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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Virtual tower measurements during the American WAKE ExperimeNt (AWAKEN)

Dual-Doppler lidar measurements were made during the American WAKE ExperimeNt to provide height-resolved measurements of wind speed and direction at multiple locations immediately south of the leading row turbines in the King Plains wind farm in Oklahoma. These so-called virtual tower measurements were performed to characterize the inflow into the wind farm and to assess possible upwind blockage effects due to the collective action of the wind farm. The campaign was conducted from 12 November 2022 to 17 October 2023, during which time 14 unique virtual tower locations were sampled with heights ranging from 240 to 490 m AGL. The wind retrieval algorithm provided estimates of the horizontal winds and their uncertainties with a vertical resolution of about 10 m, while also accounting for the tilt of the lidar platform. The virtual tower results are compared to collocated lidar wind profiling data at the A1 site, which was located roughly 2.4 rotor diameters south of the nearest turbine. The wind speed difference between the wind profiler and the virtual tower was found to be quite sensitive to atmospheric stability and wind direction below 250 m AGL. The largest differences were observed for inflow under stable conditions, where the profiler wind speeds were observed to be about 22% lower than the virtual tower near hub height. These results suggest that there are persistent horizontal gradients in the flow upwind of the wind farm which result in biased estimates using standard ground-based lidar wind profiling methods.

17 WIND ENERGY↗
DOE OSTI2022

Green diatom mutants reveal an intricate biosynthetic pathway of fucoxanthin

Fucoxanthin is a major light-harvesting pigment in ecologically important algae such as diatoms, haptophytes, and brown algae (Phaeophyceae). Therefore, it is a major driver of global primary productivity. Species of these algal groups are brown colored because the high amounts of fucoxanthin bound to the proteins of their photosynthetic machineries enable efficient absorption of green light. While the structure of these fucoxanthin-chlorophyll proteins has recently been resolved, the biosynthetic pathway of fucoxanthin is still unknown. Here, we identified two enzymes central to this pathway by generating corresponding knockout mutants of the diatom Phaeodactylum tricornutum that are green due to the lack of fucoxanthin. Complementation of the mutants with the native genes or orthologs from haptophytes restored fucoxanthin biosynthesis. We propose a complete biosynthetic path to fucoxanthin in diatoms and haptophytes based on the carotenoid intermediates identified in the mutants and in vitro biochemical assays. It is substantially more complex than anticipated and reveals diadinoxanthin metabolism as the central regulatory hub connecting the photoprotective xanthophyll cycle and the formation of fucoxanthin. Moreover, our data show that the pathway evolved by repeated duplication and neofunctionalization of genes for the xanthophyll cycle enzymes violaxanthin de-epoxidase and zeaxanthin epoxidase. Brown algae lack diadinoxanthin and the genes described here and instead use an alternative pathway predicted to involve fewer enzymes. Our work represents a major step forward in elucidating the biosynthesis of fucoxanthin and understanding the evolution, biogenesis, and regulation of the photosynthetic machinery in algae.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Identification of a carbonic anhydrase–Rubisco complex within the alpha-carboxysome

Carboxysomes are proteinaceous organelles that encapsulate key enzymes of CO 2 fixation—Rubisco and carbonic anhydrase—and are the centerpiece of the bacterial CO 2 concentrating mechanism (CCM). In the CCM, actively accumulated cytosolic bicarbonate diffuses into the carboxysome and is converted to CO 2 by carbonic anhydrase, producing a high CO 2 concentration near Rubisco and ensuring efficient carboxylation. Self-assembly of the α-carboxysome is orchestrated by the intrinsically disordered scaffolding protein, CsoS2, which interacts with both Rubisco and carboxysomal shell proteins, but it is unknown how the carbonic anhydrase, CsoSCA, is incorporated into the α-carboxysome. Here, we present the structural basis of carbonic anhydrase encapsulation into α-carboxysomes from Halothiobacillus neapolitanus . We find that CsoSCA interacts directly with Rubisco via an intrinsically disordered N-terminal domain. A 1.98 Å single-particle cryoelectron microscopy structure of Rubisco in complex with this peptide reveals that CsoSCA binding is predominantly mediated by a network of hydrogen bonds. CsoSCA's binding site overlaps with that of CsoS2, but the two proteins utilize substantially different motifs and modes of binding, revealing a plasticity of the Rubisco binding site. Our results advance the understanding of carboxysome biogenesis and highlight the importance of Rubisco, not only as an enzyme but also as a central hub for mediating assembly through protein interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?

