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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Findings on subtask 1.6 – basin electric carbon storage research project: novel monitoring techniques

The Energy & Environmental Research Center (EERC) led a study to validate novel and emerging technologies as commercial monitoring techniques for application in carbon dioxide (CO 2 ) injection operations. This applied research was conducted at Basin Electric Power Cooperative’s (Basin Electric’s) active CO 2 -injection operations in Beulah, North Dakota, in two phases. The EERC previously completed a set of baseline (preinjection) activities in Phase 1, which included 1) design of an automated, integrated, modular (AIM) monitoring station; 2) time-lapse electromagnetic (EM) field surveys; 3) drone-based surveillance studies; 4) time-lapse monitoring with seismic methods; and 5) advanced wellbore-monitoring methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Finding the missing pieces: filling gaps that impede the translation of omics data into models

High-throughput omics technologies such as DNA sequencing have made the sequencing and computational assembly of microbial genomes recovered from the environment relatively routine. Computational inference of the protein products encoded by these genomes, and the associated biochemical functions, should enable the accurate prediction and modeling of microbial metabolism, organismal interactions, and ecosystem processes. However, a lack of scalable, probabilistic protein annotation tools limits the full potential of modeling for understanding the metabolism and biogeochemical cycles of microbial communities. Our approach to improve inference of protein annotations and metabolic models relied on learning from and emulating expert manual curation, leveraging software engineering and data science best practices to scale up the throughput and accuracy of annotations and metabolic model construction, building software to objectively evaluate different annotation strategies, and more closely linking the protein annotation and metabolic model inference process. Outcomes of this research include several improved or new computational tools, including DRAM (Distilled and Refined Annotation of Metabolism) for annotating microbial genomes with protein function and metabolic traits, CAMPER (Curated Annotations for Microbial Polyphenol Enzymes and Reactions) for annotating key polyphenol metabolisms, EC-Bench for comprehensive and unbiased benchmarking of annotation tools, and several apps available via the DOE Systems Biology Knowledgebase (KBase) for building genome-scale metabolic models. We demonstrate that these tools allow us to scalably annotate and understand thousands of genomes for microbial communities from a variety of systems and test cases, including rivers, thawing permafrost, and gut microbiomes. All of these computational tools are available as open-source software, with most broadly and easily accessible to the scientific community via KBase apps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Finding Nuclear Clusters in the Short-Baseline Near Detector

Production of nuclear clusters such as deuterons, tritons, helions and alpha particles has only recently been simulated for neutrino-nucleus interactions, and has not yet been measured in neutrino experiments. The Short-Baseline Near Detector (SBND) is the first Liquid Argon Time Projection Chamber (LArTPC) with high enough resolution and a large enough neutrino flux to accurately measure the cross-section for the production of heavier-than-proton fragments in neutrino interactions. SBND is a 112-ton LArTPC, and lies 110 m downstream from the Booster Neutrino Beam target, where it has already collected the world's largest dataset of neutrino-argon interactions. In these processes, the intranuclear cascade and nuclear de-excitation stages can both produce nuclear clusters. With LArTPC technology's excellent reconstruction capabilities and SBND's unprecedented neutrino statistics, a measurement of these nuclear clusters could distinguish between nuclear models and improve neutrino energy reconstruction. This poster presents the status of simulation, reconstruction, and prospects for a measurement of nuclear clusters in SBND.

Beever, Anna [Sheffield U.] (ORCID:000900069339924↗
DOE OSTI2026

Energy Resilience Options for the Koolauloa Community Resilience Hub – Energy Technology Innovation Partnership Project, Cohort 2: Summary of Findings and Assessment

Hui o Hau‘ula (HoH) is a community organization dedicated to the well-being of the population of the Ko‘olauloa district on the Hawaiian island of Oahu. In response to growing concerns about challenges related to extreme weather events or natural disasters, HoH formulated the concept of the Ko‘olauloa Community Resilience Hub, or KCRH. The KCRH facility would serve as a focal point for the community during normal conditions, while also providing essential services and acting as a safe space during emergencies, natural or man-made. The concept of the KCRH was initially developed in partnership with the Hawaii Natural Energy Institute, +Lab Architects, and the City and County of Honolulu. In the fall of 2023, the U.S. Department of Energy, under the Energy Technology Innovation Partnership Project Program (ETIPP), provided support to the KCRH project, in the form of technical assistance (TA) to be provided by its National Laboratory complex. In this case, the core TA was provided by Sandia National Laboratories, and it was directed to providing options for designing an energy system based at the KCRH that could support critical loads in the event of a 30- day grid outage. The National Renewable Energy Laboratory (NREL) provided communications and logistics support in the project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Adaptive Cybersecurity for Distributed Energy Resources (AdCyDER): Online Reinforcement Learning with Stackelberg-Optimized Defenses — Pipeline Architecture, Evaluation Methodology, and Findings from a Synthetic-Data Evaluation

