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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Theory of x-ray photon correlation spectroscopy for multiscale flows

Complex multiscale flows associated with instabilities and turbulence are commonly induced under high-energy density (HED) conditions, but accurate measurement of their transport properties has been challenging. X-ray photon correlation spectroscopy (XPCS) with coherent x-ray sources can, in principle, probe material dynamics to infer transport properties using time autocorrelation of density fluctuations. Here we develop a theoretical framework for utilizing XPCS to study material diffusivity in multiscale flows. We extend single-scale shear flow theories to broadband flows using a multiscale analysis that captures shear and diffusion dynamics. Our theory is validated with simulated XPCS for Brownian particles advected in multiscale flows. We demonstrate the versatility of the method over several orders of magnitude in timescale using sequential-pulse XPCS, single-pulse x-ray speckle visibility spectroscopy (XSVS), and double-pulse XSVS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

High-Performance Transmission and Distribution Co-simulation with 10,000+ Inverter-Based Resources

The inverter-based resource (IBR) has become avery important component in the distribution system. The impacts on system transient stability introduced by high IBR penetration are not fully addressed because of the lack of high-fidelity models. The aggregate IBR model at the transmission level cannot precisely reproduce the dynamics of distributed IBR at the distribution system because of the oversimplification. In this paper, we will develop a high-penetration fully-connected transmission and distribution (T&D) co-simulation platform that supports the simulation of 10,000+ dispersed IBR models. The interfacing and iterative initialization techniques for the co-simulation have been implemented to maintain stable operation and simulation of large-multitude of IBR models. The phasor-domain IBR models with grid-forming (GFM) and grid-following (GFL) control are implemented in the distribution systems simulators. The developed platform is tested on high-performance computing (HPC) resources and can be utilized to explore the hierarchical control strategies of IBRs for the large-scale T&D hybrid system.

Liu, Yuan↗
DOE OSTI2022

Behavioral and Population Data-Driven Distribution System Load Modeling

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.

American Time Use Survey↗
DOE OSTI2022

Podman-hpc v1.0

We have developed some scripts and tools that compliment an existing open-source product (Podman, https://podman.io/). This enables podman to function as a high-performance container solution. This capability is similar to Shifter and Singularity (both developed at LBNL). The advantage of leveraging Podman is it has greater support in the broader community outside of HPC and has capabilities missing from many of the HPC-only solutions. The specific enhancements we wish to share our how to enable Podman to scale to very large HPC jobs and our integrations we have done to utilize high-performance interconnects and GPUs.

Cannon, Richard↗
DOE OSTI2023

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics

We seek to transform how new and emergent variants of pandemic-causing viruses, specifically SARS-CoV-2, are identified and classified. By adapting large language models (LLMs) for genomic data, we build genome-scale language models (GenSLMs) which can learn the evolutionary landscape of SARS-CoV-2 genomes. By pre-training on over 110 million prokaryotic gene sequences and fine-tuning a SARS-CoV-2-specific model on 1.5 million genomes, we show that GenSLMs can accurately and rapidly identify variants of concern. Thus, to our knowledge, GenSLMs represents one of the first whole-genome scale foundation models which can generalize to other prediction tasks. We demonstrate scaling of GenSLMs on GPU-based supercomputers and AI-hardware accelerators utilizing 1.63 Zettaflops in training runs with a sustained performance of 121 PFLOPS in mixed precision and peak of 850 PFLOPS. We present initial scientific insights from examining GenSLMs in tracking evolutionary dynamics of SARS-CoV-2, paving the path to realizing this on large biological data.

Zvyagin, Maxim↗
DOE OSTI2022

Assessing the Effectiveness of Generalized Likelihood Ratio Test Detector Schemes in Seismic Event Detection and the Avoidance of Nontarget Signals

Cross-correlation techniques have played a long-standing and pivotal role in seismic event monitoring. However, the performance of correlation-based detectors is challenged by nuisance seismicity, or nontarget signals. Such detections are a problem when the mission is to automatically map events to the correct source region. Using aftershocks of the 2014 $M_w$ 6.0 South Napa, California, earthquake, we demonstrate the effectiveness of utilizing a dynamic correlation processor framework in a generalized likelihood ratio test (GLRT) detector configuration to minimize nontarget detections. A GLRT maximizes a detection statistic with respect to one or more unknown parameters. In this case, the detection statistic is a template signal match against the waveform in a window sliding over a data stream, and the unknown parameter is an index variable indicating group membership of the template event or events. Detected events are assigned to the event group that yields the largest detection statistic. In this work, our results show that a GLRT detector will outperform a suite of independently operating correlation and subspace detectors in terms of having a lower nontarget detection rate at a given missed detection rate. We also show that a GLRT detector composed of a few high-rank subspace detectors has a slightly higher nontarget detection rate, but a significantly lower missed detection rate, than a GLRT detector composed of many low-rank subspace detectors. The high-rank GLRT configuration produced impressive results even with marginal data (single channel, single station, and very low time bandwidth product), which bodes well for the utility of building efficient aftershock classification systems and global monitoring systems at larger scales. However, future work is required to assess performance at the regional scale and to assess the performance of the system at detecting target events not used in the detector template creation.

