Results for “varying data rates” — Page 15

DOE OSTI2021

What is Important? Morphological Asymmetries are Useful Predictors of Star Formation Rates of Star-forming Galaxies in SDSS Stripe 82

The morphology and structure of galaxies reflect their star formation and assembly histories. We use the framework of mutual information (MI) to quantify the interdependence among several structural variables and to rank them according to their relevance for predicting the specific star formation rate (SSFR) by comparing the MI of the predictor variables with the SSFR and penalizing variables that are redundant. We apply this framework to study ~3700 face-on star-forming galaxies (SFGs) with varying degrees of bulge dominance and central concentration and with stellar mass $M$ * ≈ 10 9 $M$ ⊙ at redshift $z$ = 0.02–0.12. We use the Sloan Digital Sky Survey (SDSS) Stripe 82 deep $i$-band imaging data, which improve measurements of asymmetry and bulge dominance indicators. We find that star-forming galaxies are a multiparameter family. In addition to $M$ * , asymmetry emerges as the most powerful predictor of SSFR residuals of SFGs, followed by bulge prominence/concentration. Star-forming galaxies with higher asymmetry and stronger bulges have higher SSFR at a given $M$ * . In conclusion, the asymmetry reflects both irregular spiral arms and lopsidedness in seemingly isolated SFGs and structural perturbations by galaxy interactions or mergers.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Case Study: Hybrid Carbon Conversion Using Low-Carbon Energy Sources in Coal-Producing States

The demand for more carbon efficient power sources and a decrease in natural gas prices has decreased the desire for coal power. This decrease in demand has led to massive job losses in coal mining regions over the past decade. The purpose of this project is to develop a hybrid energy system utilizing both a coal power plant and advanced reactor, which is competitive with natural gas by improving on profitability and decreasing carbon emissions. This report details the problem with a summary of the impact on the coal industry and the availability of renewable energy sources in the Appalachian region. Because of the geography of the region, variable renewable energy sources are not available without significant size and siting restrictions. However, biomass in the form of wood waste is abundant and can be used as a carbon neutral energy source. Combining biomass and coal processing, in addition to thermal power plants, can increase system profits and efficiency by providing peaking power and conversion opportunities for secondary markets. The electric load is based on publicly available demand data from Appalachian Power, which services the western Virginia and southern West Virginia in the Appalachian region. The demand information is combined by service, normalized, and scaled to an average demand of 1000 kW, which will be the basis for sizing the hybrid energy system. A traditional screening curve analysis for a coal plant and advanced reactor shows that the least cost design varies significantly based on the assumed discount rate and capital recovery period. An optimization program to size the design in TEAL based on the load curve gives 10 optimal designs, all with a negative resulting net present value (NPV) and a coal plant capacity of less than 15%. Including profits from selling captured carbon at a flat rate results in a positive NPV; however, the coal capacity factor only increases to about 40%. There are limitations with this optimization as well since the price of CO2 is likely to decrease as more is sold to the conversion market. The suggested design will combine coal power, an advanced reactor, and coal and biomass coprocessing to produce a variety of products that can be sold to the conversion market while increasing system efficiency. The analysis of conversion pathways for coal and biomass reveals that multiple options will need to be included in the analysis to produce the optimal system design. Three systems will be optimized and compared to determine the best design based on the figures of merit of total NPV and cost of carbon avoided. The first system will include a coal power plant and an advanced reactor that will sell electricity to the grid to meet demand and sell captured carbon to the conversion market. The second system adds a high-temperature steam electrolysis plant, which will utilize electricity during times of low demand to produce hydrogen and sell it to the conversion market. The third system adds biomass and coal processing with options for hydrocarbon oils, syngas to be produced for the conversion market, and electricity generation to power components within the system or provide peaking power. This analysis will be based on a new approach that combines traditional screening curve methods with a dispatch algorithm that optimizes the system based on the opportunity cost of different production options. The resulting optimization algorithm should provide results with less processing time than HERON’s decision tree method. The results from this analysis will determine an optimal design and reinforce the benefits of coal power when used in a hybrid energy system. The initial results show that the addition of a secondary market for carbon sales could result in a positive NPV and increases the capacity factor of the coal plant as compared to a design with only sales to the electricity market. The addition of more markets and additional coal consumption from biomass coprocessing could increase NPV further, replace carbon in other markets through the sale of biomass-derived hydrocarbons, and demonstrate the value of coal power technology.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2021