This report documents the design and evaluation of an integrated online-learning pipeline developed within the AdCyDER project for Distributed Energy Resource (DER) cybersecurity. The pipeline couples a Reinforcement Learning (RL) attack classifier — which produces an attack-type probability distribution — with a Stackelberg game-theoretic (GT) defense selector that consumes those distributions alongside SME-encoded priors over (defense, attack) effectiveness pairings and perdefense costs to choose grid-health-preserving defenses. The objective is not attack classification per se but production of distributions that drive effective defense selection through the Stackelberg layer, learned from delayed grid-health feedback rather than labeled attack data. AdCyDER as a whole is broader than the work presented here; this report covers the specific RL/GT loop integration and its evaluation. We present the integrated pipeline (SCADA telemetry with Fronius inverter physics, Suricata IDS, time-windowed aggregation, per-facility LSTM classifier, Stackelberg optimizer, OpenC2 actuators), an experimental campaign of 28 eight-hour iterations across three baseline modes, and a pipeline-ordered diagnostic protocol. The protocol identifies two distinct failure modes within the loop: paired supervised ceilings on the same features establish that the deployed online RL classifier (macro F1 ≈ 0.07) sits at least 4.7× below a same-architecture supervised LSTM (≈ 0.34) and 10–11× below a linear feature-signal ceiling (≈ 0.70–0.79 depending on per-facility isolation), localizing the dominant failure to the training procedure; and the reward signal driving online updates carries weak directional coupling with classifier correctness in the methodology-expected direction (multi-lens convergent: top-decile P(true) records produce more frequent state changes and slightly larger improvements, top-vs-bot Cohen’s 𝑑 ≈ −0.19), but at effect magnitudes too small to drive gradient-based learning at the campaign sample size. The original learning hypothesis is not supported by the data. The primary contributions are the diagnostic methodology — proposed as a transferable falsification protocol for online RL/GT defense pipelines learning from delayed environmental reward — and the open, reproducible experimental infrastructure. We outline reward reformulation as the highest-priority aspirational next step given the underpowered-but-aligned Q6 reading, with hardware-in-the-loop evaluation as the broadest scope-expansion option.

Blakely, Benjamin [Argonne National Laboratory (AN↗
DOE OSTI2026

DAOS Benchmarks and Findings

We benchmark DAOS on a 127-node, 4,064-target pool using fio, IOR, IO500, and mdtest, comparing the DFS API against DFuse+POSIX. Single-client fio sweeps show larger block sizes and writes yield higher bandwidth, with similar random/sequential performance. Multi-node IOR (1–32 nodes, 6–48 tasks/node) shows bandwidth saturating around 32 tasks/node, with large transfer sizes hurting read throughput at high concurrency and write latency growing faster than read latency. An 8-node IO500 run shows DFS achieving ~5x higher bandwidth and ~190x higher IOPS than POSIX. We also detail operational issues: SCM target-filling under mdtest, intermittent NA_HOSTUNREACH errors, fio CPU-affinity failures, and a pool error storm resolved by recreation.

George, Rebecca [Thomas Jefferson National Acceler↗
DOE OSTI2022

Finding values in lignin: A promising yet under-utilized component of the lignocellulosic biomass

This article outlines the technical and economic potentials of lignin in unlocking sustainable biorefineries. The benefits of using this highly functionalized biopolymer for the growth of sustainable economy have been highlighted. But practically, the possibility of commercially substituting petroleum oil with lignin is still not very high as the estimated biofuel production cost is 2–3 times higher than the former one. However, with the advancement in technology and more efficient measures by biorefineries such as storing and processing the biomass near the field so as to reduce the transportation cost, it is possible to gain higher profits. Companies like Domtar, Stora Enso, Borregaard’s LignoTech, VITO, and Chemelot InSciTe have been promoting commercial value of lignin. The growth of lignin market after the start-up production at various sites has been discussed in this review. Combining the complete “start-to-finish” analysis with economic evaluation gives a pragmatic overview of the possibilities whether lignin will join petroleum oil as an efficient and cost-effective renewable source.

Jassal, Vidhisha↗
DOE OSTI2022

Shifting Trade-offs: Finding the Sustainable Nexus of Hydropower and Environmental Flows in the San Joaquin River Watershed, California

Environmental flow management in watersheds with multi-objective reservoirs is often presented as an additional constraint to an already strained and over-allocated stream system. Nevertheless, environmental flow legislation and regulatory policies are increasingly being developed and implemented globally. In California, USA, recent legislative and regulatory policies place environmental flows at the forefront of the state’s water management objectives; however, the increased reliance on hydropower to support climate change mitigation goals may complicate efforts on both issues. This study modelled alternative environmental flow strategies in the major tributaries to the San Joaquin River in California. Strategies included detailed water management rules for hydropower production, flood control, and water deliveries, and three methodological approaches to environmental flow releases: minimum instream flows (“baseline”) year-round, 40% of full natural flow (FNF) during the spring runoff season and minimum releases the remainder of the year, and functional flows year-round. Results show that environmental flow strategies affect downstream flow releases in each of the San Joaquin’s four sub-basins differently depending on infrastructure capacity, water management objectives, and hydrologic year types. While hydropower production was comparable or declined in the Stanislaus, Tuolumne, and Merced basins, functional flow and 40% FNF strategies increased hydropower production in the Upper San Joaquin basin by 11%. Uncontrolled spill of high flow events decreased when high flow releases were based on hydrologic cues rather than exclusively on flood storage capacity. Water deliveries were reduced in all years regardless of environmental flow strategy. The 40% FNF and functional flow strategies both increased water released to the river relative to baseline, but in different ways. The functional flow strategy allocated water in a holistic approach that enhanced ecological functions in all years, but particularly in moderate and wet years. In contrast, the 40% FNF strategy provided increased flows relative to baseline and some ecological benefit in dry years, but less ecological benefit in other years. This study shows that alternative environmental flow strategies will have different and important trade-offs for integrated water management, and may mutually benefit seemingly conflicting objectives.

