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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Real-Time Radiological Source Term Estimation for Multiple Sources in Cluttered Environments

A particle filter algorithm is presented to estimate the position, strength, and cardinality of an unknown number of radioactive point sources in an obstacle-rich environment using count measurements. The algorithm addresses gaps in the prior literature by incorporating two novel elements. The first is a precomputation step in which local terrain and obstacle data is processed to compute attenuation kernels throughout the search area. This enables rapid estimation performance in obstacle-rich environments as measurements are gathered. The second novel feature is a dynamic particle allocation technique in which the number of particles is adjusted in real time to meet convergence goals. This feature allows the algorithm to scale more efficiently to scenarios with a larger number of sources. A series of computational experiments using simulated data demonstrates the algorithm’s performance in a cluttered environment with up to eight sources.

Kemp, Samuel↗
DOE OSTI2025

A Brief Survey on High Performance Computing Systems Power Management

This paper provides a survey of software-based power management techniques in High Performance Computing (HPC) systems. Seven existing power management and monitoring tools and frameworks are discussed. These are: Variorum, dynamic energy-performance optimizer (DEPO), Powersched, Bull Dynamic Power Optimizer (BDPO), Energy Aware Runtime (EAR), Global Extensible Open Power Manager (GEOPM), and PoLiMEr. Each of these tools is evaluated based on hardware abstraction, optimization methods, usability, and experimental validation. This survey highlights the diversity of approaches in managing energy efficiency, from vendor-neutral APIs to algorithm-driven power capping, and dynamic frequency adjustments. Given that energy requirements for large computational systems is increasing quickly, the importance of integrating these tools into existing HPC environments and the need for further research in this rapidly evolving field is also discussed.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Surface oxidation and crystallization of FeNi-Based soft magnetic nanocrystalline and amorphous nanocomposite alloys

In this work, we present characterization of surface oxidation and crystallization behavior of Metal Amorphous Nanocomposite (MANC) (Fe 70 Ni 30 ) 80 Nb 4 Si 2 B 14 alloys via complementary x-ray diffraction, electron microscopy, and x-ray photoelectron spectroscopy techniques. An adherent amorphous oxide of roughly 15–20 nm in thickness is observed on the wheel side of the ribbon, enriched in Fe relative to Ni and with significant content of Si and Nb as would be expected based upon a comparison of the Gibbs free energy of formation of the stable oxide phases. The amorphous character of the oxide is likely to result from the significant Si-content coupled with the amorphous and nanocrystalline character of the underlying metallic layer. Higher density of FeNi-enriched nanocrystals is also observed within ~100 nm of the wheel-side surface, presumably due to reduction in local glass former concentration. A rough, crystalline copper oxide layer is also present and forms through contact between the copper wheel and the solidifying melt during the rapid solidification process. On the air side of the ribbon, a crystalline oxide layer of ~15–20 nm is formed as well as intergranular oxidation that penetrates along the grain boundaries between highly textured surface crystallites of ~50–100 nm in size. The crystalline nature of the oxide on these surfaces is asserted to result from the lack of significant Si-content and the pre-existing surface crystallites that form during the rapid solidification process which promote a crystalline oxide surface layer. Furthermore, these results shed new insights into the details of surface oxidation and can help to inform processing and alloy composition adjustments which seek to tailor surface properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Rapid retreat of the southwestern Laurentide Ice Sheet during the Bølling-Allerød interval

