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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Leading Edge Microspoilers for Load Dumping in Wind Turbine Rotors

As rotor designs have grown in size and flexibility, new design challenges have arisen that could benefit from novel control strategies. Leading edge microspoilers (LEMS) have been shown to effectively reduce forces on airfoils by inducing stall at lower angles of attack. This paper investigates how LEMS can be used to reduce blade loading on large, flexible, downwind rotors during design-limiting design load cases. During shutdown in turbulent flows near cutout speed, LEMS could reduce the tip deflection spikes toward the tower by over 50% and keep flapwise root bending moments below normal operating conditions, thus eliminating these as design-driving load cases. In power producing load cases, deploying LEMS for a short period of time during a load spike can be effective to reduce toward-tower tip deflections without significantly impacting power produced, assuming these load spikes can be identified by a controller. Leveraging this technology could allow for lower specific power machines to be designed.

17 WIND ENERGY↗
DOE OSTI2024

Study of ablation and shock generation across three orders of magnitude of laser intensity with 100 ps laser pulses

The laser ablation and subsequent shock generation in solid targets plays an important role in a variety of research topics from equation of state models for materials to inertial confinement fusion. One of the long-standing issues is the knowledge of ablation depth in the picosecond time regime. Here, we report on a direct technique for determining the ablation depth in aluminum using x-ray diffraction data from Linac Coherent Light Source at the Stanford Linear Accelerator Center. This technique gives a direct measurement of the shock wave propagation in the bulk target, enabling an ability to discern early timescale physics from late timescale effects not available in postmortem analysis. We find that the ablation depths only vary by 0.2 μm across three orders of magnitude of laser intensity, while the pressure increased by a factor of 10 following a square root dependence on laser pulse energy. We further observe that the ablation depth in this intensity range (10 11 –10 13 W/cm 2 in intensity, corresponding to 0.8–80 J/cm 2 in fluence) cannot be modeled by a universal scaling law, given the complexity of the mechanisms governing laser ablation in this intensity regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Global Barotropic Tide Modeling Using Inline Self‐Attraction and Loading in MPAS‐Ocean

Abstract We examine ocean tides in the barotropic version of the Model for Prediction Across Scales (MPAS‐Ocean), the ocean component of the Department of Energy Earth system model. We focus on four factors that affect tidal accuracy: self‐attraction and loading (SAL), model resolution, details of the underlying bathymetry, and parameterized topographic wave drag. The SAL term accounts for the tidal loading of Earth's crust and the self‐gravitation of the ocean and the load‐deformed Earth. A common method for calculating SAL is to decompose mass anomalies into their spherical harmonic constituents. Here, we compare a scalar SAL approximation versus an inline SAL using a fast spherical harmonic transform package. Wave drag accounts for energy lost by breaking internal tides that are produced by barotropic tidal flow over topographic features. We compare a series of successively finer quasi‐uniform resolution meshes (62.9, 31.5, 15.7, and 7.87 km) to a variable resolution (45 to 5 km) configuration. We ran MPAS‐Ocean in a single‐layer barotropic mode forced by five tidal constituents. The 45 to 5 km variable resolution mesh obtained the best total root‐mean‐square error (5.4 cm) for the deep ocean ( 1,000 m) tide compared to TPXO8 and ran twice as fast as the quasi‐uniform 8 km mesh, which had an error of 5.8 cm. This error is comparable to those found in other forward (non‐assimilative) ocean tide models. In future work, we plan to use MPAS‐Ocean to study tidal interactions with other Earth system components, and the tidal response to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