Kinetic and temperature sensitivity properties of soil exoenzymes through the soil profile down to one-meter depth at a temperate coniferous forest (Blodgett, CA)

This dataset contains data on kinetic and temperature sensitivity parameters of the exoenzymes β-glucosidase (BG), leucine/leucyl aminopeptidase (LAP) and acid phosphatase (AP) across a soil profile down to 90 cm depth at Blodgett forest, CA, USA. These data were generated to determine if kinetic and thermal properties of microbial exoenzymes involved in organic matter decomposition varied with soil depth, following variation in soil properties and microbial communities, as part of the study: Alves et al. (2021). Kinetic Properties of Microbial Exoenzymes Vary with Soil Depth but Have Similar Temperature Sensitivities Through the Soil Profile. Frontiers in Microbiology 12:3618. https://doi.org/10.3389/fmicb.2021.735282. This research was performed within the framework of the TES Belowground Biogeochemistry SFA project, in particular association with a long-term field warming experiment of the whole soil profile at Blodgett forest. Samples for this work were collected from locations representative of the field experimental plots. Potential enzyme activity rates were measured using laboratory fluorometric assays with soils collected at 0-10, 10-20, 30-40, 50-60, 60-70 and 80-90 cm deep in biological triplicates (i.e., three soil cores collected at different representative locations). Assays with each soil were conducted over a gradient of eight substrate concentrations per enzyme, and incubated at 4, 10, 16, 25, 35 or 50°C. Enzyme Michaelis-Menten kinetics were modeled over the eight substrate concentrations at each temperature, and the temperature sensitivity of the kinetic parameters was modeled over the six temperatures using linear Arrhenius/Q10 and non-linear Macromolecular Rate Theory (MMRT) models. The dataset includes the fully processed enzyme activity rate data used to model Michaelis-Menten kinetics, calculated kinetic and temperature sensitivity parameters, and basic soil and microbial biomass chemistry for each sample. All data is provided for each individual biological replicate, and kinetic and temperature sensitivity parameters are also provided as means of the biological replicates. The dataset also includes all raw measurement data and code used to parse, combine and perform the analyses described by Alves et al. (2021). For file descriptions, see the file-level metadata files: “enzymes_dataProcessed_flmd.xlsx” (processed data); “enzymes_dataRaw_flmd.xlsx” (compressed raw data and metadata); and “enzymes_code_flmd.xlsx” (compressed code). The experimental design, list of parameters measured, soil and microbial biomass chemistry data, and means of biological replicates for kinetic and temperature sensitivity parameters are also provided as human-readable tables in file “enzymeTraits_design_results_tables.xlsx”.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Characterization and Analysis of the Energy-Reporting Accuracy of Connected Devices