The timing of Laurentide Ice Sheet deglaciation along its southwestern margin controlled the evolution of large glacial lakes and has implications for human migration into the Americas. Accurate reconstruction of the ice sheet’s retreat also constrains glacial isostatic adjustment models and is important for understanding ice-sheet sensitivity to climate forcing. Despite its significance, retreat of the southwestern Laurentide Ice Sheet (SWLIS) is poorly constrained by minimum-limiting 14 C data. We present 26 new cosmogenic 10 Be exposure ages spanning the western Interior Plains, Canada. Using a Bayesian framework, we combine these data with geomorphic mapping, 10 Be, and high-quality minimum-limiting 14 C ages to provide an updated chronology. This dataset presents an internally consistent retreat record and indicates that the initial detachment of the SWLIS from its convergence with the Cordilleran Ice Sheet began by ca. 15.0 ka, concurrent with or slightly prior to the onset of the Bølling-Allerød interval (14.7–12.9 ka) and retreated >1200 km to its Younger Dryas (YD) position in ~2500 yr. Ice-sheet stabilization at the Cree Lake Moraine facilitated a meltwater drainage route to the Arctic from glacial Lake Agassiz within the YD, but not necessarily at the beginning. Our record of deglaciation and new YD constraints demonstrate deglaciation of the Interior Plains was ~60% faster than suggested by minimum 14 C constraints alone. In conclusion, numerical modeling of this rapid retreat estimates a loss of ~3.7 m of sea-level equivalent from the SWLIS during the Bølling-Allerød interval.

58 GEOSCIENCES↗
DOE OSTI2022

DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance Computing

Cluster schedulers are crucial in high-performance computing (HPC). They determine when and which user jobs should be allocated to available system resources. Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads. However, the increasing complexity of computing systems and the highly dynamic nature of application workloads have placed tremendous burden on manually designed and tuned scheduling heuristics. More aggressive optimization and automation are needed for cluster scheduling in HPC. In this work, we present an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for Scheduling) by leveraging deep reinforcement learning. DRAS is built on a hierarchical neural network incorporating special HPC scheduling features such as resource reservation and backfilling. An efficient training strategy is presented to enable DRAS to rapidly learn the target environment. Once being provided a specific scheduling objective given by the system manager, DRAS automatically learns to improve its policy through interaction with the scheduling environment and dynamically adjusts its policy as workload changes. We implement DRAS into a HPC scheduling platform called CQGym. CQGym provides a common platform allowing users to flexibly evaluate DRAS and other scheduling methods such as heuristic and optimization methods. Furthermore, the experiments using CQGym with different production workloads demonstrate that DRAS outperforms the existing heuristic and optimization approaches by up to 50%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

In situ Synchrotron X‐ray Metrology Boosted by Automated Data Analysis for Real‐time Monitoring of Cathode Calcination

Abstract Synchrotron X‐ray‐based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on‐site, on‐the‐fly analysis needed for real‐time process monitoring. Here, a weighted lagged cross‐correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X‐ray diffraction metrology to monitor the calcination process of the archetypal nickel‐based cathode, LiNiO 2 . The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO 2 , providing information for agile on‐site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early‐stage phase nucleation and growth. Expanding this data‐powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Miniaturized Ignition Screening Rapid Compression Machine for probing low-to-intermediate temperature chemistry

With an objective to achieve net-zero emissions by no later than 2050, biofuels hold enormous potential as sustainable, renewable fuels that can be produced on a large scale. However, fuels of a similar type, derived from limited feedstock batch can dramatically vary in terms of their chemical structures and compositions, causing significant differences in physicochemical properties mainly influencing ignition-relevant properties. Rigorous prescreening and testing efforts are involved to evaluate fuels to comply with engines equipped with advanced combustion modes. The testing of some of these fuels implies low quantity fuel samples, access to relevant temperature and pressure conditions, high reproducibility, and a high throughput rate. In light of these considerations, a Miniaturized Ignition Screening Rapid Compression Machine (MISR) has been developed and built at the University of Illinois Chicago (UIC). Some design features include a smaller bellows-actuated driver section compared to other RCM designs, an adjustable brake section, and full optical access. The setup is designed to achieve reaction conditions (T > 600K and P < 100 bar with repetition rates of about 8 seconds per experiment). These conditions are relevant to low-to-intermediate temperature auto-ignition and can generate data for developing relevant chemical kinetics models or ignition maps. A brief insight into MISR design and its operating mechanism is shown. Also, to demonstrate the performance characteristics of the MISR, Ignition Delay (I.D) experiments for ethanol fuel at different equivalence ratios (Φ) are discussed in the context of plans with additional optical diagnostics, high-speed imaging, and gas sampling experiments.

