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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

X-ray and gamma-ray tomographic imaging of fuel relocation inside sodium fast reactor test assemblies during severe accidents

The present work reports on x-ray and gamma-ray high-spatial resolution computerized tomography measurements of the Pin Bundle Metallic Fuel Relocation (PBR) assemblies tested in the Metallic Uranium Safety Experiment (MUSE) facility at Argonne National Lab (ANL). The aim of the study was to characterize fuel relocation structures that develop during severe core accidents pertaining to SFR assemblies; these include but are not limited to advanced core disruption recreated in the PBR-1 assembly, and cladding breach recreated in the PBR-2 assembly. We report the x-ray tomography measurements were able to resolve small quantities of relocation fuel; with increased presence of relocation fuel, the x-ray 1measurements spatially mapped the material but could not resolve the inner regions of these. The gamma-tomography measurements showed improved results, resolving the relocation structures in great detail. The upper plenum of the PBR-1 assembly where the molten uranium was initially inserted presented high structural damage, reflected by the partial and complete disintegration of the central rods. Relocation fuel filled the subchannels, adhering to surviving cladding walls and the assembly casing. In the lower portion of the measured section, the tomogram degrades due to photon starvation effects hinting at the increased amount of relocation fuel potentially plugging the assembly; flow blockage in this section was difficult to determine due to the tomogram’s degradation from photon starvation. Small fragments were observed further down the assembly, dislodged from the initial insertion of the molten material. This section was used as an unperturbed assembly reference, with a calculated blockage of less than 1% from the present fragments. The PBR-2 assembly was characterized by columnar relocation structures propagating through the subchannels. Three of the relocation structures were captured in the measured section, with evidence of cross migration on to adjacent subchannels. The measured section in this assembly captures the leading edge of two structures. The calculated flow blockage was 16% in the planes where the three relocation structures are present, but this quickly decreases to approximately 5% past the leading edge of two of the structures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean ( Glycine max ) nested association mapping population

We report photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500–2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R 2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Filling data analysis gaps in time-resolved crystallography by machine learning

There is a growing understanding of the structural dynamics of biological molecules fueled by x-ray crystallography experiments. Time-resolved serial femtosecond crystallography (TR-SFX) with x-ray Free Electron Lasers allows the measurement of ultrafast structural changes in proteins. Nevertheless, this technique comes with some limitations. One major challenge is the quality of data from TR-SFX measurements, which often faces issues like data sparsity, partial recording of Bragg reflections, timing errors, and pixel noise. To overcome these difficulties, conventionally, large volumes of data are collected and grouped into a few temporal bins. The data in each bin are then averaged and paired with the mean of their corresponding jittered timestamps. This procedure provides one structure per bin, resulting in a limited number of averaged structures for the entire time interval spanned by the experiment. Therefore, the information on ultrafast structural dynamics at high temporal resolution is lost. This has initiated research for advanced methods of analyzing experimental TR-SFX data beyond the standard binning and averaging method. To address this problem, we use a machine learning algorithm called Nonlinear Laplacian Spectral Analysis (NLSA), which has emerged as a promising technique for studying the dynamics of complex systems. In this work, we demonstrate the power of this algorithm using synthetic x-ray diffraction snapshots from a protein with significant data incompleteness, timing uncertainties, and noise. Our study confirms that NLSA is a suitable approach that effectively mitigates the effects of these artifacts in TR-SFX data and recovers accurate structural dynamics information hidden in such data.

