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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improved Biofuel Production through Discovery and Engineering of Terpene Metabolism in Switchgrass

Project Objectives - Of the myriad specialized metabolites that plants deploy to adapt to environmental challenges, terpenes form the largest group. In many major crops, unique terpene blends serve as key stress defenses that directly impact plant fitness and yield. In addition, terpenes, such as bisabolene and pinene, are used for producing renewable biofuels. Essential to advancing a broader use of terpenes for biofuel feedstock engineering is a system-wide knowledge of the diverse biosynthetic machinery and defensive potential of often species-specific terpene blends. The proposed project would merge genome-wide enzyme discovery with comparative –omics, protein structural and plant microbiome studies to define the biosynthesis and stress-defensive functions of the switchgrass (Panicum virgatum) terpene network. These insights would be combined with developing and applying non-transgenic genome editing tools to design plants with desirable terpene blends for higher productivity and biofuel production on marginal lands. As a dedicated lignocellulosic feedstock for U.S. biofuel production with high net energy yield, stress tolerance, and available genome resources, switchgrass is well-suited for devising new avenues for biofuel production. Project Description – The diversity of plant terpene defenses is governed by species-specific families of terpene synthase (TPS) and cytochrome P450 monooxygenase (P450) enzymes. Mining of the switchgrass genome (genotype Alamo) identified ~100 TPS and P450 candidate genes, and combinatorial biochemical analysis of synthesized TPSs and P450s revealed more than a dozen enzymes with common and novel activities. In addition, several identified terpene metabolites and the corresponding transcripts were up-regulated in response to abiotic stressors. These findings demonstrate a unique switchgrass terpene network with probable importance to abiotic stress tolerance, thus providing a large chemical portfolio for optimizing crop resistance, yield, and biofuel composition. Leveraging these preliminary data, we propose to generate a genome-wide map of the switchgrass terpene metabolic network through multi-gene co-expression analyses that allow the efficient cross-validation of TPS and P450 functions. Key enzymes would further be applied to structure-function studies via X-ray protein crystallography, homology modeling and site-directed mutagenesis to gain mechanistic insight into the catalytic specificity of switchgrass terpene metabolism and provide gene and amino acid targets for genome editing. In tandem with terpene pathway discovery, system-wide metabolomics, transcriptomics and proteomics studies in switchgrass accessions of contrasting drought tolerance would define the role of switchgrass terpene metabolism in conferring abiotic stress resilience. Metabolic changes would be assessed in a combined approach of targeted (terpenes) and untargeted metabolite profiling using a high-resolution LC-MS/MS approach, differential gene expression analyses through multiplexed Illumina RNA sequencing, and quantitative analysis of high-priority pathway enzymes using multiple reaction monitoring (MRM). Drawing on these insights, knock-down/out mutants of stress-associated pathway nodes would be generated by optimizing transient virus-induced gene silencing (VIGS) and CRISPR/Cas9 systems under control of the Tobacco Rattle Virus (TRV). The resulting mutant lines would then be analyzed for stress susceptibility and the impact on the root microbiome to define gene functions in planta. Knowledge of terpene pathways, enzyme mechanisms and bioactivities would be applied to enhance switchgrass stress resilience and to tailor-make terpene blends for biofuel production. Here, TRV-enabled CRISPR/Cas9 genome editing, including allele-specific knock-out of redundant genes, engineering of enzyme specificity via structure-guided point mutations, and overexpression of terpene genes relevant to stress-protection or biofuel production, would be used to increase metabolic flux toward desired pathways. Broader Impacts - Integrating the system-wide discovery, mechanistic analysis and non-transgenic genome engineering of the switchgrass terpene network aligns the required steps to unlock the chemical potential of this important metabolite class to generate crops that are more resistant to stress and provide advanced biofuel production in light of rising climate pressures as foreseeable challenges for bioenergy crop cultivation. The proposed project would further offer interdisciplinary student training through active involvement in the project and integration of research concepts and outcomes into newly-developed graduate and undergraduate courses on Plant Biotechnology.

