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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 307 records · Page 17

Mid-Latitude Ionospheric Disturbances Due to Geomagnetic Storms at ISS Altitudes

Spacecraft charging of the International Space Station (ISS) is dominated by interaction of the US high voltage solar arrays with the F2-region ionosphere plasma environment. ISS solar array charging is enhanced in a high electron density environment due to the increased thermal electron currents to the edges of the solar cells. High electron temperature environments suppress charging due to formation of barrier potentials on the charged solar cell cover glass that restrict the charging currents to the cell edge [Mandell et al., 2003]. Environments responsible for strong solar array charging are therefore characterized by high electron densities and low electron temperatures. In support of the ISS space environmental effects engineering community, we are working to understand a number of features of solar array charging and to determine how well future charging behavior can be predicted from in-situ plasma density and temperature measurements. One aspect of this work is a need to characterize the magnitude of electron density and temperature variations that occur at ISS orbital altitudes (approximately 400 km) over time scales of days, the latitudes over which significant variations occur, and the time periods over which the disturbances persist once they start. This presentation provides examples of mid-latitude electron density and temperature disturbances at altitudes relevant to ISS using data sets and tools developed for our ISS plasma environment study. "Mid-latitude" is defined as the extra-tropical region between approx. 30 degrees to approx. 60 degrees magnetic latitude sampled by ISS over its 51.6 degree inclination orbit. We focus on geomagnetic storm periods because storms are well known drivers for disturbances in the ionospheric plasma environment.

Minow, Joseph I.↗

Thermospheric Nitric Oxide Cooling Responses to the 14 December 2020 Solar Eclipse

The behaviors of the nitric oxide (NO) cooling in the lower thermosphere during the 14 December 2020 solar eclipse are studied using Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) measurements and WACCM-X simulations. We found that NO cooling rate decreases during the solar eclipse in both SABER measurements and WACCM-X simulations. The maximum decrease of the NO cooling is 40% in SABER measurements and 25% in WACCM-X simulations. The NO cooling process is initiated almost entirely through the collisions with atomic oxygen (O) which depends linearly on NO and O densities and non-linearly on the neutral temperature. During the eclipse, the NO concentration and temperature decreases are larger than that of O concentration. Consequently, the eclipse-time NO concentration and temperature decreases are the major drivers of the NO cooling rate decrease. The decreases of the temperature and the NO concentration contribute comparably to the eclipse-time NO cooling rate decrease.

solar eclipse↗

Understanding the Drivers of Mobility during the COVID-19 Pandemic in Florida, USA Using a Machine Learning Approach

As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases.

60 APPLIED LIFE SCIENCES↗

Soil Water Content Effects on Net Ecosystem CO2 Exchange and Actual Evapotranspiration in a Mediterranean Semiarid Savanna of Central Chile

Biosphere-atmosphere water and carbon fluxes depend on ecosystem structure, and their magnitudes and seasonal behavior are driven by environmental and biological factors. We studied the seasonal behavior of net ecosystem CO2 exchange (NEE), Gross Primary Productivity (GPP), Ecosystem Respiration (RE), and actual evapotranspiration (ETa) obtained by eddy covariance measurements during two years in a Mediterranean Acacia savanna ecosystem (Acacia caven) in Central Chile. The annual carbon balance was −53 g C/sq. m in 2011 and −111 g C/sq. m in 2012, showing that the ecosystem acts as a net sink of CO2, notwithstanding water limitations on photosynthesis observed in this particularly dry period. Total annual ETa was of 128 mm in 2011 and 139 mm in 2012. Both NEE and ETa exhibited strong seasonality with peak values recorded in the winter season (July to September), as a result of ecosystem phenology, soil water content and rainfall occurrence. Consequently, the maximum carbon assimilation rate occurred in wintertime. Results show that soil water content is a major driver of GPP and RE, defining their seasonal patterns and the annual carbon assimilation capacity of the ecosystem, and also modulating the effect that solar radiation and air temperature have on NEE components at shorter time scales.