Emerging energy-efficient building systems increasingly exhibit greater functionality, often requiring multiple operating modes (e.g. white-tunability for lighting products and data traffic for devices with networked, integrated sensors). This increased functionality makes energy consumption estimates more complex. Given that these functions consume energy, the energy performance of such building systems is dependent on what operating modes they use and how much time they spend in each mode. Devices and systems that can report their own energy consumption mitigate this energy-performance uncertainty. This study explores the energy-reporting accuracy of market-available connected electrical outlets. The study considers two residential-market products (five units each, one outlet per unit) and three commercial-market products (two units each, 18 to 24 outlets per unit) with the ability to report power drawn and/or energy consumed by devices connected to their receptacles. The products were purchased through typical market channels. Pacific Northwest National Laboratory (PNNL) conducted testing in December 2018 at its Connected Lighting Test Bed (CLTB), using a custom-developed test setup and method adapted from industry standards. The setup collected energy-consumption data reported by the outlet devices under test (DUTs) at one-minute intervals and compared that data with measurements taken by a reference meter over a range of test conditions. The residential products reported power draw but not interval or cumulative energy consumption. The commercial products reported both power draw and cumulative energy consumption. Relative reporting error (RRE) was calculated for all measurements, and analysis of the results revealed variations across devices and test conditions. The total number of measurements (50 for each residential product, 60 for each commercial product) offers an appreciable comparison of performance at the make/model level. The average RRE of the residential products derived from reported power draw was -0.02% and -1.20%. The average RRE for two of the three the commercial products derived from reported power draw was worse than those of the residential products (-2.40%, -2.72%, -0.36%). The internal integration of power over time, used to calculate cumulative energy consumption, typically occurs at current and voltage sampling rates much higher than once per minute. This suggests that the average commercial-product RRE derived from reported energy consumption should be very consistent and better than performance based on reported power draw. However, the RRE derived from reported energy consumption varied significantly across the three makes of commercial-market products and was uniformly less accurate than performance based on reported power draw. Subsequent analysis identified a number of root causes for this decrease in performance, most of which were related to reporting resolution. The goals of this study are to generate awareness of building systems capable of reporting their own energy consumption, further interest in the value of energy data for a variety of uses, draw attention to how the accuracy of reported metrics can be characterized, and quantify the performance variation found in marketavailable products. The results of this study and subsequent related work may be relevant to stakeholders in industry-specification and standards-development organizations. The methods this study employs could inform test and measurement procedures and performance classifications for connected outlets, lighting products, and other building systems capable of reporting their own energy consumption. The study concludes with stakeholder recommendations, including the following: • Energy-reporting device and system manufacturers developing products that report energy consumption should characterize the accuracy of reported metrics using a reference meter calibrated by an independent laboratory that was accredited by an ILAC MRA signatory (and whose scope of accreditation explicitly covers energy measurement), and should include this information on product data sheets. • Standards and specification development organizations should develop application-specific performance classifications that end users can understand and relate to their energy-data use needs (e.g., 2% accuracy class for utility streetlight energy billing needs, or 10% accuracy class for ESCO performance verification needs). • Current or potential owners, operators, and specifiers of energy-reporting building systems should rigorously analyze the dependency of current and planned energy-data use cases on accuracy, noting in particular the dependence (or lack thereof) on relative vs. absolute accuracy, and on trueness vs. precision (i.e., repeatability), and should communicate use-case needs to industry standards and specification organizations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Characterization and Analysis of the Energy-Reporting Accuracy of Connected Devices

Emerging energy-efficient building systems increasingly exhibit greater functionality, often requiring multiple operating modes (e.g. white-tunability for lighting products and data traffic for devices with networked, integrated sensors). This increased functionality makes energy consumption estimates more complex. Given that these functions consume energy, the energy performance of such building systems is dependent on what operating modes they use and how much time they spend in each mode. Devices and systems that can report their own energy consumption mitigate this energy-performance uncertainty. This study explores the energy-reporting accuracy of market-available connected electrical outlets. The study considers two residential-market products (five units each, one outlet per unit) and three commercial-market products (two units each, 18 to 24 outlets per unit) with the ability to report power drawn and/or energy consumed by devices connected to their receptacles. The products were purchased through typical market channels. Pacific Northwest National Laboratory (PNNL) conducted testing in December 2018 at its Connected Lighting Test Bed (CLTB), using a custom-developed test setup and method adapted from industry standards. The setup collected energy-consumption data reported by the outlet devices under test (DUTs) at one-minute intervals and compared that data with measurements taken by a reference meter over a range of test conditions. The residential products reported power draw but not interval or cumulative energy consumption. The commercial products reported both power draw and cumulative energy consumption. Relative reporting error (RRE) was calculated for all measurements, and analysis of the results revealed variations across devices and test conditions. The total number of measurements (50 for each residential product, 60 for each commercial product) offers an appreciable comparison of performance at the make/model level. The average RRE of the residential products derived from reported power draw was -0.02% and -1.20%. The average RRE for two of the three the commercial products derived from reported power draw was worse than those of the residential products (-2.40%, -2.72%, -0.36%). The internal integration of power over time, used to calculate cumulative energy consumption, typically occurs at current and voltage sampling rates much higher than once per minute. This suggests that the average commercial-product RRE derived from reported energy consumption should be very consistent and better than performance based on reported power draw. However, the RRE derived from reported energy consumption varied significantly across the three makes of commercial-market products and was uniformly less accurate than performance based on reported power draw. Subsequent analysis identified a number of root causes for this decrease in performance, most of which were related to reporting resolution. The goals of this study are to generate awareness of building systems capable of reporting their own energy consumption, further interest in the value of energy data for a variety of uses, draw attention to how the accuracy of reported metrics can be characterized, and quantify the performance variation found in marketavailable products. The results of this study and subsequent related work may be relevant to stakeholders in industry-specification and standards-development organizations. The methods this study employs could inform test and measurement procedures and performance classifications for connected outlets, lighting products, and other building systems capable of reporting their own energy consumption. The study concludes with stakeholder recommendations, including the following: • Energy-reporting device and system manufacturers developing products that report energy consumption should characterize the accuracy of reported metrics using a reference meter calibrated by an independent laboratory that was accredited by an ILAC MRA signatory (and whose scope of accreditation explicitly covers energy measurement), and should include this information on product data sheets. • Standards and specification development organizations should develop application-specific performance classifications that end users can understand and relate to their energy-data use needs (e.g., 2% accuracy class for utility streetlight energy billing needs, or 10% accuracy class for ESCO performance verification needs). • Current or potential owners, operators, and specifiers of energy-reporting building systems should rigorously analyze the dependency of current and planned energy-data use cases on accuracy, noting in particular the dependence (or lack thereof) on relative vs. absolute accuracy, and on trueness vs. precision (i.e., repeatability), and should communicate use-case needs to industry standards and specification organizations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Measurement of Photoneutrons from Depleted Uranium and Comparison Study Using MCNPX-PoliMi