Tomar, Mukul↗
DOE OSTI2026

APSO-enhanced algebraic derivative estimation approach for real-time traffic flow prediction on critical road sections during wildfire evacuation

In rapid-onset disaster scenarios such as wildfires, evacuation traffic often significantly deviates from historical patterns, rendering conventional data-driven forecasting methods less effective. To address this challenge, we propose an improved algebraic derivative estimation (ADE) incorporating particle swarm optimization (PSO) for real-time traffic flow prediction. Our approach dynamically adjusts the ADE prediction time window at each step by minimizing a cost function based on the mean and variance of accumulated forecasting errors within the window, thereby balancing bias and variability. We evaluate the method using traffic data from the January 2025 California wildfires, focusing on key road segments critical for large-scale evacuations. The results demonstrate that our approach surpasses established machine learning and deep learning models—XGBoost, LSTM, and GRU—in predictive accuracy and maintains high computational efficiency. Notably, the proposed method eliminates the need for offline model training. Moreover, rapid PSO-based tuning enables real-time deployment, which provides a crucial advantage in scenarios where evacuation timings and road closures change dynamically. In conclusion, these findings highlight the benefits of the PSO-enhanced ADE framework for emergency traffic management, where rapid, data-sparse forecasts are essential for effective evacuation planning.

Algebraic derivative estimation↗
DOE OSTI2025

Quantum-enhanced detection of viral cDNA via luminescence resonance energy transfer using upconversion and gold nanoparticles

Abstract The COVID-19 pandemic has profoundly impacted global economies and healthcare systems, revealing critical vulnerabilities in both. In response, our study introduces a sensitive and highly specific detection method for cDNA, leveraging Luminescence Resonance Energy Transfer (LRET) between upconversion nanoparticles (UCNPs) and gold nanoparticles (AuNPs), and achieves a detection limit of 242 fM for SARS-CoV-2 cDNA. This innovative sensing platform utilizes UCNPs conjugated with one primer and AuNPs with another, targeting the 5′ and 3′ ends of the SARS-CoV-2 cDNA, respectively, enabling precise differentiation of mismatched cDNA sequences and significantly improving detection specificity. Through rigorous experimental analysis, we established a quenching efficiency range from 10.4 % to 73.6 %, with an optimal midpoint of 42 %, thereby demonstrating the superior sensitivity of our method. Our work uses SARS-CoV-2 cDNA as a model system to demonstrate the potential of our LRET-based detection method. This proof-of-concept study highlights the adaptability of our platform for future diagnostic applications. Instrumental validation confirms the synthesis and formation of AuNPs, addressing the need for experimental verification of the preparation of nanomaterial. Our comparative analysis with existing SARS-CoV-2 detection methods revealed that our approach provides a low detection limit and high specificity for target cDNA sequences, underscoring its potential for targeted COVID-19 diagnostics. This study demonstrates the superior sensitivity and adaptability of using UCNPs and AuNPs for cDNA detection, offering significant advances in rapid, accessible diagnostic technologies. Our method, characterized by its low detection limit and high precision, represents a critical step forward in developing next-generation biosensors for managing current and future viral outbreaks. By adjusting primer sequences, this platform can be tailored to detect other pathogens, contributing to the enhancement of global healthcare responsiveness and infectious disease control.