Meza, Francisco J.↗

Systematic Assessment of Retrieval Methods for Canopy Far-Red Solar-Induced Chlorophyll Fluorescence Using High-Frequency Automated Field Spectroscopy

Remote sensing of solar-induced chlorophyll fluorescence (SIF) offers potential to infer photosynthesis across scales and biomes. Many retrieval methods have been developed to estimate top-of-canopy SIF using ground-based spectroscopy. However, inconsistencies among methods may confound interpretation of SIF dynamics, eco-physiological/environmental drivers, and its relationship with photosynthesis. Using high temporal- and spectral resolution ground-based spectroscopy, we aimed to (1) evaluate performance of SIF retrieval methods under diverse sky conditions using continuous field measurements; (2) assess method sensitivity to fluctuating light, reflectance, and fluorescence emission spectra; and (3) inform users for optimal ground-based SIF retrieval. Analysis included field measurements from bi-hemispherical and hemispherical-conical systems and synthetic upwelling radiance constructed from measured downwelling radiance, simulated reflectance, and simulated fluorescence for benchmarking. Fraunhofer-based differential optical absorption spectroscopy (DOAS) and singular vector decomposition (SVD) retrievals exhibit convergent SIF-PAR relationships and diurnal consistency across different sky conditions, while O2A-based spectral fitting method (SFM), SVD, and modified Fraunhofer line discrimination (3FLD) exhibit divergent SIF-PAR relationships across sky conditions. We find that such behavior holds across system configurations, though hemispherical-conical systems diverge less across sky conditions. O 2 A retrieval accuracy, influenced by atmospheric distortion, improves with a narrower fitting window and when training SVD with temporally local spectra. This may impact SIF-photosynthesis relationships interpreted by previous studies using O 2 A-based retrievals with standard (759–767.76 nm) fitting windows. Fraunhofer-based retrievals resist atmospheric impacts but are noisier and more sensitive to assumed SIF spectral shape than O 2 A-based retrievals. We recommend SVD or SFM using reduced fitting window (759.5–761.5 nm) for robust far-red SIF retrievals across sky conditions.

59 BASIC BIOLOGICAL SCIENCES↗

Experimental study of outdoor propagation of spherically speading periodic acoustic waves of finite amplitude

The outdoor propagation of spherically spreading sound waves of finite amplitude was investigated. The main purpose of the experiments was to determine the extent to which the outdoor environment, mainly random inhomogeneity of the medium, affects finite amplitude propagation. Periodic sources with fundamental frequencies in the range 6 to 8 kHz and source levels SPLlm from 140 to 149 dB were used. The sources were an array of 7 to 10 horn drivers and a siren. The propagation path was vertical and parallel to an 85 m tower, whose elevator carried the traveling microphone. The general conclusions drawn from the experimental results were as follows. The inhomogeneities caused significant fluctuations in the instantaneous acoustic signal, but with sufficient time averaging of the measured harmonic levels, the results were comparable to results expected for propagation in a quiet medium. Propagation data for the fundamental of the siren approached within 1 dB of the weak shock saturation levels. Extra attenuation on the order of 8 dB was observed. The measurements generally confirmed the predictions of several theoretical models. The maximum propagation distance was 36 m. The narrowbeam arrays were much weaker sources. Nonlinear propagation distortion was produced, but the maximum value of extra attenuation measured was 1.5 dB. The maximum propagation distance was 76 m. The behavior of the asymetric waveforms received in one experiment qualitatively suggested that beam type diffraction effects were present. The role of diffraction of high intensity sound waves in radiation from a single horn was briefly investigated.

Theobald, M. A.↗

Storing Affordability: Battery Storage as an Asset to Reduce Data Center Cost Shifts

This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.

