TTP - WREF LLNL Streamline Scanning Lidar / Raw Data
These data are from a deployment of the LLNL Halo Streamline scanning lidar in Summer 2025. The scanning lidar was deployed in a clearing area 880 m south of the NEON tower.
17 WIND ENERGY↗
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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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TTP - WREF LLNL Streamline Scanning Lidar / Standardized Data
These data are from a deployment of the LLNL Halo Streamline scanning lidar in Summer 2025. The scanning lidar was deployed in a clearing area 880 m south of the NEON tower.
17 WIND ENERGY↗
TTP - WREF 3D Sonic Anemometer Tower D / Raw Data
Tower D is one of four subcanopy towers that were installed in the WREF canopy across a transect distance of 200m. Tower D had one CSAT3-3D sonic mounted at a height of 2.7 m a.g.l. The sonic is facing 290 degrees. The towers were installed to observe flow just above the forest floor and to identify any advection flows in the canopy. These data were also part of a larger ICOS project looking at forest flow coupling/decoupling with tower sites in Italy and Czech Republic.
17 WIND ENERGY↗
TTP - WREF 3D Sonic Anemometer Tower C / Raw Data
Tower C is one of four subcanopy towers that were installed in the WREF canopy across a transect distance of 200m. Tower C had one CSAT3-3D sonic mounted at a height of 2.7 m a.g.l. The sonic is facing 270 degrees. The towers were installed to observe flow just above the forest floor and to identify any advection flows in the canopy. These data were also part of a larger ICOS project looking at forest flow coupling/decoupling with tower sites in Italy and Czech Republic.
17 WIND ENERGY↗
TTP - WREF 3D Sonic Anemometer Tower B / Raw Data
Tower B is one of four subcanopy towers that were installed in the WREF canopy across a transect distance of 200m. Tower B had two CSAT3-3D sonics mounted at a height of 2.7 m a.g.l. One (B1) is facing 270 degrees; the second (B2) is facing 90 degrees. The towers were installed to observe flow just above the forest floor and to identify any advection flows in the canopy. These data were also part of a larger ICOS project looking at forest flow coupling/decoupling with tower sites in Italy and Czech Republic.
17 WIND ENERGY↗
Hydrogen Contaminant Detector Transition to Production (HCD-TTP II)
Explore the source record for details and available documents.
Moscoso, Jerome↗
Metamodeling Time-Temperature Creep Parameters
Abstract There exist many time-temperature parameter (TTP) models for creep rupture prediction of components including the Larson–Miller (LM), Manson–Haferd (MH), Manson–Brown (MB), Orr–Sherby–Dorn (OSD), Manson–Succop (MS), Graham–Walles (GW), Chitty–Duval (CD), Goldhoff–Sherby (GS) models. It remains a challenge to determine which model is “best”, capable of accurate interpolation and physically realistic extrapolation of creep rupture data for a given material. In this study, metamodeling is applied to create a unified TTP metamodel that combines and regresses into twelve TTP models (eight existing and four newly derived). An analysis of the mathematical problems that exist in TTP models is provided. A matlab code is written that can: (1) calibrate the material constants of any of the twelve TTP models (using the metamodel); (2) determine the most suitable stress-parameter function; (3) and report the normalized mean square error (NMSE) of rupture predictions for a given material database. Using the metamodel, and code, a design engineer can make an intelligent selection of the “best” TTP model for creep resistant design. This process is demonstrated using four isotherms of alloy P91 creep rupture data. To assess the influence of material, further validation is performed on alloys Hastelloy X, 304SS, and 316SS. It is determined that the “best” model is dependent on material type and the quality and quantity of available data.
Engineering↗
A medium-range structure motif linking amorphous and crystalline states
Amorphous materials have no long-range order, but there are ordered structures at short range (2–5 Å), medium range (5–20 Å) and even longer length scales. While regular and semiregular polyhedra are often found as short-range ordering in amorphous materials, the nature of medium-range order has remained elusive. Consequently, it is difficult to determine whether there exists any structural link at medium range or longer length scales between the amorphous material and its crystalline counterparts. Moreover, an amorphous material often crystallizes into a phase of different composition, with very different underlying structural building blocks, further compounding the issue. Here, we capture an intermediate crystalline cubic phase in a Pd-Ni-P amorphous alloy and reveal the structure of the medium-range order, a six-membered tricapped trigonal prism cluster (6M-TTP) with a length scale of 12.5 Å. We find that the 6M-TTP can pack periodically to several tens of nanometres to form the cube phase. Our experimental observations provide evidence of a structural link between the amorphous and crystalline phases in a Pd-Ni-P alloy at the medium-range length scale and suggest that it is the connectivity of the 6M-TTP clusters that distinguishes the crystalline and amorphous phases. These findings will shed light on the structure of amorphous materials at extended length scales beyond that of short-range order.