High-energy photon interrogation is a nondestructive technique that is used to detect special nuclear materials and characterize nuclear waste. The development of such systems is complex and requires Monte Carlo simulations to optimize system performance. Monte Carlo simulations rely on various scattering, absorption, and photonuclear cross-section data. While the scattering and absorption cross-section data have been extensively studied and validated with experiments, the results obtained from photonuclear simulations are often found to underpredict measured results, indicating uncertainties in the photonuclear cross sections themselves. Thus, there is a need for new measured results that can be used to quantify underpredictions in simulations using photonuclear cross-section data. In the present work, we interrogated depleted uranium with a 9-MV electron linac and detected photoneutrons with trans-stilbene organic scintillators. The measurement of photoneutrons with organic scintillators is challenging due to the presence of the intense photon flux, which causes issues such as pulse pile-up, detector saturation, and poor signal-to-background ratio. To mitigate these challenges, we used iron and polyethylene shielding of varying thicknesses around the depleted uranium target and a neural network–based digital pulse processing algorithm to recover neutron and photon information from piled-up events. Our goal was to compare the measured photoneutron count rate with the simulated rate obtained using the MCNPX-PoliMi transport code. For a light output window of 0.28 to 2.67 MeVee (1.66- to 6.85-MeV proton recoil energy), we found that the simulated count rate obtained using the ENDF/B-VII photonuclear cross-section library underpredicts the measured rate by 32.8% 3.2%. Additionally, we compared the simulated and measured photoneutron light output distributions. For the least thicknesses of shielding, the simulation was found to underpredict measurements in the 0.70- to 2.67-MeVee light output window. For the greatest thicknesses of shielding, the simulation was found to underpredict the measurement across the entire light output window of 0.28 to 2.67 MeVee.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2026

Rapid neutron and gamma-ray source localization using machine learning

Rapid localization of radiation sources is critical for applications including nuclear emergency response, safeguards, and security. However, conventional imaging systems such as neutron scatter cameras and Compton cameras depend on rare coincidence events, which often result in long acquisition times. In this work, we address the challenge of rapid source localization by developing a machine learning approach to predict the direction of a single radiation source using only count rates from an array of neutron and gamma-ray detectors. The proposed model is a fully connected neural network (FCNN) trained using Monte Carlo simulation data from a 252 Cf source. The model hyperparameters are optimized with a small set of routine 252 Cf measurements. We benchmarked the performance of the trained and optimized machine learning model using additional 252 Cf , 137 Cs , and PuBe measurements under laboratory conditions with varying source-detector configurations. For these measurements, the machine learning model achieved a mean localization error smaller than 30° with 3 x 10 3 system counts, corresponding to 8 s measurement time for the imaging system used in this work. In this low-statistics regime, the method outperformed traditional scatter-based imaging by more than 75% in localization accuracy for the evaluated measurement configurations. These results demonstrate that a machine learning-based approach can significantly reduce the time required for accurate single-source localization, providing a robust and computationally efficient alternative to traditional imaging systems in time-critical nuclear security and emergency response scenarios.

