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

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Information Leakage Analysis using a Co-design-Based Fault Injection Technique on a RISC-V Microprocessor

The RISC-V instruction set architecture open licensing policy has spawned a hive of development activity, making a range of implementations publicly available. The environments in which RISC-V operates have expanded correspondingly, driving the need for a generalized approach to evaluating the reliability of RISC-V implementations under adverse operating conditions or after normal wear-out periods. Fault injection (FI) refers to the process of changing the state of registers or wires, either permanently or momentarily, and then observing execution behavior. The analysis provides insight into the development of countermeasures that protect against the leakage or corruption of sensitive information which might occur because of unexpected execution behavior. In this paper, we develop a hardware-software co-design architecture that enables fast, configurable fault emulation and utilize it for information leakage and data corruption analysis. Modern System-on-chip FPGAs enable building an evaluation platform where control elements run on a processor(s) (PS) simultaneously with the target design running in the programmable logic (PL). Software components of the FI system introduce faults and report execution behavior. A pair of RISC-V FI-instrumented implementations are created and configured to execute the Advanced Encryption Standard and Twister algorithms. Key and plaintext information leakage and degraded pseudo-random sequences are both observed in the output for a subset of the emulated faults.

42 ENGINEERING↗
DOE OSTI2024

Rapid Monitoring and Defense Approach for Resilience Improvement of Grid Cyber Security

Cyber-physical systems and electric utilities significantly depend on the reliability and efficiency of information and operational technology. However, false data injection attacks based on synchrophasor measurement data pose a serious threat to the safe and reliable operation of modern power systems. Here, to mitigate this problem, a rapid monitoring and defense approach is proposed to defend against cyber attacks. Initially, the Time and Frequency based Convolutional neural Network (TFCN) is proposed to detect different types of attacks. Within the TFCN, the advances are that both time and frequency domain information can be fused without extra spectrum analysis methods, and can save detection time to speed the calculation efficiency using the developed time-frequency block. Next, a comprehensive defense strategy is developed for multiple cyber attacks to ensure the stability and resilience of the power system according to the feedback detection results. The advances of this strategy are that different control strategies can be automatically selected to recover the stability to the greatest extent according to the detected attacks. To verify the effectiveness of the proposed approach, the high-speed frequency measurements collected from the wide-area monitoring system are used. The results demonstrate that the cyber attack detection performance can reach 95.57% accuracy, outperforming both traditional and some advanced neural networks. Importantly, the defense strategy is conducted and verified in a modified IEEE 39 bus system as well, which illustrates profound performance in faster stability restoration.

Comprehensive defense strategy↗
DOE OSTI2022

The K2 Galactic Archaeology Program Data Release 3: Age-abundance Patterns in C1–C8 and C10–C18

We present the third and final data release of the K2 Galactic Archaeology Program (K2 GAP) for Campaigns C1–C8 and C10–C18. We provide asteroseismic radius and mass coefficients, κ R and κ M , for ~19,000 red giant stars, which translate directly to radius and mass given a temperature. As such, K2 GAP DR3 represents the largest asteroseismic sample in the literature to date. K2 GAP DR3 stellar parameters are calibrated to be on an absolute parallactic scale based on Gaia DR2, with red giant branch and red clump evolutionary state classifications provided via a machine-learning approach. Combining these stellar parameters with GALAH DR3 spectroscopy, we determine asteroseismic ages with precisions of ~20%–30% and compare age-abundance relations to Galactic chemical evolution models among both low- and high-α populations for α, light, iron-peak, and neutron-capture elements. We confirm recent indications in the literature of both increased Ba production at late Galactic times as well as significant contributions to r-process enrichment from prompt sources associated with, e.g., core-collapse supernovae. With an eye toward other Galactic archeology applications, we characterize K2 GAP DR3 uncertainties and completeness using injection tests, suggesting that K2 GAP DR3 is largely unbiased in mass/age, with uncertainties of 2.9% (stat.) ± 0.1% (syst.) and 6.7% (stat.) ± 0.3% (syst.) in κ R and κ M for red giant branch stars and 4.7% (stat.) ± 0.3% (syst.) and 11% (stat.) ± 0.9% (syst.) for red clump stars. We also identify percent-level asteroseismic systematics, which are likely related to the time baseline of the underlying data, and which therefore should be considered in TESS asteroseismic analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Hubble Frontier Field Clusters and Their Parallel Fields: Photometric and Photometric Redshift Catalogs

