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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Sample glue layer investigation and mitigation for laser induced prompt impulse experiments

Understanding longer timescale material reactions under dynamic stress loading is critical for applications in materials engineering, shock physics, and planetary science. Prompt impulse experiments generate lower pressures since the ablator—the material first removed by the laser—is thicker and farther from the diagnostic plane, capturing aggregate material responses from the initial shock wave, rarefaction waves, and later time effects. This complexity demands thorough material characterization and simulation support. Since traditional sample construction is specific to supported shock experiments, designing prompt impulse experiments requires reconsideration around target design and sample engineering. Here, we present sample preparation techniques, experimental investigations, and theoretical simulations to investigate glue layer impacts, aiming to standardize samples for consistent data at lower laser fluences. We find that glue layers <30 μm have a minimal impact on peak velocity and pulse shape. The peak velocity scales linearly with glue layer thickness until a glue layer of 75 μm. For glue layers >75 μm, the peak velocity no longer scales with thickness; however, the pulse shape continues to degrade as described by simulations.

Lasers↗
DOE OSTI2025

Mixing-Controlled Combustion of Ethanol Enabled by Prechamber Ignition (PC-MCC): A Preliminary Experimental Demonstration

This experimental study presents preliminary investigations of prechamber-enabled mixing-controlled combustion (PC-MCC) at −2 bar brake mean effective pressure (BMEP) and 2200 rpm with fuel-grade ethanol (E98). Experimental results are conducted on a prechamber retrofitted single-cylinder Caterpillar C9.3B test engine. First, a series of prechamber-only experiments were conducted with a motored engine to evaluate the salient combustion trends in response to relevant prechamber operating parameters. Under firing conditions, the prechamber operating strategy was assessed with respect to the impact on ignition assistance of direct-injected E98 and overall engine performance. The preliminary results indicate the jet-induced ignition process is robust and prompts diffusion combustion of E98 at diesel-like boundary conditions. Here, the effect of external exhaust gas recirculation (EGR) on the residual tolerance of the prechamber combustion process was also investigated and showed stable combustion in both the main chamber and prechamber up to 30% EGR. Experiments were also conducted with the stock diesel engine for baseline comparison. At matched combustion phasing, mixing-controlled combustion of ethanol enabled by prechamber ignition was able to achieve heightened gross thermal efficiency while simultaneously reducing NOx and practically eliminating smoke emissions relative to diesel combustion. In addition, the covariance of load and standard deviation of combustion phasing was diesel-like and less than 2% and 1 CAD, respectively.

PC-MCC↗
DOE OSTI2025

FLEX-FUEL MIXING CONTROLLED COMBUSTION ENABLED BY PRECHAMBER IGNITION

There is an imminent need to displace fossil diesel fuel with cleaner burning, domestically produced, renewable fuels for use in heavy-duty engines. Bioethanol is a prime candidate as it widely adopted in the U.S. as a gasoline additive ranging in volume percentage from 10% (E10) up to 85% (E85). Direct substitution of market available ethanol-gasoline blends for diesel fuel is not plausible as the stark reactivity differences would not constitute the same ignition quality nor achieve auto-ignition at all. This work focuses on the development of prechamber enabled mixing-controlled combustion (PC-MCC) as an advanced combustion strategy to facilitate reliable ignition and diffusion style combustion ethanol-gasoline fuel blends. PC-MCC involves integration of an actively fueled prechamber (PC) into a conventional compression ignition combustion system. When ignited, the PC ejects hot turbulent jets into the main combustion chamber that then interact with the direct injected fuel, prompting immediate ignition. The PC jet flames provide a robust thermal ignition source that allows the engine to operate agnostic of fuel composition, or flex-fuel. Computational fluid dynamics (CFD) modeling was used to assess critical design features of the PC while garnering insights into the ignition strategies that facilitate robust performance. A key finding was the ignition performance benefits of fuel-rich PC operation which yield exothermic jets. Based on the numerical findings, a prototype igniter was tested experimentally on both single and multi-cylinder engine platforms at a variety of operating conditions. The experimental results indicate flex-fuel PC-MCC is well capable of diesel-like combustion processes by demonstrating matched or improved gross thermal efficiencies and load variability within 2%. Fuel grade ethanol (E98) exhibited consistently lower NOx and immeasurable soot across the load space. E98 also demonstrated a significant improvement in thermal efficiency at light loads.