25 ENERGY STORAGE↗

A dataset for understanding self-reported patterns influencing residential energy decisions

Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Truck Platooning Performance with ADAS and Onboard Camera Data Describing Traffic Interactions

This project was part of the Characterizing Behaviors and Capabilities for Emerging Connected and Automated Vehicle Technologies, Sensors, and Connectivity project. The National Laboratory of the Rockies partnered with Cummins Inc. to collect data from Class 8 tractor trailer combinations in platoon (cooperative adaptive cruise control) operations on public roads in southern Indiana. Data collected include J1939 CAN bus, radar, intervehicle position, and video data. The video data could not be shared in the raw form, so they were processed to extract information on the other vehicles on the road, their relative positions, and intrusion events. This information was then columnized for modeling use and further enhanced by appending road information including road type, speed limit, altitude, and grade. The test route included free-flowing traffic, highway interchanges, and construction zones, as well as low-, medium-, and high-grade sections. Individual test conditions varied by day, with advanced driver-assistance system (ADAS) features engaged or disengaged and different combined vehicle masses tested in addition to uncontrolled variables such as weather and traffic interactions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Dynamic Response of a Semiactive Suspension System with Hysteretic Nonlinear Energy Sink Based on Random Excitation by means of Computer Simulation

This paper aims to investigate the property and behavior of the hysteretic nonlinear energy sink (HNES) coupled to a half vehicle system which is a nine-degree-of-freedom, nonlinear, and semiactive suspension system in order to improve the ride comfort and increase the stability in shock mitigation by using the computer simulation method. The HNES model is a semiactive suspension device, which comprises the famous Bouc–Wen (B-W) model employed to describe the force produced by both the purely hysteretic spring and linear elastic spring of potentially negative stiffness connected in parallel, for the half vehicle system. Nine nonlinear motion equations of the half vehicle system are derived in terms of the seven displacements and the two dimensionless hysteretic variables, which are integrated numerically by employing the direct time integration method for studying both the variables of vertical displacements, velocities, accelerations, chassis pitch angle, and the ride comfort and driver safety, respectively, based on the bump and random road inputs of the pseudoexcitation method as excitation signal. Simulation results show that, compared with the HNES model and the magnetorheological (MR) model coupled to the half vehicle system, the ride comfort and stability have been evidently improved. A successful validation process has been performed, which indicated that both the ride comfort and driver safety properties of the HNES model coupled to half vehicle significantly improved.

Chen, Hui↗

Modeling Interplanetary Expansion and Deformation of CMEs With ANTEATR-PARADE: Relative Contribution of Different Forces

Coronal Mass Ejections (CMEs) are key drivers of space weather activity but most predictions have been limited to the expected arrival time of a CME, rather than the internal properties that affect the severity of an impact. Many properties, such as the magnetic field density and mass density, follow conservation laws and vary systematically with changes in the size of a CME. We present ANTEATR-PARADE, the newest version of the ANTEATR arrival time model, which now includes physics-driven changes in the size and shape of both the CME's central axis and its cross section. Internal magnetic and thermal and external drag forces affect the acceleration of the CME in different directions, inducing asymmetries between the radial and perpendicular directions. These improvements should lead to more realistic CME velocities, both bulk and expansion, sizes and shapes, and internal properties. We present the model details, an initial illustration of the general behavior, and a study of the relative importance of the different forces. The model shows a pancaking of both the cross section and central axis of the CME so that their radial extent becomes smaller than their extent in the perpendicular direction. We find that the initial velocities, drag, any form of cross section expansion, and the precise form of thermal expansion have strong effects. The results are less sensitive to axial forces and the specific form of the cross section expansion.

C. Kay↗

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↗

Instantaneous mesh load factor ( K γ ) measurements in a wind turbine gearbox using fiber-optic strain sensors

The mesh load factor, K γ , describes how loads are shared between planet gears and has become one of the key design challenges in modern wind turbine gearboxes. Planet load sharing directly impacts tooth root stresses, a critical driver of torque density and gearbox reliability. Experimental evaluation of K γ is typically performed from sun gear tooth root strain gauge measurements, which are complex. Furthermore, such measurements can only provide an average value of load sharing. The present study describes an alternative method to evaluate the mesh load factor in wind turbine gearboxes based on fiber-optic strain sensors installed on the outer surface of the fixed ring gear. We present the results of an extensive measurement campaign to evaluate this novel sensing solution installed on the input planetary stage of a 2-MW wind turbine gearbox at the National Renewable Energy Laboratory's Flatirons Campus (Colorado, USA). The number of strain sensors on the ring gear was selected as an integer multiple of the number of planets, which has enabled an instantaneous evaluation of the mesh load factor. The effect of operating conditions on the planet load-sharing behavior of the gearbox has been investigated. The mesh load factor measured for operating conditions close to rated was below 1.05, well below IEC 61400-4 standard requirements.

