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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Principled Schedulability Analysis for Distributed Storage Systems Using Thread Architecture Models

In this article, we present an approach to systematically examine the schedulability of distributed storage systems, identify their scheduling problems, and enable effective scheduling in these systems. We use Thread Architecture Models (TAMs) to describe the behavior and interactions of different threads in a system, and show both how to construct TAMs for existing systems and utilize TAMs to identify critical scheduling problems. We specify three schedulability conditions that a schedulable TAM should satisfy: completeness, local enforceability, and independence; meeting these conditions enables a system to easily support different scheduling policies. We identify five common problems that prevent a system from satisfying the schedulability conditions, and show that these problems arise in existing systems such as HBase, Cassandra, MongoDB, and Riak, making it difficult or impossible to realize various scheduling disciplines. We demonstrate how to address these schedulability problems using both direct and indirect solutions, with different trade-offs. To show how to apply our approach to enable scheduling in realistic systems, we develop Tamed-HBase and Muzzled-HBase, sets of modifications to HBase that can realize the desired scheduling disciplines, including fairness and priority scheduling, even when presented with challenging workloads.

Computer Science↗
DOE OSTI2025

User-based I/O Profiling for Leadership Scale HPC Workloads

I/O constitutes a significant portion of most of the application run-time. Spawning many such applications concurrently on an HPC system leads to severe I/O contention. Thus, understanding and subsequently reducing I/O contention induced by such multi-tenancy is critical for the efficient and reliable performance of the HPC system. In this study, we demonstrate that an application’s performance is influenced by the command line arguments passed to the job submission. We model an application’s I/O behavior based on two factors: past I/O behavior within a time window and user-configured I/O settings via command-line arguments. We conclude that I/O patterns for well-known HPC applications like E3SM and LAMMP are predictable, with an average uncertainty below 0.25 (A probability of 80%) and near zero (A probability of 100%) within a day. However, I/O pattern variance increases as the study time window lengthens. Additionally, we show that for 38 users and at least 50 applications constituting approximately 93000 job submissions, there is a high correlation between a submitted command line and the past command lines made within 1 to 10 days submitted by the user. We claim the length of this time window is unique per user.

Yazdani, Ahmad Hossein [Virginia Tech, Blacksburg]↗
DOE OSTI2020

Transport Processes in a Li-ion Cell during an Internal Short-Circuit

Internal short-circuit in a lithium-ion cell causes an abrupt increase in cell temperature and triggers subsequent thermal runaway. In this work, we present a detailed electrochemical-thermal model to investigate the physical behavior during an internal short-circuit. Simulations at wide range of heat transfer coefficients and short-circuit resistances are conducted to illustrate electrochemical and thermal behavior under a wide range of conditions. The Joule heating at the shorted region promotes electrochemical reactions nearby, causing in-plane non-uniformity of electrolyte and active material transport. Furthermore, it is found that diffusion in solid active materials plays a significant role at very high shorting currents (~20 C), as electrochemical reactions rate are being controlled progressively more by availability of Li + at the interface, due to limitations in diffusion through the active material with increasing discharge rates. This diffusion limitation causes a drop in available energy, and subsequently a decrease in cell equilibrium potential and the heat generation rate at the location of the short. On the other hand, rapid depletion of lithium concentration in the electrolyte and accumulation on the electrode surface results in highly non-uniform transport properties resulting in higher heat generation rates. Hence, the heating regime shifts from "local heating" to "global heating". Based on the findings, important design parameters for battery safety are discussed.

25 ENERGY STORAGE↗
DOE OSTI2024

ExaDigiT/RAPS

ExaDigiT is a framework for developing comprehensive digital twins of liquid-cooled supercomputers, which has three main modules: (1) a python-based Resource Allocator and Power Simulator (RAPS), (2) a Modelica-based Thermo-Fluidic cooling model, and (3) a C++-based augmented reality model built on Unreal Engine 5. RAPS either simulates workloads or replays historical workloads from system telemetry, and is able to predict dynamic energy consumption, as well as interact with the cooling model to predict cooling system behavior. Such a tool can be used together with reinforcement learning algorithms to provide an end-to-end optimization tool for data centers.