Trujillo, Justin (ORCID:0000000285505360)↗
DOE OSTI2021

Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy

Tropical forests are one of the main carbon sinks on Earth, but the magnitude of CO 2 absorbed by tropical vegetation remains uncertain. Terrestrial biosphere models (TBMs) are commonly used to estimate the CO 2 absorbed by forests, but their performance is highly sensitive to the parameterization of processes that control leaf-level CO 2 exchange. Direct measurements of leaf respiratory and photosynthetic traits that determine vegetation CO 2 fluxes are critical, but traditional approaches are time-consuming. Reflectance spectroscopy can be a viable alternative for the estimation of these traits and, because data collection is markedly quicker than traditional gas exchange, the approach can enable the rapid assembly of large datasets. However, the application of spectroscopy to estimate photosynthetic traits across a wide range of tropical species, leaf ages and light environments has not been extensively studied. Here, we used leaf reflectance spectroscopy together with partial least-squares regression (PLSR) modeling to estimate leaf respiration ( R dark25 ), the maximum rate of carboxylation by the enzyme Rubisco ( V cmax25 ), the maximum rate of electron transport ( J max25 ), and the triose phosphate utilization rate ( T p25 ), all normalized to 25°C. We collected data from three tropical forest sites and included leaves from fifty-three species sampled at different leaf phenological stages and different leaf light environments. Our resulting spectra-trait models validated on randomly sampled data showed good predictive performance for V cmax25 , J max25 , T p25 and R dark25 (RMSE of 13, 20, 1.5 and 0.3 μmol m -2 s -1 , and R 2 of 0.74, 0.73, 0.64 and 0.58, respectively). The models showed similar performance when applied to leaves of species not included in the training dataset, illustrating that the approach is robust for capturing the main axes of trait variation in tropical species. We discuss the utility of the spectra-trait and traditional gas exchange approaches for enhancing tropical plant trait studies and improving the parameterization of TBMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Radiative Heat Transport and Optical Characterization of High Temperature Molten Salts

The goal of this project was to advance the field of molten fluoride radiative heat transfer (RHT), through optical property measurements, heat transfer loop construction and operation, and computational fluid dynamics simulations. The following outcomes have been accomplished. A comprehensive review has been conducted to compile all available RHT-relevant optical absorption data for molten fluorides. The various measurement methods have also been compiled and critiqued. Thermal emission measurement methods have been refined and documented. A unique thermal emissivity measurement facility has been constructed and used to measure the emissivity of flowing FLiBe-exposed SS316 samples. A slight increase has been observed in the emissivity compared to unexposed SS316. A dual reflectance-emission setup was partially demonstrated for the purpose of molten fluoride absorption measurement. Preliminary infrared edge data for FLiNaK was obtained. A visible near-infrared (NIR) measurement setup has been constructed and will be used to measure absorption of corrosion products in FLiBe. A new multi-band thermal radiation method using blackbody-weighted spectral bands for the absorption-coefficient was deployed to investigate the thermal performance of the UW Natural Circulation FLiBe Loop under various power levels and operating conditions. The results showed an increasing and decreasing trend in the overall heat transfer with optical thickness with a maximum at optical thickness τ D ≈Ο(1). A forced convection FLiBe loop was constructed for the purpose of studying corrosion and thermal hydraulic phenomena. A vertical cantilever pump is used to achieve up to 5 gpm and 5 m/s flow velocity in hot and cold test sections. A methodology has been established for modeling radiative heat transfer (RHT) in molten salt coolants for the purpose of quantifying the contribution of thermal radiation in conditions relevant to FHR or MSR systems. The comparison between the results obtained for forced and mixed convection showed that in the studied ranges of optical thickness, the maximum effect of RHT on overall heat transfer was 60% improvement observed at z/D = 250 for forced convection; whereas for mixed convection, that value was higher at 170% observed at z/D = 30. The highest effect of RHT on the overall energy distribution was observed at τ D ≈3.4. Negligible effect (<20%) was observed above τ D =18 and below τ D ≈0.002. A CFD code-to-code comparison was conducted using COMSOL Multiphysics at UW-Madison and STAR-CCM at MIT, for a forced and mixed convection laminar flow for a grey medium in a vertical heated using both COMSOL Multiphysics at UW-Madison and STAR-CCM at MIT. The relative error was the largest at τ D =40 for the radiative heat flux, bulk temperature, and Nusselt number. On the other hand, the relative error was the least at τ D =0.002 for the conductive heat flux, bulk temperature, and Nusselt number.

Anderson, Mark↗
DOE OSTI2021

Potential ecological impacts of climate intervention by reflecting sunlight to cool Earth