Esmaeili, Shahriar [Institute for Quantum Science ↗
DOE OSTI2024

Performance of a Drifting Acoustic Instrumentation SYstem (DAISY) for characterizing radiated noise from marine energy converters

Marine energy converters can generate electricity from energetic ocean waves and water currents. Because sound is extensively used by marine animals, the radiated noise from these systems is of regulatory interest. However, the energetic nature of these locations poses challenges for performing accurate passive acoustic measurements, particularly with stationary platforms. The Drifting Acoustic Instrumentation SYstem (DAISY) is a modular hydrophone recording system purpose-built for marine energy environments. Using a flow shield in currents and mass–spring–damper suspension system in waves, we demonstrate that DAISYs can effectively minimize the masking effect of flow noise at frequencies down to 10 Hz. In addition, we show that groups of DAISYs can utilize time-delay-of-arrival post-processing to attribute radiated noise to a specific source. Consequently, DAISYs can rapidly measure radiated noise at all frequencies of interest for prototype marine energy converters. Furthermore, the resulting information from future operational deployments should support regulatory decision-making and allow technology developers to make design adjustments that minimize the potential for acoustic impacts as their systems are scaled up for utility-scale power generation.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Online Alpha Monitoring of High Cs-137 Hanford and SRS High Level Waste with Tensioned Metastable Fluid Detectors

The Department of Energy’s Hanford and Savannah River Sites maintain millions of gallons of caustic supernate and salt high activity waste in their high-level waste (HLW) tank farm inventories. The Savannah River Site is currently treating this waste with a calixarene-based solvent extraction of Cs-137 to reduce these inventories. Hanford is employing an at-tank crystalline silico titanate (CST) based solid phase extraction methodology to reduce their liquid HLW inventories. Due to the high solubility of Cs-137 and the relative insolubility of the actinides in these caustic waste forms, the beta to alpha radioactivity ratio can often exceed six orders of magnitude in the feed solutions to these treatment processes. This unique characteristic leads to significant technical challenges in making rapid gross alpha measurements in the presence of the overwhelming beta, gamma, as well as dissolved sodium salt in these HLW matrices. Conventional radioanalytical techniques, such as liquid scintillation analysis or gas flow proportional counting require significant radiochemistry preparation prior to the radiometric measurements for gross alpha activity. These required pretreatments render these technologies untenable for rapid quantification of gross alpha activity that could be required to support on or at-line measurements ensuring a waste stream will meet regulatory requirements. The radiation measurement properties of Tensioned Metastable Fluid Detectors (TMFDs) have been studied by Purdue University’s Taleyarkhan research group for well over a decade. Fluids tensioned to the appropriate degree will rupture when struck by radiation, resulting in a measurable cavitation event. The negative pressure generating this tension can be adjusted by centrifugal rotation or by acoustic means in such a way that these cavitation events can be generated from alpha radiation but will not be generated by beta or gamma radiation. Purdue University and the Savannah River National Laboratory are currently collaborating to develop a gamma/beta blind, spectroscopic alpha measurement system based on the Tensioned Metastable Fluid Detector technology to provide a potential solution for performing rapid gross alpha measurements on these high gamma/beta sample matrices. Measurements using the Indirect Drive Acoustically Tensioned Metastable Fluid Detectors developed as part of this collaboration were performed with an alpha emitting radionuclide. Successful determination of gross alpha activity was observed, indicating a potential pathway for rapid gross alpha measurements in remote-handled shielded cells or in process situations requiring online alpha monitoring. Measurements using this system have been conducted on high beta activity solutions, demonstrating the beta blind capability of this system. Measurements are currently underway to test the system’s capability to measure gross alpha activity on Savannah River Site high level waste high Cs-137 samples that have been measured by the SRNL radiochemistry team. This work was supported by the DOE EM Technology Development program.

