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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AdditiveFOAM: A Continuum Multiphysics Code for Additive Manufacturing

AdditiveFOAM is a computational framework that simulates transport phenomena in Additive Manufacturing (AM) processes. It is built on OpenFOAM (Weller et al., 1998), the leading free, open-source software package for computational fluid dynamics (CFD). OpenFOAM offers an extensible platform for solving complex multiphysics problems using state-of-the-art finite volume methods. AdditiveFOAM leverages these capabilities to develop specialized tools aimed at addressing challenges in AM processing. Metal additive manufacturing, also known as metal 3D printing, is an advanced manufacturing technique that creates physical parts from a three-dimensional (3D) digital model by melting metal powder or wire feedstock. A significant area of research in metal AM focuses on process planning to mitigate anomalous features during printing that are deleterious to part performance (e.g., porosity and cracking), as well as controlling localized microstructure and material properties. Given the high costs and substantial time requirements associated with experimental methods for qualifying new materials and processes, there is a compelling incentive for researchers to utilize advanced computational simulations. In this context, AdditiveFOAM offers a simulation framework to better understand undesirable features in printing, thereby enhancing process planning and reducing the reliance on labor-intensive experimental campaigns.

Coleman, John [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2024

Controlling Vibronic Coupling in Chlorophyll Proteins: The Effects of Excitonic Delocalization and Vibrational Localization

Vibrational-electronic (vibronic) coupling plays a critical role in excitation energy transfer in molecular aggregates and pigment-protein complexes (PPCs). But the interplay between excitonic delocalization and vibronic interactions is complex, often leaving even qualitative questions as to what conceptual framework (e.g., Redfield versus Forster theory) should be used to interpret experimental results. To shed light on this issue, we report here on the interplay between excitonic delocalization and vibronic coupling in site-directed mutants of the water soluble chlorophyll protein (WSCP), as reflected in 77 K fluorescence spectra. Experimentally, we find that in PPCs where excitonic delocalization is disrupted (either by mutagenesis or heterodimer formation), the relative intensity of the vibrational sideband (VSB) in fluorescence spectra is suppressed by up to 37% compared to the native protein. Furthermore, numerical simulations reveal that this effect results from the localization of high-frequency vibrations in the coupled system; while excitonic delocalization suppresses the purely electronic transition due to H-aggregate-like dipole-dipole interference, high-frequency vibrations are unaffected, leading to a relative enhancement of the VSB. By comparing VSB intensities of PPCs both in the presence and absence of excitonic delocalization, we extract a set of “local” Huang-Rhys (HR) factors for Chl a in WSCP. More generally, our results suggest a significant role for geometric effects in controlling energy-transfer rates (which depend sensitively on absorption/fluorescence line shapes) in molecular aggregates and PPCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Visual Analytics Framework for Reviewing Streaming Performance Data

Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is challenging because the rate of receiving data and limited time to comprehend data make it difficult for the analysts to sufficiently examine the data without missing important changes or patterns. To support streaming data analysis, we introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization. The data management module collects various computing and communication performance metrics from the monitored system using streaming data processing techniques and feeds the data to the other two modules. The analysis module automatically identifies important changes and patterns at the required latency. In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results. Finally, the interactive visualization module provides the analysts with a coherent view of the changes and patterns in the continuously captured performance data. Through a multi-faceted case study on performance analysis of parallel discrete-event simulation, we demonstrate the effectiveness of our framework for identifying bottlenecks and locating outliers.

Human-centered computing↗
DOE OSTI2026

Hydrological Control on Soil Redox Condition and Carbon Loss of Coastal Wetland Under Sea-Level Rise

Coastal wetlands are critical carbon sinks with their biogeochemical and ecological functioning shaped by dynamic hydrological conditions that are increasingly influenced by climate change. A key unresolved question is how hydrologic flow, vegetation response, and rising sea levels interact to regulate soil redox condition and carbon loss in coastal wetlands. Using a field-tested hydrological–biogeochemical–ecological modeling framework, we reveal how the interplay between terrestrial groundwater discharge and tidal fluctuations generates complex groundwater flow patterns at the terrestrial–aquatic interface, and how these patterns modulate soil redox conditions, in turn influencing soil organic matter decomposition and carbon loss. Notably, rising sea levels suppress soil CO2 emissions while reducing lateral dissolved carbon losses, thereby enhancing litter carbon sequestration under anoxic conditions. As vegetation responds to sea-level rise and carbon inputs diminish, litter carbon subsequently declines. These findings underscore a critical hydrological control on carbon cycling, advancing our understanding of coastal ecosystem resilience in a warming world.