As the size of commercial wind turbines keeps increasing, having accurate ways to vertically extrapolate wind speed is essential to obtain a precise characterization of the wind resource for wind energy production. Recently, machine learning has been proposed and applied to extrapolate wind speed to hub heights. However, previous studies trained and tested the machine learning methods at the same site, giving them an unfair advantage over the conventional extrapolation techniques, which are instead more universal. Here, we use data from four sites in Oklahoma to test a round-robin validation approach for machine learning, under which we train a random forest at a site, and test it at a different site, where the model has no prior knowledge of the wind resource. We quantify how the accuracy of this technique varies with distance from the training site, and we find that it outperforms conventional techniques for wind extrapolation at all the considered spatial separations. We then assess how the accuracy of the machine-learning based approach varies when it is used to predict wind speed in a wind farm far wake. Finally, we explore as case study the performance of the random forest in extrapolating winds during a low-level jet event.

17 WIND ENERGY↗
DOE OSTI2021

Conceptual monopile and tower sizing for the IEA Wind Task 37 Borssele reference wind farm

This paper explores the preliminary design of the support structures for the IEA Wind Task 37 reference wind farm at the Borssele site III and IV. The study looks at two different design methods that might be used within larger wind farm system design optimization (i.e. lay-out, electrical systems, etc.). The first consists of a scaling tool that scales a detailed design according to the rated power, water depth, hub height and rotor diameter. The second is a physics-based optimization approach that relies on the WISDEM® design tool. This research compares the results of these two methods for the design of both the tower and monopile of the IEA 10-MW and 15-MW reference wind turbines at a range of sea depths (25 m, 30 m, 35 m, 40 m). The two tools yield very similar results in terms of monopile base diameter, support structure natural frequency and mass. WISDEM is then used to also investigate the sensitivity of the design to the tower top forces, wave conditions and the soil conditions. It is shown that tower top forces dominate the design. In general, large diameter structures can carry additional costs associated with manufacturing and transportation requirements. Thus, the paper is concluded with a trade-off study between mass and diameter that quantifies the effect of the reduction in monopile diameter with the increase in structural mass.

WIND ENERGY↗
DOE OSTI2024

Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US

Current wind resources within the United States (US) indicate a potential to profitably install nearly 1,400 gigawatts of distributed wind (DW) capacity. This amount is equivalent to over half of the United States’ current energy demand from electricity, making it enough to power millions of homes and businesses and replace countless fossil fuel-based generating plants. Despite the potential growth of DW in the US, deployments are presently hindered by a lack of confidence in resource estimation methods. One potential challenge is that smaller-scale turbines, with hub heights of 40 meters or less, are disproportionately impacted by obstacles such as buildings and vegetation. These obstacles may produce complex wake effects, best modeled with high-fidelity complex fluid dynamics (CFD) models that are too computationally expensive to use for routine siting and resource assessment. Thus, installers today make use of heuristics and simple equations to approximate the impact of obstacles while also leveraging long-term resource data from commercial or publicly available atmospheric models. This study evaluates these historical and commonly used methods alongside new lower-order obstacle models produced from CFD simulations and measurement-based bias correction. The preliminary results from this study show the importance of taking care in the choice and application of mesoscale atmospheric models and the significant value of bias correction using measurements from nearby meteorological towers. Detailed obstacle modeling provides only modest additional gains in performance and, in some cases, can add error, especially at sites where turbines have already been located to avoid obvious impact from upwind obstacles. These findings reinforce the importance of collecting in situ measurements and suggest that obstacle models may be better applied in practice to automated or computer-aided siting, rather than in economic wind resource assessments.