58 GEOSCIENCES↗
DOE OSTI2020

Fast and Accurate Core Analysis by the Full-Immersion Pressure-Pulse Decay: Part 1—Theory

Summary Core-scale measurements are considered the ground truth that oil and gas or electric utility companies use to predict the migration of fluids such as oil, natural gas, carbon dioxide, or brine deep underground during their extraction or injection operations. To provide a greater understanding of petrophysical properties of low-permeability geologic formations such as shales and tight gas sandstones, this study introduces a novel core-analysis procedure. The technique follows conventional pressure-pulse-decay permeametry, where the pressure in an inlet chamber adjacent to a cylindrical core plug undergoes a rapid pressurization, the system is shut in, and the pressure reaches a new equilibrium. However, unlike a standard unidirectional pressure-pulse decay, the full-immersion pressure-pulse decay (Hannon 2019) applies a pressure disturbance to the entire outer surface area of the sample. This article covers the numerical simulator designed to model flow through an anisotropic porous sample in this scenario. The model assumes distinct but uniform permeabilities along the radial and axial directions of the cylindrical plug sample. When extracting a plug vertically (or perpendicular to bedding), the permeability along the radial direction associates with the horizontal permeability (i.e., parallel to bedding), whereas in the axial direction, flow occurs perpendicular to bedding (a vertical permeability). Investigations of these model outputs demonstrate an approximately 20-fold decrease in time to complete a full-immersion experiment compared with conventional pressure-pulse decay. Furthermore, the pressure-decay curves resulting from the full-immersion method have slightly different shapes than those resulting from other unidimensional transient methods. These differences begin to demonstrate that, under achievable experimental conditions, the analysis of pressure data from one full-immersion test could enable the simultaneous estimation of the apparent permeabilities parallel and perpendicular to bedding of a cylindrical sample in addition to its porosity. A follow-up article finalizes the proof of this capability with a parameter-estimation procedure and presents experimental verification through a proof-of-concept study. Described in greater detail in that article, the parameter estimator requires multiple forward simulations to analyze the data, which behooves minimizing the compute time for each simulation. By using an alternating direction implicit time-marching scheme and a structured but variably spaced grid, the numerical simulator built for this purpose provides a forward model output with suitable accuracy in approximately 0.5 seconds.

Energy & Fuels↗
DOE OSTI2020

Demand Response Optimization and Management System for Real-TIme (DROMS-RT)

To design and demonstrate DROMS-RT, a highly distributed Demand Response Optimization and Management System for Real-Time (DROMS-RT) power flow control to support large scale integration of distributed renewable generation into the grid. AutoGrid developed a novel control and communications platform to allow highly dispatchable demand response (DR) services in time frames suitable for providing ancillary services to the transmission grid. These services will be substantially less expensive and more efficient than other forms of ancillary services options currently available to manage the intermittency associated with large-scale renewable integration. DROMSRT successfully leveraged Automated Demand Response (ADR) by fundamentally re-thinking the architecture of the DR platform from the ground up and by developing innovative new technologies in a number of areas related to DR. DROMS-RT leveraged the low-cost, open, interoperable DR signaling technology, OpenADR, and low-cost, internet-protocol based telemetry solutions to reduce the cost of hardware. This allowed DROMS-RT to provide dynamic price signals to millions of OpenADR clients. Statistically rigorous signal processing techniques were developed to reliably detect even small load reductions in the presence of noisy baseline profiles. Novel forecasting engines based on modern online machine learning algorithms enabled accurate individualized forecasts for customer loads in the presence of dynamic pricing signals, and a real-time decision engines enabled continuous optimization and optimal dispatch of DR resources across a large portfolio of heterogeneous loads that respond at varying time-scales. Moreover, the real-time optimization conducted by the decision engine can utilize grid physics to maximize load reduction at the transmission system in addition to the distribution sites, for more efficient grid operation. Finally, the Software-as-a-Service (SaaS) availability of the DROMS-RT platform has reduced the cost of deployment and enable participation of small commercial and residential customers in DR who otherwise would not be able to do so.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Exploration of Quantum Machine Learning and AI Accelerators for Fusion Science

