Methods Appl. Mech. Eng. 429: 117135, 2024), allowing different activation functions across material interfaces. Further, this ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.

42 ENGINEERING↗
DOE OSTI2026

Rapid organic carbon spiraling in a headwater stream linked with streamflow, biogeochemistry, and canopy phenology

Headwater streams are abundant worldwide and important to global biogeochemical cycles, serving as critical processors and transporters of C. C spiraling is a useful way to understand the retention and mineralization of organic C (OC) in streams. However, analyses of seasonal and interannual variability in OC spiraling are currently limited. In this study, we aimed to understand the temporal patterns and driving mechanisms of OC spiraling, which will inform our understanding of future OC changes under climate change. We used 7 y of daily data in a small headwater stream (Walker Branch, Tennessee, USA) to assess seasonal and interannual variability in OC spiraling length (S OC ) and mineralization velocity (v fOC ), as well as their potential related variables. On average, S OC in Walker Branch was ~10× shorter than in previously studied small streams, indicating strong connections between the water column and the benthic environment where OC mineralization mostly takes place. OC spiraling was faster during the more biologically active periods of spring and autumn compared with more elongated OC spiraling in summer and winter, when OC retention was lower and downstream transport was higher. Gross primary production (GPP) was most strongly related to S OC and v fOC . Photosynthetically active radiation (PAR) and NO 3 − were also positively and negatively related to v fOC , respectively. Trends toward earlier and longer canopy cover and reduced GPP and PAR may result in longer S OC and slower v fOC , reducing localized instream processing of OC and potentially shunting more OC downstream. However, long-term observations indicate reduced NO 3 − at Walker Branch, suggesting opposing effects to those of GPP and PAR, leading to faster v fOC and greater OC retention. Time-series analyses of OC spiraling in streams can enhance our understanding of current and future responses of OC processing and downstream transport to climate change, as well as implications for downstream OC dynamics.

biological activity↗
DOE OSTI2024

Core inductive electric field during sawtooth crashes on DIII-D

Sawtooth crashes on tokamak plasmas exhibit relaxation much faster than resistive time scales via a mechanism not fully understood. Using core magnetic measurements from the Radial Interferometer-Polarimeter (RIP) diagnostic on the DIII-D tokamak, Grad–Shafranov equilibria constrained by internal magnetic measurements that have high time resolution (<1μs) can be computed, allowing analysis of how equilibrium parameters such as safety factor q, current density J, and parallel electric field E ∥ , particularly on-axis, evolve. At the sawtooth crash, on-axis safety factor q 0 is observed to rise by 5% but remain below 1 throughout the cycle, and on-axis current density J 0 is observed to drop by 5%. On-axis parallel electric field E ∥ (0) is found to be balanced by ηJ 0 (resistivity times on-axis current density) except during the 200 µs crash period, where E ∥ (0) reaches 22 V m –1 , exceeding ηJ 0 by a factor of more than 2000. These first measurements in tokamak plasmas verify that generalized Ohm's law is not balanced during the crash by resistive effects alone; this is a finding expected due to the relaxation being much faster than resistive timescales. As a result, measurement of the electric field during the tokamak sawtooth serves to illuminate the physical mechanisms at work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Expedited desalination permitting enables adaptive planning and water system cost reduction

Seawater desalination is a drought-proof water supply for coastal cities, but widespread development of desalination plants in the U.S. has been limited by both cost and the complexity of permitting processes designed to minimize environmental impact . This work estimates the value of accelerating the permitting timeline without changing environmental or social standards. On average, faster permitting reduces the frequency of desalination plant construction and operation, the overall costs of robust water system operation, the environmental impacts of drought-tolerant water supplies due to shorter duration of plant operation. Expedited permitting allows fundamental changes in how water infrastructure is deployed, facilitating a transition from anticipatory construction and continuous operation of seawater desalination capacity as a redundant drought buffer to just-in-time (i.e. adaptive) deployment of seawater desalination capacity when critical drought thresholds are crossed. We demonstrate the value of expedited desalination permitting in enabling adaptive planning and reducing water system costs using a simple case study in Santa Barbara, CA. We discuss additional forms of adaptive water infrastructure planning as enabled by faster permitting and address their challenges and opportunities. Lastly, we identify synergies between innovation in adaptive planning, innovation in expedited permitting practices, and innovation in water technology.