Zeman, Jared↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2026

Mechanism, Thermochemistry, and Kinetics for the CH + N 2 Reaction Leading to Prompt NO Formation in Combustion

Here, the reaction of CH + N 2 forming H + NCN is a remarkable example of activation of the nitrogen triple bond and is an important source of prompt NO in combustion. The reaction pathway is complex and proceeds through two competing mechanisms: a cyclic addition channel initiated by c-HC(NN) and a chain-addition channel initiated by HCNN, both of which eventually form HNCN prior to dissociation to H + NCN. This work reinvestigates this reaction with composite coupled cluster protocols, including a novel spin-flip equation of motion coupled cluster scheme, combined with pragmatic two-dimensional master equation simulations of the resulting rate coefficients. These improved calculations predict the CH + N 2 rate coefficient between the two more recent previous theoretical results and reduce the uncertainties of the best theoretical models of this reaction to less than a factor of 1.3. Additionally, we provide a closer theoretical investigation of the simultaneous dependence of the CH + N 2 rate coefficient on pressure and temperature, and affirm that collisionally stabilized HNCN, another potential source of prompt NO, emerges as an appreciable product of this reaction under conditions relevant to automotive internal combustion engines and aircraft gas turbine engines.

Nguyen, Thanh Lam [Univ. of Florida, Gainesville, ↗
DOE OSTI2024

Radiation characterization summary of the NETL beam port 1/5 free-field environment at the 128-inch core centerline adjacent location

The characterization of the neutron, prompt gamma-ray, and delayed gamma-ray radiation fields in the University of Texas at Austin Nuclear Engineering Teaching Laboratory (NETL) TRIGA reactor for the beam port (BP) 1/5 free-field environment at the 128-inch location adjacent to the core centerline has been accomplished. NETL is being explored as an auxiliary neutron test facility for the Sandia National Laboratories radiation effects sciences research and development campaigns. The NETL reactor is a TRIGA Mark-II pulse and steady-state, above-ground pool-type reactor. NETL is intended as a university research reactor typically used to perform irradiation experiments for students and customers, radioisotope production, as well as a training reactor. Initial criticality of the NETL TRIGA reactor was achieved on March 12, 1992, making it one of the newest test reactor facilities in the US. The neutron energy spectra, uncertainties, and covariance matrices are presented as well as a neutron fluence map of the experiment area of the cavity. For an unmoderated condition, the neutron fluence at the center of BP 1/5, at the adjacent core axial centerline, is about 8.2×10 12 n/cm 2 per MJ of reactor energy. About 67% of the neutron fluence is below 1 keV and 22% above 100 keV. The 1-MeV Damage-Equivalent Silicon (DES) fluence is roughly 1.6×10 12 n/cm 2 per MJ of reactor energy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Bayesian Optimization of Catalysis with In-Context Learning

Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning (ICL), allowing the model to observe query-relevant examples at inference time and eliminating the need for additional weight updates to generalize beyond its original training data. We extend this capability to regression with uncertainty estimation using frozen LLMs (e.g., GPT-4o, Gemini), enabling Bayesian optimization (BO) in natural language without explicit model training or feature engineering. We apply this to materials discovery by representing materials as synthesis and testing procedures for use in natural language prompts. This Bayesian, design-first approach prioritizes optimization toward target material properties before detailed characterization, in contrast to conventional experimental workflows that often emphasize characterization of suboptimal materials. On benchmarks like aqueous solubility and oxidative coupling of methane (OCM), BO-ICL matches or outperforms Gaussian processes. In live experiments on the reverse water–gas shift (RWGS) reaction, BO-ICL identifies multimetallic catalysts that approach equilibrium CO yield within 6 and 10 iterations from a pool of 3,700 and 360,000 candidates, respectively. Our method redefines materials representation and accelerates discovery, with broad applications across catalysis, materials science, and AI.