Chen, Kewei [ORNL] (ORCID:000000032580514X)↗
DOE OSTI2022

Crystallization of potassium-zeolites in organic-free media

A ubiquitous phenomenon in zeolite synthesis is the generation of polymorphs leading to either single or multi-phase products. The use of organic structure-directing agents (OSDAs) often ensures a high degree of phase purity for a desired zeolite; however, OSDAs can be economically and environmentally prohibitive. The use of alkali metals as inorganic structure-directing agents (ISDAs) is a common alternative, although reductions in polymorph specificity and/or purity in organic-free media are significant limitations. Developing more controlled, facile synthesis strategies without the aid of OSDAs relies on understanding how zeolite growth conditions impact polymorph selection and alter the physicochemical properties of crystalline products. Relatively few studies of organic-free syntheses have systematically explored potassium growth media. Here, in this study, we examine synthesis conditions that promote the formation of five K-zeolites: CHA, LTL, MER, EDI, and BPH frameworks. Time-resolved analyses reveal distinct trends in crystallization kinetics and interzeolite transformations for potassium compared to prior studies that have largely focused on Na-zeolites. We also highlight methods to achieve nanosized crystals and markedly reduce crystallization times for zeolites, such as chabazite, which are notoriously difficult to synthesize in organic-free media at reasonable timescales. Collectively, these findings identify routes to control K-zeolite polymorphism and provide general design strategies to tailor their crystallization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

HPDR: High-Performance Portable Scientific Data Reduction Framework

The rapid growth in scientific data generation is outpacing advancements in computing systems necessary for efficient storage, transfer, and analysis, particularly in the context of exascale computing. With the deployment of first-generation exascale computing systems and next-generation experimental facilities, this gap is widening and necessitates effective data reduction techniques to manage enormous data volumes. Over the past decade, various data reduction methods, including lossless compression, error-controlled lossy compression, and data refactoring, have been developed to accelerate I/O in scientific workflows. Despite significant reductions in data volume, these methods introduce considerable computational overhead, which can become the new bottleneck in data processing. To mitigate this, GPU-accelerated data reduction algorithms have been introduced. However, challenges remain in their integration into exascale workflows, including limited portability across different GPU architectures, substantial memory transfer overhead, and reduced scalability on dense multi-GPU systems. To address these challenges, we propose HPDR, a high-performance and portable data reduction framework. HPDR is designed to enable the execution of state-of-the-art reduction algorithms across diverse processor architectures while reducing memory transfer overhead to 2.3 % of the original, resulting in up to 3.5× faster throughput compared to existing solutions. It also achieves up to 96% of the theoretical speedup in multi-GPU settings. In addition, evaluations on accelerating I/O operations at scale up to 1,024 nodes of the Frontier supercomputer demonstrate that HPDR can achieve up to 103 TB/s reduction throughput, providing up to 4× acceleration in parallel I/O performance compared to existing data reduction routines. This work highlights the potential of HPDR to significantly enhance data reduction efficiency in exascale computing environments.

Chen, Jieyang [University of Oregon]↗
DOE OSTI2025

Effects of threshold resummation for large-x PDF in large momentum effective theory

Parton distribution functions (PDFs) at large x are challenging to extract from experimental data, yet they are essential for understanding hadron structure and searching for new physics beyond the Standard Model. Within the framework of the large momentum P z expansion of lattice quasi-PDFs, we investigate large x PDFs, where the matching coefficient is factorized into the hard kernel, related to the active quark momentum xP z , and the threshold soft function, associated with the spectator momentum (1 − x)P z . The renormalization group equation of the soft function enables the resummation of the threshold double logarithms α k ln 2k (1 − x), which is crucial for a reliable and controllable calculation of large x PDFs. Our analysis with pion valence PDFs indicates that perturbative matching breaks down when the spectator momentum (1 − x)P z approaches Λ QCD , but remains valid when both xP z and (1 − x)P z are much larger than Λ QCD . Additionally, we incorporate leading renormalon resummation within the threshold framework, demonstrating good perturbative convergence in the region where both spectator and active quark momenta are perturbative scales.

factorization↗
DOE OSTI2026

Extension of Clad Damage Propagation Model for Fission Gas Dispersal and Two-Phase Flow Effects in MOOSE SubChannel Module