As the effects of anthropogenic climate change become more severe, several approaches for deliberate climate intervention to reduce or stabilize Earth’s surface temperature have been proposed. Solar radiation modification (SRM) is one potential approach to partially counteract anthropogenic warming by reflecting a small proportion of the incoming solar radiation to increase Earth’s albedo. While climate science research has focused on the predicted climate effects of SRM, almost no studies have investigated the impacts that SRM would have on ecological systems. The impacts and risks posed by SRM would vary by implementation scenario, anthropogenic climate effects, geographic region, and by ecosystem, community, population, and organism. Complex interactions among Earth’s climate system and living systems would further affect SRM impacts and risks. We focus here on stratospheric aerosol intervention (SAI), a well-studied and relatively feasible SRM scheme that is likely to have a large impact on Earth’s surface temperature. We outline current gaps in knowledge about both helpful and harmful predicted effects of SAI on ecological systems. Desired ecological outcomes might also inform development of future SAI implementation scenarios. In addition to filling these knowledge gaps, increased collaboration between ecologists and climate scientists would identify a common set of SAI research goals and improve the communication about potential SAI impacts and risks with the public. Without this collaboration, forecasts of SAI impacts will overlook potential effects on biodiversity and ecosystem services for humanity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Virtual Growth of SRF Materials

Niobium's native surface oxide affects SRF cavity and superconducting qubit performance, motivating interest in controlling its crystalline structure. We combine a literature-derived machine-learning analysis with temperature-dependent XRD to study crystalline ordering in Nb2O5. Random Forest models, trained on 74 processing conditions from 17 papers and validated by leave-one-group-out cross-validation, predicted broad crystallinity outcomes well (balanced accuracy 0.809), but struggled with specific polymorph identity (0.577). Annealing temperature was the dominant predictor across all targets; oxygen partial pressure showed negligible importance, reflecting narrow literature coverage rather than physical irrelevance. Temperature-dependent XRD on anodized and H2O2-treated Niobium showed structural evolution consistent with the machine learning predictions. Our model and overall approach provide a data-driven framework for identifying and optimizing conditions that promote crystallization in initially amorphous oxides. This framework can guide the selection of growth and post-annealing conditions for Nb surfaces by narrowing the experimental parameter space, thereby reducing trial-and-error efforts in developing oxide structures relevant to SRF applications.

Tilkin, Anthony [Fermilab]↗
DOE OSTI2026

Virtual Growth of SRF Materials: A Machine Learning Approach to Predict the Crystalline Structural Ordering in Nb Surface Oxides

Niobium's native surface oxide affects SRF cavity and superconducting qubit performance, motivating interest in controlling its crystalline structure. We combine a literature-derived machine-learning analysis with temperature-dependent XRD to study crystalline ordering in Nb2O5. Random Forest models, trained on 74 processing conditions from 17 papers and validated by leave-one-group-out cross-validation, predicted broad crystallinity outcomes well (balanced accuracy 0.809), but struggled with specific polymorph identity (0.577). Annealing temperature was the dominant predictor across all targets; oxygen partial pressure showed negligible importance, reflecting narrow literature coverage rather than physical irrelevance. Temperature-dependent XRD on anodized and H2O2-treated Niobium showed structural evolution consistent with the machine learning predictions. Our model and overall approach provide a data-driven framework for identifying and optimizing conditions that promote crystallization in initially amorphous oxides. This framework can guide the selection of growth and post-annealing conditions for Nb surfaces by narrowing the experimental parameter space, thereby reducing trial-and-error efforts in developing oxide structures relevant to SRF applications.

Tilkin, Anthony [Unlisted, US, IL; Fermilab]↗
DOE OSTI2021

Understanding Selectivity in CO2 Hydrogenation to Methanol for MoP Nanoparticle Catalysts Using In Situ Techniques

Molybdenum phosphide (MoP) catalyzes the hydrogenation of CO, CO2, and their mixtures to methanol, and it is investigated as a high-activity catalyst that overcomes deactivation issues (e.g., formate poisoning) faced by conventional transition metal catalysts. MoP as a new catalyst for hydrogenating CO2 to methanol is particularly appealing for the use of CO2 as chemical feedstock. Herein, we use a colloidal synthesis technique that connects the presence of MoP to the formation of methanol from CO2, regardless of the support being used. By conducting a systematic support study, we see that zirconia (ZrO2) has the striking ability to shift the selectivity towards methanol by increasing the rate of methanol conversion by two orders of magnitude compared to other supports, at a CO2 conversion of 1.4% and methanol selectivity of 55.4%. In situ X-ray Absorption Spectroscopy (XAS) and in situ X-ray Diffraction (XRD) indicate that under reaction conditions the catalyst is pure MoP in a partially crystalline phase. Results from Diffuse Reflectance Infrared Fourier Transform Spectroscopy coupled with Temperature Programmed Surface Reaction (DRIFTS-TPSR) point towards a highly reactive monodentate formate intermediate stabilized by the strong interaction of MoP and ZrO2. This study definitively shows that the presence of a MoP phase leads to methanol formation from CO2, regardless of support and that the formate intermediate on MoP governs methanol formation rate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Reactor Power Level Estimation by Fusing Multi-Modal Sensor Measurements