We present a multiband analysis of the six Hubble Frontier Field clusters and their parallel fields, producing catalogs with measurements of source photometry and photometric redshifts. We release these catalogs to the public along with maps of intracluster light and models for the brightest galaxies in each field. This rich data set covers a wavelength range from 0.2 to 8 μm, utilizing data from the Hubble Space Telescope, Keck Observatories, Very Large Telescope array, and Spitzer Space Telescope. We validate our products by injecting into our fields and recovering a population of synthetic objects with similar characteristics to those in real extragalactic surveys. The photometric catalogs contain a total of over 32,000 entries, with 50% completeness at a threshold of mag AB ~ 29.1 for unblended sources and magAB ~ 29 for blended ones, in the IR-weighted detection band. Photometric redshifts were obtained by means of template fitting and have an average outlier fraction of 10.3% and scatter σ = 0.067 when compared to spectroscopic estimates. The software we devised, after being tested in the present work, will be applied to new data sets from ongoing and future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Implementing nonlinear optics from Off-Energy closed orbit at NSLS-II

To characterize the second-order (chromatic sextupole) magnet lattice with high precision, we implemented nonlinear optics from off-energy closed orbit (NOECO) tool based on the linear optics from closed orbit modulation (LOCOM) method, named LOCOM-NOECO. The preliminary numerical study indicates that 1–2% precision can be achieved for the calibration of chromatic sextupoles. Further, this accuracy could potentially help in resolving some long-standing challenges of NSLS-II (e.g., the discrepancy between the designed and measured dynamic apertures) if such high precision can be fulfilled. As an independent crosscheck, we also implemented NOECO based on the independent component analysis (ICA) method using turn-by-turn (TBT) BPM data, named ICA-NOECO. Both ICA-NOECO and LOCOM-NOECO have been successfully applied to identify the pre-dialed random errors of a chromatic sextupole family including five power supplies, and achieved the root mean square (RMS) residual error of 1% and peak error less than 2%. Moreover, to mitigate the chromatic sextupole error effect, we applied the correction and achieved significant improvements in the injection efficiency as well as the dynamic apertures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

WISP: Watching grid Infrastructure Stealthily through Proxies (Final Technical Report)

