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DOE OSTI2026

Neutron measurement from neutrino interactions in NOvA

The NuMI Off-Axis Appearance (NOvA) experiment is a long-baseline neutrino oscillation experiment based at Fermilab, consisting of two functionally identical liquid-scintillator detectors positioned slightly off-axis from the NuMI beam. It aims to probe CP violation in the lepton sector and determine the neutrino mass ordering by comparing the oscillation rates between the detectors. Given the energy dependence of oscillations, energy reconstruction is key--and a key limitation in the precision and accuracy of this kinematic variable is an understanding of primary neutron-related uncertainties since many may go undetected. While recent improvements have reduced discrepancies, NOvA's simulation still overpredicts neutron-linked activity by about 20\%, particularly for neutrons between 5–200 MeV. We present ongoing progress toward a measurement of neutron multiplicity in the NOvA Near Detector using spatially displaced and delayed energy deposits associated with neutron interactions, aiming to constrain neutron-related systematics and improve neutrino energy reconstruction in future oscillation analyses.

Kufatty, G. [Florida State U.]↗

DOE OSTI2026

From Ensemble Climate to Ensemble Impacts

Many climate-risk tools rely on ensemble mean projections or endpoint climate snapshots to characterize future hazards. Although convenient for communication, these representations remove the statistical, temporal, and physical information that real infrastructure systems respond to. Infrastructure degradation and failure arise from extremes, sequences, cumulative stress, compound hazards, and nonlinear fragility relationships, none of which survive ensemble averaging or temporal compression. Power-system failure statistics and cascading failure models further show that infrastructure risk is dominated by tail events and path-dependent dynamics rather than by mean conditions. This paper demonstrates why ensemble mean or endpoint-only climate representations are mathematically and physically inconsistent with engineering-grade risk analysis. We outline a model-resolved, time-series-based workflow that preserves extremes, variability, and sequencing by propagating each climate-model realization independently through hazard formation, exposure, fragility, and cascading failure mechanisms. Taking the ensemble of impacts—rather than the ensemble of climate—provides a defensible, physically coherent foundation for infrastructure resilience planning, regulatory compliance, and long-term investment decisions.

54 - ENVIRONMENTAL SCIENCES/GLOBAL CLIMATE CHANGE ↗

DOE OSTI2025

Preferential adsorption of noble gases in zeolitic tuff with variable saturation: A modeling study of counter-intuitive diffusive-adsorptive behavior

Noble gas transport through geologic media has important applications in the prediction and characterization of measured gas signatures related to underground nuclear explosions (UNEs). Retarding processes such as adsorption can cause significant species fractionation of radionuclide gases, which has implications for measured and predicted signatures used to distinguish radioxenon originating from civilian nuclear facilities or from UNEs. Accounting for the effects of variable water saturation in geologic media on tracer transport is one of the most challenging aspects of modeling gas transport because there is no unifying relationship for the associated tortuosity changes between different rock types, and reactive transport processes such as adsorption that are affected by the presence of water likewise behave differently between gas species. In this study, we perform numerical diffusive-adsorptive transport simulations to estimate gas transport parameters associated with bench-scale laboratory diffusion cell experiments measuring breakthrough in zeolitic and non-zeolitic rocks for a gaseous mixture of xenon, krypton, and SF 6 at varying degrees of water saturation (S w ). Counter-intuitive transport behavior was observed in the zeolitic rock experiments whereby breakthrough concentrations were significantly higher when the core was partially saturated (S w = 17 %) than under dry (S w = 0 %) conditions. Breakthrough of xenon was especially retarded in the dry core – likely due to comparatively high affinity of xenon for zeolitic adsorption sites – and estimated effective diffusion coefficients for all gases were approximately an order of magnitude lower than what is predicted by porosity-tortuosity models. We propose the counter-intuitive behavior observed is because water infiltration of zeolite nanopores reduces both the adsorptive capacity of the rock and the tortuosity of connected flow paths. We developed a two-site competitive kinetic Langmuir adsorption reaction for the porous media transport simulator in order to constrain transport parameters within zeolitic tuff, where differential adsorption to zeolite and non-zeolite pores was observed. We determined that liquid saturation-dependent diffusive-adsorptive transport is affected by subtle and at times competing processes that are specific to different gases, which have a significant overall influence on effective transport parameters.