Estimates of the power level of a nuclear reactor based on measurements from an independent monitoring sensor system can help in the compliance verification of its declared operations. We present a three-level fusion method to estimate the power level of a nuclear reactor using features derived from infrared, electromagnetic, and acoustic sensor measurements collected in proximity to the reactor. Based on a simplified analytical model of the secondary coolant system of the reactor, we identify partial regression functions of the power level in terms of the temperature difference between inlet and outlet of coolant pipes, and the activity levels of four fans and four pumps, which are estimated as features from the sensor measurements. The power level estimator employs a combination of aggregate and complementary fusion steps at three levels to incorporate the multi-modal features in a structure that reflects the secondary cooling system and its partial regression functions. Using the measurements from a test campaign at an operational reactor, we show that this estimator achieves 3.47% or lower root mean square error under 5-fold cross validation. More generally, these results illustrate a progressive reduction in estimation error as additional modalities are appropriately incorporated, and that the fuser outperforms single modality features and their sub-combinations.

Rao, Nageswara↗
DOE OSTI2021

Detection of the Metabolic Response to Drought Stress Using Hyperspectral Reflectance

Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49 to 0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between droughted and watered plants based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Performance Comparison of Low-Cost Near-Infrared (NIR) Spectrometers to a Conventional Laboratory Spectrometer for Rapid Biomass Compositional Analysis

The performance of a conventional laboratory near-infrared (NIR) spectrometer and two NIR spectrometer prototypes (a Texas Instruments NIRSCAN Nano evaluation model (EVM) and an InnoSpectra NIR-M-R2 spectrometer) are compared by collecting reflectance spectra of 270 well-characterized herbaceous biomass samples, building calibration models using the partial least squares (PLS-2) algorithm to predict five constituents of the samples from the reflectance spectra, and comparing the resulting model statistics. The prediction models developed using spectra from the Foss XDS spectrometer were slightly better than the prediction models developed using spectra from either the TI NIRSCAN Nano EVM and the InnoSpectra NIR-M-R2 as measured by the root mean square error (RMSECV) and the correlation coefficient (R 2 _cv) for “leave-one-out” cross-validation (CV). The models built from the two prototype units were not statistically significantly different from each other (p = 0.05). The Foss spectrometer has a larger wavelength range (400–2500 nm) compared with the two prototypes (900–1700 nm). When the spectra from the Foss XDS spectrometer were truncated so their wavelength range matched the wavelength range of the two prototype units, the resulting model was not statistically significantly different from the models from either prototype.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Evolution of charge density wave soft phonon modes in Pd 𝑥 ⁢ErTe 3

Here, we investigated the lattice dynamics of quasi-two-dimensional Pd-intercalated ErTe 3 in relation to its charge-density-wave (CDW) transitions by means of x-ray diffuse and meV-resolution inelastic x-ray scattering. In pristine ErTe 3 , CDW order develops at orthogonal in-plane wave vectors q$^𝑐_1$ =(0,0,0.29) (the 𝑐−CDW ) and q$^𝑎_2 $= (0.31,0,0) (the 𝑎−CDW ), with transition temperatures $𝑇^𝑐_1$ = 270K and $𝑇^𝑎_2$ = 160K, respectively. Remarkably, we observe diffuse x-ray scattering already near the higher transition temperature $𝑇^𝑐_1$ along 𝑎−CDW but at a slightly different wave vector q$^𝑎_1$ = (0.29,0,0). Inelastic x-ray scattering for Pd 0.01 ⁢ErTe 3 shows that a partial phonon softening at q$^𝑎_1$, underscoring the strong competition between ordering tendencies along the nearly equivalent in-plane axes of the orthorhombic lattice. For intercalation levels 𝑥 ≥ 0.02, the 𝑎−CDW state is suppressed. Nevertheless, a similar correlation between phonon softening and diffuse scattering persists along the [100] direction, again observed at q$^𝑎_1$ = (0.29,0,0) and $𝑇^𝑐_1$. These findings suggest that the 𝑎−CDW is fully suppressed for 𝑥 ≥ 0.02, and that the residual diffuse scattering at originates from the partial phonon softening associated with the 𝑐−CDW, reflected by the near equality of the absolute size of q$^𝑐_1$ and q$^𝑎_1$.

