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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Unified Metric for Fast Frequency Response in Low-Inertia Power Systems

Future power systems with more inverter-based resources (IBRs), will be vulnerable to frequency decline contingencies. Fast frequency response (FFR) provided by IBRs is a good candidate to arrest frequency excursions. Diverse types of FFR have been proposed, and some have been deployed in our power systems. Without a unified quantification of FFR, it is hard for the grid operators to compare and fully leverage the FFR capabilities of IBRs. This work introduces a potential unified metric that quantifies two key characteristics of FFR and describes its application to three prevailing FFR types. We then use metric-to-frequency mapping to validate the accuracy of the metric in predicting the impact of a given FFR on the trajectory of a frequency event. The results show that the proposed metric is simple yet accurately captures the ability of diverse forms of FFR to improve system frequency dynamics.

effective inertia↗
DOE OSTI2021

Evaluation of residual gas fraction estimation methods for cycle-to-cycle combustion variability analysis and modeling

Cycle-to-cycle combustion variability in spark-ignition engines during normal operation is mainly caused by random perturbations of the in-cylinder conditions such as the flow velocity field, homogeneity of the air-fuel distribution, spark energy discharge, and turbulence intensity of the flame front. Such perturbations translate into the variability of the energy released observed at the end of the combustion process. During normal operating conditions, the cycle-to-cycle variability (CCV) of the energy release behaves as random uncorrelated noise. However, during diluted combustion, in either the form of exhaust gas recirculation (EGR) or excess air (lean operation), the CCV tends to increase as dilution increases. Moreover, when the ignition limit is reached at high dilution levels, the combustion CCV is exacerbated by sporadic occurrences of incomplete combustion events, and the uncorrelation assumption no longer holds. The low or null energy released by partial burns and misfires has an impact on the following combustion event due to the residual gas that carries burned and unburned gases, which contributes to the deterministic coupling between engine cycles. Many residual gas fraction estimation methods, however, only address the nominal case where complete combustion occurs and combustion events are uncorrelated. Here we evaluate the efficacy of such methods on capturing the effects of partial burns and misfires on the residual gas estimate for high-EGR operation. The advantages and disadvantages of each method are discussed based on their ability to generate cycle-to-cycle estimates. Finally, a comparison between the different estimation techniques is presented based on their usefulness for control-oriented modeling.

42 ENGINEERING↗
DOE OSTI2024

Effects of fire and fire-induced changes in soil properties on post-burn soil respiration

Boreal forests cover vast areas of land in the northern hemisphere and store large amounts of carbon (C) both aboveground and belowground. Wildfires, which are a primary ecosystem disturbance of boreal forests, affect soil C via combustion and transformation of organic matter during the fire itself and via changes in plant growth and microbial activity post-fire. Wildfire regimes in many areas of the boreal forests of North America are shifting towards more frequent and severe fires driven by changing climate. As wildfire regimes shift and the effects of fire on belowground microbial community composition are becoming clearer, there is a need to link fire-induced changes in soil properties to changes in microbial functions, such as respiration, in order to better predict the impact of future fires on C cycling. We used laboratory burns to simulate boreal crown fires on both organic-rich and sandy soil cores collected from Wood Buffalo National Park, Alberta, Canada, to measure the effects of burning on soil properties including pH, total C, and total nitrogen (N). We used 70-day soil incubations and two-pool exponential decay models to characterize the impacts of burning and its resulting changes in soil properties on soil respiration. Laboratory burns successfully captured a range of soil temperatures that were realistic for natural wildfire events. We found that burning increased pH and caused small decreases in C:N in organic soil. Overall, respiration per gram total (post-burn) C in burned soil cores was 16% lower than in corresponding unburned control cores, indicating that soil C lost during a burn may be partially offset by burn-induced decreases in respiration rates. Simultaneously, burning altered how remaining C cycled, causing an increase in the proportion of C represented in the modeled slow-cycling vs. fast-cycling C pool as well as an increase in fast-cycling C decomposition rates. Together, our findings imply that C storage in boreal forests following wildfires will be driven by the combination of C losses during the fire itself as well as fire-induced changes to the soil C pool that modulate post-fire respiration rates. Moving forward, we will pair these results with soil microbial community data to understand how fire-induced changes in microbial community composition may influence respiration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Impact of fire on chemical properties and respiration of boreal forest soils