Brewer, Wesley [Oak Ridge National Laboratory (ORN↗
DOE OSTI2023

A Deep Learning Filter for the Intraseasonal Variability of the Tropics

Abstract This paper presents a novel application of convolutional neural network (CNN) models for filtering the intraseasonal variability of the tropical atmosphere. In this deep learning filter, two convolutional layers are applied sequentially in a supervised machine learning framework to extract the intraseasonal signal from the total daily anomalies. The CNN-based filter can be tailored for each field similarly to fast Fourier transform filtering methods. When applied to two different fields (zonal wind stress and outgoing longwave radiation), the index of agreement between the filtered signal obtained using the CNN-based filter and a conventional weight-based filter is between 95% and 99%. The advantage of the CNN-based filter over the conventional filters is its applicability to time series with the length comparable to the period of the signal being extracted. Significance Statement This study proposes a new method for discovering hidden connections in data representative of tropical atmosphere variability. The method makes use of an artificial intelligence (AI) algorithm that combines a mathematical operation known as convolution with a mathematical model built to reflect the behavior of the human brain known as artificial neural network. Our results show that the filtered data produced by the AI-based method are consistent with the results obtained using conventional mathematical algorithms. The advantage of the AI-based method is that it can be applied to cases for which the conventional methods have limitations, such as forecast (hindcast) data or real-time monitoring of tropical variability in the 20–100-day range.

Stan, Cristiana↗
DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗
DOE OSTI2021

Investigating the dielectric properties of barium titanate nanocomposites using transmission electron microscopy image processing

Abstract Barium titanate (BTO) is a ferroelectric perovskite material used in energy storage applications because of its high dielectric constant. A previous study showed that the dielectric constant for BTO nanoparticles drastically increases to over 15,000 at a particle size of 70 nm. This result is highly contested, but its implications to energy storage motivated our investigation into the dielectric constants of BTO nanoparticles that are incorporated into a polymer matrix. We developed a novel method of using image processing techniques on transmission electron microscope images of BTO-polymer nanocomposites. Data on the positions, shapes, sizes, and orientations of BTO nanoparticles were used to build more realistic computational models that simulate the dielectric behavior of the nanocomposites. Here, we investigate the relationship between regions of enhanced electric field and the composite dielectric constant.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Design Optimization of A generic Fissile Solution for Mo99 production using Electron Beam-based Neutron Generator using MCNP+CFD

Molybdenum-99 ( 99 Mo) is a medical isotope used in 80% of all medical imaging procedures today. However, 99 Mo is not directly administered to patients for imaging; its decay product, technetium-99m ( 99 mTc), is a metastable isotope with a half-life of ~6 hours. Such a short half-life does not allow for production, separation, and application to a patient before decaying further. Therefore, the solution is to produce the precursor to 99 mTc, 99 Mo. 99 Mo has a half-life of 66 hours, long enough to produce the material and send it to the desired location before it fully decays. Under National Nuclear Security Administration’s (NNSA) highly enriched uranium (HEU) minimization mission, the Material Management and Minimization (M3) office leads the Molybdenum-99 (Mo-99) program. A major goal of the Mo-99 program is to develop methods of producing 99 Mo using low enriched uranium (LEU) opposed to the traditionally used HEU domestically in the U.S. Internationally, LEU is the new standard for use in research reactors and isotope production facility. In this study, we introduce a generic fissile solution system for 99 Mo production facility meeting the design requirement of 1) LEU usage as fissile material and 2) subcritical fissile system coupled with a horizontal electron beam (E-beam) accelerator. The focus of this research is to optimize a fissile solution configuration for maximum 99 Mo production yield using MCNP model, and ultimately implement the optimized design with computational fluid dynamics (CFD) model to understand thermal hydraulic behavior and solution convection characteristic. The report consists of two parts: 1) isotope yield calculation and criticality analysis with a wide range of parametric test matrix, and 2) thermal hydraulic analysis for solution mixing and cooling assessment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Metastability and Long-Term Degradation in CIGS Devices: Effect of Alkali Treatments, Back Contact and Emitter Layers

Thin-film solar cell technologies often suffer from metastable and long-term performance degradation, which may or may not be reversible. The presence of the transient or degradation issues pose challenges for energy-yield prediction in the field for these technologies. Cu(In,Ga)(S,Se) 2 solar cells are a promising thin-film technology, however, the underlying mechanisms of instability in these devices are not well understood. The most common mechanism of metastability in CIGS solar cells has been speculated to be the configuration changes of (VSe-VCu) divacancy complex. However, the role of metastability in light soaking behavior and the effect of alkali treatments and buffer layers is not understood. The goal of the current project is to understand the effect of alkali treatments and buffer materials on metastability and light soaking behavior and develop a device model to simulate experimental results. Input parameters are taken from literature or derived from first-principle calculations, and are fine-tuned through validation experiments.