17 WIND ENERGY↗
DOE OSTI2022

The CMS Barrel Calorimeter Processor demonstrator (BCPv1) board evaluation

For the CERN LHC phase 2, the barrel region of the CMS electromagnetic (ECAL EB) and hadronic calorimeters (HCAL HB) require new back-end electronics for their readout. To this purpose, a first version of the ATCA-based blade, the Barrel Calorimeter Processor (BCPv1), has been developed. The performance of the optical links as well as clock distribution are also presented here. Here, the BCPv1 has been tested together with front-end and trigger boards, as well as with the new DAQ and TCDS Hub (DTH) [1], to demonstrate that the BCPv1 meets the required specifications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

N 2 Onet: a global collaborative network facilitating advances in measurement, modeling, and mitigation of agricultural soil nitrous oxide emissions

Nitrogen (N) fertilizer supports global food production, but its use and overuse drive emissions of nitrous oxide (N 2 O), a potent and long-lived greenhouse gas. Understanding the drivers of N 2 O fluxes remains elusive, making it difficult to predict emissions in time and space and to develop and evaluate ways to lower emissions through management. Major scientific uncertainties underlying the understanding of the drivers of N 2 O fluxes identified in a workshop of N 2 O emissions experts include poor process-based understanding of controls on soil N 2 O emissions in the field; insufficient data to reduce uncertainty in N 2 O budgets from the field to regional scales, including N 2 O emission measurements and importantly, field-scale N balances; and high uncertainty in model predictions of soil N 2 O emissions across environmental and management conditions. To reduce these uncertainties, we present the concept of N 2 Onet, a global collaborative initiative to accelerate advances in N 2 O measurement, analyses, and mitigation. N 2 Onet will serve as an observational network of supersites with multi-scale measurements; a database hub for N 2 O flux and ancillary data; and a catalyst for community building, information sharing, and training. By coalescing and coordinating the global community of researchers, N 2 Onet will provide a roadmap for reducing N 2 O emissions from agriculture worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Statistically-informed deep learning for gravitational wave parameter estimation

We introduce deep learning models to estimate the masses of the binary components of black hole mergers, $(m_1,m_2)$, and three astrophysical properties of the post-merger compact remnant, namely, the final spin, $a_\mathrm f$, and the frequency and damping time of the ringdown oscillations of the fundamental $\ell = m = 2$ bar mode, $(\omega_\mathrm R, \omega_\mathrm I)$. Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters $(m_1,m_2, a_\mathrm f, \omega_\mathrm R, \omega_\mathrm I)$ of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. Furthermore, this neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Applications of physics informed neural operators

Abstract We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code , an interactive website to visualize the predictions of our PINOs, and a tutorial for their use at the Data and Learning Hub for Science .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Information-incorporated gene network construction with FDR control

Abstract Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Zap1 is required for Candida glabrata response to fluconazole

ABSTRACT The increasing prevalence of fluconazole-resistant clinical isolates of Candida spp. strongly hinders the widespread use of the drug. To tackle this problem, great efforts have been made to fully understand the fungal response to fluconazole. In this work, we show that the role of Zap1 in Candida glabrata goes beyond regulating yeast adaptation to zinc deficiency. In line with our previous observation that deletion of ZAP1 makes yeast cells more sensitive to fluconazole, we found that the mutant CgΔzap1 accumulates higher levels of the drug, which correlates well with its lower levels of ergosterol. Surprisingly, Zap1 is a negative regulator of the drug efflux transporter gene CDR1 and of its regulator, PDR1. The apparent paradox of drug accumulation in cells where genes encoding transporters relevant for drug extrusion are being overexpressed led us to postulate that their activity could be impaired. In agreement, Zap1-depleted cells present, in addition to decreased ergosterol levels, an altered composition of membrane phospholipids, which together should impact membrane function and impair the detoxification of fluconazole. Overall, our study brings to light Zap1 as an important hub in Candida glabrata response to fluconazole.