58 GEOSCIENCES↗

NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

The field of prognostics and health management provides quantitative methods for monitoring and predicting the health of physical systems. Prognostics algorithms are useful in that they can be employed to assess the current state of a system, propagate the system state throughout time, and predict potential anomalies or failures that may occur. However, effective prognosis can be challenging to achieve in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for some prognostics applications. In this work, we implement three new algorithmic approaches to prediction (sampling methods, variable prediction time step, variable prediction sample size) with the goal of improving computational efficiency while minimizing decrease in model accuracy. To quantitatively analyze our results, we examine a use-case of degradation of a Lithium-ion battery. Notably, through this work it was found that none of the sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. However, our results show that prediction accuracy is highly dependent on the time step used, and that an appropriate time step can optimize both model accuracy and simulation efficiency. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment in an application-specific manner may prove useful in some applications. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency of prognosis.

Prognostics↗

DOE OSTI2026

Understanding aerosol properties in convective outflows during TRACER

Convective outflow boundaries commonly form across the southeastern U.S., often originating from storms initiated along sea-breeze fronts. As outflows spread, merge, and collide, they frequently trigger secondary convection in air masses already modified by primary storm outflow. This resulting air mass carries a distinctive aerosol population, which can influence the development of secondary convective cells. During the U.S. DOE TRACER field campaign in summer 2022, we investigated how outflows affect aerosol size, composition, and hygroscopicity across Greater Houston, Texas. Using a novel detection algorithm, we identified and verified 72 outflow boundaries from surface meteorological observations. In the relatively simple continental environment northwest of Houston, a common relationship emerged between CCN-inferred hygroscopicity (κ) and size-resolved aerosol concentrations. The κ of aerosols typically decreased in size ranges where aerosol concentrations increased following outflow passage and increased where concentrations decreased. In contrast, at the Atmospheric Radiation Measurement’s (ARM) fixed site in La Porte, Texas, the aerosol response depended on outflow direction, shaped by concentrated industrial and shipping activity to the north and south/southeast along the Houston Ship Channel. Concurrent size distribution and composition changes suggested case-specific variability in κ, with either reinforcing or offsetting effects. Outflows passing over industrial/shipping sectors often promoted less hygroscopic aerosol populations while cleaner residential outflows often favored more hygroscopic aerosol populations. These results highlight key aerosol changes following outflow passage, providing a foundation for improved understanding of aerosol-cloud interactions related to secondary convection initiation along or in the wake of outflow boundaries.

Thompson, Seth A. [Texas A & M Univ., College Stat↗

NASA NTRS1999

Coupled Land Atmosphere Predictability

We have designed and executed a set of predictability experiments, designed around the driest and wettest June soil moisture anomalies from a CCM3 simulation forced by observed SST for the period from 1958 through 1998. Each set contains an ensemble of five runs, all begun on June I radiation date. One set of these experiments helps to assess the extent to which the wet or dry conditions depend solely on the initial state of the atmosphere. The other set of experiments helps to assess the extent to which the wet or dry conditions depend sole on the initial state of the land surface. Preliminary analysis of these experiments suggests that the initial atmospheric state is more important than the initial state of the surface soil moisture in predicting the occurrence of wet or dry periods. These results suggest that when the atmosphere is inclined to generate dry surface conditions (through reduced moisture availability and increased evaporation) it matters little what initial levels of soil water are at; the soil will rapidly dry out. The ensemble forcing the ensembles with dry soil conditions, but utilizing 'normal' atmospheric conditions show little if any indication of the sharp reduction in soil moisture experienced in the control, indicating that the 'normal' atmospheric state is more important than the initial state of the soil moisture in predicting the occurrence of drought. Our work to date has documented the response of surface hydrologic variability, particularly over North America, to atmospheric forcing. While we have seen some suggestion that pre-existing surface anomalies can affect atmospheric circulation over this region, by far the strongest signal is the atmospheric anomalies leading those of soil moisture and surface energy balance changes. This is not an unexpected result since wintertime NA precipitation links to remote atmospheric forcing by ENSO are known to be statistically significant. While warm season links to remote forcing are more tenuous and not well explored, the present results encourage us to examine in more detail the SST forcing from the tropical and North Pacific.

