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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Linewidth narrowing in self-injection-locked on-chip lasers

Abstract Stable laser emission with narrow linewidth is of critical importance in many applications, including coherent communications, LIDAR, and remote sensing. In this work, the physics underlying spectral narrowing of self-injection-locked on-chip lasers to Hz-level lasing linewidth is investigated using a composite-cavity structure. Heterogeneously integrated III–V/SiN lasers operating with quantum-dot and quantum-well active regions are analyzed with a focus on the effects of carrier quantum confinement. The intrinsic differences are associated with gain saturation and carrier-induced refractive index, which are directly connected with 0- and 2-dimensional carrier densities of states. Results from parametric studies are presented for tradeoffs involved with tailoring the linewidth, output power, and injection current for different device configurations. Though both quantum-well and quantum-dot devices show similar linewidth-narrowing capabilities, the former emits at a higher optical power in the self-injection-locked state, while the latter is more energy-efficient. Lastly, a multi-objective optimization analysis is provided to optimize the operation and design parameters. For the quantum-well laser, minimizing the number of quantum-well layers is found to decrease the threshold current without significantly reducing the output power. For the quantum-dot laser, increasing the quantum-dot layers or density in each layer increases the output power without significantly increasing the threshold current. These findings serve to guide more detailed parametric studies to produce timely results for engineering design.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Tank Waste Characterization: History, Challenges, and Success Stories

The preparation and chemical and radiochemical analysis of Hanford tank waste samples can be performed with standard laboratory equipment and instruments as relatively routine processes that are not particularly challenging. Rather, the main challenges of tank waste characterization are associated with radiological dose and sampling limitations. Accurate, representative and effective sampling techniques are difficult with the waste tanks because they were not designed for routine sampling. There are a finite number of sampling locations for each tank based on riser positioning, depth and the operational functionality of the sampling riser. For example, in one recently emptied SST, there was one riser that was found to have had concrete dumped down it, thereby eliminating that sampling port. Additionally, the waste within the tank; especially true for the saltcake and sludge, is not homogenous. The ability to adequately mix a million-gallon double shell tank (DST) is a concern for data reproducibility. Another real challenge that must be addressed for sampling single shell tanks, is how to dissolve the salt cake waste in a compromised (leaking) SST. These physical constraints mean that uncertainty in the representativeness of samples must be considered when applying analytical results to the bulk contents of the tank. The tank waste is highly radioactive and thus can only be handled initially by facilities that can receive samples into concrete-shielded hot cells with remote operation with an example provided in Figure 1. The shielding protects the worker from the radiological dose while mineral oil windows and remotely operated manipulators enables the samples to be handled. At Hanford, analytical laboratories with these hot cell capabilities are limited to the Pacific Northwest National Laboratory and the main Hanford operations support laboratory, 222-S Laboratory. Because of their highly radioactive nature, samples must be sufficiently diluted to facilitate their analysis outside of a shielded cell. In some cases, this means some accuracy must be compromised to complete the analysis beyond that normally encountered for non-radioactive material.

Waste Characterization, BBI, PHOENIX: Tank Farms: ↗
DOE OSTI2025

Ligand substituents modulate excited-state lifetime and energy-transfer reactivity in Cu( I ) photosensitizers supported by salicylaldimine and isocyanide ligands

The design of earth-abundant molecular photosensitizers with desirable photophysical properties and good excited-state reactivity is critical for sustainable photochemical applications. Herein, we report a new family of three-coordinate heteroleptic Cu(I) complexes supported by monoanionic salicylaldimine (N^O) chelating ligands and aryl isocyanides. By systematically tuning the steric bulk on each ligand, we establish clear structure–property relationships that govern the excited-state lifetimes and photocatalytic performance metrics of these complexes. Increasing steric congestion on the salicylaldimine ligand, which contributes to the HOMO, results in faster nonradiative decay and shortens excited-state lifetimes. In contrast, introducing steric bulk on the isocyanide ligand, where the LUMO is primarily localized, suppresses nonradiative decay, most likely by inhibiting excited-state geometric relaxation, thereby extending the lifetime up to 375 ns. These photophysical trends correlate directly with performance in triplet–triplet energy transfer (TTET) photocatalysis, where longer-lived complexes enable faster E/Z isomerization of trans-stilbene. This work demonstrates that remote steric modulation of ligand frameworks offers a simple yet powerful strategy for tuning the excited-state dynamics and catalytic properties of this new class of Cu(I) photosensitizers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Towards Superior Software Portability with SHAD and HPX C++ Libraries