14 SOLAR ENERGY↗
DOE OSTI2021

High Dose and Dose Rate 60 Co γ-Irradiation of High-Density Polyethylene

We have irradiated high-density polyethylene ribbons and cylinders to ~200 kGy at a dose rate of ~5 kGy day -1 , using a 60 Co source. The radiolytically-generated gas consists almost entirely of H 2 . Based on the time- and dose-dependence of the outgassing, we have determined the radiation- chemical yield for the evolved H 2 , as well as the diffusion coefficient of H 2 in HDPE. We find that the evolved gas yield G evolved depends on the interplay of sample geometry, experimental timescale, and the diffusion coefficient of H 2 in HDPE. When these factors facilitate completion of diffusion prior to gas quantification, such as for sub-mm-thickness ribbons, we measure a time- and dose-invariant value of G evolved = 0.0076 cm 3 std. H 2 J -1 that is in agreement with literature reports. When sample geometry and experiment timescale do not permit completion of diffusion, we measure a time- and dose-dependent value of G evolved . We have developed a model for the transient outgassing behavior of the cylinders, and fitted it to the experimental data. Based on these fits, we estimate a room-temperature diffusivity of D H 2 /HDPE ≈ 1.67 ± 0.12 x 10 -6 cm 2 s -1 for H 2 in HDPE, in good agreement with literature values. At present, the effects of dose rate and γ-photon energy on HDPE gas yield are not known and require further investigation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comparison of ML-Based Proxy Modeling Strategies: Lessons Learned from the SMART Initiative

Teams of researchers on Task 5 of the SMART project have developed a variety of modeling architectures to predict subsurface behavior during carbon injection and post-injection periods. One important part of this task was to compare the candidate approaches in terms of accuracy, reliability, speed, and memory use, all using a common set of metrics and visualizations for an “apples to apples” comparison. Dr. Jared Schuetter will share the details of this task, the results that were obtained, and the lessons learned.

Schuetter, Jared↗
DOE OSTI2025

Benchmark Exercise Report for Experimental Study of Bubble Scrubbing in Water Coolant Pool

Mechanistic assessments of radionuclide release during postulated accidents are expected to be included in advanced reactor license applications. The mechanistic source term (MST) provides an opportunity for vendors to realistically evaluate the radiological consequences of an incident, and may aid in justifying reduced emergency planning zones and plant sites. However, the development of MSTs for advanced nuclear reactors is challenging because there are numerous phenomena that can affect the transport and retention of radionuclides. As part of a trial MST assessment for a metal-fueled, pool-type sodium cooled fast reactor (SFR), led by Argonne National Laboratory, a simplified radionuclide transport code (SRT code) was developed, which includes models to estimate the quantity of fission product aerosols scrubbed in the sodium pool during postulated accident scenarios. In a pool-type SFR, when fission products are released into the coolant pool due to failure of fuel pins, most of the radionuclides are scrubbed by the coolant pool, but some have the potential to migrate to the cover gas region through entrainment within gas bubbles. The SRT code contains a model that evaluates this scrubbing behavior and calculates the fraction of fission product aerosols that reach the cover gas. Due to a lack of available validation data for sodium pool scrubbing, the U.S. Department of Energy funded an experiment at the University of Wisconsin-Madison to measure aerosol scrubbing by injecting air bubbles containing aerosol into a coolant pool. Prior to performing an experiment with liquid sodium, a water loop experiment was performed. Their experiment evaluated the effect of changing the aerosol size, aerosol density, aerosol concentration, bubble size, and pool depth on the aerosol scrubbing efficiency of the pool. In this benchmark experiment, the base tests were conducted by repeated tests of isolated bubbles. Afterwards, more prototypic tests with bubble swarms were performed to evaluate the interactions between the bubbles. The bubble swarm test was able to confirm that a larger amount of aerosol scrubbing occurred than the single bubble test. It was also confirmed that as the bubble size, aerosol density, and pool height increase, the extent of pool scrubbing also increases and does not change with the aerosol concentration. In addition, since the degree of scrubbing is the lowest at aerosol sizes between 0.01 and 1 μm, that is, the largest amount of aerosol is emitted, it was confirmed that the analysis of this size in MST is the most important. This benchmark experiment informs the direction of future sodium experiments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A Generative Model for Synthetic Electroluminescence Images