BICEP Array: 150 GHz Detector Module Development

The Background Imaging of Cosmic Extragalactic Polarization (BICEP)/Keck (BK) collaboration is currently leading the quest for the highest-sensitivity measurements of the polarized cosmic microwave background (CMB) anisotropies on a degree scale with a series of cryogenic telescopes, of which BICEP Array (BA) is the latest Stage-3 upgrade with a total of ~ 32,000 detectors. The instrument comprises 4 receivers spanning 30–270 GHz, with the low-frequency 30/40 GHz deployed to the South Pole Station in late 2019. The full complement of receivers is forecast to set the most stringent constraints on the tensor-to-scalar ratio r. Building on these advances, the overarching small-aperture telescope concept is already being used as the reference for further Stage-4 experiment design. This paper describes the development of the BICEP Array 150 GHz detector module and its fabrication requirements, with highlights on the high-density time division multiplexing (TDM) design of the cryogenic circuit boards. The low-impedance wiring required between the detectors and the frst stage of superconducting quantum interference device amplifers is crucial to maintaining a stable bias current on the detectors. Here, a novel multilayer FR4 Printed Circuit Board with superconducting traces, capable of reading out up to 648 detectors, is detailed along with its validation tests. An ultra-high-density TDM detector module concept we developed for a CMB-S4-like experiment that allows up to 1920 detectors to be read out is also presented. TDM has been chosen as the detector readout technology for the Cosmic Microwave Background Stage-4 (CMB-S4) experiment based on its proven low-noise performance, predictable costs, and overall maturity of the architecture. The heritage for TDM is rooted in mm- and sub-mm-wave experiments dating back 20 years and has since evolved to support a multiplexing factor of 64x in Stage-3 experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Quantifying Progress Across Different CMIP Phases With the ESMValTool

Abstract More than 40 model groups worldwide are participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), providing a new and rich source of information to better understand past, present, and future climate change. Here, we use the Earth System Model Evaluation Tool (ESMValTool) to assess the performance of the CMIP6 ensemble compared to the previous generations CMIP3 and CMIP5. While CMIP5 models did not capture the observed pause in the increase in global mean surface temperature between 1998 and 2013, the historical CMIP6 simulations agree well with the observed recent temperature increase, but some models have difficulties in reproducing the observed global mean surface temperature record of the second half of the twentieth century. While systematic biases in annual mean surface temperature and precipitation remain in the CMIP6 multimodel mean, individual models and high‐resolution versions of the models show significant reductions in many long‐standing biases. Some improvements are also found in the vertical temperature, water vapor, and zonal wind speed distributions, and root‐mean‐square errors for selected fields are generally smaller with reduced intermodel spread and higher average skill in the correlation patterns relative to observations. An emerging property of the CMIP6 ensemble is a higher effective climate sensitivity with an increased range between 2.3 and 5.6 K. A possible reason for this increase in some models is improvements in cloud representation resulting in stronger shortwave cloud feedbacks than in their predecessor versions.

Bock, L.↗
DOE OSTI2023

First report of barley root-knot nematode, Meloidogyne naasi from turfgrass in Idaho, with multigene molecular characterization

Barley root-knot nematode, Meloidogyne naasi Franklin, 1965, is one of the most important pest nematodes infecting monocots (Franklin, 1965). Two-inch core soil samples collected from a golf course in Ada County, Idaho were submitted for identification in November of 2019. A high number of Meloidogyne sp. juveniles were recovered from both soil samples using sieving and decantation followed by the sugar centrifugal flotation method. They were examined by light microscopy, morphometric measurements, and multiple molecular markers, including the ribosomal 28S D2–D3 and intergenic spacer 2 (IGS-2) regions, mitochondrial markers cytochrome oxidase I (COI) and the interval from COII to 16S, and the protein-coding gene Hsp90. Morphometrics as well as BlastN comparisons with other root-knot nematode sequences from GenBank were consistent with identification as M. naasi. Phylogenetic trees inferred from 28S, IGS-2, COI, or Hsp90 alignments each separated the Idaho population into a strongly supported clade with other populations of M. naasi, while the COII-16S interval could not resolve M. naasi from M. minor. This report represents the first morphological and molecular characterization of Meloidogyne naasi from turfgrass in Idaho.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Artificial ground reflector size and position effects on energy yield and economics of single‐axis‐tracked bifacial photovoltaics