DiPrete, David [Savannah River National Laboratory↗
DOE OSTI2026

Intelligent industrial demand response to increase grid flexibility and reliability: A review

The rapid transition toward renewable energy has introduced challenges in grid stability due to the intermittency of non-dispatchable sources like solar and wind. Industrial Demand Response (IDR) offers a promising, cost-effective solution that adjusts energy consumption patterns to align with supply, increases renewable utilization, and reduces costs. This review provides an updated analysis of IDR, sorting technologies into five categories: energy storage, scheduled energy usage, operational flexibility, on-site generation, and intelligent operations. Energy storage solutions, while requiring little flexibility, often have the longest payback periods. While slightly better, on-site generation also has longer payback periods, ranging from 5 to 20 years or more. Scheduled energy usage, operational flexibility, and intelligent operations allow significant peak reduction at lower capital costs but require greater flexibility. While 15–20 % peak reduction is within the range of all five categories, scheduled energy use and on-site energy generation are shown to have reductions of up to 70–80 % in select scenarios. Combining multiple IDR strategies from these five categories maximizes both financial and operational benefits. Synergistic approaches are shown to enhance grid stability while reducing costs. As the grid evolves, IDR will enable a more flexible, renewable-powered future that will benefit industrial facilities and the broader energy system.

Demand flexibility↗
DOE OSTI2022

Optimising response to an introduction of African swine fever in wild pigs

African swine fever virus (ASFv) is a virulent pathogen that threatens domestic swine industries globally and persists in wild boar populations in some countries. Persistence in wild boar can challenge elimination and prevent disease-free status, making it necessary to address wild swine in proactive response plans. In the United States, invasive wild pigs are abundant and found across a wide range of ecological conditions that could drive different epidemiological dynamics among populations. Information on the size of the control areas required to rapidly eliminate the ASFv in wild pigs and how this area should change with management constraints and local ecology is needed to optimize response planning. We developed a spatially explicit disease transmission model contrasting wild pig movement and contact ecology in two ecosystems in Southeastern United States. We simulated ASFv spread and determined the optimal response area (reported as the radius of a circle) for eliminating ASFv rapidly over a range of detection times (when ASFv was detected relative to the true date of introduction), culling capacities (proportion of wild pigs in the culling zone removed weekly) and wild pig densities. Large radii for response areas (14 km) were needed under most conditions but could be shortened with early detection (≤ 8 weeks) and high culling capacities (≥ 15% weekly). Under most conditions, the ASFv was eliminated in less than 22 weeks using optimal control radii, although ecological conditions with high rates of wild pig movement required higher culling capacities (≥ 10% weekly) for elimination within 1 year. The results highlight the importance of adjusting response plans based on local ecology and show that wild pig movement is a better predictor of the optimal response area than the number of ASFv cases early in the outbreak trajectory. Furthermore, our framework provides a tool for determining optimal control plans in different areas, guiding expectations of response impacts, and planning resources needed for rapid elimination.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Centrifugal Generation of Droplet-Based 3D Cell Cultures

Quickly and easily producing uniform populations of microsphere-based 3D cell cultures using droplet-based templating methods has the potential to enable widespread use of such platforms in drug discovery or cancer research. Here, we advance the design of centrifuge-based droplet generation devices, describe the use of this platform for droplet generation with controlled cell occupancy, and demonstrate weeklong culture duration. Using simple-to-construct devices and easily implemented protocols, the initial concentration of encapsulated cells is adjustable up to hundreds of cells per microsphere. This work demonstrates the first instance of using centrifugal droplet-generating devices to produce large numbers of cell-encapsulating microspheres. Applications of this versatile methodology include the rapid formation of templated 3D cell culture populations suitable for suspension culture or large batch bioreactor studies that require uniform populations.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Characterization of Connected Lighting System Potential for Grid Services Under Real-Time Pricing