This work will develop modular, open-source model and analysis components including crack detection workflow and parameterization for quantitative inspection of large EL large datasets. These tools will allow users to quickly and accurately assess the extent and types of cracking in their modules. Measured statistical distributions of crack parameters, together with the imposed stress and electrical properties will be used to generate models to predict future crack behavior and power loss.

Pierce, Benjamin Garrett↗
DOE OSTI2025

KIPM Detector Characterization in QUIET

Kinetic Inductance Phonon‑Mediated (KIPM) detectors are superconducting microwave resonators on silicon that sense bursts of energy through tiny shifts in their resonance. In this work, we installed new devices in the QUIET facility's cryogenic test setup and used a network analyzer to drive them across a range of power levels. At each setting, we measured the transmitted signal and fit it to a simple notch‑filter model, allowing us to extract key performance metrics. And despite the increased noise at low power, our model consistently captured the resonance behavior. We observed that the resonant frequency stayed stable across most powers, the device s internal quality factor improved with stronger signals, and its coupling strength peaked mid‑range before leveling off. Overall, these findings confirm the detector s performance, laying groundwork for future work on energy calibration with pulsed optical sources.

Savitala, Akash [Washington U., Seattle; Fermilab]↗
DOE OSTI2026

Cluster Dynamics Simulations of Intra-Granular Fission Gas Bubble Size and Pressure Evolution in UO 2

Fission gases such as xenon (Xe) play a critical role in determining the behavior and response of nuclear fuel. Given that Xe has little solubility in UO 2 , it accumulates and forms bubbles, which significantly impact fuel performance. Intra- and inter-granular bubble nucleation and growth can lead to fuel swelling, and once bubbles interconnect at grain boundaries, fission gas can be released into the plenum. At low temperatures, limited uranium vacancy mobility can restrict swelling, therefore causing the bubbles to become highly pressurized. Consequently, this can induce micro-cracking, promote fission gas release (increasing the likelihood of cladding failure), and even lead to fuel pulverization under accident conditions such as a loss of coolant accident. As bubble evolution is strongly influenced by local temperature and fission rate, markedly different behavior occurs across the radial profile of the fuel pellet. Capturing the mechanisms that underpin bubble evolution is therefore important to predict these behaviors in the fuel. Previous models describing important mechanisms informed by lower length scale simulations have been developed under the NEAMS program. These can describe the evolution of a single bubble type (i.e., single value for radius and pressure) at each position in the pellet, for instance using the Centipede cluster dynamic code. However, in reality, a full distribution in bubble sizes and pressures exists within the microstructure at a given position in the pellet. To address this the cluster dynamics code Xolotl, which can predict Xe and vacancy phase space (i.e., bubble distributions) for intra-granular bubbles, has been used before. Prior work benchmarked the Xolotl code against the Centipede cluster dynamics code to ensure compatibility and to verify that mobile defect properties are adequately transferred between the two codes, along with some physics improvements. In this work, we go further by introducing a physics-based set of improvements that will allow us to accurately predict bubble size distributions and internal bubble pressures under representative UO 2 irradiation conditions. The improvements include (i) coupling bubble-defect reaction energies to a virial equation of state (EOS), (ii) including a bubble surface tension contribution, (iii) incorporating radiation-induced re-solution of Xe and vacancies, (iv) enabling pressure-driven dislocation loop punching through an effective emission of interstitial clusters informed by interstitial loop energetics, (v) accounting for radiation induced athermal diffusion of Xe, and (vi) implementing a Booth-type grain boundary sink representation for all mobile defects and defect clusters. After these modifications, we observe good agreement of Xolotl fission gas bubble size and concentration predictions with legacy experimental measurements. Additionally, it allows the distribution of Xe bubble pressures and radius to also be predicted and compared to data produced through the Advanced Fuels Campaign (AFC) program. Here, we have done this by running simulations under conditions similar to the AFC post-irradiation examination (PIE) samples irradiated at North Anna 2 light water reactor (LWR). Our results shows excellent agreement with these experimental measurements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