We collected both organic-rich and sandy soil cores from 12 sites (site_location_and_texture.csv) within Wood Buffalo National Park, Alberta, Canada. Laboratory burns were conducted by exposing intact soil cores to 60 kW m^-2 in a mass loss calorimeter to simulate boreal forest crown fires in order to measure the effects of burning (temp_data.csv) on soil properties including pH, total C, and total nitrogen (N) (metadata.csv). We used 70-day soil incubations and two-pool exponential decay models to characterize the impacts of burning and burn-induced changes in soil properties on soil respiration (soil_moisture_at_end_of_incubation.csv; soil_mass_at_end_of_incubation.csv; respiration_data.csv). Laboratory burns successfully captured a range of soil temperatures that were realistic for natural wildfire events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Crush Gun Impact of Lightly Confined Explosive with Rounded Projectiles at Nominal Velocity 35 m/s

Blunt, crushing impact of high explosives can cause internal shear heating sufficient to ignite a deflagration. Low-velocity impact of this sort is plausible in handling accident scenarios, e.g. if a charge is dropped or an object is dropped onto a charge. This mechanism can also be activated during sub-shock fragment impact. Efforts to model this viscous shear heating mechanism are currently being developed and refined. The Crush Gun apparatus was created to provide a controlled low-velocity impact stimuli to explosive samples. Infrared thermography captures temporal- and spatially-resolved heating that occurs during the impact event.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Overview of NNSS Capabilities

This presentation is required for the DHS CWMD T&E week event. Submission to DHS is July 21. Material in this presentation was captured from publicly released information such as NNSS external website, Fact sheets, SDRD annual report.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Towards a Robust Sampling Approach: A Computational Review and Design

As pointed out in several other works, the estimation of the reliability of the electrical grid can not be conducted without the estimation of the stochastic phenomena of electicity demand and electricity production by variable renewable sources. Therefore, sampling procedures have become integral in the design of engineering structures and analysis. Commonly referred to as Monte Carlo uncertainty analysis or integration, the general objective of these procedures is to establish specifics about the uncertainty of an output characteristic of such an engineering system, given uncertainty about its input characteristics. These sampling procedures are applied in a context in which establishing such specifics cannot be performed through other means. Variance-reduction techniques are designed to lessen the variability among estimators to estimate statistics of those output uncertainties. Importance-sampling techniques, on the other hand, are designed to reduce the number of samples needed to estimate a particular statistic—e.g., a tail probability. The combination of these approaches can reduce the computational burden considerably for a particular estimator and statistic. Importance sampling—geared and designed as it is toward improving a particular estimator—suffers, unfortunately, from the unintended consequence of reducing the performance of other estimators in terms of their variance. The objective of this paper is to offer an alternative sampling procedure where this variance does not grow unacceptably large for a suite of estimators. Moreover, it is anticipated that, with additional knowledge of how an engineering output characteristic responds to its input characteristics, tuning parameters of the input’s sampling procedure can be set to improve the output characteristic’s estimation. This work proves the effectiveness of the suggested alternative approach. Such positive outcome will lead to a decrease of the computational burden of performing stochastic optimization of integrated energy systems (e.g., components dispatch, and portfolio composition). In particular, capturing the contribution to the overall system cost of rare and unlikely events and patterns of the electricity demand and production will become less computationally expensive. This is due to the fact that the approach demonstrated here will allow the sampling of those rare occurrences more frequently without misrepresenting their probabilistic impacts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Parameter Reduction of Composite Load Model Using Active Subspace Method