Increasingly over the last decade, the management of end-use electricity demand has been examined as a potential source of services for the electric power grid beyond contracts with large commercial and industrial customers. Lighting represents about 17\% of U.S. commercial building annual electricity consumption; however, its potential rapid speed of response may position it to uniquely contribute to services that improve the reliability and resilience of the grid. Connected lighting systems (CLS), which build upon solid-state lamp technology, can change state by adjusting power demand more quickly than most other building electricity end uses and could be used as a platform to monitor lighting electricity use and space conditions. But the potential of CLS to provide grid services has not been extensively studied. In this paper, we describe initial research to evaluate the potential of CLS for grid services. We develop a model for CLS using a set of parameters to represent operation behaviors and constraints: maximal power, minimal power, nominal power, ramp rate, and time delay. Parameter values are generated for representative building types. CLS demand curves are constructed, indirectly capturing building occupant preferences for lighting as functions of electricity price. The CLS model and the demand curves are incorporated into a Transactive Control and Coordination (TCC) Platform to simulate CLS providing grid services. Previous research has shown TCC to be a powerful tool to enable end uses to provide grid services through a hybrid economic-control approach. Initial quantitative results will be provided for CLS grid services potential.

Wang, Peng↗
DOE OSTI2022

Novel frequency tunable systems for superconducting cavities (Abbreviated Report)

Ultra-low loss microwave resonant cavities are the backbone of quantum information science (QIS) and axion/dark matter detection (ADMX), but one of the challenges in developing 3D cavity systems has been finding a tuning mechanism that does not drastically reduce the cavity quality factor (Q) but still provides a large operating bandwidth. To alleviate this limitation, we proposed to fabricate a high-Q, 3D microwave cavity with an integrated hybrid mechanical/electrical tuning scheme to achieve both coarse and rapid fine tuning. We implemented two tuning schemes: one using a shift in kinetic inductance and one utilizing mechanical motion. We found that the kinetic inductance method did not yield any tunability in the cavity mode, but the mechanical adjustment successfully moved the cavity resonance. The quality factors obtained were lower than originally proposed, but there is potential to improve the design on further iterations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Rapid Viscoelastic Deformation Slows Marine Ice Sheet Instability at Pine Island Glacier

The ice sheets of the Amundsen Sea Embayment (ASE) are vulnerable to the marine ice sheet instability (MISI), which could cause irreversible collapse and raise sea levels by over a meter. The uncertain timing and scale of this collapse depend on the complex interaction between ice, ocean, and bedrock dynamics. The mantle beneath the ASE is likely less viscous (~10 18 Pa s) than the Earth's average mantle (~10 21 Pa s). In this paper we show that an effective equilibrium between Pine Island Glacier's retreat and the response of a weak viscoelastic mantle can reduce ice mass lost by almost 30% over 150 years. Other components of solid Earth response—purely elastic deformations and geoid perturbations—provide less stability than the viscoelastic response alone. Uncertainties in mantle rheology, topography, and basal melt affect how much stability we expect, if any. Our study indicates the importance of considering viscoelastic uplift during the rapid retreat associated with MISI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A practical control strategy for demand flexibility with ensured occupant comfort in grid-interactive efficient buildings

This study proposes a practical and simplified demand response (DR) control strategy from the perspective of grid-interactive efficient buildings (GEBs), aiming to secure demand flexibility while ensuring occupant thermal comfort. Focusing on summer on-peak periods, a linear demand response (LDR) strategy that integrates cooling setpoint adjustment and lighting dimming was designed, and its performance was quantitatively evaluated. A case study was conducted using EnergyPlus-based simulations for a U.S. DOE small office prototype building under summer on-peak weather conditions. Compared with a conventional rapid demand response (RDR) strategy, the proposed LDR approach gradually reduced electrical loads while maintaining occupant thermal comfort indices, including predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD), within acceptable comfort ranges. Quantitative analysis of demand flexibility using the grid-interactive impact index (GII) and the flexibility strength index (FSI) showed that the LDR strategy provided approximately 12.5% demand flexibility during DR periods and achieved an electricity cost reduction of about 8.8%. In addition, the results of the part-load ratio (PLR)-based cooling system performance analysis showed that, in the on-peak period, the LDR strategy exhibited improved cooling performance compared with the baseline. Overall, this study shows the potential of a practical DR control strategy that can simultaneously achieve occupant comfort and demand flexibility without relying on complex advanced control technologies.

Jung, Dong Eun↗