adaptive planning↗
DOE OSTI2025

NEAR: Neural Embeddings for Amino acid Relationships

Protein language models (PLMs) have recently demonstrated potential to supplant classical protein database search methods based on sequence alignment, but are slower than common alignment-based tools and appear to be prone to a high rate of false labeling. Here, we present NEAR, a method based on neural representation learning that is designed to improve both speed and accuracy of search for likely homologs in a large protein sequence database. NEAR’s ResNet embedding model is trained using contrastive learning guided by trusted sequence alignments. It computes per-residue embeddings for target and query protein sequences, and identifies alignment candidates with a pipeline consisting of residue-level k-NN search and a simple neighbor aggregation scheme. Tests on a benchmark consisting of trusted remote homologs and randomly shuffled decoy sequences reveal that NEAR substantially improves accuracy relative to state-of-the-art PLMs, with lower memory requirements and faster embedding and search speed. While these results suggest that the NEAR model may be useful for standalone homology detection with increased sensitivity over standard alignment-based methods, in this manuscript we focus on a more straightforward analysis of the model’s value as a high-speed pre-filter for sensitive annotation. In that context, NEAR is at least 5x faster than the pre-filter currently used in the widely-used profile hidden Markov model (pHMM) search tool HMMER3, and also outperforms the pre-filter used in our fast pHMM tool, nail.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Dynamics and observational signatures of core-collapse supernovae with central engines: hydrodynamics simulations with Monte Carlo post-processing

A long-lived central engine embedded in expanding supernova ejecta can alter the dynamics and observational signatures of the event, producing an unusually luminous, energetic, and/or rapidly evolving transient. We use 2D hydrodynamics simulations to study the effect of a central energy source, varying the amount, rate, and isotropy of the energy deposition. We post-process the results with a time-dependent Monte Carlo radiation transport code to extract observational signatures. The engine excavates a bubble at the centre of the ejecta, which becomes Rayleigh–Taylor unstable. Sufficiently powerful engines are able to break through the edge of the bubble and accelerate, shred, and compositionally mix the entire ejecta. The breakout of the engine-driven wind occurs at distinct rupture points, and the outflowing high-velocity gas may eventually give rise to radio emission. The dynamical impact of the engine leads to faster rising optical light curves, with photon escape facilitated by the faster expansion of the ejecta and the opening of low-density channels. For models with strong engines, the spectra are initially hot and featureless, but later evolve to resemble those of broad-line Ic supernovae. Under certain conditions, line emission from ionized, low-velocity material near the centre of the ejecta may be able to escape and produce narrow emission similar to that seen in interacting supernovae. We discuss how variability in the engine energy reservoir and injection rate could give rise to a heterogeneous set of events spanning multiple observational classes, including the fast blue optical transients, broad-line Ic supernovae, and superluminous supernovae.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Cas3-Mediated Genome Reduction: Demonstration in Cupriavidus Necator H16 Improves Growth on Heterotrophic and Autotrophic Carbon Sources

Genome reduction is widely used to improve microbial bioprocessing hosts by reducing the burden of inessential physiology. Rationally identifying genomic regions that are dispensable or even detrimental to bioprocessing is challenged by our inability to map genome sequence to function across complex regulation and physiology. Thus, there is a need for tools that rapidly generate reduced genome strains with improved performance in process-relevant conditions. Here, we report a Cascade-Cas3-enabled method called TRIM3 that generates large deletions by targeting a randomly integrated transposon, enabling facile generation of a genome-reduced mutant library. Mutants with improved performance were isolated following growth-coupled selection and analyzed by long-read DNA sequencing to identify deletions in their genomes. We deploy this system iteratively in the industrial host Cupriavidus necator H16 on fructose and on formate. After two rounds of TRIM3, we isolate a strain containing a total reduction of 1.4 Mb (18.4% of the genome) that grows 25% faster in a bioreactor on fructose and a strain with a total reduction of 0.5 Mb (7.3% of the genome) that grows 14% faster on formate. This work demonstrates a method for random, iterative, growth-selectable genome reduction that represents a new avenue for large-scale genome modifications and the development of improved bioprocessing hosts.

09 BIOMASS FUELS↗
DOE OSTI2026

Efficient many-jet event generation with flow matching

We apply for the first time, to the best of our knowledge, the flow matching method to the problem of phase-space sampling for event generation in high-energy collider physics. By training the model to remap the random numbers used to generate the momenta and helicities of the scattering matrix elements as implemented in the portable partonic event generator pepper, we find substantial efficiency improvements in the studied processes. We focus our study on the highest final-state multiplicities in Drell-Yan and top-antitop pair production used in simulated samples for the Large Hadron Collider, which computationally are the most relevant ones. We find that the unweighting efficiencies improve by factors of 184 and 25, respectively, when compared to the standard approach of using a vegas-based optimization. We also compare continuous normalizing flows trained with flow matching against the previously studied normalizing flows based on coupling layers and find that the former leads to better results, faster training and a better scaling behavior across the studied multiplicity range, while the latter evaluate faster. When combining the advantages of both methods using the regflow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest final-state multiplicities.