Over the past decades, the increasing penetration of distributed energy resources (DERs) has dramatically changed the power load composition in the distribution networks. The traditional static and dynamic load models can hardly capture the dynamic behavior of modern loads especially for fault-induced delayed voltage recovery (FIDVR) events. Thus, a more comprehensive composite load model with combination of static load, different types of induction motors, single-phase A/C motor, electronic load and DERs has been proposed by Western Electricity Coordinating Council (WECC). However, due to the large number of parameters and model complexity, the WECC composite load model (WECC CMLD) raises new challenges to power system studies. To overcome these challenges, in this paper, a cutting-edge parameter reduction (PR) approach for WECC CMLD based on active subspace method (ASM) is proposed. Firstly, the WECC CMLD is parameterized in a discrete-time manner for the application of the proposed method. Then, parameter sensitivities are calculated by discovering the active subspace, which is a lower-dimensional linear subspace of the parameter space of WECC CMLD in which the dynamic response is most sensitive. The interdependency among parameters can be taken into consideration by our approach. Finally, the numerical experiments validate the effectiveness and advantages of the proposed approach for WECC CMLD model.

active subspace↗
DOE OSTI2020

Microseismic Event Denoising: Removal of Borehole Waves

The vertical borehole array at Farnsworth Unit, TX is used to monitor microseismic activity in the subsurface around the Carbon Capture and Sequestration (CCS) reservoir. The array consists of 16 3-component seismometers spaced vertically in a single borehole. Tube or borehole waves traveling up or down the borehole can corrupt signals of interest, such as microseismic events. A denoising convolutional neural network (DCNN) was trained to remove borehole waves from seismic waveforms of microseismic events for the purpose of reducing unwanted signal detections and better characterizing events of interest. This R&D leverages the work of Sandia colleague Rigo Tibi, who used a DCNN developed by Greg Beroza at Stanford University to improve the signal-to-noise ratio (SNR) of teleseismic events detected by the International Monitoring System.

58 GEOSCIENCES↗
DOE OSTI2025

Future Intensity‐Duration‐Frequency Curves of Extreme Precipitation in the Midwest United States From Convection‐Permitting Modeling

Abstract During the last four decades, global warming has statistically significant intensified extreme precipitation events in the Midwestern United States (defined here as the region covering Illinois, Indiana, Ohio, and Kentucky), leading to increased risks to human life, property, and infrastructure. To enable climate change adaptation and resilience across various economic and social sectors in this region, updated information about future climate changes, specifically at finer spatial scales, is essential. Leveraging a new 150‐year dynamical downscaling data set at convection‐permitting resolution, this study introduces a framework to construct the projected future intensity‐duration‐frequency (IDF) curves of heavy precipitation, which are prominent tools for infrastructure design and water resources management. This framework generates IDF curves at both sub‐daily and multi‐day duration utilizing hourly in situ observations as well as quantile‐based statistical techniques in bias‐correction and return levels selection. The assumption of non‐stationarity in the distribution parameter fitting process is also implemented in this workflow. Compared to historical IDF curves for 1980–2022, future projected IDF curves for 2058–2100 under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios indicate an average intensity increase of approximately 15% and 25%, respectively, across 74 stations, considering both annual and seasonal timescales. Future projections suggest that extreme precipitation events may become more severe across six investigated return periods, with longer return periods showing a greater increase. The frequency of future extreme precipitation events in the Midwest region is also projected to double. Furthermore, current results reveal spatial heterogeneity of future trends across stations owing to the high‐resolution input data set. Plain Language Summary This study investigates the evolving nature of extreme precipitation events in the Midwestern United States under a changing climate. By leveraging a high‐resolution dynamical downscaling data set, we construct projected intensity‐duration‐frequency (IDF) curves for future extreme rainfall events. These curves serve as vital tools for infrastructure planning and water resource management. Our analysis reveals a significant increase in both the intensity and frequency of extreme precipitation events in the region. Future projected IDF curves for the late century indicate an average intensity increase of approximately 15%–25% compared to historical values. Moreover, the frequency of such events is expected to double. Spatial heterogeneity in future trends is observed across different stations within the Midwest, highlighting the importance of high‐resolution modeling in capturing localized climate variability. These findings underscore the urgent need for climate adaptation strategies to mitigate the increasing risks associated with extreme precipitation events in the region. Key Points This study introduces a workflow to construct future intensity‐duration‐frequency (IDF) curves over the Midwest United States using a new convection‐permitting modeling data set The current IDF construction workflow reproduces well the historical observed IDF 30 curves in summer months with median relative errors of 2.4% among 74 stations and 6 investigated durations The projected IDF curves show diverse future trends of extreme precipitation across stations, with intensity increases of approximately 15% and 25% under RCP4.5 and RCP8.5 climate scenarios, respectively, and a doubling of frequency on average