Gamma-ray imaging↗

DOE OSTI2021

Burning velocities of R-32/O 2 /N 2 mixtures: Experimental measurements and development of a validated detailed chemical kinetic model

This work entails characterizing the flammability of the refrigerant R-32 (CH 2 F 2 ) by both experimental measurements and modeling. Burning velocities S u were measured using a constant-volume spherical-flame method for R-32/O2/N 2 mixtures with O 2 /N 2 ratios ranging from 21% (synthetic air) to 40%, pressures of (1 to 3) bar, and equivalence ratios $\phi$ of (0.8 to 1.3). Based on a critical assessment of available data, and extended by our own calculations, a detailed chemical kinetic model was developed and key reactions determined using reaction path and sensitivity analyses. Initiation and combustion were identified as distinct kinetic regimes and burning velocities were found to be controlled by two primary reactions: unimolecular decomposition of CH 2 F 2 → CHF + HF and the subsequent reaction, CHF + O 2 → CHFO + O, the latter reaction initiating the radical chain propagating and branching by producing O atoms. Sensitive rate constants in the kinetic model were critically adjusted within their uncertainties and current knowledge bounds to best fit the experimental burning velocities. We found that rate constants in the model could be adjusted to match a given experimental S u for specific conditions (O 2 loading, P, T, $\phi$). This, however, then fixes predicted burning velocities for other all conditions within (3 to 4)% if physically realistic rate parameters are maintained. Thus, the entire set of experimental data is fit, not just to particular conditions. Relative random uncertainties in the experimental Su measurements were (4 to 6)%, but assumptions made for thermal radiation lost by the burned gas in the spherical-flame experiments add an additional systematic uncertainty. Systematic differences between the limiting cases of adiabatic (no thermal radiation lost) and optically-thin (all thermal radiation lost) varied significantly with conditions and ranged from (4 to 30)% at high to low velocities, respectively, translating into uncertainties of (2 to 15)% considering the average of two limiting cases. Comparison of experimental and kinetically modeled Su values suggests that the burned gas tends towards the optically-thin limit at the lowest pressures and fuel loadings and toward the adiabatic limit at the highest pressures and loadings. We tested and found support for this conclusion with a detailed analysis as a function of all the conditions (T, P, % O 2 , $\phi$). This behavior appears to transition from optically-thin to adiabatic as the density of the initial fuel increases, which results in increased CO 2 in the burned gas and thus increased absorption of the thermal radiation (consistent with the Beer-Lambert Law). The validated detailed model based on evaluated kinetics is shown to accurately predict burning velocities for R-32 O 2 /N 2 mixtures over a wide range of conditions and provides a reliable basis for extrapolation to other conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations

Polyhydroxyalkanoates (PHAs) have emerged as a promising class of biosynthesizable, biocompatible, and biodegradable polymers to replace petroleum-based plastics for addressing the global plastic pollution problem. Although PHAs offer a wide range of chemical diversity, the structure–property relationships in this class of polymers remain poorly established. In particular, the available experimental data on the mechanical properties is scarce. In this contribution, we have used molecular dynamics simulations employing a recently developed forcefield to predict chemical trends in mechanical properties of PHAs. Specifically, we make predictions for Young’s modulus, and yield stress for a wide range of PHAs that exhibit varying lengths of backbone and side chains as well as different side chain functional groups. Deformation simulations were performed at six different strain rates and six different temperatures to elucidate their influence on the mechanical properties. Our results indicate that Young’s modulus and yield stress decrease systematically with increase in the number of carbon atoms in the side chain as well as in the polymer backbone. In addition, we find that the mechanical properties were strongly correlated with the chemical nature of the functional group. The functional groups that enhance the interchain interactions lead to an enhancement in both the Young’s modulus and yield stress. Finally, we applied the developed methodology to study composition-dependence of the mechanical properties for a selected set of binary and ternary copolymers. Overall, our work not only provides insights into rational design rules for tailoring mechanical properties in PHAs, but also opens up avenues for future high throughput atomistic simulation studies geared towards identifying functional PHA polymer candidates for targeted applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