Calibration↗
DOE OSTI2024

Dataset for "Large Language Models as molecular design engines"

This dataset contains data and results associated with the paper "Large Language Models as molecular design engines" The paper investigates the use of large language models, specifically Claude 3 Opus, for generating and analyzing chemical structures based on various prompts from A-H (as mentioned in the manuscript), and guided design related to electron-withdrawing groups (EWG), electron-donating groups (EDG).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

ON THE EFFECTIVENESS OF LLMS IN UNIT TEST GENERATION FOR STRUCTURED TEXT PROGRAMS

The reliability of industrial automation systems heavily depends on the correctness of Programmable Logic Controller (PLC) programs, which are often written in Structured Text (ST). While Large Language Models (LLMs) have shown promise in automating test generation for mainstream programming languages, their effectiveness for the syntactically strict ST language remains underexplored. This thesis presents a systematic empirical evaluation of three state-of-the-art LLMs—GPT-4o, Gemini 2.5 Pro, and Claude Sonnet 4.5—for generating ST unit tests. We examine three prompting strategies: Natural Language (NL), Code Language (CL), and Chain-of-Thought (CoT), across a curated set of 11 ST function blocks. The quality of the generated tests is assessed using Compilation Success Rate (CSR), Statement Coverage (SC), and Branch Coverage (BC). In the zero-shot setting, Claude Sonnet 4.5 achieves the highest CSR, while Gemini 2.5 Pro consistently delivers the best statement and branch coverage, particularly under CL prompts. By incorporating a one-shot CL prompt, all models exhibit substantial improvements—most notably GPT-4o, whose CSR increases from 45.45% to 90.91%, with substantial gains in both SC and BC. To further contextualize these findings, we compare GPT-4o’s one-shot results with PLCAutoTester, a state-ofthe- art ST unit test generation tool, on an additional benchmark dataset. While LLMgenerated tests approach competitive coverage levels, PLCAutoTester maintains significantly higher and more stable coverage across programs. This study provides the first comprehensive benchmark of modern LLMs for ST unit testing, highlighting their strengths, limitations, and improvements through one-shot prompting, and positioning their performance relative to specialized automated testing tools in industrial automation.

42 ENGINEERING↗
DOE OSTI2024

General Applications for Hamilton Vantage (GenApps for Vantage) v0.6.1

General applications for Hamilton Vantage is a flexible liquid handling method used to automate the most widely applicable types of automated liquid transfers. General applications can also be used as a tool to onboard new fully-automated workflows by breaking them down step by step into single transfers. The goal of General Applications is to make using the Hamilton Vantage liquid handler as easy & practical as using a handheld pipette for the end user. The software supports plate-to-plate transfers for a variety of method types including: Stamp: One-to-One, Stamp: One-To-Many, Split, Combine, Hitpick, and Qtray plating. General Applications eliminates the need for automation engineers to customize individual methods for each new workflow that gets onboarded. Steps can be customized within GenApps according to the needs of the researcher. The software utilizes a GUI to prompt the users to input variables – Allowing for flexible control over plate types, transfer volumes, number of replicates, tip types, liquid classes, mixing steps, aspiration/dispense heights and more. General Applications also generates a deck image and setup instructions to guide the researcher on how to load the deck and start the instrument.