Abstract Artificial ground reflectors improve bifacial energy yield by increasing both front and rear‐incident irradiance. Studies have demonstrated an increase in energy yield due to the addition of artificial reflectors; however, they have not addressed the effect of varying reflector dimensions and placement on system performance and the impact of these parameters on the reflectors' financial viability. We studied the effect of high albedo (70% reflective) artificial reflectors on single‐axis‐tracked bifacial photovoltaic systems through ray‐trace modeling and field measurements. In the field, we tested a range of reflector configurations by varying reflector size and placement and demonstrated that reflectors increased daily energy yield up to 6.2% relative to natural albedo for PERC modules. To confirm the accuracy of our model, we compared modeled and measured power and found a root mean square error (RMSE) of 5.4% on an hourly basis. We modeled a typical meteorological year in Golden, Colorado, to demonstrate the effects of artificial reflectors under a wide range of operating conditions. Seventy percent reflective material can increase total incident irradiance by 1.9%–8.6% and total energy yield by 0.9%–4.5% annually after clipping is considered with a DC–AC ratio of 1.2. Clipping has a significant effect on reflector impact and must be included when assessing reflector viability because it reduces reflector energy gain. We calculated a maximum viable cost for these improvements of up to $2.50–4.60/m 2 , including both material and installation, in Golden. We expanded our analysis to cover a latitude range of 32–48°N and demonstrated that higher‐latitude installations with lower energy yield and higher diffuse irradiance content can support higher reflector costs. In both modeling and field tests, and for all locations, the ideal placement of the reflectors was found to be directly underneath the module due to the optimized rear irradiance increase.

14 SOLAR ENERGY↗
DOE OSTI2020

Lattice $ \mathcal{N} $ = 4 super Yang-Mills at strong coupling

In this paper we present results from numerical simulations of N = 4 super Yang-Mills for two color gauge theory over a wide range of ’t Hooft coupling 0 < λ ≤ 30 using a supersymmetric lattice action. Numerical study of this lattice theory has been stymied until recently by both sign problems and the occurrence of lattice artifact phases at strong coupling. We have recently developed a new action that appears capable of solving both problems. The resulting action possesses just SU(2) rather than U(2) gauge symmetry. By explicit computations of the fermion Pfaffian we present evidence that the theory possesses no sign problem and exists in a single phase out to arbitrarily strong coupling. Furthermore, preliminary work shows that the logarithm of the supersymmetric Wilson loop varies as the square root of the ’t Hooft coupling λ for large λ in agreement with holographic predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15–385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Further, quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R 2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

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

Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization

Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low energy structures of Pd n (n = 1-21) clusters supported on CeO 2 (111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pdn clusters of any structure. Low energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters and large cluster isomers share similarities in both shape and energy, and elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Editorial: Forest Rhizosphere Interactions: Cascading Consequences for Ecosystem-Level Carbon and Nutrient Cycling

Mycorrhizal fungimine organicmatter and subsequently transfer plant-available nutrients to roots. While there is disagreement considering the role of mycorrhizal fungi in ecosystem carbon (C) and nutrient cycling relative to factors such as climate (Cotton, 2018; Bennett and Classen, 2020), soil nutrient availability (Brzostek et al., 2015; Frey, 2019), or host plant community composition (Read and Perez-Moreno, 2003), it is now clear that mycorrhizal fungal symbionts control a substantial proportion of C and nutrient flow belowground (Steidinger et al., 2019). Yet, determining the relative importance of arbuscular mycorrhizal (AM) and ectomycorrhizal (EM) fungi to these processes is still an open field of inquiry in ecosystemecology (Averill et al., 2019a) and earth system modeling (Sulman et al., 2019). At large scales, ecosystems dominated by EM fungi are thought to store more soil C per unit nitrogen (N) (Averill et al., 2014), have slower N cycles (Phillips et al., 2013), and respond more strongly to global change drivers such as N-deposition (Jo et al., 2019). However, it is unclear how generalizable these patterns are across sites, and plant and mycorrhizal taxa. If site or species-dependent relationships exist between mycorrhizal plants, fungi, and soil biogeochemical cycles, forecasting hot spots and hot moments of mycorrhizal fungal activities and their consistency across space and time would be evenmore challenging (Averill et al., 2019b; Kivlin and Hawkes, 2020).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction

Recent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.