As hardware architectures and software stacks complexity grows, development productivity, performance and software portability, quickly evolve from desirable features to actual needs. SHAD, the Scalable High-performance Algorithms and Data-structures C++ library is designed to mitigate these issues: it provides general purpose building blocks as well as high-level custom utilities, and offers a shared-memory programming abstraction which facilitates the programming of complex systems, scaling up to High Performance Computing clusters. SHAD’s portability is achieved through an abstract runtime interface, which decouples the upper layers of the library and hides the low level details of the underlying architecture. This layer enables SHAD to interface with different runtime/threading systems, e.g. Intel TBB and Global Memory and Threading (GMT). However, current backends targeting distributed systems, rely on a centralized controller which may possibly limit scalability up to hundreds of nodes and creates a network hot spot due to all to one communication for synchronization, and possibly resulting in degraded performance at high process counts. In this research, we explore HPX, the C++ standard library for parallelism and concurrency, as an additional backend in support of the SHAD library, and present the methodologies in support of local and remote task executions in SHAD with respect to HPX. Finally, we evaluate the proposed system by comparing against existing backends of SHAD and analyzing their performance on C++ Standard Template Library algorithms.

Wu, Nanmiao↗
DOE OSTI2025

Development and preliminary results of 270 GHz microwave forward scattering diagnostic system on the experimental advanced superconducting tokamak (EAST)

To measure localized (intermediate, high) poloidal wavenumber electron density fluctuations, a tangential millimeter-wave collective scattering system has been designed and successfully developed on the experimental advanced superconducting tokamak (EAST). This innovative system employs a 270 GHz mm-wave probe beam, emitted from the mid-plane of Port K and directed towards Port P after passing through a combination of two in-vessel mirrors. Here these two ports are located on the EAST device’s midplane and are 110° apart. The downward scattered signals pass through two in-vessel mirrors, and exit through the P window. The received multi-channel separated scattering signals are arranged along the poloidal direction. This optical arrangement makes the monitored turbulent wave number is poloidal dominant and up to 40 cm −1 . The tangential microwave scattering scheme enables this diagnostic to monitor local density fluctuations, with typically a radial spatial resolution △ R = 5 cm, and a wavenumber resolution of △ k = 0.4 cm −1 . The scattering system underwent comprehensive laboratory testing in 2023, with installation finalized in 2024. The system features steerable launch and receiver optics, allowing remote control to position the scattering volume from normalized radius ρ –0 to the pedestal region on a shot-by-shot basis. Effective experimental data, characterized by distinct off-center spectral peaks, have been successfully obtained in neutral beam-heated plasmas on EAST. The beam tracing forward modeling has been employed for experimental data interpretation. The advanced millimeter-wave scattering system provides fluctuation measurement capability from ion temperature gradient to electron temperature gradient scale on low field side. Preliminary experimental density fluctuation data have been successfully obtained in neutral beam-heated plasmas on EAST. The localized measurement will be used for kinetic turbulence transport numerical simulation validation.

high-k collective scattering↗
DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Mass Optimization of a Multilayered Shield for Transportable Microreactors