The dawn of the noisy intermediate-scale quantum (NISQ) era sparked rapid development in variational quantum algorithms. These algorithms, utilizing parameterized quantum circuit optimized by classical computers with feedback, are practical under the constraints of the current hardware and can potentially show quantum advantage. In recent years, these variational circuit are applied to neural networks, hoping to boost the triumphant success of deep learning. We simulate various of quantum-classical hybrid neural networks applied to scientific tasks, and seek to understand their capabilities and limitations. Specifically, we devise quantum convolutional layers and apply them to deep neural networks used for predicting plasma disruption in fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

SRNL Sludge Batch 10 Qualification SRAT and SME Off-Gas Results

Savannah River National Laboratory (SRNL) completed a small-scale demonstration of the Defense Waste Processing Facility (DWPF) Chemical Process Cell (CPC) utilizing the nitric-glycolic acid (NGA) flowsheet to support Sludge Batch 10 (SB10) qualification. The demonstration utilized a Tank 51 slurry sample washed by SRNL (with added H-canyon material). The purpose of this document is to report the observed off-gas results from the demonstration. With the NGA flowsheet, DWPF has a CPC hydrogen generation limit of 2.4×10 -2 lb/h. The peak observed rate was nearly 90 times less than that limit during SRNL testing.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

TCF-20-20213: Advanced Power Distribution Sensing and Communications through the Cable TV Broadband Network (Final Report. Period of Performance: June 2020 to June 2022)

To enable the rapid, widespread commercial availability of secondary distribution grid voltage and phase angle data, the DOE OE Technology Commercialization Fund (TCF) project: 1) created a new American National Standard for next-generation grid power quality sensing and communications, 2) created a new prototype broadband-based standard-compliant grid power sensing system for use by utilities and others, and 3) improved by several orders of magnitude the spatiotemporal scale of the in-progress DOE CESER project: Situational Awareness of Grid Anomalies (SAGA) for Visual Analytics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Developing Inorganic Resins for Radium and Actinium Generators and Purifications

The inorganic, normal-phase sorbents alumina, titania, and zirconia were evaluated and screened for the separation of Ba, La, 228 Ra, 228 Ac, and 212 Pb in a dilute acid matrix. The results show that the sorbents are capable of fractionating the ions between pH 1–8. Acetate and total metal ion concentrations were experimentally evaluated as factors that could affect retention using La and Ba surrogates. Pseudo-second order rate constants were derived and the uptake of La was determined to be endothermic. A stationary zirconia column was used to isolate Ba from La and Ra from Ac/Pb; elution profiles of each separation are presented. The utility and implications of these sorbents are discussed with respects to large-scale production of 225 Ac from radium targets. This work was published in 2020 and in 2022.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Mercury Testing with Sludge Batch 10 Tank 40 Simulant

Savannah River Mission Completion (SRMC) requested that researchers at Savannah River National Laboratory (SRNL) perform testing designed to examine why the Defense Waste Processing Facility (DWPF) is not collecting elemental mercury in the Mercury Water Wash Tank (MWWT). In order for DWPF to recover mercury, mercuric oxide must first be reduced to elemental mercury. The elemental mercury must then be steam stripped, condense, and coalesce in the Mercury Water Wash Tank (MWWT) during chemical processing in the Sludge Receipt and Adjustment Tank (SRAT). The efficiency of these steps was investigated in a series of laboratory scale SRAT experiments under the nitric-glycolic and nitric-formic flowsheets utilizing Momentive Y-17112 and Antifoam 747. Mercury speciation in the Slurry Mix Evaporator Condensate Tank (SMECT) and condensate streams was also examined. The key conclusions from these experiments are as follows: Mercury II Oxide may not be fully reduced to elemental mercury during acid addition at 93°C. Higher temperatures, i.e., boiling may be necessary to fully reduce Mercury II Oxide. The highest percent mercury recovery (71 %) in the MWWT was observed in the MS-NGA-17112 experiment (nitric-glycolic acid flowsheet with Momentive Y-17112), which is how DWPF is currently operating the SRAT.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Extraction of Drell-Yan Angular Parameters in $pp$ Collisions with a 120 GeV Beam Energy Using a Deep-Learning Unfolding Algorithm