Yoder, Sam↗
DOE OSTI2026

Admissible Powertrain Alternatives for Heavy-Duty Fleets: A Case Study on Resiliency and Efficiency

Heavy-duty vehicles dominate global freight movement and primarily rely on fossil-derived diesel fuel. However, fluctuations in crude oil prices and evolving emissions regulations have prompted interest in alternative powertrains to enhance fleet energy resiliency. This study paired real-world operational data from a large commercial fleet with high-fidelity vehicle models to evaluate the potential for replacing diesel internal combustion engine (ICE) trucks with alternative powertrain architectures. The baseline vehicle for this analysis is a diesel-powered ICE truck. Alternatives include ICE trucks fueled by bio- and renewable diesel, compressed natural gas (CNG) or hydrogen (H 2 ), as well as plug-in hybrid (PHEV), fuel cell electric (FCEV), and battery electric vehicles (BEV). While most alternative powertrains resulted in some payload capacity loss, the overall fleetwide impact was negligible due to underutilized payload capacity for the specific fleet considered in this study. For sleeper cab trucks, CNG-powered trucks achieved the highest replacement potential, covering 85% of the fleet. In contrast, H 2 and BEV architectures could replace fewer than 10% and 1% of trucks, respectively. Day cab trucks, with shorter daily routes, showed higher replacement potential: 98% for CNG, 78% for H 2 , and 34% for BEVs. However, achieving full fleet replacement would still require significant operational changes such as route reassignment and enroute refueling, along with considerable improvements to onboard energy storage capacity. Additionally, the higher total cost of ownership (TCO) for alternative powertrains remains a key challenge. This study also evaluated lifecycle impacts across various fuel sources, both fossil and bio-derived. Bio-derived synthetic diesel fuels emerged as a practical option for diesel displacement without disrupting operations. Conversely, H 2 and electrified powertrains provide limited lifecycle impacts under the current energy scenario. This analysis highlights the complexity of replacing diesel ICE trucks with admissible alternatives while balancing fleet resiliency, operational demands, and emissions goals. These results reflect a US-based fleet’s duty cycles, payloads, GVWR allowances, and an assumption of depot-only refueling/recharging. Applicability to other fleets and regions may differ based on differing routing practices or technical features such as battery swapping.

BEV↗
DOE OSTI2024

Early photometric and spectroscopic observations of the extraordinarily bright INTEGRAL-detected GRB 221009A

Context. GRB 221009A, initially detected as an X-ray transient by Swift, was later revealed to have triggered the Fermi satellite about an hour earlier, marking it as a post-peak observation of the event’s emission. This GRB distinguished itself as the brightest ever recorded, presenting an unparalleled opportunity to probe the complexities of GRB physics. The unprecedented brightness, however, challenged observation efforts, as it led to the saturation of several high-energy instruments.Aims. Our study seeks to investigate the nature of the INTEGRAL-detected GRB 221009A and elucidate the environmental conditions conducive to these exceptionally powerful bursts. Moreover, we aim to understand the fundamental physics illuminated by the detection of teraelectronvolt (TeV) photons emitted by GRB 221009A.Methods. We conducted detailed analyses of early photometric and spectroscopic observations that span from the Fermi trigger through to the initial days following the prompt emission phase in order to characterize GRB 221009A’s afterglow, and we complemented these analyses with a comparative study.Results. Our findings from analyzing INTEGRAL data confirm GRB 221009A as the most energetic event observed to date. Early optical observations during the prompt phase negate the presence of bright optical emissions with internal or external shock origins. Spectroscopic analyses enabled us to measure GRB 221009A’s distance and line-of-sight properties. The afterglow’s temporal and spectral analysis suggests prolonged activity of the central engine and a transition in the circumburst medium’s density. Finally, we discuss the implications for fundamental physics of detecting photons as energetic as 18 TeV from GRB 221009A.Conclusions. Early optical observations have proven invaluable for distinguishing between the potential origins of optical emissions in GRB 221009A, underscoring their utility in GRB physics studies. However, the rarity of such data underscores the need for dedicated telescopes capable of synchronous multiwavelength observations. Additionally, our analysis suggests that the host galaxies of TeV GRBs share commonalities with those of long and short GRBs. Expanding the sample of TeV GRBs could further solidify these findings.Key words: techniques: photometric / techniques: spectroscopic / gamma-ray burst: general / gamma-ray burst: individual: GRB 221009A