The ability to easily transport microreactors is a major selling point for deploying microreactors to remote areas. However, this creates a unique shielding challenge, especially when the microreactor is being shipped after irradiation. A traditional reactor configuration utilizes a separate biological shield and pressure vessel to meet radiological shielding and pressure needs. The limited space available for transportable microreactors for both shielding and pressure vessels requires a revised assessment of separating out the biological shield and pressure vessel. To address these concerns, we examine a nuclear-grade sandwich composite (NGSC) that combines the reactor pressure vessel and biological shielding functions into a single component. Through a series of optimization problems for both transportation and operational use cases, the NGSC is able to minimize dose, minimize the vessel cost, and ensure that weight requirements are met for transportation. Initial results show that using a tungsten-tetraboride cermet in the first two layers of a six-layer NGSC provides adequate shielding for both use cases. These results show promise that an NGSC has enough overlap between operational and transportation cases to help reduce the design space for future analysis and assessment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Revisiting the Terawatt Challenge

Richard E. Smalley, in 2003, defined the Terawatt (TW) Challenge as “Adapting our energy infrastructure to simultaneously address diminishing oil resources and rising levels of atmospheric CO 2 .” Smalley, best known for the discovery of C 60 , for which he received the 1996 Nobel Prize in Chemistry, continued to address the challenges of anthropomorphic and natural global energy flows until he passed away in 2005. Smalley challenged the world to transform the energy sector. He envisioned electricity transmitted by high-voltage direct current (DC) lines from massively deployed solar plants in sunny areas and remotely sited nuclear plants. He also envisioned using advanced batteries for local storage of energy. To meet the needs of ~10 people in a world with a dwindling oil supply, Smalley asserted that the world would need to transform its fossil-fuel-driven 14-TW (average power) energy used in 2003 to a largely renewable-energy-driven 30–60 TW (average power) in 2050. This would be possible only if solar-electricity costs could be drastically reduced. The challenges associated with this transition have been called the “Terawatt Challenge.” Fifteen years later, solar-module costs have been reduced by tenfold and annual deployment of solar photovoltaic (PV) modules has grown by a factor of 100,from ~1 gigawatt (GW) in 2004 to ~100 GW in 2018, with a total of 500 GW installed worldwide, producing 2% of the planet’s electricity. As global installed solar generating capacity approaches1 TW, we revisit Smalley’s TW challenge to identify what has changed and quantify the TW Challenge for a baseline scenario and for two scenarios designed as upper and lower bounds determined by the degree we implement electrification and storage. In this paper, we show that the energy choices we make today will dramatically affect the magnitude of future global energy requirements.

SOLAR ENERGY↗
DOE OSTI2020

Highly Efficient Magnetocaloric Natural Gas Liquefaction (Final Report)

Over the twelve-month project the following achievements were accomplished: • Liquefication of methane as primary component of natural gas was accomplished with a magnetocaloric liquefier (MCL) prototype. This was the first time an MCL was used to liquefy methane. • Improved MCL designs were completed. • Cooling to 135 K from room temperature was achieved for the first time in a single MCL stage with dual, reciprocating four-layer regenerators. • Techno-economic analysis for a multi-stage 5 tonne/day magnetocaloric liquefier was completed. The detailed thermodynamic analysis of this MCL design showed a figure of merit (FOM) of 0.6 was achievable. This is a ~2x improvement over current state of the art. • Cost of an efficient 5 tonne/day LNG multi-stage liquefier was projected to be ~$\$$3.1 MM for the 5 tonne/day MCL with achievable design assumptions. • Market study of U.S. merchant LNG demand by energy sector was completed showing that at beginning of 2019, total use was ~2.5 million gallons/day primarily in three sectors that was filled by ~20 small companies in the merchant LNG supply business producing ~2.3 million gpd. These data exclude LNG produced at dedicated peak shaving and large export plants. • Business case for MCL technology based on LNG market study was completed showing the high FOM feature of MCL reduces cost of plant power, and lower capital cost reduces debt repayment and plant depreciation operating expenses. However, today’s demand for U.S. merchant LNG in most sectors is satisfied with conventional technology. Further, with today’s extremely low natural gas (NG) feedstock costs (e.g., ~$\$$1.8/MMBtu), the cost of fuel for NG gensets and for LNG feedstock is already very low. Therefore, possible new merchant LNG liquefier plant developers anticipating demand growth in the transportation, industrial, and electricity generation sectors do not obtain sufficient cost benefits to adopt new, commercially unproven MCL technology. • Other market factors such as remoteness from existing NG pipelines, or unfilled or unsatisfied applications such as boil-off gas re-liquefaction in LNG vessels, or policy factors such as some form of emissions-related fees may make lower capital costs and higher FOM of MCL technology attractive for new small-scale (~50 tonne/day) LNG plants. Two potentially attractive niche markets for MCL technology were identified: i) re-liquefaction of boil-off gas from large LNG transport vessels where severe transport conditions are problematic for conventional technology; and ii) providing LNG in distributed-scale plants that eliminate road transport costs to meet diverse, smaller-scale, distributed LNG bunkering fuel demands. By eliminating significant cryogenic tanker delivery costs and create an attractive cost-savings benefit with small-scale MCL plants.