The complex interdependencies of cyber systems (sensors and communications), physical grids and associated electricity market operations make protecting electric power grids a significant challenge. The energy sector is constantly under new, targeted, advanced and dangerous cyber-attacks that have the potential to result in the loss of human life. These threats are further exacerbated by our need to modernize the grid. One focus of cyber security research in smart grids is the securing of the SCADA system through advanced intrusion detection systems (IDS) and bad data detection algorithms in state estimation. These methods either require full knowledge of the system topology and parameters or fail to understand the physical behaviors under attack. WISP (Watching grid Infrastructure Stealthily through Proxies) is designed to provide additional protection to the power grid using only publicly available data. In particular, WISP exploits the spatio-temporal nature of the real time locational marginal prices (LMPs), in conjunction with other information such as bids, weather, outages and load data to analyze anomalous power pricing behaviors and then correlate those observations to localize regions of interest and identify potential cyber events. WISP is non-intrusive as the tool is deployed as a service in the Cloud or on premise and provides reliable information to system operators for enhanced situational awareness, without impeding energy delivery functions. The WISP technology comprises three modules: the data-driven anomaly detection core, the vulnerability and risk analysis and the root cause analysis. The data-driven anomaly detection core performs the tasks of feature selection, anomaly detection and attack region localization. The vulnerability and risk analysis module provides system level information of the vulnerable variables and times, assisting the operators in selecting monitoring and protection nodes. The root cause analysis module takes the detection results and identifies potential operational conditions that contribute to the detected anomalies. In Phase I, we have demonstrated the feasibility and effectiveness of WISP. We developed a realistic electricity market simulator capable of generating normal and attack market data under various operational conditions. We developed a series of cyber-attack detection and analysis algorithms and evaluated them under multiple data sources. Finally, we integrated all modules into an end-to-end software, providing functions for data management, data analytics and visualization. Specifically, we have achieved: (i) real-time data acceptance from external utility interfaces with >99% acceptance rate; (ii) high performance anomaly detection algorithms with >98% detection accuracy and <0.1% false alarm rate; and (iii) ultra-low computing delay <50 milliseconds. Additionally, our team developed algorithms to identify the vulnerable variables in electricity market operations and root cause analysis functions to identify major contributors to the price spikes. These ancillary modules are necessary when deploying WISP in real world industry environment. In Phase II, we have demonstrated the effectiveness of WISP software on realistic largescale power systems. We performed red team testing for the Phase I WISP software and identified software vulnerabilities and implemented corresponding mitigation solutions. We adapted the electricity market simulator for the Texas synthetic 2000-bus system and generated datasets for the false data injection attacks. We created database and visualization interfaces for the Texas system and the ISO New England system. We performed software optimization in terms of operation efficiency, computing speed and detection accuracy. Finally, we tested the software on the Texas system and the ISO New England system and evaluated the detection performance. Overall, we achieved above 89% detection rate, below 3% false alarm rate and below 37 seconds of end-to-end detection delay.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A probabilistic creep model incorporating test condition, initial damage, and material property uncertainty

Uncertainty is prevalent in the creep resistance of alloys, where at elevated temperature and low pressure, rupture can range across logarithmic decades. In this study, a probabilistic continuum-damage-mechanics (CDM)-based model is derived to capture the uncertainty of creep resistance. To meet this objective, creep data for alloy 304 Stainless Steel is gathered. A constitutive model, “Sinh”, is calibrated deterministically to determine the statistical variability of the material properties. Three sources of uncertainty are injected into the model: test condition (stress and temperature), initial damage, and material properties. Probabilistic simulations are carried out by (a) calibrating probability distribution functions (pdfs) for each source of uncertainty (b) randomly sampling the pdfs using Monte Carlo methods and (c) executing simulations to replicate the uncertain creep behavior. A sensitivity analysis is performed to evaluate the relative effect of each source of uncertainty. In full probabilistic simulations, the cumulative uncertainty of creep behavior is evaluated. The probabilistic model accurately predicts the creep deformation and rupture of the available experiments. The probabilistic model is validated for interpolation but lacks extrapolation ability. Several future works are proposed to further improve the model.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Controlling pollutant emissions in a high-pressure combustor with fuel-diluent blending

This paper investigates the formation of nitric oxide (NO) and carbon monoxide (CO) emissions as a function of nitrogen (N 2 ) or carbon dioxide (CO 2 ) diluent content, added to a premixed reacting jet in crossflow. Here, reaction characteristics of a rich methane-air jet injected into a lean vitiated crossflow were analyzed at an elevated pressure of 5 atm. The jet was pre-heated and enriched with 0%, 15%, 30%, and 50% mass fraction to quantify the effect on pollutant emissions. Simulated results of the full chemistry Star-CCM+CFD model were verified with data taken in the experimental high-pressure combustion facility, which provides pressure, temperature and velocity profiles, as well as line-of-sight chemiluminescence and exit emission measurements. The analysis revealed the significant influence of the diluent to delay axial combustion; this mixing delay increased the axial flame lift-off and controlled any thermal ignition limitations. Increasing the diluent content increased the timescale for the flame to stabilize, which allowed for greater entrainment of crossflow oxidizer into the axial jet stream, and led to decreased pollutant emissions. Hence, crossflow entrainment is a critical driving force at high diluent content. Local nitric oxide (NO) emission formation in the axial stage was predicted numerically, showing the correlation between diluent addition, axial heat release, and the formation of nitric oxide pollution. The high diluent levels leaned out the jet mixture and delayed axial combustion, while reducing prompt and thermal NOx by mitigating flame hotspots and minimizing the timescale that the products remain at high temperatures.