Nguyen, Trung↗
DOE OSTI2021

Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks

Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Combining Simulation and Optimization to Derive Operating Policies for a Concentrating Solar Power Plant

Optimizing short-term decisions over a rolling horizon and/or using deterministic penalties to capture system stochasticity can lead to myopic policies that fail to consider unplanned events and their long-term adverse effects. We present a methodology that integrates an off-line optimization model with a simulation procedure to determine the profitability of different operating strategies; specifically, the latter is used to generate additional constraints for the former when failures occur according to system component operating lifetimes that (i) are subject to exogenous uncertainty, and (ii) may degrade more quickly under specific operating conditions. We use the feedback provided by the simulation model in a parametric analysis to obtain penalties that can be used in short-term operations scheduling to maximize the long-term revenues obtained by the optimization model. We apply this research to a concentrating solar power plant; our results show that the methodology can be used to choose an operating policy that balances maximizing profit while accounting for maintenance costs. Integrating the optimization model with a simulation procedure reveals that aggressive prices for cycling yield about 55% fewer startups and 30% fewer failures compared to using a more typical start-up operating strategy, and can save hundreds of thousands to millions of dollars in repair costs over the lifetime of the plant.

concentrating solar power↗
DOE OSTI2020

A Retrospective of Findings From the FORTE Satellite Mission

We revisit the findings from the FORTE satellite program (1997–2004), which collected optical imaging of lightning as well as optical and radio frequency time series waveforms globally from low-earth-orbit. These include surveys of the earth's radio frequency anthropogenic noise environment; earth surface reflectivity at radio frequencies; a scheme for classifying lightning discharge types on the basis of their very high frequency time domain power envelope; insights into the polarization and radiation pattern characteristics of different lightning types, with implications for the underlying discharge processes; and estimates of cloud optical properties based on the statistics of scattered light. Most significantly, FORTE was uniquely suited to capture large samples of data from the rare discharge known as “narrow bipolar events,” enabling detailed examination of their basic characteristics and confirming that they appear to result from fundamentally distinct physical processes compared to other lightning. In particular, despite representing huge charge transfer they evidently produce little-to-no light output.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Celestial-body focused dark matter annihilation throughout the Galaxy

Indirect detection experiments typically measure the flux of annihilating dark matter (DM) particles propagating freely through galactic halos. We consider a new scenario where celestial bodies “focus” DM annihilation events, increasing the efficiency of halo annihilation. In this setup, DM is first captured by celestial bodies, such as neutron stars or brown dwarfs, and then annihilates within them. If DM annihilates to sufficiently long-lived particles, they can escape and subsequently decay into detectable radiation. This produces a distinctive annihilation morphology, which scales as the product of the DM and celestial body densities, rather than as DM density squared. We show that this signal can dominate over the halo annihilation rate in γ -ray observations in both the Milky Way Galactic center and globular clusters. We use Fermi and H.E.S.S. data to constrain the DM-nucleon scattering cross section, setting powerful new limits down to ~10 -39 cm 2 for sub-GeV DM using brown dwarfs, which is up to 9 orders of magnitude stronger than existing limits. We demonstrate that neutron stars can set limits for TeV-scale DM down to about 10 -47 cm 2 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Rare K 40 Decay with Implications for Fundamental Physics and Geochronology