03 NATURAL GAS↗
DOE OSTI2024

Challenges in the Use of AI-Driven Non-Destructive Spectroscopic Tools for Rapid Food Analysis

Routine, remote, and process analysis for foodstuffs is gaining attention and can provide more confidence for the food supply chain. A new generation of rapid methods is emerging both in the literature and in industry based on spectroscopy coupled with AI-driven modelling methods. Current published studies using these advanced methods are plagued by weaknesses, including sample size, abuse of advanced modelling techniques, and the process of validation for both the acquisition method and modelling. This paper aims to give a comprehensive overview of the analytical challenges faced in research and industrial settings where screening analysis is performed while providing practical solutions in the form of guidelines for a range of scenarios. After extended literature analysis, we conclude that there is no easy way to enhance the accuracy of the methods by using state-of-the-art modelling methods and the key remains that capturing good quality raw data from authentic samples in sufficient volume is very important along with robust validation. A comprehensive methodology involving suitable analytical techniques and interpretive modelling methods needs to be considered under a tailored experimental design whenever conducting rapid food analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

AMTS update for STTR

Advanced nuclear reactor systems currently in development include designs that utilize higher energy neutron spectrums, higher temperatures, and corrosive environments that are quite different from the current fleet of nuclear power plants in use today. As a result, new structural materials must be developed and qualified for use to support these advanced systems. For example, research is currently being conducted on a new class of materials known as multi-principle-element-alloys. These alloys show some promise to be able to meet the structural needs of these challenging reactor systems. However, due to the vast available compositional space, and the limited amount of research conducted thus far, Idaho National Laboratory (INL)sought a way to advanced mechanical testing capability for highly irradiated structural materials to increase both throughput and higher fidelity data analysis through incorporation of digital image correlation. The Automated Mechanical Testing System accomplishes these tasks by automating the testing and incorporating digital image correlation to provide higher fidelity data. INL has now received this system and is currently performing functional testing and training. INL is also performing some modifications to the system to allow for installation into a remote shielded enclosure where testing of irradiated structural materials can be performed. After testing, training, and modifications of the system are complete, INL will install the system into a shielded enclosure and begin using the system to test irradiated structural materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Launch Alaska Transportation and Energy Accelerator (LATEA)

The Launch Alaska Transportation and Energy Accelerator (LATEA), funded through the U.S. Department of Energy Office of Technology Commercialization’s Energy Program for Innovation Clusters (EPIC),advanced deployment of innovative and efficient transportation and energy technology in Alaska from October 2021 through June 2025. The project was designed to leverage Launch Alaska’s accelerator model to identify, recruit, and support transportation technology companies with novel solutions to market needs while building the stakeholder networks, demonstration opportunities, and institutional capacity necessary to accelerate commercialization in one of the most challenging operating environments in the United States.