A data-driven framework for predicting machining stability: employing simulated data, operational modal analysis, and enhanced transfer learning

Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. Addressing this challenge requires a reliable predictive model that can accommodate the complex dynamics of various machining scenarios. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets and employing advanced techniques such as operational modal analysis (OMA), enhanced transfer learning (TL), and receptance coupling substructure analysis (RCSA). By integrating these methodologies, the framework effectively classifies and predicts chatter across diverse operational modes, achieving robust and accurate outcomes. Our model utilizes a Random Forest (RF) classifier trained with the comprehensive dataset, which demonstrates substantial improvements in both predictive accuracy and robustness. Specifically, the RF model achieved an accuracy rate of 85%, an area under the curve (AUC) of 0.90, and an F1 score of 0.88, underscoring its capability to adapt to varying machining configurations. These results highlight the framework’s potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining parameters. In conclusion, this research thus offers a significant advancement in predictive maintenance for machining processes, enabling more stable and efficient manufacturing operations.

42 ENGINEERING↗

DOE OSTI2024

Rheology characterization of ionic liquids under high pressure and high temperature

Ionic liquids (ILs) are liquid salts that exist at or below ambient temperatures and are composed of ion pairs. They offer promising alternatives to toxic, hazardous, highly flammable, and volatile solvents in various applications such as solution preparation, dispersion, gel formation, composites, and polymer melts. ILs possess unique and interesting characteristics, including excellent chemical and thermal stability and low vapor pressures. Understanding the rheological properties of ILs is essential to optimizing IL performance. This paper presents a comparative analysis of the rheological properties of two ionic liquids, Nsingle bondHexylpyridinium tetrafluoroborate (HPyBF4) and Nsingle bondHexylpyridinium bromide (HPyBr), under different shear rates, temperatures, and pressures. Rheological measurements were performed under varying controlled pressure and temperature conditions. The experimental investigation covered a pressure range of 689–12,411 kPa [100–1800 psi] and a temperature range from room temperature up to 522 kelvin (K) [480°F]. The primary objective is to explore and compare the flow behavior and viscoelastic characteristics of HPyBF4 and HPyBr under high-pressure and high-temperature conditions. The experimental data showed that HPyBF4 and HPyBr exhibited shear-thinning behavior, and pressure had an insignificant effect on rheology compared to the temperature effect. Under the same testing conditions, HPyBr showed higher shear stress and viscosity than HPyBF4. This research significantly contributes to the improved understanding of the rheological behavior of these specific ionic liquids and their suitability for diverse industrial and scientific applications, particularly in high-pressure and high-temperature environments.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Regolith, rock and fluid distributions at the Upper Colorado River Basin via a multicomponent seismic imaging approach

In October 2018, we conducted an exploratory seismic survey of the East River Watershed Function Science Focus Area, near Crested Butte, CO. Our survey goals were to map seismic bedrock, regolith and sediment properties and relate them to geomorphic and hydrologic processes. We acquired ~12 km of seismic data along roads with a 72-channel, 1.25m spaced streamer, an average shot spacing of 2.5m and an average acquisition rate of 3 km/day. We also obtained ~3km of planted geophone data with geophone and shot spacing between 2.5 and 5 meters and an acquisition rate of 0.5 km/day. Vp results show a sharp transition between regolith and bedrock along hillslopes suggesting a similarly abrupt change in porosity. Vp anisotropy in the Mancos Shale is observable in the lower reaches of the watershed, but is absent in regions near crystalline intrusions where contact metamorphism has likely modified pore structure within the shale. Alluvium thickness varies along the river axis from 0 to > 20 meters reflecting variations in depositional processes as function of stream gradient. We show that when strong discontinuities at the regolith base are present, inhomogeneous waves that travel along subsurface interfaces are generated and can be measured on vertical component geophones. We present a summary manuscript in the report appendix.