Potassium-40 is a widespread, naturally occurring isotope whose radioactivity impacts subatomic rare-event searches, nuclear structure theory, and estimated geological ages. A predicted electron-capture decay directly to the ground state of argon-40 has never been observed. Here, the KDK (potassium decay) collaboration reports strong evidence of this rare decay mode. A blinded analysis reveals a nonzero ratio of intensities of ground-state electron-captures (I EC 0 ) over excited-state ones (I EC $\star$ ) of I EC 0 /I EC $\star$ = 0.0095 $^{stat}_{±}$ 0.0022 $^{sys}_{±}$ 0.0010 (68% C.L.), with the null hypothesis rejected at 4σ. In terms of branching ratio, this signal yields I EC 0 = 0.098 % $^{stat}_{±}$ 0.023 % $^{sys}_{±}$ 0.010 % , roughly half of the commonly used prediction, with consequences for various fields [L. Hariasz et al., companion paper, Phys. Rev. C 108, 014327 (2023)].

39 ≤ A ≤ 58↗
DOE OSTI2024

Investigating Electro-Nuclear Interactions in a New Dark Matter Search

Electro-nuclear (EN) interactions are interactions in which an incident electron collides with a nucleus, scattering the electron and creating byproduct particles. Such interactions are of interest to neutrino physicists, who use EN interactions to inform model building of neutrino-nucleus interactions. The Light Dark Matter Experiment (LDMX) is a small-scale, fixed-target, electron beam experiment which seeks to probe for dark matter and mediator particle production in the sub-GeV mass region. The 8GeV LDMX electron beam will serve as an opportunity to study electro-nuclear interactions in their final states in the multi-GeV region. LDMX’s missing energy trigger however, will not be sufficient to efficiently capture EN interactions. An additional trigger needs to be created. Using simulated background events, including EN interactions, for the LDMX experiment, a trigger on momentum was developed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Investigating ultrafine aerosol turbulent fluxes during atmospheric new particle formation events

New particle formation (NPF) is an important source of atmospheric aerosols, yet quantitatively predicting its occurrence remains challenging, partly because ground-based measurements cannot capture NPF accurately if they occur in the upper atmosphere. While observations have confirmed the presence of new particles near the top of the planetary boundary layer (PBL), their origins and the direction of their vertical transport have remained ambiguous. Here, we propose and validate an analytical framework using airborne eddy-covariance (EC) and continuous wavelet transform (CWT) to directly quantify the vertical flux of newly formed particles and determine its dominant vertical transportation direction. Analyzing data from a dedicated airborne campaign over the Southern Great Plains, we observed a persistent and strong downward particle flux during NPF events, with a mean value of 133.8 cm?3 m s?¹ downward flux in the entrainment zone, whereas fluxes on non-NPF days were negligible. Our spectral analysis further confirms that these directional fluxes can be reliably captured using standard 1 Hz aerosol instrumentation. These findings suggest that new particle formation, driven by the entrainment of air from the overlying residual/stable layer during PBL growth, is a significant and potentially understudied source of boundary layer aerosols. The framework presented here provides a methodology to correctly attribute NPF events to specific altitudes, thereby improving the mechanistic understanding required for accurate atmospheric models.

Zhang, Ruoyu↗
DOE OSTI2023

Seismological Stress Drops for Confined Ruptures Are Invariant to Normal Stress

Seismic moment and rupture length can be combined to infer stress drop, a key parameter for assessing earthquakes. In natural earthquakes, stress drops are largely depth-independent, which is surprising given the expected dependence of frictional stress on normal stresses and hence overburden. We have developed a transparent experimental fault that allows direct observation of thousands of slip events, with ruptures that are fully contained within the fault. Surprisingly, the observed stress drops are largely independent of both the magnitude of normal stress and its heterogeneity, capturing the independence seen in nature. However, we observe larger, normal stress-dependent stress drops when the fault area is reduced, which allows slip events to frequently reach the edge of the interface. We conclude that confined ruptures have normal stress independent stress drops, and thus the depth-independent stress drops of tectonic earthquakes may be a consequence of their confined nature.

58 GEOSCIENCES↗