AWAKEN

The American WAKE experimeNt (AWAKEN) is a landmark collaborative international wake observation and validation campaign. Wake interactions are among the least understood and most impactful physical interactions in wind plants today, leading to unexpected power losses and increased operations and maintenance costs. The AWAKEN campaign is designed to gather observational data to address the most pressing science questions about wind turbine wake interactions and aerodynamics and to further understand wake behavior and validate wind plant models. Simultaneously, the AWAKEN campaign will also focus on testing of wind farm control strategies that have been shown to increase wind plant power production. Leveraging the expertise and resources of a large body of National Laboratories, academic institutions, and industry partners will lead to improved wind farm layout with greater power production and improved reliability, ultimately leading to lower wind energy costs.

17 WIND ENERGY↗
DOE OSTI2025

Stability Analysis of Power Systems with High Penetration of State-of-the-Art Inverter Technologies

With the increasing level of inverter-based resources (IBRs) in modern power systems, this paper presents a small-signal stability analysis for power systems comprising synchronous generators (SGs) and IBRs. Four types of inverter controls are considered: two grid-following (GFL) controls, with or without grid support functions; droop-based grid-forming (GFM) controls; and virtual oscillator control-based GFM. We also analyze the impact of STATCOM and synchronous condensers on system stability to assess their role in the energy mix transition. With the small-signal dynamic behavior of the major technologies modeled, this paper provides stringent stability assessments using the IEEE 39-bus benchmark system modified to simulate future power systems. The exhaustive test cases allow for (a) assessing the impacts of different types and controls of generation and supplementary grid assets, as well as system inertia and line impedance on grid stability, and (b) elucidating pathways for the stabilization of IBR-dominated power systems. The analysis also indicates that future power systems can be stabilized with only a fraction of the total generation as voltage sources without SGs or significant system inertia if they are well distributed. This study provides insights into future power system operations with a high level of IBRs that can also be used for planning and operation studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimizing Rate of Penetration and Tripping Decision-Making using Real-Time Bit Wear Monitoring While Drilling Geothermal Wells

Understanding bit wear while drilling is critical to minimizing non-productive time (NPT) and optimizing rate of penetration (ROP). Lengthening drilling runs with damaged bits does not only lower the ROP, but also elevates the risk of inducing severe bit damage, which could potentially lead to time-consuming fishing operations. When drillers believe the bit has worn off substantially, the bit is tripped out to be replaced. On geothermal wells, tripping can take up to 20% of the overall well construction time, and this is generally acknowledged as an opportunity for improvement. Ideally, a bit run should be terminated before the bit is damaged beyond repair. At the same time, premature bit pulls are to be avoided as well. This study aims to leverage bit and tooth wear metrics that can be obtained in real time to characterize bit condition in order to optimize ROP and determine the optimal time to pull the bit. Two metrics were explored in this study: a bit wear metric that incorporated depth-of-cut, and a tooth wear metric developed by Bourgoyne & Young characterizing the state of bit teeth dull. Both metrics were computed using recorded data from 12¼ inches roller cone insert bit runs in five geothermal wells targeting a granodiorite formation in the western United States. Together with the actual dull grades, determined after the bits were pulled to surface, the metric trends were interpreted to characterize the downhole bit condition and identify the point at which the bit should have optimally been tripped out. The insights from studying the actual dull grades and how they relate to the two metrics were used to establish a reliable bit pull criterion. The bit wear metric trend correctly showed a noticeable departure from baseline for bits experiencing major dulling behavior. Additionally, the tooth wear model predicted the cutter dull within two dull grades for most runs, with better performance in predicting the inner teeth dull. Moreover, the combination of the bit wear and tooth wear metrics was effective in revealing the cause of the bit performance impairment. Proactive tracking of these two metrics in real-time can facilitate geothermal drilling ROP optimization and better-informed tripping decision-making, thereby avoiding wasted time and cost.

Ashari, Rahmat↗