09 BIOMASS FUELS↗
NASA NTRS2003

Biomimetic, Self-Healing Nanocomposites for Aerospace Applications

This final report contains a summary of significant findings, and bibliographies of publications and patents resulting from the research. The findings are grouped as follows: A) Lustrin-Mimetic Self-Healing Polymer Networks; B) Nanostructure-Directing Catalysis of Synthesis of Electronically and Optoelectronically Active Metallo-oxanes and Organometallics; C) New Discovery that Molecular Stencils Control Directional Growth to Form Light-Weight Mineral Foams.

Morse, Daniel E.↗
NASA NTRS2010

The Global Precipitation Measurement (GPM) Mission: Overview and U.S. Status

The Global Precipitation Measurement (GPM) Mission is an international satellite mission designed to unify and advance precipitation measurements from a constellation of research and operational microwave sensors. NASA and JAXA will deploy the GPM Core Observatory carrying an advanced radar-radiometer system to serve as a physics observatory and a transfer standard for inter-calibration of constellation radiometers. The GPM Core Observatory is scheduled for launch in July 2013. NASA will provide a second radiometer to be flown on a partner-provided GPM Low-Inclination Observatory to enhance the near real-time monitoring of hurricanes and mid-latitude storms. JAXA will also contribute data from the Global Change Observation Mission-Water (GCOM-W) satellite. Additional partnerships are under development to include microwave radiometers on the French-Indian Megha-Tropiques satellite and U.S. Defense Meteorological Satellite Program (DMSP) satellites, as well as cross-track scanning humidity sounders on operational satellites such as the NPP, POES, JPSS, and MetOp satellites, which are used to improve the precipitation sampling over land. Brazil has in its national space plan for a GPM low-inclination radiometer, and data from Chinese and Russian microwave radiometers could potentially become available through international collaboration under the auspices of the Committee on Earth Observation Satellites (CEOS) and Group on Earth Observations (GEO). The current generation of global rainfall products combines observations from a network of uncoordinated satellite missions using a variety of merging techniques. GPM will provide "next-generation" precipitation data products characterized by: (1) more accurate instantaneous precipitation measurement (especially for light rain and cold-season solid precipitation), (2) more frequent sampling by an expanded constellation of microwave radiometers including operational humidity sounders over land, (3) intercalibrated microwave brightness temperatures from constellation radiometers within a unified framework, and (4) physical-based precipitation retrievals from constellation radiometers using a common a priori cloud/hydrometeor database constructed from GPM Core sensor measurements. As a science mission with integrated application goals, GPM will (1) provide new measurement standards for precipitation estimation from space, (2) improve understanding of precipitation physics, the global water cycle variability, and freshwater availability, and (3) advance weather/climate/hydrological prediction capabilities to directly benefit the society. An overview of the GPM mission concept, NASA program status, science activities in the United States, as well as a wide range of international scientific collaborations in radiometer inter-calibration, retrieval algorithm development, and ground validation will be presented.

Hou, Arthur Y.↗
DOE OSTI2023

Phonon-informed Neural Thermal Scattering (NeTS) Optimization for Crystalline Graphite and Beryllium Metal