Gaspar-Cordeiro, A. (ORCID:0000000202488965)↗
DOE OSTI2024

Combining compositional data sets introduces error in covariance network reconstruction

Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both theoretical and practical points of view using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and we survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while the two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applications including identification of strong correlations and identification of possible keystone taxa (i.e. hub nodes in the network). Furthermore, we identify a signature of the error caused by transkingdom network inference and demonstrate that it appears in networks constructed using real-world environmental microbiome data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Mock light-cones and theory friendly catalogues for the CANDELS survey

Here we present mock catalogues created to support the interpretation of the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS). We extract haloes along past light-cones from the Bolshoi Planck dissipationless N-body simulations and populate these haloes with galaxies using two different independently developed semi-analytic models of galaxy formation and the empirical model UNIVERSEMACHINE. Our mock catalogues have geometries that encompass the footprints of observations associated with the five CANDELS fields. In order to allow field-to-field variance to be explored, we have created eight realizations of each field. In this paper, we present comparisons with observable global galaxy properties, including counts in observed frame bands, luminosity functions, colour–magnitude distributions and colour–colour distributions. We additionally present comparisons with physical galaxy parameters derived from SED fitting for the CANDELS observations, such as stellar masses and star formation rates. We find relatively good agreement between the model predictions and CANDELS observations for luminosity and stellar mass functions. We find poorer agreement for colours and star formation rate distributions. All of the mock light-cones as well as curated ‘theory friendly’ versions of the observational CANDELS catalogues are made available through a web-based data hub.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Search for brown dwarfs in IC 1396 with Subaru HSC: interpreting the impact of environmental factors on substellar population

ABSTRACT Young stellar clusters are predominantly the hub of star formation and hence, ideal to perform comprehensive studies over the least explored substellar regime. Various unanswered questions like the mass distribution in brown dwarf regime and the effect of diverse cluster environment on brown dwarf formation efficiency still plague the scientific community. The nearby young cluster, IC 1396 with its feedback-driven environment, is ideal to conduct such study. In this paper, we adopt a multiwavelength approach, using deep Subaru HSC along with other data sets and machine learning techniques to identify the cluster members complete down to ∼ 0.03 M⊙ in the central 22 arcmin area of IC 1396. We identify 458 cluster members including 62 brown dwarfs which are used to determine mass distribution in the region. We obtain a star-to-brown dwarf ratio of ∼ 6 for a stellar mass range 0.03–1 M⊙ in the studied cluster. The brown dwarf fraction is observed to increase across the cluster as radial distance from the central OB-stars increases. This study also compiles 15 young stellar clusters to check the variation of star-to-brown dwarf ratio relative to stellar density and ultraviolet (UV) flux ranging within 4–2500 stars pc−2 and 0.7–7.3 G0, respectively. The brown dwarf fraction is observed to increase with stellar density but the results about the influence of incident UV flux are inconclusive within this range. This is the deepest study of IC 1396 as of yet and it will pave the way to understand various aspects of brown dwarfs using spectroscopic observations in future.

Gupta, Saumya (ORCID:0000000161843958)↗
DOE OSTI2021

The SvFUL2 transcription factor is required for inflorescence determinacy and timely flowering in Setaria viridis

Inflorescence architecture in cereal crops directly impacts yield potential through regulation of seed number and harvesting ability. Extensive architectural diversity found in inflorescences of grass species is due to spatial and temporal activity and determinacy of meristems, which control the number and arrangement of branches and flowers, and underlie plasticity. Timing of the floral transition is also intimately associated with inflorescence development and architecture, yet little is known about the intersecting pathways and how they are rewired during development. Here, we show that a single mutation in a gene encoding an AP1/FUL-like MADS-box transcription factor significantly delays flowering time and disrupts multiple levels of meristem determinacy in panicles of the C 4 model panicoid grass, Setaria viridis. Previous reports of AP1/FUL-like genes in cereals have revealed extensive functional redundancy, and in panicoid grasses, no associated inflorescence phenotypes have been described. In S. viridis, perturbation of SvFul2, both through chemical mutagenesis and gene editing, converted a normally determinate inflorescence habit to an indeterminate one, and also repressed determinacy in axillary branch and floral meristems. Our analysis of gene networks connected to disruption of SvFul2 identified regulatory hubs at the intersection of floral transition and inflorescence determinacy, providing insights into the optimization of cereal crop architecture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Modeling Offshore Wind Farm Performance in Coastal Low-Level Jets Using Coupled Mesoscale-Microscale Large Eddy Simulations