Bothmann, E. [CERN; Gottingen U.] (ORCID:000000016↗
DOE OSTI2024

Exploring the Landscape of Distributed Graph Clustering on Leadership Supercomputers

The rapid growth of large-scale datasets in fields like biology and social networks has driven the need for advanced graph analytics techniques. Community detection, a fundamental task in graph analytics, identifies closely connected groups of nodes within a network, providing valuable insights across various disciplines. This study focuses on two classic community detection methods, the Louvain algorithm and Markov Clustering (MCL), and evaluates the performance of two prominent distributed community detection algorithms: HiPDPL-GPU, our prior implementation, and HipMCL. We conduct experiments on GPU-accelerated heterogeneous HPC systems, Summit and Frontier, to assess their performance under varying conditions. Our objective is to identify the strengths and weaknesses of these algorithms in terms of scalability, and quality of solutions. We evaluate these algorithms on a diverse set of 70+ networks spanning 13 domains, with sizes ranging up to 4.2 billion edges. Our results demonstrate that HiPDPL-GPU consistently outperforms HipMCL, especially for large-scale networks. HiPDPL-GPU achieves significantly faster runtimes (47x to 1439x), higher modularity scores, and improved scalability. These findings highlight HiPDPL-GPU as a promising solution for efficient and effective large-scale graph analytics in diverse application domains, and provide insights into the feasibility of using MCL-based approaches for certain application domains.

Community detection, graph algorithms↗
DOE OSTI2025

Machine Learning-Assisted Distribution System Network Reconfiguration Problem

High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load delivery, reduced losses, and the operation between voltage limits. However, computations to decide the optimal feeder configuration are often computationally expensive and intractable, making it unfavorable for real-time operations. This is mainly due to the existence of binary variables in the network reconfiguration optimization problem. To tackle this issue, we have devised an approach that leverages machine learning techniques to reshape distribution networks featuring multiple substations. This involves predicting the substation responsible for serving each part of the network. Hence, it leaves simple and more tractable Optimal Power Flow problems to be solved. This method can produce accurate results in a significantly faster time, as demonstrated using the IEEE 37-bus distribution feeder. Compared to the traditional optimization-based approaches, a feasible solution is achieved approximately ten times faster for all the tested scenarios.

deep neural networks↗
DOE OSTI2024

Stability-Constrained Learning for Frequency Regulation in Power Grids With Variable Inertia

The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the power system at timescales comparable to the fast converter dynamics. This paper proposes a combined linear and neural network controller for inverter-based primary frequency control that is stable at time-varying levels of inertia. We model the time-variance in inertia via a switched affine hybrid system model. We derive stability certificates for the proposed controller via a quadratic candidate Lyapunov function. We test the proposed control on a 12-bus 3-area test network, and compare its performance with a base case linear controller, optimized linear controller, and finite-horizon Linear Quadratic Regulator (LQR). Our proposed controller achieves faster mean settling time and over 50% reduction in average control cost across 100 inertia scenarios compared to the optimized linear controller. Unlike LQR which requires complete knowledge of the inertia trajectories and system dynamics over the entire control time horizon, our proposed controller is real-time tractable, and achieves comparable performance to LQR.

data-driven control↗
DOE OSTI2024

Genetic Basis of Non–Photochemical Quenching and Photosystem II Efficiency Responses to Chilling in the Biomass Crop Miscanthus

Miscanthus holds a promise as a biocrop due to its high yield, perenniality and ability to grow on infertile soils. However, the current commercial biomass production of Miscanthus is mostly limited to a single sterile triploid clone of M. × giganteus. Nevertheless, parental species of M. × giganteus, Miscanthus sacchariflorus and Miscanthus sinensis contain vast genetic diversity for crop improvement. With M. sacchariflorus having a natural geographic distribution in cold-temperate northeast China and eastern Russia, we hypothesised that it has substantial variation in physiological response to chilling. Using a semi-high-throughput method, we phenotyped 209 M. sacchariflorus genotypes belonging to six genetic groups for non-photochemical quenching (NPQ) and photosystem II efficiency (ΦPSII) kinetics under warm and chilling treatments in three growing seasons. In response to the chilling treatment, all genetic groups exhibited an increase in NPQ induction rate indicating faster activation of NPQ in light. Notably, under chilling, the Korea/NE China/Russia 2x and N China 2x groups stood out for the highest NPQ rate in light and the highest steady-state NPQ in light. This NPQ phenotype may contribute adaptation to chilling during bright, cold mornings of spring and early autumn in temperate climates, when faster NPQ would better protect from oxidative stress. Such enhanced adaptation could expand the growing season and thus productivity at a given location or expand the range of economically viable growing locations to higher latitudes and altitudes. A genome-wide association study identified 126 unique SNPs associated with NPQ and ΦPSII traits. Among the identified candidate genes were enzymes involved in the ascorbate recycle and shikimate pathway, gamma-aminobutyric acid and cation efflux transporters. Identifying natural variation and genes involved in NPQ and ΦPSII kinetics considerably enlarges the toolbox for breeding and/or engineering Miscanthus with optimised photosynthesis under warm and chilling conditions for sustainable feedstock production for bioenergy.

09 BIOMASS FUELS↗