Fast neutrons born from fission lose energy through scattering interactions in the process of slowing-down. As neutrons thermalize to the order of $k$ $b$ $T$ (where $k$ $b$ is the Boltzmann constant, and $T$ is the temperature of the medium), their de Broglie wavelength and energy approaches the order of inter-atomic spacing and quantized lattice vibrations, i.e., phonons. At thermal energies, the thermal scattering law (TSL), i.e., $S$($α, β$), captures crystal binding contributions to the total reaction rate, or cross section. This dimensionless material property describes the energy ($β$) and momentum ($α$) exchanges available in a medium. Currently, $S$($α, β$) is evaluated in the Full Law Analysis Scattering System Hub (FLASSH) code for discrete inputs and stored as ENDF/B File 7 for 0-phonon elastic (MT 2) and n-phonon inelastic (MT 4) processes. Further processing recasts $S$($α, β$) into cumulative distribution functions for sampling post-collision scattering kinematics. In practice, interpolation schemes are employed to access data between tabulated values. An improvement to this juncture of the nuclear data pipeline is supplying cross sections on-the-fly (OTF), as has been developed for the un-resolved resonance region to minimize non-physical interpolation errors. This capability may improve simulation accuracy for accident and transient analyses, where rapidly varying changes in temperature and pressure are difficult to predict beforehand. To do so, deep artificial neural networks (ANNs) can be employed which collapse non-linear, complex data into a lightweight dictionary of neural weights and biases. This has been successfully demonstrated for the hydrogen in light water $S$($α, β$) dataset in the form of a Neural Thermal Scattering (NeTS) module. In this work, the NeTS framework is extended to consider the impact of material-dependent dynamical features on optimal neural pre-processing and architecture design decisions, such as number of neurons per hidden layer, residual skip connections and neural depth. New NeTS modules for crystalline graphite and beryllium metal illuminate a novel correlation between dynamical nonlinearity and optimal neural parametrization when deploying $S$($α, β$) on-the-fly.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Regulation of Energy and Mass Transport in a Hydrogen-Bonded Framework for Visible-Light-Driven CO2 Reduction in Water

Photoenzymatic reduction of CO2 to formate is a promising strategy for carbon valorization, yet its efficiency is still limited by inefficient energy and mass transport. Here, we design a series of isostructural hydrogen-bonded organic frameworks (HOFs) that establish confinement effects to promote photocatalytic NADH regeneration and the subsequent NADH-dependent enzymatic CO2-to-formate reduction. We demonstrate that spatial confinement within the framework channels localizes exciton migration to nanoscale domains and promotes interfacial dissociation. Additionally, Rh-induced electronic-structure modulation enables ultrafast electron transfer, while the intrinsic hydrogen-bond network furnishes directional proton conduction to NAD+. These synergistic regulations afford a photocatalytic NADH regeneration efficiency of 99.8% with a record apparent quantum efficiency of 32.8%, and drive formate production at a rate of 3020 μmol g-1 h-1 with 100% selectivity─the highest rate reported to date for all light-driven systems in water. The HOF-based catalyst retains 86.3% of its initial activity over five cycles, highlighting its robustness. This work offers mechanistic insight into how microenvironment engineering within HOF architectures regulates energy and mass transport in photoenzymatic catalysis, paving the way for the rational design of advanced hybrid catalytic systems.

Xu, Jiaxing↗
DOE OSTI2020

Impacts of Experimentally Obtained Harmonic Spectrums of Residential Appliances on Distribution Feeder

Owing to the increased use of power electronic based appliances and energy-efficient home equipment such as modern lighting loads, the percentage of nonlinear loads has increased in the distribution system, which can impact system performance, loss, and stability. Thus a comprehensive knowledge of their power quality and harmonic analysis is essential to improve the load models, voltage stability assessment, determination of possible interactions at harmonic frequencies, protection planning, and the effect of system impedance. This paper focuses on harmonic load flow analysis for multiple residential load types to determine the expected impacts on an modeled distribution secondary. For this study, the household appliances include lighting, power electronic, resistive, and motor loads with a nominal supply voltage of 120 V single-phase or 240 V split phase at a nominal frequency of 60 Hz. The harmonic spectrums, as obtained from the experimental evaluation of real loads, are used to inform the harmonic load flow for a detailed secondary network, including the distribution service transformer extracted from a real distribution feeder's model. Additionally, the impact of voltage harmonics on a three-phase test load is presented with experimental results. The harmonic data of the transformer terminal voltage, as obtained from the former load flow study, are scaled to generate the source voltage for the three-phase configuration of the grid simulator to make the test setup very similar to a typical secondary design in the U.S.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
NASA NTRS1995