13 HYDRO ENERGY↗
DOE OSTI2024

Estimation of the Chances to Find New Phenomena at the LHC in a Model-Agnostic Combinatorial Analysis

In this paper, we estimate the number of event topologies that have the potential to be produced in 𝑝𝑝 collisions at the Large Hadron Collider (LHC) without violating kinematic and other constraints. We use numerical calculations and combinatorics, guided by large-scale Monte Carlo simulations of Standard Model (SM) processes. Then, we set the upper limit on the probability that new physics may escape detection, assuming a model-agnostic approach. The calculated probability is unexpectedly large, and the fact that LHC has not found new physics until now is not entirely surprising. Theoretical limitations and experimental challenges in observing new physics within the studied exclusive event classes are examined.

BSM↗
DOE OSTI2022

Finding Fast Transients in Real Time Using a Novel Light-curve Analysis Algorithm

The current data acquisition rate of astronomical transient surveys and the promise for significantly higher rates in the next decade necessitate the development of novel approaches to analyze astronomical data sets and promptly detect objects of interest. The Deeper, Wider, Faster (DWF) program is a survey focused on the identification of fast-evolving transients, such as fast radio bursts, gamma-ray bursts, and supernova shock breakouts. It employs multifrequency simultaneous coverage of the same part of the sky over several orders of magnitude. Using the Dark Energy Camera mounted on the 4 m Blanco telescope, DWF captures a 20 s g -band exposure every minute, at a typical seeing of ~1'' and an air mass of ~1.5. These optical data are collected simultaneously with observations conducted over the entire electromagnetic spectrum—from radio to γ -rays—as well as cosmic-ray observations. In this paper, we present a novel real-time light-curve analysis algorithm, designed to detect transients in the DWF optical data; this algorithm functions independently from, or in conjunction with, image subtraction. We present a sample of fast transients detected by our algorithm, as well as a false-positive analysis. Our algorithm is customizable and can be tuned to be sensitive to transients evolving over different timescales and flux ranges.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep Learning of Dark Energy Spectroscopic Instrument Mock Spectra to Find Damped Lyα Systems

We have updated and applied a convolutional neural network (CNN) machine-learning model to discover and characterize damped Ly α systems (DLAs) based on Dark Energy Spectroscopic Instrument (DESI) mock spectra. We have optimized the training process and constructed a CNN model that yields a DLA classification accuracy above 99% for spectra that have signal-to-noise ratios (S/N) above 5 per pixel. The classification accuracy is the rate of correct classifications. This accuracy remains above 97% for lower S/N ≈1 spectra. This CNN model provides estimations for redshift and H i column density with standard deviations of 0.002 and 0.17 dex for spectra with S/N above 3 pixel -1 . Also, this DLA finder is able to identify overlapping DLAs and sub-DLAs. Further, the impact of different DLA catalogs on the measurement of baryon acoustic oscillations (BAO) is investigated. The cosmological fitting parameter result for BAO has less than 0.61% difference compared to analysis of the mock results with perfect knowledge of DLAs. This difference is lower than the statistical error for the first year estimated from the mock spectra: above 1.7%. We also compared the performances of the CNN and Gaussian Process (GP) models. Our improved CNN model has moderately 14% higher purity and 7% higher completeness than an older version of the GP code, for S/N > 3. Both codes provide good DLA redshift estimates, but the GP produces a better column density estimate by 24% less standard deviation. A credible DLA catalog for the DESI main survey can be provided by combining these two algorithms.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Finding Multiply Lensed and Binary Quasars in the DESI Legacy Imaging Surveys

The time delay between multiple images of strongly lensed quasars is a powerful tool for measuring the Hubble constant ( H 0 ). To achieve H 0 measurements with higher precision and accuracy using the time delay, it is crucial to expand the sample of lensed quasars. We conduct a search for strongly lensed quasars in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys. The DESI Legacy Surveys comprise 19,000 deg 2 of the extragalactic sky observed in three optical bands ( g , r , and z ), making it well suited for the discovery of new strongly lensed quasars. We apply an autocorrelation algorithm to ~5 million objects classified as quasars in the DESI Quasar Sample. These systems are visually inspected and ranked. Here, we present 436 new multiply lensed and binary quasar candidates, 65 of which have redshifts from Sloan Digital Sky Survey Data Release 16. We provide redshifts for an additional 18 candidates from the SuperNova Integral Field Spectrograph.

79 ASTRONOMY AND ASTROPHYSICS↗