17 WIND ENERGY↗

Patterned anodes with sub-millimeter spatial resolution for large-area MCP-based photodetector systems

Micro-channel-plate-based photo-detectors are unique in being capable of covering very large areas such as those required in elementary particle and nuclear physics, while providing sub-millimeter space resolution, time resolutions of less than 10 picoseconds for charged particles, and time resolutions of 30 psec–50 psec for single photons. In such systems the electronic channel count is a major cost driver. Incorporating a capacitively-coupled anode allows the use of external pickup electrodes with patterns of individual channels optimized for occupancy, rate, and time/space resolution. The signal pickup antenna can be economically implemented as a printed circuit card with a 2-dimensional array of pads for high-occupancy/high-rate applications such as in particle colliders and medical imaging, or a 1-dimensional array of strips for a lower channel count in low-occupancy/low-rate applications such as large neutrino detectors. In this paper we present pad patterns that enhance signal-sharing between pads to lower the channel count per unit area in large-area systems by factors up to 4, while maintaining spatial resolutions of approximately 100 to 200 μ m for charged particles and 400 to 1000 μ m for single photons. Patterns that use multiple signal layers in the signal-pickup board can lower the channel count even further, moving the scaling behavior in the number of pads versus total area from quadratic to linear.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Large Scale Cryogenic Storage with Active Refrigeration

Storage and transfer of cryogenic liquefied gases on volume scales from under 10 liters for lab use, up to hundreds of millions of liters for industrial applications is of paramount importance across a vast range of industries. Traditionally, these commodities have been stored at or near the normal boiling point due to relative ease of operation and safety-related considerations; however, this also means that some percentage will always be lost due to environmental heat leaking into the vessel and causing boil-off. These losses become more concerning as scales increase, and are of particular importance for high-cost commodities such helium and hydrogen. Additionally, the normal boiling point marks the highest liquid density achievable; which becomes a strong driver of end-use system designs such as space launch vehicles. Recent development and testing of an Integrated Refrigeration and Storage (IRAS) system for liquid hydrogen has proven that next generation cryogenic storage operations such as zero boil-off and densification are feasible on a large scale. This IRAS system married an 850 Watt at 20 Kelvin reverse-Brayton cycle commercial cryogenic refrigerator with a 125,000 liter LH2 storage tank via and internal tubular heat exchanger. This allowed heat to be removed directly from the hydrogen, and by extension, provided a means to control the bulk thermodynamic state. Zero boil-off, in-situ liquefaction, and densification down to the triple point was performed during testing, and data such as fluid temperature profiles and tank pressure were gathered. Details regarding the design, setup, and testing of the IRAS system are discussed herein, and the data used to anchor various physics models created to predict the behavior of the system during transient and steady state operation. It is hoped that these efforts will provide a useful basis for the design and implementation of future large scale IRAS systems across numerous industries.