Science Rationale for a Micro-Met Mission to Augment InterMarsNet

The 2003 opportunity has the potential to carry out for the first time in Mars exploration history, coordinated measurements from the surface and from orbit that can address fundamental issues associated with the Martian global circulation and climate system. Coordinated measurements are defined here to mean collecting meteorological data from a network of 12-16 globally distributed surface stations simultaneously with an orbiter carrying an atmospheric sounder. With such measurements it is possible to define the horizontally varying (barotropic) and vertically varying (baroclinic) components of the global circulation from which the full 3-dimensional horizontal wind field can be reconstructed. It is also possible to precisely define the CO2 cycle, the main component of the current climate system. InterMarsNet, as currently envisioned, consists of 3-4 landers with a supporting communications orbiter that may carry some instrumentation. The landers are likely to touch down in low latitudes and will probably be configured to optimize seismological objectives. We propose to augment the InterMarsNet meteorological objectives by flying an additional 10-15 "MicroMet" landers equipped to measure surface pressure and nothing else. Surface pressure is the most fundamental meteorological parameter and it is the easiest to measure. The sensors are light, operate with minimal power, and do not require orientation or deployment. Consequently the landers can be very small (< 10 kg) and 10-15 of them can be delivered by a Med-lite launcher. This would enable global network science for meteorology and, when combined wAh the more sophisticated measurements from the InterMarsNet landers and the simultaneous temperature and dust profiling measurements from an atmospheric sounder aboard the orbiter, it would enable us to reconstruct global scale circulation patterns.

Haberle, Robert M.↗
DOE OSTI2026

A review of United States energy-only generator interconnection service policy and considerations for reform

Grid interconnection has emerged as a significant obstacle to the development of new electricity resources. There is growing interest in energy-only interconnection, which is an interconnection service option meant to allow the interconnection of new generators without ensuring their energy deliverability during all hours through the transmission system to customers. This approach potentially avoids upfront congestion-related transmission upgrades but could increase curtailment risk. Interest in energy-only interconnection is shaped by incomplete understanding of how interconnection policy functions in different jurisdictions, a knowledge gap that makes it difficult to determine how energy-only interconnection might be better used or re-designed. In this paper, we provide a regulatory review of energy-only interconnection in U.S. interconnection policy and practice, identifying jurisdictions in which rules are close to -or farther from-the theoretical concept of energy-only interconnection service. We find substantial jurisdictional differences in how energy-only interconnection is implemented, driven by differences in resource adequacy frameworks, real-time transmission operations, and state-level procurement practices. U.S. regulators have preferred local jurisdictional flexibility over federal prescription of interconnection study methods and procedures, which also contributes to differences among regions. Such findings raise fundamental questions about whether competition policies in electricity markets should extend beyond spot energy markets and into more prescriptive guidelines around interconnection rules and market entry. This paper sheds light on tensions that energy-only interconnection raises in allowing generators to access the transmission system on an as available basis and discusses how controlling thresholds for congestion-related network upgrades may be a barrier to electricity market entry.

Gorman, Will↗
DOE OSTI2022

Realistic galaxy image simulation via score-based generative models

ABSTRACT We show that a denoising diffusion probabilistic model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies. Our method is tested with Dark Energy Spectroscopic Instrument (DESI) grz imaging of galaxies from the Photometry and Rotation curve OBservations from Extragalactic Surveys (PROBES) sample and galaxies selected from the Sloan Digital Sky Survey. Subjectively, the generated galaxies are highly realistic when compared with samples from the real data set. We quantify the similarity by borrowing from the deep generative learning literature, using the ‘Fréchet inception distance’ to test for subjective and morphological similarity. We also introduce the ‘synthetic galaxy distance’ metric to compare the emergent physical properties (such as total magnitude, colour, and half-light radius) of a ground truth parent and synthesized child data set. We argue that the DDPM approach produces sharper and more realistic images than other generative methods such as adversarial networks (with the downside of more costly inference), and could be used to produce large samples of synthetic observations tailored to a specific imaging survey. We demonstrate two potential uses of the DDPM: (1) accurate inpainting of occluded data, such as satellite trails, and (2) domain transfer, where new input images can be processed to mimic the properties of the DDPM training set. Here we ‘DESI-fy’ cartoon images as a proof of concept for domain transfer. Finally, we suggest potential applications for score-based approaches that could motivate further research on this topic within the astronomical community.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Classification of Semiconductors Using Photoluminescence Spectroscopy and Machine Learning