08 HYDROGEN↗
DOE OSTI2021

Building Intelligent Cyberinfrastructure to Learn Iteratively from both Observations and Models for Understanding Watershed Dynamics

Focal Area(s): Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection). Science Challenge: Watershed processes, such as the fate and transport of sediment, carbon and nutrients across landscapes and their fluxes to water bodies (e.g., streams, rivers and lakes), have important implications for global and regional carbon and nutrient dynamics, biogeochemical functioning of terrestrial ecosystems, and soil functions. The magnitude of lateral surface/subsurface transport and fluxes of sediment, carbon and nutrients are key factors controlling the vulnerability of watersheds to climate extremes such as droughts, wildfires, and floods. Recent field observations and other scientific evidence suggest that the magnitudes of lateral transport and fluxes of sediment, carbon and nutrients are governed primarily by the spatial and vertical heterogeneity of landscape and soil properties and by pedogenic processes. However, the current generation of land surface and watershed models do not mechanistically couple the terrestrial and hydrologic systems, nor do they represent sufficiently the spatial and vertical heterogeneity of land surface and subsurface properties. On the other hand, increasing complexity of coupled watershed and land surface models requires more data to parameterize, calibrate and validate. Remote sensing (RS) provides a means to acquire spatial data and characterize their heterogeneity at the watershed scale, overcoming a major limitation associated with conventional point measurements. To improve the representation of land-surface and surface/subsurface process coupling and sub-grid heterogeneity in watershed models, it is essential to build our predictive understanding by learning from both the multi-scale multi-process modeling and diverse multi-scale data while leveraging powerful artificial intelligence (AI) techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bishop Paiute's Residential Solar Program Phase III

The project consisted of the design, installation, inspection, interconnection and monitoring of 40 grid-tied solar electric systems, totaling up to 108 kW rated capacity, on qualified existing low-income single-family homes located within the Bishop Paiute Reservation. The systems are to provide at least 30-75% savings in displaced electricity totaling 175,000 kWh/year. After the DOE grant contract was signed, the Tribal Employment Rights Ordinance (TERO) board voted to reduce the tribal tax to work on the reservation for these low-income projects from 4% to 1%. This generous reduction was applied back into the community solar project and two additional tribal homes were added for a new total of 40 grid-tied solar electric systems, totaling up to 113 kW rated capacity (two more than originally planned) projects. Additionally, each homeowner was educated on energy efficiency and how solar works and saves them money. The estimated 75% savings on monthly electric bills brings financial relief to tribal homeowners and makes a significant difference on the reservation; giving low-income families money to spend on other essential items, while reducing their carbon footprint in this remote tribal reservation. The GRID IE program and this tribal project had a training component for tribal members to get free hands-on job training on 8 of the 40 homes. Those tribal members that came out to train with GRID IE could then be eligible for the “train to hire” portion of the program, on the remaining 32 homes. Overall, the triple impact of the Bishop Paiute Tribe Residential Solar Program Phase III—affordable energy for low-income families, on-site clean energy production, and hands-on solar installation jobs for local workers—these all help build the Tribe’s energy, economic, environmental goals and offers local self-sufficiency while supporting energy independence to the neediest on the Reservation.

14 SOLAR ENERGY↗
DOE OSTI2026

Artificial Intelligence in Nuclear Safeguards; Evaluating Safeguards and Security Risks and Benefits for Advanced and Small Modular Reactor Deployments

Rapidly growing interest in advanced and small modular reactor (A/SMR) technologies presents challenges as well as opportunities for implementing international safeguards and security. A/SMR deployments are expected to be more numerous, more geographically dispersed, and more varied in their designs, placing new demands on the data systems and analytical tools used to support oversight (Alberti et al., 2023; Canadian Nuclear Safety Commission et al., 2024). Because of this variability, the importance and reliance on data systems for A/SMR deployments is expected to be higher than for previous reactor generations. Artificial Intelligence and Machine Learning (AI/ML) offer potential capabilities to address the high variability inherent in A/SMR technology. The beneficiaries of AI-assisted tools include facility operators, government regulators, IAEA inspectors, and A/SMR vendors. This report analyzes how AI/ML-assisted technologies can strengthen the implementation of IAEA safeguards and security measures. It also identifies AI-assisted tools to strengthen operator, facility, and regulator knowledge management practices and examines the potential risks AI/ML-based tools may introduce to IAEA safeguards and security efforts. It concludes with a set of hypothetical, standards-style requirements for AI/ML systems used in safeguards contexts, grounded in an inspector-centric view of system verification. Despite the potential benefits of AI/ML systems, understanding potential intentional and unintentional failure modes is critical for ensuring adequate protection of nuclear materials and facilities. Unique features of A/SMRs including sealed cores, remote and novel paradigms of operation, off-site reactor fabrication, novel fuel forms, and varied refueling requirements, introduce challenges for traditional safeguards technological approaches (Pensado et al., 2024; Federation of American Scientists, 2025). AI/ML systems deployed to address these challenges may introduce new risks requiring systematic evaluation rooted in both AI-specific risk frameworks, such as the NIST AI Risk Management Framework (NIST AI RMF), and established cyber risk management standards such as NIST SP 800-30 (National Institute of Standards and Technology [NIST], 2023; NIST, 2012).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A stress-sensitive precipitate nucleation model beyond classical nucleation theory