Maity, Avishek [Technische Univ. München, Garching↗
DOE OSTI2020

Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

The photosynthetic capacity or the CO 2 -saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C 4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that V max can be well predicted from spectra (r=0.81). Here, the integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of V max (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to V max prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Source:sink imbalance detected with leaf and canopy-level spectroscopy in a field-grown crop

The finely-tuned balance between sources and sinks determines plant resource partitioning and regulates growth and development. Understanding and measuring metabolic indicators of source or sink limitation forms a vital part of global efforts to increase crop yield for future food security. Here, we measured metabolic profiles of Cucurbita pepo (zucchini) grown in the field under carbon sink limitation and control conditions. We demonstrate that these profiles can be measured non-destructively using hyperspectral reflectance at both leaf and canopy scales. Total non-structural carbohydrates (TNC) increased 82% in sink-limited plants; leaf mass per unit area (LMA) increased 38%; free amino acids increased 22%. Partial least-squares regression models link these measured functional traits with reflectance data, enabling high-throughput estimation of traits comprising the sink limitation response. Leaf- and canopy-scale models for TNC had R 2 values of 0.93 and 0.64 and %RMSE of 13% and 38% respectively. For LMA, R 2 values were 0.91 and 0.60 and %RMSE 7% and 14%; for free amino acids, R 2 was 0.53 and 0.21 with %RMSE 20% and 26%. Remote sensing can enable accurate, rapid detection of sink limitation in the field at the leaf and canopy scale, greatly expanding our ability to understand and measure metabolic responses to stress.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multi–year analysis of rain–snow levels at Marquette, Michigan

This study uses observations from a ground-based instrument suite to investigate the rain-snow level in stratiform rainfall from January 2014 to April 2020 in the Upper Great Lakes Region. The height above the surface where ice melts to rain, the rain-snow level (RSL), influences microphysical assumptions in remote sensing precipitation retrievals and the ability of space-based radar to discriminate surface precipitation phase because of ground clutter. The instrument suite is installed at the Marquette, MI (MQT) National Weather Service station adjacent to Lake Superior. Rain events and the RSL are studied through a ground-based vertically profiling radar (Micro Rain Radar), a custom NASA-developed video disdrometer (Precipitation Imaging Package), and reanalyses products from ECMWF ERA5 and NASA MERRA-2. Further, distinct macro and micro physical characteristics are observed in precipitation events with shallow RSL ( < 1.8 km above ground level [AGL]) and intermediate RSL ( > 1.8 km AGL). Intermediate RSL correspond to rain events with relatively higher rain rates and a higher concentration of small drops in the drop size distributions (DSDs). Shallow RSL DSDs contain relatively higher concentrations of large drops with lower fall speeds suggesting that partially melted snowflakes may be reaching the surface. Reflectivity-rain-rate relationships are also impacted by microphysical differences associated with RSL regimes. Radar-detected RSL agree with reanalysis-derived melt levels- especially at wet-bulb isotherms of 0.5°C and 1°C. Seasonal differences such as shallow rain-snow levels in winter, fall, and spring have subsequent implications for satellite detectability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study

# 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The [National Institute for Congestion Reduction](https://nicr.usf.edu/) provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants. The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2024 University of Puerto Rico at Mayagüez Civic Innovation Challenge Study

# 2024 University of Puerto Rico at Mayagüez Civic Innovation Challenge Study The 2024 University of Puerto Rico at Mayagüez Civic Innovation Challenge (CIVIC) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of shared mobility strategies—such as collaborative ride-sharing programs—that could address the mobility needs of rural communities in Puerto Rico. The Civic Innovation Challenge is a multiagency, federal government research and action competition that funds ready-to-implement, research-based pilot projects that have the potential for scalable, sustainable, and transferable impact on community-identified priorities. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source [NREL OpenPATH platform](https://www.nrel.gov/transportation/openpath). The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 31 participants. ## Survey Records, Data, and Documentation Survey records include 31 participants. The total number of trips was 1,373 and the total non-air-miles traveled was approximately 8,260.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