Accurately predicting wind farm reliability under complex offshore atmospheric conditions remains a key challenge, particularly during noncanonical meteorological events such as coastal low-level jets (LLJs). LLJs, characterized by strong nonmonotonic vertical shear and directional veer, depart significantly from the simplified inflow assumptions embedded in conventional design standards, low-fidelity engineering models, and microscale large eddy simulations of the atmospheric boundary layer. In this work, we use the virtual wind farm framework—an exascale, graphics processing unit–accelerated large eddy simulation platform coupled with high-fidelity aeroservoelastic turbine models and advanced mesoscale-microscale coupling via the ExaWind software stack—to investigate turbine responses under realistic LLJ forcing. Simulations are performed over the U.S. North Atlantic offshore domain with the use of meteorological inputs from New York State Energy Research and Development Authority buoy data, focusing on a representative LLJ case impacting the International Energy Agency 15 MW reference turbine. Our results show that LLJs can cause up to 50% power deficits in downstream turbine rows and significantly amplify low-speed shaft and tower loads through nonlinear coupling between complex inflow characteristics and turbine structural dynamics. Two primary mechanisms drive these load amplifications: (1) unique LLJ inflow features—including veer and vertical/lateral shear—and (2) the downstream evolution of the flow under stable thermal stratification, which suppresses turbulence mixing and alters wake recovery. These mechanisms produce streamwise variations in turbine loading not captured by standard hub height–based metrics or existing design load case (DLC) definitions. This study highlights the critical role of rotor-scale flow gradients in driving fatigue and system-level aeroelastic responses, challenging current DLC and control strategies. We advocate the integration of full-flow field, environment-aware wind inputs into load modeling and control algorithms. By leveraging exascale computing to resolve mesoscale-microscale coupling, this work lays the groundwork for next-generation offshore wind turbine design and operation in meteorologically complex marine environments.

17 WIND ENERGY↗
DOE OSTI2026

Submodule-Level Decentralized Control for Cascaded Quadruple Active Bridge-Based Medium-Voltage Converters

High-power, low-voltage DC (LVDC) loads, such as AI data centers, electric vehicle fast-charging hubs, and hydrogen electrolyzers, are accelerating the deployment of solid-state transformers (SSTs) that directly interface medium-voltage AC (MVAC) to LVDC, streamlining power delivery. This paper proposes a submodule-level decentralized control strategy for an SST comprising a cascaded H-bridge (CHB) active rectifier on the MVAC side and per-module quadruple-active-bridge (QAB) converters on the LVDC side with local controllers. The proposed strategy eliminates the need for high-fidelity, real-time cross-isolation communication by synchronizing all active bridges to a common heartbeat signal and enabling the primary- and secondary-side controllers to generate independent local control signals (phase shifts). The resultant phase shifts naturally govern average and double-line frequency power transfer between active bridges, enabling each side to meet its control objectives using only local measurements. Detailed controller design considerations are presented based on a stability analysis. Comprehensive electromagnetic-transient simulations demonstrate tight DC-link voltage regulation, high-quality grid currents, and robust operation under load transients and grid disturbances, without real-time communication among the submodule controllers. These results indicate that the proposed scheme can significantly enhance the reliability, scalability, and flexibility of cascaded bridge-based medium-voltage converters.

24 POWER TRANSMISSION AND DISTRIBUTION↗