The dynamic evolution of precipitates and second phases dictates the strength and stability of most engineering alloys. By design, or as a consequence of thermo-mechanical aging, engineering metals and alloys often form precipitates of second phases when subjecting to diverse thermal and mechanical loads. Precipitation is governed by several factors, including the alloy’s composition, processing/operating temperature, and stresses — either as a result of external loads or from residual stresses. However, state-of-the-art models for precipitate nucleation (i.e., classical nucleation theory) typically lacks consistent method to capture the effects of externally applied and/or internal stresses on nucleation; thereby severely limiting the applicability of these models to complex materials systems and to representative loading scenarios. Here, in this work, we extend upon classical nucleation theory to account for the effect of stresses on precipitation kinetics and thermodynamics. This is achieved via the use of an Eshelbian micromechanics framework keeping track of (i) the stress build up resulting from second phase formation as a function of mechanical load and, (ii) the effects of dislocations on precipitate formation. This new model is applied to σ precipitate in Fe–Cr binary alloys and M 23 C 6 precipitate in 316H stainless steel (SS). Simulations demonstrate the important role of both the remotely applied loads and dislocation pile ups on precipitate nucleation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling

High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth system models (ESMs) and ultra-high-resolution urban climate modeling, over large domains. Here, we present U-Surf, a first-of-its-kind 1 km resolution present-day (circa 2020) global continuous urban surface parameter dataset. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for satisfying dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet and canopy level. Generated using a systematically unified workflow, U-Surf ensures internal consistency among key parameters, making it the first globally coherent urban canopy surface dataset. U-Surf significantly improves the representation of the urban land heterogeneity both within and across cities globally; provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs; enables detailed city-to-city comparisons across the globe; and supports next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf can also be used as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to advance the research frontier of urban system science, climate-sensitive urban design, and coupled human–Earth systems in the future. The dataset is publicly available at https://doi.org/10.5281/zenodo.11247598 (Cheng et al., 2024).

Cheng, Yifan [Univ. of Illinois at Urbana-Champaig↗
DOE OSTI2023

GPS Spoofing Mitigation and Timing Risk Analysis in Networked Phasor Measurement Units via Stochastic Reachability

To address phasor measurement unit (PMU) vulnerability to spoofing, we propose the use of a set-valued state estimation technique known as stochastic reachability (SR)-based distributed Kalman filter (DKF) that computes secure global positioning system (GPS) timing across a network of receivers. Utilizing SR, we estimate not only GPS time but also its stochastic reachable set, which is parameterized by probabilistic zonotope (p-Zonotope). While requiring known measurement error bounds in only non-spoofed conditions, we designed a two-tiered approach. We first performed measurement-level spoofing mitigation via deviation of a measurement innovation from its expected p-Zonotope. We then performed state-level timing risk analysis via a determination of the intersection probability of the estimated p-Zonotope with an unsafe set that violates IEEE C37.118.1a-2014 standards. Finally, we validated our SR-DKF algorithm by subjecting it to a simulated receiver network to coordinate signal-level spoofing. We demonstrate improved timing accuracy and successful spoofing mitigation via the use of our SR-DKF algorithm. We also validated the robustness of the estimated timing risk as the number of receivers were varied.

47 OTHER INSTRUMENTATION↗