Photoluminescence spectroscopy is a nondestructive optical method that is widely used to characterize semiconductors. In the photoluminescence process, a substance absorbs photons and emits light with longer wavelengths via electronic transitions. This paper discusses a method for identifying substances from their photoluminescence spectra using machine learning, a technique that is efficient in making classifications. Neural networks were constructed by taking simulated photoluminescence spectra as the input and the identity of the substance as the output. Here, six different semiconductors were chosen as categories: gallium oxide (Ga 2 O 3 ), zinc oxide (ZnO), gallium nitride (GaN), cadmium sulfide (CdS), tungsten disulfide (WS 2 ), and cesium lead bromide (CsPbBr 3 ). The developed algorithm has a high accuracy (>90%) for assigning a substance to one of these six categories from its photoluminescence spectrum and correctly identified a mixed Ga 2 O 3 /ZnO sample.

47 OTHER INSTRUMENTATION↗
NASA NTRS1999

Spaceborne Fiber Optic Data Bus (SFODB)

Spaceborne Fiber Optic Data Bus (SFODB) is an IEEE 1393 compliant, gigabit per second, fiber optic network specifically designed to support the real-time, on-board data handling requirements of remote sensing spacecraft. The network is fault tolerant highly reliable, and capable of withstanding the rigors of launch and the harsh space environment. SFODB achieves this operational and environmental performance while maintaining the small size, light weight, and low power necessary for spaceborne applications. On December 9, 1998, SFODB was successfully demonstrated at NASA's Goddard Space Flight Center (GSFC).

Bretthauer, Joy W.↗
NASA NTRS1999

IEEE 1393 Spaceborne Fiber Optic Data Bus: A Standard Approach to On-Board Payload Data Handling Networks for the AIAA Space Technology Conference and Exposition "Partnering in the 21th Century"

The Spaceborne Fiber Optic Data Bus (SFODB) is the next generation in on-board data handling networks. It will do for high speed payloads what SAE 1773 has done for on-board command and telemetry systems. That is, it will significantly reduce the cost of payload development, integration and test through interface standardization. As defined in IEEE 1393, SFODB is a 1 Gb/s, fiber optic network specifically designed to support the real-time, on-board data handling requirements of remote sensing spacecraft. The network is highly reliable, fault tolerant, and capable of withstanding the rigors of launch and the harsh space environment. SFODB achieves this operational and environmental performance while maintaining the small size, light weight, and low power necessary for spaceborne applications. SFODB was developed jointly by DoD and NASA GSFC to meet the on-board data handling needs of Remote Sensing satellites. This jointly funded project produced a complete set of flight transmitters, receivers and protocol ASICS; a complete Development & Evaluation System; and, the IEEE 1393 standard.

Andrucyk, Dennis J.↗
NASA NTRS1999

Systems Engineering Design Via Experimental Operation Research: Complex Organizational Metric for Programmatic Risk Environments (COMPRE)

Unique and innovative graph theory, neural network, organizational modeling, and genetic algorithms are applied to the design and evolution of programmatic and organizational architectures. Graph theory representations of programs and organizations increase modeling capabilities and flexibility, while illuminating preferable programmatic/organizational design features. Treating programs and organizations as neural networks results in better system synthesis, and more robust data modeling. Organizational modeling using covariance structures enhances the determination of organizational risk factors. Genetic algorithms improve programmatic evolution characteristics, while shedding light on rulebase requirements for achieving specified technological readiness levels, given budget and schedule resources. This program of research improves the robustness and verifiability of systems synthesis tools, including the Complex Organizational Metric for Programmatic Risk Environments (COMPRE).