03 NATURAL GAS↗
DOE OSTI2022

Transient mixing behavior of a supercritical fluid injected into supercritical and subcritical environments

Transient, near-field mixing of a supercritical fluid injected into supercritical and subcritical environments is investigated experimentally. Fuel (propane) from a 100 μm diameter Bosch diesel injector is injected into a high-pressure test rig with a steady flow of heated, low-velocity nitrogen to control the ambient pressure and temperature. The fuel supply pressure is approximately 190 bar, allowing short injections lasting 3–4 ms to be performed in quick succession to acquire statistically significant datasets to represent the unsteady fuel–air mixing. Two test cases with the same ambient density and injector inlet pressure and temperature were chosen in an attempt to isolate effects of mixture phase behavior. Microscopic rainbow Schlieren deflectometry (RSD) is used for visualization and quantitative data acquisition. In the case of the supercritical ambient, the injector inlet and exit and ambient conditions are all supercritical. The RSD analysis reveals a mixing process similar to the single phase fluid mixing, though condensation is also observed locally, and can be explained by the adiabatic mixing theory presented in the paper. For the subcritical ambient case, the injector inlet is supercritical, but the injector exit and ambient conditions are both subcritical. The results show a rapid expansion in the near-field promoting mixing between propane and nitrogen. At downstream locations, significant condensation is also observed as predicted by the adiabatic mixing theory. As a result, the supercritical ambient case exhibited longer and narrower jet penetration compared to the subcritical ambient case.

02 PETROLEUM↗
DOE OSTI2021

Two-zone stratified wetwell model development and implementation for RELAP-7

The Reactor Core Isolation Cooling (RCIC) system consists of a turbine, pump and wetwell and is used in many US Boiling Water Reactors (BWRs) as an important heat removal system. The wetwell is a major heat sink within containment. Experimental investigations have shown that thermal stratification may occur in the wetwell during RCIC operation. Current systems-level analysis codes lack stratified wetwell models. Here, this paper describes the development of a stratified wetwell model for the RELAP-7 code. This paper uses a simple two-zone model for the wetwell water that can capture thermal stratification. This model assumes that a buoyant plume transports heat and mass from the steam injection site to the upper water zone. The buoyant plume assumption requires that steam is injected at a low enough rate so it adds negligible momentum. Model validation against experimental data shows similar trends of temperature field development in the suppression pool.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Influence of living grass Roots and endophytic fungal hyphae on soil hydraulic properties

Soil hydraulic properties are often estimated based on laboratory data or pedotransfer functions dependent on soil physical properties, which often do not consider potential impacts of soil roots or fungal hyphae. Here, we first review current knowledge of how these soil biotic components affect hydraulic properties, then we conducted laboratory experiments to specifically test if the presence of roots and mycorrhizal fungi had a significant effect on the hydraulic properties of two soils with contrasting textures: Flint sand and Hamblen silt loam. Soil cores were seeded with (Panicum virgatum) and grown in a greenhouse over three separate growth periods. The endophytic fungus Serendipita indica was injected as liquid inoculant into designated mycorrhizal cores. Saturated hydraulic conductivity (K sat ) measurements were made with a constant head permeameter, and soil water retention curves were obtained by the evaporation method, supplemented at the dry end for Hamblen silt loam with water activity meter data. Retention curve parameters were obtained by fitting the van Genuchten equation to the resulting measurements. Mean root volume ratios were higher in the mycorrhizal inoculated treatment than in the uninoculated treatment for both soils. For Flint sand, analysis of variance revealed that K sat was reduced by the presence of roots as compared to bare soil. This was likely due to roots clogging soil pores. Results also indicated the presence of roots changed the shape of the water retention curve for Flint sand by increasing water content at saturation and by reducing the slope of the curve. These changes suggested roots created additional porosity and broadened the pore-size distribution. The presence of mycorrhizal fungi accentuated the root effects. The influence of roots and mycorrhizal fungi on hydraulic properties was less obvious for the Hamblen silt loam, as none of the treatments differed from each other at p < 0.05. The results highlight the necessity to consider the impact of root and fungal structures on models of soil hydraulic properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Energetic particle-induced geodesic acoustic modes on DIII-D