Roads, John↗

NASA NTRS2012

Solar Spectral Irradiance and Climate

Spectrally resolved solar irradiance is recognized as being increasingly important to improving our understanding of the manner in which the Sun influences climate. There is strong empirical evidence linking total solar irradiance to surface temperature trends - even though the Sun has likely made only a small contribution to the last half-century's global temperature anomaly - but the amplitudes cannot be explained by direct solar heating alone. The wavelength and height dependence of solar radiation deposition, for example, ozone absorption in the stratosphere, absorption in the ocean mixed layer, and water vapor absorption in the lower troposphere, contribute to the "top-down" and "bottom-up" mechanisms that have been proposed as possible amplifiers of the solar signal. New observations and models of solar spectral irradiance are needed to study these processes and to quantify their impacts on climate. Some of the most recent observations of solar spectral variability from the mid-ultraviolet to the near-infrared have revealed some unexpected behavior that was not anticipated prior to their measurement, based on an understanding from model reconstructions. The atmospheric response to the observed spectral variability, as quantified in climate model simulations, have revealed similarly surprising and in some cases, conflicting results. This talk will provide an overview on the state of our understanding of the spectrally resolved solar irradiance, its variability over many time scales, potential climate impacts, and finally, a discussion on what is required for improving our understanding of Sun-climate connections, including a look forward to future observations.

Pilewskie, P.↗

DOE OSTI2025

Energy dependence of polarized 𝛾⁢𝛾 → 𝑒 + ⁢ 𝑒− in peripheral Au+Au collisions at $\sqrt{s_{NN}}$ = 54.4 and 200 GeV with the STAR experiment at RHIC

We report the differential yields at mid-rapidity of the Breit-Wheeler process (𝛾⁢𝛾 → 𝑒 + ⁢𝑒 − ) in peripheral Au+Aucollisions at $\sqrt{s_{NN}}$ = 54.4 and 200 GeV with the STAR experiment at the Relativistic Heavy Ion Collider (RHIC), as a function of energy $\sqrt{s_{NN}}$, 𝑒 + ⁢𝑒 − transverse momentum 𝑝 T , 𝑝$^{2}_{T}$, invariant mass 𝑀 𝑒⁢𝑒 , and azimuthal angle. In the invariant mass range of 0.4 < 𝑀 𝑒⁢𝑒 < 2.6GeV/𝑐 2 at low transverse momentum (𝑝 T < 0.15GeV/𝑐), the yields increase while the pair √⟨𝑝$^{2}_{T}$⟩ decreases with increasing $\sqrt{s_{NN}}$, a feature that is correctly predicted by the QED calculation. Here, the energy dependencies of the measured quantities are sensitive to the nuclear form factor, infrared divergence and photon polarization. The data are compiled and used to extract the charge radius of the Au nucleus.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

WIND Toolkit Long-Term Ensemble Dataset

WIND Toolkit Long-term Ensemble Dataset (WTK-LED), an updated version of the meteorological WIND Toolkit, is a meteorological dataset providing high-resolution time series, including interannual variability and model uncertainty of wind speed at every modeling grid point to indicate ranges of possible wind speeds. The data were produced using the Weather Research and Forecasting Model (WRF). The vertical grid used in WTK-LED includes many vertical layers in the atmospheric boundary layer to provide information of atmospheric quantities across the rotor layer of utility scale and distributed wind turbines. The WTK-LED includes: (1) Numerical simulations of wind speed and other meteorological variables covering the contiguous United States (CONUS) and Alaska, with high-resolution (5-minute [min], 2-kilometer [km]) data for 3 years (2018-2020): WTK-LED CONUS, WTK-LED Alaska. (2) Climate simulations from Argonne National Laboratory covering North America, including Alaska, Canada, and most of Mexico and the Caribbean islands. These simulations complement the new WTK-LED to offer a 4-km, hourly dataset covering 20 years (2001-2020): WTK-LED Climate. (3) Specific long-term, high-resolution offshore simulations have been conducted separately for the U.S. coasts, Hawaii, and the Great Lakes, leading to the 2023 National Offshore Wind dataset: NOW-23. The data for Hawaii include land-based data and are part of WTK-LED Hawaii. Because the accuracy of simulations from a mesoscale model, such as WRF, varies depending on the location and weather situation, and can reach up to several m/s for wind speed, we provide simulated wind speed uncertainty estimates to the community to be used in conjunction with the deterministic model simulations. This dataset was developed to satisfy a wide group of stakeholders across various wind energy disciplines, including but not limited to stakeholders in the distributed and utility scale wind industry, the new emerging airborne wind energy field, grid integration, power systems modeling, environmental modeling, and researchers in academia, and to close some of the gaps that current public datasets have. Based on our validation results to date, we suggest use cases and applications for each dataset of the WTK-LED as shown in "WTK-LED Use Cases" resource below.