Mog, Robert A.↗
NASA NTRS2019

Developing a Machine-Learning-Based Processing Framework for Twitter and Other Crowdsourced Data

Crowdsourced data streams such as Twitter and other social media are important sources of real-time and historical global information for Earth science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we have been exploring the Twitter data stream for its potential in augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. To realize this potential, we need to increase the information density and enhance the quality of filtered precipitation tweets. We have implemented various components of a machine learning (ML)-based processing infrastructure for crowdsourced data that outputs, in this instance, useful and usable information derived from precipitation tweets. We have test enriched the Twitter stream with higher quality active tweets from those knowingly contributing to our effort and from existing crowdsourced programs (e.g., mPING, CoCoRaHS). We have experimented with various algorithms for processing tweets, including Naà ve Bayes, Convolutional Neural Network (CNN), Hierarchical Attention Network (HAN), and semi-supervised learning (with tri-training). Our current work focuses on (1) automated review of Earth science-related publications to determine relationships between discipline research needs and ML algorithms; (2) investigating Sequential Generative Adversarial Network (SeqGAN) for processing precipitation tweets for anomaly detection; and (3) managing crowdsourced data in a way that is compatible with existing NASA satellite data archives and using the data for ML applications. Key results include (1) network visualization of NLP-processed publications in various Earth science disciplines; (2) difference between GPM-linked, generated tweets and collected actual tweets that is small for GPM-determined light to moderate rain cases and high for GPM-determined heavy rain cases; and (3) identification of MongoDB for storing raw tweets and Zarr format for gridded tweets (compatible with GPM data). Our results have taken us a step closer to an operational ML-based tweet processing infrastructure and have already demonstrated that tweet-derived precipitation information is potentially useful for validation of Earth science satellite data.

Teng, William↗
NASA NTRS2020

Flare Statistics for Young Stars from a Convolutional Neural Network Analysis of TESS Data

All-sky photometric time-series missions have allowed for the monitoring of thousands of young (t(age) < 800 Myr) stars in order to understand the evolution of stellar activity. Here, we developed a convolutional neural network (CNN), stella, specifically trained to find flares in Transiting Exoplanet Survey Satellite (TESS) short-cadence data. We applied the network to 3200 young stars in order to evaluate flare rates as a function of age and spectral type. The CNN takes a few seconds to identify flares on a single light curve. We also measured rotation periods for 1500 of our targets and find that flares of all amplitudes are present across all spot phases, suggesting high spot coverage across the entire surface. Additionally, flare rates and amplitudes decrease for stars t(age) > 50 Myr across all temperatures T(eff) ≥ 4000 K, while stars from 2300 ≤ T(eff) < 4000 K show no evolution across 800 Myr. Stars of T(eff) ≤ 4000 K also show higher flare rates and amplitudes across all ages. We investigate the effects of high flare rates on photoevaporative atmospheric mass loss for young planets. In the presence of flares, planets lose 4%–7% more atmosphere over the first 1 Gyr. stella is an open-source Python toolkit hosted on GitHub and PyPI.