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Evaluating Polymer Properties with Different Additives for Carbon Capture and Other Applications

Anthropogenic climate change is one of this generation’s most pressing concerns, with the potential to completely alter the delicate balance we’ve struck with nature. Already, global temperatures have risen 1.29°C, leading to disrupted weather systems, extinctions, increased risks of wildfires, and sea level rise, to name a few effects. Carbon dioxide emission from the combustion of fossil fuels and other industrial activity is a large driver of this phenomenon, as it absorbs heat before it can be radiated away from Earth, trapping it. Carbon dioxide has reached unprecedented levels in our atmosphere, showing a 50% increase from preindustrial averages to a whopping 430 ppm. Thus, reducing the amount of carbon dioxide via carbon capture technology is an important endeavor that serves to benefit everyone. The Microencapsulated CO 2 Sorbent (MECS) team at Lawrence Livermore National Laboratory (LLNL) has turned to microencapsulation to approach this endeavor. Microcapsules provide an attractive approach to carbon capture, combining large surface areas for more efficient mass transfer, regenerative abilities, reduced solvent loss, and improved handling. Additionally, while existing carbon capture technology relies on industrial plants, capsules could present a modular approach to carbon capture, reducing the need for extensive physical infrastructure. The MECS team’s design consists of a polymer membrane that contains a liquid carbon sequestering sorbent, aqueous sodium carbonate. The carbon capturing reaction occurs in three distinct steps, the first of which is the dissolution of carbon dioxide into the sorbent solution and its conversion into carbonic acid (H 2 CO 3 ), shown in equations 1 and 2 respectively. Because this step hinges upon the ability of carbon dioxide to reach the solution inside the capsule, it is necessary that the microcapsule shell is permeable to carbon dioxide gas. The MECS team produces these microcapsules using the in-air droplet encapsulation apparatus (IDEA) shown in figure 1, which can produce uniform micron-scale droplets at speeds much faster than traditional single-dispersal microfluidic-based techniques. The IDEA Is 100 times faster than these current techniques and can reach up to 1000 times their speed when incorporating a multi-nozzle design. Additionally, because droplets are produced in-air via vibration, IDEA can decrease post-processing times and material waste by 99% and can fabricate microgels that are 10 to 100 times more viscous than can be produced via traditional microfluidics. While this design represents a breakthrough in the throughput, efficiency, and tunability of microcapsule production, it imposes a major constraint on the microcapsule curing process. Because microcapsule shells are crosslinked with UV light while falling 30 cm through the air, this gives them a reaction window of approximately 0.2 seconds. Thus, the system and shell formulations must be optimized such that the shells can be fully crosslinked within this very narrow window, prompting investigations into curing behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Mk-IV Salt Crystallization Hot Finger Apparatus for Partitioning Used Electrorefiner Salt

Electrorefining is a controlled redox process used to regulate the behavior of ionic species. Through this process, metals can be deposited onto a cathode from an electrolyte solution in a controlled manner. The Mk-IV electrorefiner (Mk-IV ER) at Idaho National Laboratory is an engineering-scale, molten salt-based electrorefining cell that has been used for decades to recover metallic uranium from spent fuel. As a result, highly stable fission product chlorides have accumulated in the electrolyte. This accumulation results in changes to the salt’s properties, such as melting temperature, thermal conductivity, and density, as well as elevated product impurity and fissile materials criticality margin. These factors prompt the need for a salt regeneration process, such as melt-crystallization and species drawdown. This work focuses on providing a conceptual design to regenerate ER salt from used Mk-IV-ER salt in-situ, while minimizing salt waste volumes by concentrating the fission products in a final processed salt heal. We propose using a hot-finger crystallization apparatus design to fractionally crystallize salt in the Mk-IV-ER head space (or baffle space), allowing the collection of solid and liquid fractions. By using a cup-drain design, the used salt will be allowed to slowly solidify on the walls of a stainless-steel cup. The apparatus drain plug will then open to allow the liquid salt phase to drain to a lower cup, effectively separating the liquid phase from the solid phase. Under the hypothesis that the liquid phase salt concentrates the fission products, which is under examination in the accompanying work package, this separation allows the recovered solid salt to be reused while minimizing the high-level salt waste volume of used ER salt.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Rheinheimera sp . T2C2 Bacterial Biofilm for Bioremediation of Cobalt(II)