36 MATERIALS SCIENCE↗
A heterogeneous iridium single-atom-site catalyst for highly regioselective carbenoid O–H bond insertion
Transition-metal-catalysed carbenoid insertion of hydroxyl groups represents a robust and versatile method to forge C–O bonds. Achieving site-selective functionalization of alcohols using this transformation has undoubted synthetic value but remains challenging. Here we report a strategy for selective carbenoid O–H insertion that exploits an engineered heterogeneous iridium single-atom catalyst, thus providing opportunities for organic transformations by merging material science and catalysis. This catalytic protocol delivers excellent selectivities (up to 99:1) for the functionalization of aliphatic over phenolic O–H bonds, whereas the analogous homogeneous catalyst, Ir(ttp)COCl (ttp = 5,10,15,20-tetra-p-tolylporphyrinato), provided modest preferences. Density-functional-theory calculations suggest that the site-selectivity derives from the lower oxidation state of the iridium metal centre in the heterogeneous catalyst and its impact on the absorption energies of the reactants. These results showcase an example of a heterogeneous single-atom catalyst providing superior site-selectivity and provide a complementary strategy to address challenges in catalysis for organic synthesis. [Figure not available: see fulltext.]
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Novel Authentication Management for the Data Security of Smart Grid
Bidirectional wireless communication is employed in various smart grid components such as smart meters and control and monitoring applications where security is vital. The Trusted Third Party (TTP) and wireless connectivity between the smart meter and the third party in the key management-based encryption techniques for the smart grid are expected to be totally trustworthy and dependable. In a wired/wireless medium, however, a man-in-the-middle may seek to disrupt, monitor and manipulate the network, or simply execute a replay attack, revealing its vulnerability. Recognizing this, this study presents a novel authentication management (model) comprised of two layer security schema. The first layer implements an efficient novel encryption method for secure data exchange between meters and control center with the help of two partially trusted simple servers (constitutes the TTP). In this setting, one server handles the data encryption between the meter and control center/central database, and the other server administers the random sequence of data transmission. The second layer monitors and verifies exchanged data packets among smart meters. It detects abnormal packets from suspicious sources. To implement this node-to-node authentication, One class support vector machine algorithm is proposed which takes advantages of the location information as well as the data transmission history (node identification, packet size, and data transmission frequency). This schema secures data communication, and imposes a comprehensive privacy throughout the system without considerably extending the complexity of the conventional key management scheme.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Long-term thermal aging behavior and strength reduction in a laser powder bed fusion 316H stainless steel
The long-term thermal stability of structural alloys is essential for ensuring the safe and reliable operation of nuclear reactors and other power plants. While extensive research has explored the effects of thermal aging on conventional stainless steels, the behavior of additively manufactured (AM) alloys remains less understood. This study examines the thermal aging response of laser powder bed fusion (LPBF) 316H stainless steel (SS) at temperatures ranging from 550 °C to 750 °C over durations of up to 10,000 h (approximately 1.14 years). Advanced characterization techniques, including electron microscopy and synchrotron X-ray diffraction, were used to investigate dislocation recovery and phase evolution. Based on these findings, a time-temperature-precipitation (TTP) diagram was developed for LPBF 316H SS, revealing a 10- to100-fold acceleration in precipitation kinetics compared to wrought 316H SS. A physics-informed model was calibrated using the short-term experimental data, enabling predictions of average precipitate sizes, volume fractions of M 23 C 6 and Laves phases, and changes in molybdenum solute concentration for aging up to 1 × 10⁶ h (114 years). These microstructural insights were further utilized to estimate yield strength and extrapolate strength reduction factors over the extended aging period. Despite the accelerated aging kinetics, LPBF 316H SS demonstrated superior yield strength retention compared to its wrought counterpart. In conclusion, this study establishes a framework for evaluating long-term performance using short-term experimental data and supports the accelerated qualification of AM materials for high-temperature structural applications.
Laser powder bed fusion↗
XY-like incommensurate magnetic order in Ce 2 SnS 5
We report the synthesis of single crystals of Ce 2 SnS 5 through a two-stage chemical vapor transport method. The Ce 2 SnS 5 system is a member of the orthorhombic Pbam (No. 55) space group and realizes a distorted trigonal tricapped prism (TTP) crystal field around each cerium site. We characterized the sample through orientation-dependent magnetization and heat capacity measurements to probe the magnetic anisotropy in the system characteristic of XY-like anisotropic Heisenberg model behavior. Ce 2 SnS 5 furthermore enters a zero-field ordered phase under 𝑇 𝑁 =2.4 K; powder neutron diffraction measurements reveal incommensurate magnetic order near 𝑇 𝑁 . Furthermore, the system then locks into a commensurate, two-𝑞 magnetic structure below approximately 1.2 K. This commensurate structure belongs to the Shubnikov group 𝑃𝑏′𝑎′𝑚′ (MSG 55.359) and realizes the propagation vectors $\overrightarrow{𝑞}$ = (1/3, 0, 0) and $\overrightarrow{𝑞}$ = (0, 0, 0).