Various properties of the energetic particle-induced geodesic acoustic mode (EGAM) are explored in this large database analysis of DIII-D experimental data. EGAMs are n = 0 modes with m = 0 electrostatic potential fluctuations (where n/m = toroidal/poloidal mode number), m = 1 density fluctuations, and m = 2 magnetic fluctuations. The fundamental frequency (~20–40 kHz) of the mode is typically below that of the traditional geodesic acoustic mode frequency. EGAMs are most easily destabilized by beams in the counter plasma current (counter-I p ) direction as compared to co-Ip and off-axis beams. During counter beam injection, the mode frequency is found to have the strongest linear dependence (correlation coefficient r = –0.71) with the safety factor (q). Here, the stability of the mode in the space of q and poloidal beta (β p ) shows a clear boundary for the mode stability. The stability of the mode depends more strongly on damping rate than on fast-ion drive for a given injection geometry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Mechanisms for Microseismicity Occurrence Due to CO 2 Injection at Decatur, Illinois: A Coupled Multiphase Flow and Geomechanics Perspective

Here, we numerically investigate the mechanisms that resulted in induced seismicity occurrence associated with CO 2 injection at the Illinois Basin–Decatur Project (IBDP). We build a geologically consistent model that honors key stratigraphic horizons and 3D fault surfaces interpreted using surface seismic data and microseismicity locations. We populate our model with reservoir and geomechanical properties estimated using well-log and core data. We then performed coupled multiphase flow and geomechanics modeling to investigate the impact of CO 2 injection on fault stability using the Coulomb failure criteria. We calibrate our flow model using measured reservoir pressure during the CO 2 injection phase. Our model results show that pore-pressure diffusion along faults connecting the injection interval to the basement is essential to explain the destabilization of the regions where microseismicity occurred, and that poroelastic stresses alone would result in stabilization of those regions. Slip tendency analysis indicates that, due to their orientations with respect to the maximum horizontal stress direction, the faults where the microseismicity occurred were very close to failure prior to injection. These model results highlight the importance of accurate subsurface fault characterization for CO 2 sequestration operations.

58 GEOSCIENCES↗
DOE OSTI2022

Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction

Carbon capture and storage (CCS) is one approach being studied by the U.S. Department of Energy to help mitigate global warming. The process involves capturing CO 2 emissions from industrial sources and permanently storing them in deep geologic formations (storage reservoirs). However, CCS projects generally target “green field sites,” where there is often little characterization data and therefore large uncertainty about the petrophysical properties and other geologic attributes of the storage reservoir. Consequently, ensemble-based approaches are often used to forecast multiple realizations prior to CO 2 injection to visualize a range of potential outcomes. In addition, monitoring data during injection operations are used to update the pre-injection forecasts and thereby improve agreement between forecasted and observed behavior. Thus, a system for generating accurate, timely forecasts of pressure buildup and CO 2 movement and distribution within the storage reservoir and for updating those forecasts via monitoring measurements becomes crucial. This study proposes a learning-based prediction method that can accurately and rapidly forecast spatial distribution of CO 2 concentration and pressure with uncertainty quantification without relying on traditional inverse modeling. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO 2 storage site operators with an effective tool for timely and informative decision making based on limited simulation and monitoring data.