Toxic metals, including cobalt, are often the cause of the contamination of rivers and lakes in mining regions. Heavy metal water pollution has been linked to numerous human health problems, prompting the need for environmental remediation. Existing techniques for removing heavy metals from water, such as chemical precipitation and filtration, produce toxic waste, are costly, or require high power consumption for pumping. Biosorption is a potential alternative strategy that is cost-effective and uses readily available and naturally produced biomass and living material to absorb pollutants. Engineering living materials, such as biofilms, which consist of living cells and a secreted polymer matrix, offer the potential to integrate toxin sensing, sequestration, and metabolism capabilities of cells to improve pollution remediation strategies. Alternative biofilm producing candidates need to be explored to implement these material capabilities. Previous biosorption studies have primarily used bacterial biofilms from known pathogens and/or generated toxic waste in the form of the absorbent material combined with the heavy metal. Here, we describe a recently isolated bacterium called Rheinheimera sp. T2C2 that forms biofilms with promising biosorption characteristics. T2C2 is an aquatic bacterium with low nutrient requirements and high biofilm production that is not known to be pathogenic. We demonstrate (1) the efficacy of Rheinheimera sp. T2C2 as a biosorbent for cobalt bioremediation; (2) how biosorption is altered by water conditions to establish the efficacy of this strategy in different environments; and (3) how the metal can be released from the biofilm for metal recycling. Our findings will provide a living materials strategy that overcomes the existing barriers for bioremediation and improves the health of ecosystems and humans through heavy metal removal and recycling.

Rheinheimera↗
DOE OSTI2024

Trust-Based Detection and Mitigation of Cyber Attacks in Distributed Cooperative Control of Islanded AC Microgrids

In this study, we address the challenge of detecting and mitigating cyber attacks in the distributed cooperative control of islanded AC microgrids, with a particular focus on detecting False Data Injection Attacks (FDIAs), a significant threat to the Smart Grid (SG). The SG integrates traditional power systems with communication networks, creating a complex system with numerous vulnerable links, making it a prime target for cyber attacks. These attacks can lead to the disclosure of private data, control network failures, and even blackouts. Unlike machine learning-based approaches that require extensive datasets and mathematical models dependent on accurate system modeling, our method is free from such dependencies. To enhance the microgrid’s resilience against these threats, we propose a resilient control algorithm by introducing a novel trustworthiness parameter into the traditional cooperative control algorithm. Our method evaluates the trustworthiness of distributed energy resources (DERs) based on their voltage measurements and exchanged information, using Kullback-Leibler (KL) divergence to dynamically adjust control actions. We validated our approach through simulations on both the IEEE-34 bus feeder system with eight DERs and a larger microgrid with twenty-two DERs. The results demonstrated a detection accuracy of around 100%, with millisecond range mitigation time, ensuring rapid system recovery. Additionally, our method improved system stability by up to almost 100% under attack scenarios, showcasing its effectiveness in promptly detecting attacks and maintaining system resilience. These findings highlight the potential of our approach to enhance the security and stability of microgrid systems in the face of cyber threats.

Computer Science↗
DOE OSTI2025

High-Fidelity Modeling of a Type-5 Wind Turbine Gearbox

Type-5 wind turbines are characterized by their use of a hydraulic torque converter and permanent synchronous generator. This combination promotes steady and grid-ready energy without the use of a power converter. Thus, researchers were prompted to study the potential impact on grid reliability, stability, and resilience using a Real Time Digital Simulator (RTDS) model of the type-5 turbine, including a high-fidelity model of its gear box.

13 - HYDRO ENERGY↗