58 GEOSCIENCES↗

DOE OSTI2025

Understanding Soil Microbial Sources of Nitrous Acid and their Effect on Carbon-Nitrogen Cycle Interactions

Descriptions of soil emissions of reactive nitrogen (NO y ) in climate models are underdeveloped or non-existent, due to the fact that details of the mechanisms leading to nitrous acid (HONO) and nitrogen di-oxide (NO 2 ) formation in soil are lacking. This represents a major gap in our understanding of a significant land-atmosphere interaction that prevents us from scaling these processes from the laboratory scale to the ecosystem and global scales. There is a critical need to include these mechanisms into climate models since NO y controls the oxidative capacity of the atmosphere and the lifetime of greenhouse gases and the rate of secondary aerosol formation that directly and indirectly affect climate. The first objective of this proposal is to conduct laboratory and field measurements of NO y fluxes from diverse soil types and determine the mechanism of biogenic NO y formation. The working hypothesis based on preliminary data is that soil HONO and NO 2 is ultimately derived from ammonia-oxidizing archaea (AOA) and bacteria (AOB) that are widespread, but whose abundance varies across ecosystems. In the case of NO 2 , reactive oxygen species derived from iron-containing minerals and heterotrophic bacteria drive NO-to-NO 2 conversion. The approach is to link soil fluxes of HONO and NO 2 to AOA, AOB, and other heterotrophs using a combination of laboratory and field experiments, isotopic analysis, and molecular techniques to address how variability in land surfaces and edaphic properties impact emissions. In addition, we will determine the effect of HONO and NO 2 on nitrogen immobilization and the photo-oxidative capacity of soil. The working hypothesis is that HONO uptake in soil will be a source of nitrosonium and hydroxyl radical that will lead to thermal- and photodegradation of soil organic matter to CO 2 and CO, and the incorporation of N in soil organic matter. We will use surface-sensitive mass spectrometry techniques and gas phase detection to study N-immobilization in soil and subsequent enhancements in reactivity that lead to decomposition of organic matter. The results will be used to reduce the uncertainty in projections from the Community Earth System Model (CESM) stemming from inaccurate representations of soil NO y emissions. The proposed research is significant because, in addition to demonstrating new mechanisms of NO y formation and loss, it will be a crucial first step towards modeling the land-atmosphere exchange of HONO and NO 2 in the CESM. Improved model treatment of land-air exchange of NO y is key for understanding feedbacks between human activity and climate, and addressing societal concerns about the fate of N and C emitted to the atmosphere.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Density measurements for the National Ignition Facility (NIF) opacity platform

The Opacity Platform on the National Ignition Facility (NIF) has been developed to measure opacities at varying densities and temperatures relevant to the solar interior and thermal cooling rates in white dwarf stars. The typical temperatures reached at NIF range between 150 and 210 eV, which allow these measurements to be performed experimentally. The captured opacities are crucial to validating radiation-hydrodynamic models that are used in astrophysics. The NIF opacity platform has a unique new capability that allows in situ measurement of the sample expansion. The sample expansion data are used to better understand the plasma conditions in our experiments by inferring the sample density throughout the duration of the laser drive. Here, we present the details of the density measurement technique, data analysis, and recent results for Fe and MgO.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Lightning and Radar Measures of Mixed-Phase Updraft Variability in Tracked Storms during the TRACER Field Campaign in Houston, Texas

Properties of 7488 thunderstorms are summarized for June–September 2022 during the Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign Houston, Texas, using polarimetric weather radar and VHF 3D Lightning Mapping Array data. Automated tracking of storms linked each instrument’s measurements to a data-defined, time-evolving storm footprint. Within each storm, the depth and magnitude of episodic columns of radar differential reflectivity and specific differential phase quantified the prevalence of updrafts that activated mixed-phase precipitation pathways. Lightning measurements further distinguished the degree of rimed precipitation formation: the fraction of tracks with lightning varied from day to day and cells with lightning had stronger polarimetric columns. Track-level correlation of the lightning flash rate with radar polarimetric measures had substantial spread, showing that lightning provides an additional signal of mixed-phase precipitation processes that can complement future studies of thermodynamic and aerosol controls on cloud microphysics in the Houston region.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A first principles framework to predict the transient performance of latent heat thermal energy storage