Array↗

DOE OSTI2021

The Role of Hydrophobic Nodes in the Dynamics of Class A β-Lactamases

Class A β-lactamases are known for being able to rapidly gain broad spectrum catalytic efficiency against most β-lactamase inhibitor combinations as a result of elusively minor point mutations. The evolution in class A β-lactamases occurs through optimisation of their dynamic phenotypes at different timescales. At long-timescales, certain conformations are more catalytically permissive than others while at the short timescales, fine-grained optimisation of free energy barriers can improve efficiency in ligand processing by the active site. Free energy barriers, which define all coordinated movements, depend on the flexibility of the secondary structural elements. The most highly conserved residues in class A β-lactamases are hydrophobic nodes that stabilize the core. To assess how the stable hydrophobic core is linked to the structural dynamics of the active site, we carried out adaptively sampled molecular dynamics (MD) simulations in four representative class A β-lactamases (KPC-2, SME-1, TEM-1, and SHV-1). Using Markov State Models (MSM) and unsupervised deep learning, we show that the dynamics of the hydrophobic nodes is used as a metastable relay of kinetic information within the core and is coupled with the catalytically permissive conformation of the active site environment. Finally, our results collectively demonstrate that the class A enzymes described here, share several important dynamic similarities and the hydrophobic nodes comprise of an informative set of dynamic variables in representative class A β-lactamases.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Learning intermolecular forces at liquid–vapor interfaces

By adopting a perspective informed by contemporary liquid-state theory, we consider how to train an artificial neural network potential to describe inhomogeneous, disordered systems. Here, we find that neural network potentials based on local representations of atomic environments are capable of describing some properties of liquid-vapor interfaces but typically fail for properties that depend on unbalanced long-ranged interactions that build up in the presence of broken translation symmetry. These same interactions cancel in the translationally invariant bulk, allowing local neural network potentials to describe bulk properties correctly. By incorporating explicit models of the slowly varying long-ranged interactions and training neural networks only on the short-ranged components, we can arrive at potentials that robustly recover interfacial properties. We find that local neural network models can sometimes approximate a local molecular field potential to correct for the truncated interactions, but this behavior is variable and hard to learn. Generally, we find that models with explicit electrostatics are easier to train and have higher accuracy. We demonstrate this perspective in a simple model of an asymmetric dipolar fluid, where the exact long-ranged interaction is known, and in an ab initio water model, where it is approximated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Benchmarking Utility-Scale PV Operational Expenses and Project Lifetimes: Results from a Survey of U.S. Solar Industry Professionals

This paper draws on a survey of solar industry professionals and other sources to clarify trends in the expected useful life and operational expenditure (OpEx) of utility-scale photovoltaic (PV) plants in the United States. Solar project developers, sponsors, long-term owners, and consultants have increased project-life assumptions over time, from an average of ~21.5 years in 2007 to ~32.5 years in 2019. Current assumptions range from 25 years to more than 35 years depending on the organization; 17 out of 19 organizations surveyed or reviewed use 30 years or more. Levelized, lifetime OpEx estimates have declined from an average of ~$\$$35/kW DC -yr for projects built in 2007 to an average of ~$\$$17/kW DC -yr in 2019. Across 13 sources, the range in average lifetime OpEx for projects built in 2019 is broad, from $\$$13 to $\$$25/kW DC -yr. Operations and maintenance (O&M) costs—one component of OpEx—have declined precipitously in recent years, to $\$$5-8/kW DC -yr in many cases. Property taxes and land lease costs are highly variable across sites, but on average are—together—of similar magnitude. Other OpEx line items include security, insurance, and asset management. Given 2007-2009 values for not only project life and OpEx but also other drivers of the levelized cost of energy (LCOE, excluding the investment tax credit), the LCOE for utility-scale PV projects built from 2007 through 2009 averaged $\$$305 /MWh. Using 2019 values for all parameters yields an average LCOE of $\$$51 /MWh. The decline in LCOE from $\$$305 /MWh to $\$$51 /MWh was predominantly caused by reductions in up-front expenditures (and, to a much lesser extent, by changes in capacity factors, financing costs, and tax rates), but 9% ($\$$22 /MWh) of the overall decline is due to improvements in project life and OpEx. Project life extensions and OpEx reductions have had similarly sized impacts on LCOE over this period, at $\$$11 /MWh each. Had project life and OpEx not improved over the last decade, LCOE in 2019 would have instead been $\$$73 /MWh—43% higher. Given the limited quantity and comparability of previously available data on these cost drivers, the data and trends presented here may inform assumptions used by electric system planners, modelers, and analysts. The results may also provide useful benchmarks to the solar industry, helping developers and assets owners compare their expectations for project life and OpEx with those of their peers.