Dilepton production in pp collisions through the Drell-Yan process provides a crucial tool for studying the internal quark-gluon structure of the nucleon. By precisely measuring the $\cos2\phi$ asymmetry, where $\phi$ represents the azimuthal angle of the $l^{+}l^{-}$ pair in the Collins-Soper frame, we can gain valuable insights into the proton’s structure and the transverse momentum ($q_{T}$) dependence of the $\cos2\phi$ asymmetry. SeaQuest, a fixed-target Drell-Yan experiment at Fermilab, involved an unpolarized proton beam colliding with unpolarized LH$_{2}$ and LD$_{2}$ targets. Measurements obtained from experiments typically require corrections for detector inefficiencies, smearing, and acceptance. Traditionally, these corrections involve “unfolding” the detector-level measurements through matrix operations. However, in higher-dimensional phase space, these conventional methods fail to scale effectively. To overcome these limitations, we employ an unbinned unfolding method that utilizes deep neural networks for unfolding higher-dimensional phase space. In this presentation, we will explain the design of the neural network architecture, our training strategies, and outline our plans to achieve conclusive results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Applying Automated Detectors for Regional Infrasound Signals to Global Networks: A Case Study Using the 2022 Hunga Tonga Volcanic Eruption

The January 15, 2022 Tonga Hunga volcanic eruption was the largest event to be recorded across the International Monitoring System infrasound network. Signals from the eruption were identified at all 53 operational stations by International Data Centre analysts. This report contains descriptors for signal detection bulletins produced by infrasound researchers at Sandia National Laboratories and Los Alamos National Laboratory. Manual detection bulletins were produced by laboratory staff. Automated detection bulletins were produced using automated infrasound processing tools developed at both laboratories. Initial results are provided to evaluate the utility of extending tools developed for regional infrasound event analysis to global-scale events.

58 GEOSCIENCES↗
DOE OSTI2024

03.02.02.45: Energy Efficiency Improvement Approaches in Ice Related Processes

The primary objective of this project was to facilitate the dislocation of the interfacial ice layer by employing advanced materials and ultrasonic vibration to reduce ice adhesion strength. This work employed two technical approaches that were thoroughly investigated and previously reported. The effectiveness of these approaches, both individually and in combination, has been quantified, demonstrating notable energy savings. The projected payback period for these enhancements is approximately 2.2 years or less, contingent upon specific energy costs. Furthermore, these advancements hold significant promise for reducing carbon emissions across various equipment scales. This study particularly focused on the ultrasonic deicing technique for diverse structures, utilizing numerical simulations to evaluate performance and potential benefits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Onset of Fluidization in MP-PIC Simulations using MFIX-Exa

Fluidized bed reactors are used across a variety of industries, including for energy processes like pyrolysis that result in low-cost energy products. Design and scale-up of fluidized beds is de-risked by modeling and simulation, utilizing tools like NETL’s MFIX-Exa High-Performance Computing (HPC) code for reacting multiphase flow. This report summarizes an investigation into the breadth of problems to which MFIX-Exa may be applied, specifically with regard to low fluid velocities and the onset of fluidization. A simple fluidization study is conducted both experimentally and numerically for particles of interest, then reactor simulations are compared to cold flow experiments for uniform distributor plates. Approaches for modeling bubble caps are also presented.

discrete particle method↗
DOE OSTI2021

Opportunities for Catalytic Reactions and Materials in Buildings

Residential and commercial buildings are responsible for over 30% of global final energy consumption and accounts for ~40% of annual direct and indirect greenhouse gas emissions. Energy efficient and sustainable technologies are necessary to not only lower the energy footprint but also lower the environmental burden. Many proven and emerging technologies are being pursued to meet the ever-increasing energy demand. Catalytic science has a significant new role to play in helping address sustainable energy challenges, particularly in buildings, compared to transportation and industrial sectors. Thermally driven heat pumps, dehumidification, cogeneration, thermal energy storage, carbon capture and utilization, emissions suppression, waste-to-energy conversion, and corrosion prevention technologies can tap into the advantages of catalytic science in realizing the full potential of such approaches, quickly, efficiently, and reliably. Catalysts can help increase energy conversion efficiency in building related technologies but must utilize low cost, easily available and easy-to-manufacture materials for large scale deployment. This entry presents a comprehensive overview of the impact of each building technology area on energy demand and environmental burden, state-of-the-art of catalytic solutions, research, and development opportunities for catalysis in building technologies, while identifying requirements, opportunities, and challenges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