Antiferromagnetism↗
Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense (Final)
In the world of ever-advancing technology, Autonomous Systems (AS) find extensive application, bolstering functionalities of critical infrastructures such as nuclear power plants. These systems, however, are increasingly becoming a target for nefarious activities, namely through inference attacks, trojan attacks, and adversarial reprogramming. This paper delves into a comprehensive exploration of machine learning (ML)-driven autonomous control systems within advanced nuclear reactor designs, revealing the vulnerabilities and proposing strategies for defense against potential cyber-attacks. Advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)- based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber-physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems. As global reliance on generation III reactors begins to be critically assessed, the evolution towards advanced reactor systems utilizing digital instrumentation and controls (I&C) becomes not merely preferable, but essential. The integration of semi and fully autonomous control systems (ACS), powered by digital I&C and machine learning (ML)-based digital twinning (DT) technologies, emerges as a potent strategy to mitigate operations and maintenance costs, thereby enhancing the economic feasibility of novel reactor designs. However, with a staggering 500% and 380% increase in cyber-attacks reported against the energy sector by the United States Department of Energy (DoE) and the European Union respectively, a surge in cyber vulnerabilities specifically targeting the nuclear industry has been 2 markedly observed. Notable incidents, such as the W32.Ramnit spyware infiltration at the Gundremmingen nuclear power plant in Germany and the Dtrack spyware intrusion at the Kudankulam nuclear power plant in India, while not directly compromising core industrial control systems (ICS), underscore a compelling necessity to fortify cybersecurity protocols in safeguarding reactor systems against increasingly adept digital adversaries. In light of this, our investigation extends beyond conventional cybersecurity parameters, diving into the intricate web of potential vulnerabilities woven into ML-based DTs and ACS in advanced reactor systems. A crafted cyber-physical testbed and preliminary ACS were devised to act as a mirror, reflecting potential configurations of advanced reactor control designs. Moreover, this study is intertwined with a scrutinization of ML models, developed either through conventional, manually tuned methodologies or via automated means through AutoML, probing into their cyber-risk profiles within operational technology (OT) environments. Expanding on this, two distinct ACS blueprints were forged – one navigating through the corridors of traditional ML and the other traversing the path of AutoML – in an effort to holistically encapsulate the considerations pivotal to ML-based DT control system design. Employing the SANS Institute Industrial Control System (ICS) Kill Chain and the MITRE ATT&CK Tactics, Techniques, and Procedures (TTP) framework, a structured analysis was conducted, launching three targeted attacks against the training dataset, real-time dataset, and ML models, therein dissecting the potential cyber-attack implications against both ML frameworks within an ACS milieu. It is essential to note that three distinct categories of attacks were conducted against both ACS configurations, each encompassing three distinct ML-based DTs, cumulating in a total of 18 varied attacks. This exploration extends into the realms of Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense, unraveling vulnerabilities, and opportunities for fortified defenses against such intrusions, particularly where ML-driven technologies, and by extension, ACS, are deployed. Final recommendations, articulated through a lens of security, safeguard, and implementation considerations, are presented for both traditional and AutoML models, anchoring upon the existing knowledge landscape and ML-based DT modeling for ACS, and are offered as a beacon to guide the nuclear industry through the intricate cybersecurity challenges that lie ahead.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
UNCERTAINTIES FOR IN-REACTOR TESTING OF TRITIUM LOADED GETTERS WITH TEMPERATURE CONTROL
The fourth experiment in TPBAR Materials Irradiation Separate-Effects Test (TMIST) series (TMIST-4) is planned to experimentally determine the effect of neutron irradiation on the equilibrium vapor pressure of tritium over a tritium loaded Nickel-Plated Zirconium (NPZ) getter in a simulated TPBAR service environment. This pressure will be used to estimate the tritium permeation rate. In the thermal design in the TMIST-4, two evaluations are required. 1) The cladding temperature is calculated to estimate the feasibility of detecting potential irradiation enhancement of the partial pressure for an NPZ getter. This work is required to obtain a precise cladding temperature for measurement of a low tritium partial pressure. This work has been completed and indicated that the required/target cladding temperatures based on the TMIST-4 test plan can be satisfied under the test condition range by modifying the capsule component shapes and inserting a thermally conductive metal sleeve. 2) The evaluation of uncertainties associated with the test design and operation is performed. This includes fabrication tolerances, and power and nuclear operating conditions. These factors will impact the component temperatures inside capsules and resulting the tritium partial pressure. The tritium permeation rate is a function of temperature and tritium partial pressure. The prediction of tritium permeation rates affects the feasibility of obtaining measurement resolutions required to reach reasonable conclusion for potential irradiation enhanced performance. This work will investigate the uncertainties.
TTP, TPBAR, TMIST-4, TPBAR Materials Irradiation S↗