14 SOLAR ENERGY↗

DOE OSTI2023

Validation of LOCA2 and STAR-ESDM Statistically Downscaled Products

The National Climate Assessment (NCA) is the preeminent national report examining current and future risks posed by climate change. Countless agencies, policymakers, stakeholders and other end-users rely upon guidance from the NCA to plan for an uncertain future. These groups all depend on modern curated data, provided alongside the NCA, to quantify the impact of climate change on metrics of relevance for their decision processes. In its fifth iteration (NCA5), two statistically downscaled ensemble products, each providing data at grid spacing of approximately 5km over the contiguous United States, were selected to accompany the report. These include LOCalized Analogs version 2 (LOCA2) and Seasonal Trends and Analysis of Residuals Empirical-Statistical Downscaling Model (STAR-ESDM). Both data products are produced through a process known as statistical downscaling, where relatively coarse Global Climate Model (GCM) data is refined to locally relevant scales through the application of scientifically-supported empirical and algorithmic relationships. In support of the NCA effort, this report provides an independent validation of these two products against historical observations, with a focus on precipitation and near-surface temperature variables. Based on the results of this validation, several recommendations are provided related to the use of these data products. The structure of this report is as follows: In section 2, we review three gridded observational products that are used as part of our intercomparison. In section 3, we describe the two statistical downscaling techniques and their corresponding datasets that are the focus of this study. In section 4, the methodology we employ for validation is described. Section 5 provides results of the validation, which in turn motivate our recommendations on the use of these data products. A brief summary is provided in section 6.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1979

Comparison of quasi-specular radar scatter from the moon with surface parameters obtained from images

Quasi-specular radar data used to determine apparent surface roughness of geologic surfaces displays a variable wavelength dependence. The strongest changes with wavelength are observed in lunar maria, while scatter from lunar highlands is nearly wavelength independent. Commonly used, gently undulating surface models for electromagnetic scatter predict no wavelength dependence. Wavelength dependence occurs whenever a significant fraction of the surface has local radii of curvature comparable to the observing wavelength. This condition can be determined by comparison of the value of the integrated surface curvature spectrum with the radar wave number, multiplied by a constant that depends on the geometry. Variations in curvature statistics calculated from photogrammetric reduction of lunar images are consistent with the observed variations in quasi-specular scatter between wavelengths of 13 and 116 cm at the same locations. Variations in the strength of the wavelength dependence are correlated with the sizes of lunar craters that lie near the upper size limit for the local steady-state distribution. This correlation is also consistent with variations in the curvature spectrum calculated from crater size-frequency distributions.

Tyler, G. L.↗

NASA NTRS2018

A Markov Decision Process Framework for Optimal Airport Reconfiguration

The airport runway configuration is defined as a combination set of runways for arrivals and departures used at a point during operation of the airport. An optimal configuration of these runways depends on a number of factors, including traffic demand, wind magnitude and direction, other adverse weather conditions, and noise restrictions, among others. Based on the current state of these factors and predictions of traffic demand and weather conditions, runway configuration changes are made and coordinated between tower controller, other air traffic control facilities, pilots, and ground personnel. Reconfigurations can be quite disruptive to airport operations; minimizing their frequency and scheduling them well in advance is essential for mitigating some of the added workload for controllers and pilots. Unfortunately, deciding on an appropriate time to change is challenging for human decision makers. Not only do multiple factors need to be evaluated, but the uncertainty in their forecasts must also be considered. Previous optimization methods, such as mixed linear integer programming, have been proposed. Although these methods can reason over a large set of variables, they do not systematically handle the uncertainty associated with weather movement, traffic demands, and other variables. In this work, we introduce a Markov Decision Process (MDP)-based decision making framework which can reason effectively over the inherent uncertainties and make optimal decisions on if/when to change the airport configuration. In a prototype implementation, we present a single runway with three aircraft and utilize knowledge of the forecasted wind speed and direction to determine whether to keep or change the current runway configuration. Our aim through this work is to present a framework for airport reconfiguration which can be scalable to additional aircraft, multiple runways, and various input parameters. This technique will optimize the airport reconfiguration procedure by providing a proactive approach, optimizing not just at the next optimal opportunity for a reconfiguration based on varying atmospheric and traffic conditions in the terminal airspace, but also anticipating future necessary reconfigurations. This will eliminate the inefficiencies of frequent changes currently associated with runway reconfiguration procedures.