58 GEOSCIENCES↗
DOE OSTI2022

Improved uranium isotopic ratio determinations for the liquid sampling-atmospheric pressure glow discharge orbitrap mass spectrometer by use of moving average processing

The liquid sampling-atmospheric pressure glow discharge (LS-APGD) is a versatile combined atomic and molecular (CAM) ionization source capable of ionizing elemental species, small polar compounds, low-polarity polycyclic aromatic hydrocarbons, and proteins. While the LS-APGD has been proven capable of determining the 235U/238U isotope ratio in enriched and natural uranium, recent efforts have strived to reduce the analysis time of individual measurements by employing higher-order data processing techniques, specifically, moving average methods. Additionally, the use of a moving average (of data windows of various widths) improves the precision of the ratio measurements and reduces the number of scans needed to be collected to generate high-precision results. Additionally, use of the moving average minimizes the quantity of sample that needs to be analyzed while reducing the measurement time. These reductions have allowed isotope ratio (IR) determinations to be made using injections instead of direct infusion of the analyte. For example, employing a 40-point window width in the moving average to direct infusion data reduced the percent relative standard deviation (% RSD) of the 235U/238U values from 3.4% to 0.5%. For the injection of a 100 μL aliquot, the % RSD was reduced from 5.3% to 0.7%.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Role of Inlet Boundary Conditions on Fuel-Air Mixing at Supercritical Conditions

Advanced engine design and alternative fuels present the possibility of fuel injection at purely supercritical conditions in diesel engines and gas turbines. The complex interactions that govern this phenomenon still need significant research, particularly the boundary conditions for fuel injection are critical for accurate simulation. However, the flow inside the injector itself is often omitted to reduce the computational efforts, and thus, velocity, mass flux, or total pressure is specified at the injector exit (or domain inlet), often with simplified velocity profiles and turbulence levels. This simplified inlet boundary treatment has minimal effects on results for conventional fuel injection conditions, however, the validity of this approach at supercritical conditions has not been assessed. Comprehensive real-gas and binary fluid mixing models have been implemented for computational fluid dynamics (CFD) analysis of fuel-air mixing at supercritical conditions. The model is verified using prior CFD results from the literature. The model is used to investigate the effects of the shape of axial velocity and mass fraction profiles at the inlet boundary with the goal to improve the comparison of predictions to experimental data. Results show that the boundary conditions have a significant effect on the predictions, and none of the cases match precisely with experimental data. Furthermore, the study reveals that the physical location of the inlet boundary might be difficult to infer correctly from the experiments and highlights the need for high-quality, repeatable measurements at supercritical conditions to support the development of relevant high-fidelity models for fuel-air mixing.

02 PETROLEUM↗
DOE OSTI2023

Validation of Play Fairway Analysis of the geothermal potential of Camas Prairie, south-central Idaho, by an exploration well

Abstract Play Fairway Analysis (PFA) methodology was adapted for geothermal exploration at Camas Prairie, Idaho. Geophysical data, structural and geologic mapping, volcanic rock ages and vent locations, and the distribution of thermal springs and wells all indicated a relatively high geothermal potential along the southern margin of the Prairie. An exploration well (USU Camas-1) was drilled to a depth of 618.3 m to validate the PFA. A permeable zone was encountered at ~ 357.5 m with a maximum measured temperature of ~ 80 °C, which was suppressed following the injection of cold water. A moderate transmissivity of ~ 0.25–1 cm 2 /s estimated from an injection test as well a seasonal artesian flow at ~ 0.7 L/s corroborate the presence of a permeable zone. The existence of a lacustrine clay seal was confirmed near the bottom of the basin-fill sediment occupying the upper 314 m of the well. Geothermometers suggest the USU Camas-1 well water equilibrated at a reservoir temperature of ~ 120 °C. Based on the locations of both thermal and cold wells, geothermal fluids appear to be flowing upward along one or both of two fault systems. The presence of young basalts and elevated helium isotope ratios suggest that the heat source of Camas Prairie is magmatic. However, the faults may be acting as a conduit for geothermal fluids to rise from great depth without a shallow magmatic source being present. Camas Prairie is a promising area for geothermal development, but the relatively low reservoir temperatures indicate this resource may not be suitable for electric generation. Perhaps the best use would be for heating.

Lachmar, Thomas E. (ORCID:0000000249640119)↗