Adina D. Feinstein↗
NASA NTRSDate not supplied

Unlocking the Mysteries of the Moon’s Shadowed Regions

The Moon poles host large quantities of water-ice deposits in the permanently shadowed regions (PSRs), which are vital for enabling sustainable human space exploration, making these regions high-priority targets for upcoming Artemis missions [1]. Unfortunately, today, the best available orbital lunar imagery [2, 3] lacks the meter-scale resolution and signal needed to understand the geomorphology and trafficability of PSRs, complicating the planning and execution of future missions seeking to explore PSRs. We have developed an image enhancement tool called HORUS (Hyper-effective nOise Removal Unet Software) [4, 5], designed to enhance LRO Narrow-Angle Camera (NAC) optical low-light imagery of permanently shadowed regions by effectively removing the CCD-related, photon, and other residual noises that corrupt the images. The tool is composed of two deep learning neural networks trained on environmental metadata and real and synthetic imagery, the latter generated by a physical noise model (LROC). We demonstrated that HORUS effectively produces low-noise, high-resolution images (~1.5m/px), achieving a 5 to 10x improvement over existing long-exposure images of PSRs. HORUS allows scientists and engineers to identify geomorphic features (e.g., craters and boulders) in shadowed regions as small as 3 meters across as well as to peek inside of small shadowed regions, for the first time. The tool was deployed and thoroughly validated for NASA's VIPER mission [6], where it was applied to 20 candidate target regions across the lunar South Pole. Additionally, we conducted different approaches to validate the resulting HORUS-processed images. With HORUS denoised images, VIPER scientists can increase their confidence on what surface features (previously unseen) exist in the shadowed regions, helping them plan rover traverses more safely and efficiently (e.g., Fig. 1) In this manuscript, we will describe how VIPER scientists are utilizing HORUS denoised images to extract new information from the terrain and increase their confidence in what surface features exist in the shadowed regions. In combination with other high-resolution images and digital elevation maps, HORUS images are helping the team analyze potential lading and science sites, as well as planning traverses more safely and efficiently (e.g., Fig. 1). Additionally, we will describe how HORUS tool unlocks a broad range of scientific and exploration applications to other Artemis and CPLS missions to the lunar poles, including (but not limited to) geomorphic analysis, change detection, surface hazard detection, and terrain relative navigation.

artificial intelligence↗
DOE OSTI2025

A Generic FSC Wind Park EMT Model with IEEE Std 2800-Compliant Fault Ride-Through Capability

The modern power grid is seeing more and more electricity come from renewable sources like wind farms, which use sophisticated power electronics instead of traditional spinning generators. To keep everything running smoothly and meet industry standards such as IEEE 2800, these systems need smart control strategies. In our work, we built a flexible computer model of a full-scale wind farm converter that can handle grid disturbances without shutting down. When a fault or storm hits, the model’s built-in logic automatically adjusts the currents it sends to the grid and protects its internal energy storage, ensuring the wind farm stays connected and doesn’t damage its own equipment. Once the disturbance clears, the model restores normal operation seamlessly, so there’s no long interruption in power delivery. At the same time, it carefully regulates the voltage where the wind farm ties into the larger grid, helping to maintain safe voltage levels across the network. Our simulations show that this control setup not only meets all the requirements of IEEE Standard 2800 but also allows the wind farm to recover quickly and predictably, keeping the lights on no matter what happens on the grid.

17 WIND ENERGY↗
DOE OSTI2026

Achieving 20.1% Efficiency in Organic Solar Cells Through Interconnected Fibrillar Networks via Local Molecular Stacking

ABSTRACT The performance of organic solar cells (OSCs) is governed by how molecular packing evolves into interconnected networks that facilitate exciton dissociation and charge transport. Using an all‐small‐molecule blend DR3TSBDT:Y6 as a model system, we study how local molecular stacking evolves into performance‐relevant morphology during solvent vapor annealing (SVA) and subsequent thermal annealing (TA). SVA promotes end‐to‐end stacking of amorphous acceptors to form interconnected fibrils, while TA compacts inter‐fibril spacing without disrupting favorable local order. Such molecular‐to‐morphological refinements broaden light absorption, enhance charge transport, and markedly improve device efficiency. Extending this approach to additional blend systems (D18:Y6, D18:L8‐BO, and DR3TSBDT:L8‐BO) yields similar structural evolution and performance gains, with the D18:L8‐BO system achieving up to 20.10% PCE. Our study establishes control over local stacking in amorphous acceptors into fibrillar networks as a general and effective route to realize high‐performance OSCs.

Wu, Junying↗