runway reconfiguration↗

NASA NTRS2018

Atmosphere-Ocean Coupled Data Assimilation Using NASA GEOS: Estimation of Air-Sea Interface State Variables

Air-sea interface variables, such as the skin Sea Surface Temperature (SST) are essential for atmosphere-ocean coupling. In the NASA GMAO Data Assimilation System (DAS), the skin SST and 3-D atmospheric state are jointly estimated [1]. This presentation is focused on the prior or background error covariance that is used in this analysis. The GEOS DAS uses an ensemble-variational assimilation strategy. In that, specification of a climatological background (CB) error covariance for SST relies on the NOAA's OI SST, with estimates of standard deviation and correlation length scales based on weekly analyses of the bulk SST at 1 degree resolution. However, present analysis system is striving to resolve SST diurnal variability with six hourly analyses and assimilates a vast number of in-situ and satellite observations. The first part of this presentation re-derives the CB error covariance using OSTIA SST analyses and illustrates the impact of this update on assimilating satellite observations. In a hybrid assimilation system the CB error covariances are appended with a flow-dependent background error covariance estimate implied by the underlying ensemble. The second part of this presentation refers to: a. treatment of the skin SST in the ensemble members, b. corresponding ensemble spread, and c. impact of these additions on the data assimilation system. [1] S. Akella, et al. (2017), doi:10.1002/qj.2988

Akella, Santha↗

DOE OSTI2020

Sensitivity Effects of High Temperature Overhead Conductors to Line Rating Variables

For traditional overhead transmission lines, the maximum allowable conductor operating temperature on ASCR, AAC, or AAAC lines for determining the static line ratings can be quite low, typically well under 100 C. At these low temperatures, the primary driver for determining static line ratings are assumptions made about the wind speed and direction and the ambient temperature. Assumptions for solar loading and emitted thermal radiation, driven by line emissivity and absorptivity, are secondary factors, and only provide small changes to the static ratings. However, when considering newer high temperature conductors such as ACSS, ACCR, ZTACIR, and ACCC® the maximum allowable conductor temperatures can be significantly higher, up to 250° C. This higher temperature shifts the importance of the emissivity assumptions of the overhead transmission line, due to the nature of the T4 dependence of the radiative heat loss compared to the T1 dependence of the convective heat loss, as well as shifting the dependency on local wind conditions. Typical assumptions in the United States for the emissivity/absorptivity of overhead transmission lines in determining the static ratings are to use a value of 0.5 for both parameters or set a value in the range of 0.7 to 0.9. Recent experimental tests at EPRI have shown that higher emissivity aging assumptions may not be valid in some regions of the US, and that actual values may be much lower (0.25-0.45) than older studies have predicted (0.8-0.9). Here, the assumptions of different US Regional Transmission Operators (RTOs) and different utilities within RTOs are examined for the static ratings for traditional lower operating temperatures up to 100° C as well as with the higher maximum temperature conductors associated with newer conductor designs at 250° C. The sensitivity of the static ratings is examined at both lower and higher temperature conductors with respect to overhead line emissivity and absorptivity, wind speed, wind direction, and solar loading, and ambient temperature. It is shown that the effects of the conductor absorptivity, solar flux and ambient temperature on the static rating are increased when the transmission line has a lower maximum conductor temperature. The effects of the conductor emissivity, wind direction and wind speed have increased effects on the static rating when the transmission line has a higher maximum conductor temperature. In addition, example weather data is used to calculate the conductor temperature with different line emissivity and absorptivity assumptions. This shows the effect of assumptions made for emissivity only has a minor impact for low temperature lines, but for high temperature transmission lines can cause a 150-200° C temperature swing.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

The three major axes of terrestrial ecosystem function

The leaf economics spectrum and the global spectrum of plant forms and functions revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability. Here we derive a set of ecosystem functions from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