42 ENGINEERING↗
DOE OSTI2022

Tomato deploys defence and growth simultaneously to resist bacterial wilt disease

Abstract Plant disease limits crop production, and host genetic resistance is a major means of control. Plant pathogenic Ralstonia causes bacterial wilt disease and is best controlled with resistant varieties. Tomato wilt resistance is multigenic, yet the mechanisms of resistance remain largely unknown. We combined metaRNAseq analysis and functional experiments to identify core Ralstonia ‐responsive genes and the corresponding biological mechanisms in wilt‐resistant and wilt‐susceptible tomatoes. While trade‐offs between growth and defence are common in plants, wilt‐resistant plants activated both defence responses and growth processes. Measurements of innate immunity and growth, including reactive oxygen species production and root system growth, respectively, validated that resistant plants executed defence‐related processes at the same time they increased root growth. In contrast, in wilt‐susceptible plants roots senesced and root surface area declined following Ralstonia inoculation. Wilt‐resistant plants repressed genes predicted to negatively regulate water stress tolerance, while susceptible plants repressed genes predicted to promote water stress tolerance. Our results suggest that wilt‐resistant plants can simultaneously promote growth and defence by investing in resources that act in both processes. Infected susceptible plants activate defences, but fail to grow and so succumb to Ralstonia , likely because they cannot tolerate the water stress induced by vascular wilt.

Meline, Valerian↗
DOE OSTI2025

TropiRoot 1.0: Database of tropical root characteristics across environments

Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine-Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.

FRED↗
DOE OSTI2025

Single-Shot Reconstruction of Electron Beam Longitudinal Phase Space in a Laser Wakefield Accelerator

We report on a single-shot longitudinal phase-space reconstruction diagnostic for electron beams in a laser wakefield accelerator via the experimental observation of distinct periodic modulations in the angularly resolved spectra. Such modulated angular spectra arise as a result of the direct interaction between the ultrarelativistic electron beam and the laser driver in the presence of the wakefield. A constrained theoretical model for the coupled oscillator, assisted by a genetic algorithm, can recreate the experimental electron spectra and, thus, fully reconstructs the longitudinal phase-space distribution of the electron beam with a temporal resolution of approximately 1.3 fs. In particular, it reveals the slice energy spread of the electron beam, which is important to measure for applications such as x-ray free electron lasers. In our experiment, the root-mean-square energy spread retrieved is bounded at 9.9 MeV, corresponding to a 0.9%–3.0% relative spread, despite the overall GeV energy beam having approximately 100% relative energy spread.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Lost and found: Rediscovering microbiome-associated phenotypes that reshape agricultural sustainability

Modern agriculture faces an urgent need to improve nutrient use efficiency while reducing environmental impacts. Here, we show that ancestral traits controlling rhizosphere microbiome functions can be reintroduced into elite maize through targeted teosinte introgressions. Using near-isogenic lines, we mapped microbiome-associated phenotypes (MAPs) derived from teosinte that suppress nitrification and denitrification—key microbial processes contributing to nitrogen loss. These introgressions altered root exudate chemistry, resulting in distinct microbial assemblies and enhanced nitrogen retention. We identified candidate loci and exudate metabolites responsible for suppressive activity and demonstrated their functional effects in vitro. These findings reveal a genetic and biochemical basis for rewilding microbiome-mediated ecosystem services in crops, offering a scalable path toward sustainable nutrient management in global agriculture.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Monitoring Sulfuric Acid and Temperature Using Raman Spectroscopy and Multivariate Chemometrics

Multivariate regression models were optimized for the quantification of sulfuric acid (H 2 SO 4 ) [0–8 M] and temperature (20 °C–80 °C) in the presence of ammonium sulfate ((NH 4 ) 2 SO 4 [0–0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H 2 SO 4 . The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, in this study, partial least squares regression models were optimized for the quantification of H 2 SO 4 and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H 2 SO 4 . The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.

D-optimal design↗