Swanger, Adam M.↗

Soil Respiration Response to Rainfall Modulated by Plant Phenology in a Montane Meadow, East River, Colorado, USA

Soil respiration is a primary component of the terrestrial carbon cycle. However, predicting the response of soil respiration to climate change remains a challenge due to the complex interactions between environmental drivers, especially plant phenology, temperature, and soil moisture. In this study, we use a 1-D diffusion-reaction model to calculate depth-resolved CO 2 production rates from soil CO 2 concentrations and surface efflux observations in a subalpine meadow in the East River watershed, CO. Modeled rates are compared to in situ soil temperature and moisture conditions and MODIS satellite enhanced vegetation index (EVI) representing plant phenology across three hydrologically distinct growing seasons from 2016-2018. While soil respiration correlated with temperature on diel timescales (p<0.05), seasonal variability was dominated by soil moisture and plant phenology (p<0.05). We observed significant respiration increases in response to precipitation events; however, magnitude and duration were significantly higher in 2017 than 2016 despite similar wetting characteristics. Based on MODIS EVI, we suggest the respiration response to rainfall is controlled by plant phenology, which in turn reflects the capacity of plants to respond to precipitation via increased photosynthesis and autotrophic respiration, behavior that is not captured in typical soil respiration pulse models. Projected changes in montane climate such as earlier snowmelt and prolonged fore-summer drought may decrease soil respiration fluxes by decreasing the overlap between peak productivity and the summer monsoon. Lastly, we observed significant late-season CO 2 fluxes from the deep sub-soil (>165 cm) that support growing evidence for the importance of sub-soil processes in driving integrated respiration fluxes.

54 ENVIRONMENTAL SCIENCES↗

Emergence of Diverse Failure Patterns in Weathering‐Induced Landslides: Insights From Particle Finite Element Simulations

Weathering is a fundamental driver of landslide evolution over geological timescales. Despite its ubiquity and importance, quantifying how weathering drives the progressive destabilization of rock slopes remains challenging. In this work, we develop a unified computational framework based on the particle finite element method to investigate the evolution of weathering‐induced landslides, from long‐term weathering to short‐term slope failure and runout dynamics. The framework integrates key processes, including weathering front propagation, time‐dependent strength degradation, rupture surface development, and post‐failure runout dynamics. Through numerical simulation experiments, we elucidate how interactions among weathering characteristics (type, intensity, and rate law), bedrock strength, fracture distribution, and slope geometry govern the failure modes and kinematics of weathering‐induced landslides. Simulations show that matrix‐dominated weathering leads to shallow translational failures, whereas fracture‐dominated weathering produces deep‐seated rotational and compound landslides. Pre‐existing fractures and slope morphology also strongly influence the movement of destabilized landmasses, affecting the failure pattern (e.g., kinematic mode and rupture surface geometry) and post‐failure behavior (e.g., runout velocity). We further demonstrate that the failure time and volume of weathered slopes are governed by the competition between gravitational driving forces and cohesive resisting forces during progressive destabilization. These findings provide new insights into the fundamental mechanisms that drive the emergence of diverse failure patterns of weathering‐induced landslides with important implications for landslide hazard assessment.

Wang, Liang [Eidgenoessische Technische Hochschule↗

Evaluation of Methods for Causal Discovery in Hydrometeorological Systems

Understanding causal relations is of utmost importance in hydrology and climate research for systems identification, prediction, and understanding systems behavior in a changing climate. Traditionally, researchers in hydrometeorology attempted to study causal questions by conducting controlled experiments using numerical models. This approach, however, in most cases of interest provides uncertain results because the models are approximate representation of the natural system. An alternative approach that has recently drawn significant attention in several fields is to infer causal relations from purely observational data. It possesses several traits to its utility particularly in hydrometeorology due to the rapid accumulation of in situ and remotely sensed data records. The first objective of this study is to present a brief description of four causal discovery methods (Granger causality, Transfer Entropy, graph-based algorithms, and Convergent Cross Mapping) with special emphasis on the assumptions on which they are built. Second, using synthetic data generated from a hydrological model, we assess their performance in retrieving causal information taking into account sensitivity to sample size and presence of noise. Last, we use causal analysis to examine and formulate hypotheses on causal drivers of evapotranspiration in a shrubland region during summer and winter seasons. An interpretation of the hypotheses based on canopy seasonal dynamics and evapotranspiration processes is presented. It is hoped that the results presented here can be useful in guiding researchers studying hydrometeorological systems as to which causal method is most appropriate to the characteristics of the system under study.

54 ENVIRONMENTAL SCIENCES↗