Thermal energy storage (TES) is increasingly recognized as an essential component of efficient Combined Heat and Power (CHP), Concentrated Solar Power (CSP), Heating Ventilation and Air Conditioning (HVAC), and refrigeration as it reduces peak demand while helping to manage intermittent availability of energy (e.g., from solar or wind). Latent Heat Thermal Energy Storage (LHTES) is a viable option because of its high energy storage density. Parametric analysis of LHTES in terms of dimensionless numbers is highly desired as a tool to model LHTES systems. One approach is to develop a model equation so as to minimize the error between the model and data obtained from experiments or simulations. While this approach can produce an accurate correlation applicable within the range of data used for its creation, it does not provide physical understanding of the rate-limiting process controlling the transient behavior of the device. In this paper we present an alternative approach whereby the potential rate-limiting processes are identified from first principles and then the key process is determined as a function of time as a LHTES device is charged. For example, in a simple geometry, the melt-fraction can be expected to vary linearly in time if the heat transfer rate is limited by natural convection of the phase changing material and we show it scales with the PCM Grashof number as $Gr^1_p$ and PCM Prandtl number as $Pr_p^{(1/3)}$. On the other hand, if surface area of solid PCM limits the heat transfer rate, the melt fraction increases asymptotically to reach full melting. The existence of these linear and asymptotic regions and the $Gr^1_pP r^{1/3}_p$ shape of the melt fraction curve is verified using our database of 64 simulations. Of practical importance in designing LHTES devices is the melt fraction at which the heat transfer rate ceases to be limited by convection, after which the heat storage rate deteriorates. For our geometry, this is found to be about 90%. This test case of our methodology shows the value of our approach, that predicting heat storage rate based on the rate-limiting physical phenomenon as a function of time is an effective approach to modeling LHTES devices.

25 ENERGY STORAGE↗

DOE OSTI2026

Understanding the Costs and Barriers of Residential Electrical Panel and Service Upgrades

Upgrading electrical panels and services in U.S. homes represents a significant barrier to building modernization, imposing considerable costs, delays, and procedural uncertainties on homeowners, contractors, utilities, and building departments. Although cost databases document individual infrastructure activities, they rarely capture the integrated, whole-project perspective required to understand how electrical upgrades are planned, executed, and regulated. Existing research is often geographically constrained, narrowly scoped, or derived from limited samples, leaving significant knowledge gaps unaddressed. This study examines the timelines and costs associated with residential electrical panel and service upgrades using data from a national cross- sectional survey conducted in summer 2025. The survey captured responses from 140 stakeholders across 34 states, including building industry professionals, utility staff, and building department staff. Findings indicate that project duration and cost increase substantially with building size though patterns vary by infrastructure type. Most single-family and small multifamily projects were completed within 30 days, whereas medium and large multifamily buildings show longer and less predictable timelines. Service rating increases modestly extend project duration, but building type and size were the primary determinants of delays. Cost patterns were similar: customer-side equipment and panel replacements scale predictably with building size, while utility-side costs are highly variable, often triggered by threshold-driven infrastructure upgrades such as transformer replacements. Permit fees vary considerably between jurisdictions but generally represent a minor proportion of total project costs. These findings identify electrical upgrades as a significant barrier to residential building costs and highlight the need for improved stakeholder coordination, streamlined permitting, and strategies that reduce upgrade requirements while maintaining safety and code compliance.

Casquero-Modrego, Nuria↗

DOE OSTI2026

Search for HH → bbτ⁺τ⁻ Using Run 3 Scouting Data Analyze b-tagging and tau-tagging Performance with Unified Particle Transformer

B-tagging and tau-tagging performances play an important role in the search for the rare event HH → bbτ⁺τ⁻. A transformer-based neural network, Unified Particle Transformer, is applied for both tagging tasks, and Run 3 proton–proton collision scouting data at center-of-mass energy of 13.6 TeV is used. The scouting data stream accepts events at a much higher rate compared to traditional triggers, but stores only the objects reconstructed in the trigger, no low-level detector information. Therefore, existing taggers trained for the offline event reconstruction cannot be used. Analysis of the SoftMax plots, ROC/AUC curves, confusion matrix, accuracy and losses are used to evaluate model performance. Specifically, the tagging efficiency of the signal and misidentification probability across multiple background processes are compared for varying working points. Different training samples with distinct distributions of jet flavors are utilized and related model performances are analyzed. Interpretability methods, such as Integrated Gradients, may further be applied to study the input features’ influence on the model’s decisions, providing insights into potential improvements.

Chen, Blair [Purdue U., West Lafayette; Fermilab]↗