This report presents an extension of the Clad Damage Propagation (CDAP) model implemented in the MOOSE SubChannel Module (SCM) to capture post-failure fission-gas dispersal and two-phase flow effects in sodium-cooled fast reactor assemblies. The extended model tracks discharged gas axially and radially, computes channel-averaged flow quality and void fraction using a Lockhart–Martinelli framework, evaluates two-phase frictional pressure-drop multipliers, determines inlet mass-flow degradation under fixed core pressures, and applies an intensified-void-based heat-transfer degradation to affected fuel pins. Radial plume expansion is parameterized using mineral-oil jet experiments mapped to sodium conditions via Reynolds–Weber similarity. Implementation details are documented, along with the new methods and user inputs needed to control plume mapping and two-phase behavior. Demonstration simulations for 19- and 37-pin bundles show that breach size and inlet velocity strongly influence propagation potential: small breaches (≤0.5 mm) produce limited degradation while larger breaches (~1 mm) can drive oscillatory temperature spikes and enhanced failure propagation, especially at higher velocities. These results demonstrate that the extended CDAP model provides a more complete framework for quantifying cladding damage propagation and evaluating propagation potential in transient scenarios. The approach remains computationally efficient, consistent with subchannel-level analysis, yet incorporates sufficient physics to bridge localized post-failure effects with bundle- and assembly-scale degradation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

A deep learning-based Bayesian framework for high-resolution calibration of building energy models

Calibrating building energy models (BEMs), i.e., closing discrepancy between modeling and field measurements, is of significance to support its applications in building sustainability and resilience analysis. However, as being widely used in practice, current Bayesian calibration is mostly performed in low-resolution (annual or monthly), instead of high-resolution (hourly or sub-hourly), which is crucial to support emerging BEM applications, such as building-renewable energy integration (demand response) and smart control. This is attributable to the gaps in current Bayesian calibration process, including (1) difficulty in supporting reliable high-resolution calibration with over-parameterization and multi-solution issues, (2) inadequacy of meta-model to capture temporal building dynamics in high-resolution, and (3) excessive computational burdens of covariance matrix calculation in Bayesian inference. Therefore, to close these gaps, this research proposes a novel deep learning-based Bayesian calibration framework, involving pre-calibration mechanism, Long Short-Term Memory as surrogate models, and simplified covariance matrix calculation, to calibrate BEMs in high temporal resolution (i.e., hourly) with enhanced accuracy and computational efficiency. Finally, the case study demonstrates its effectiveness to match modeling outcomes with measurements and realize CV-RMSE of < 30 % and NMBE of < 6 % in hourly resolution, as well as a significant reduction of calibration time (by > 99 %, from > 600 h to ~ 1.5 h).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Inferring precocial Chinook Salmon production through single‐parentage assignments

Abstract Objective Parentage analysis is a routine methodology in fisheries research, but study systems exist where it is impractical to sample both parents. The ability to reliably assign offspring to a single parent is beneficial in these situations. We applied single‐parentage assignments to a naturally spawning population of Chinook Salmon Oncorhynchus tshawytscha to quantify production of anadromous returns by unsampled precocial males. Methods We used an approach that focused on two important aspects of parentage analyses: (1) addressing the presence of family structure within the set of sampled parents and (2) controlling for false‐positive and false‐negative assignments. Result Results indicated that 30% of reproductively successful males were precocial males, which produced 20% of the returning anadromous offspring. Conclusion This study provides a framework for applying single‐parent assignments in a salmonid study system while explicitly addressing sources of assignment errors.

Steele, Craig A.↗
DOE OSTI2025

Inverse Thermodynamics: Designing Interactions for Targeted Phase Behavior

The traditional goal of inverse self-assembly is to design interactions that drive particles toward a desired target structure. However, achieving successful self-assembly also requires tuning the thermodynamic conditions under which the structure is stable. In this work, we extend the inverse design paradigm to explicitly address this challenge by developing a framework for inverse thermodynamics, i.e., the design of interaction potentials that realize specific thermodynamic behavior. As a step in this direction, using patchy particle mixtures as a model system, we demonstrate how precise control over both bonding topology and bond energetics enables the programming of targeted phase behavior. In particular, we establish design principles for azeotropic demixing and show how to create mixtures that exhibit azeotropy at any prescribed composition. Our predictions are validated through Gibbs-ensemble simulations [Panagiotopoulos, Mol. Phys. 1987, 61, 813−826]. These results highlight the necessity of coupling structural design with thermodynamic engineering, and provide a blueprint for controlling complex phase behavior in multicomponent systems.

Azeotropes↗
DOE OSTI2026

Cryogenic Reduction of Surface-Bound HKUST-1Crystallites

HKUST-1 is a canonical Cu-based metal-organic framework (MOF) with potential roles in catalysis. A convenient hydroxy double salt templating synthesis was used to produce a thin layer of HKUST-1 crystallites amenable to surface science spec-troscopies under controlled conditions. To study the nature of the Cu sites, N2 and CO2 were adsorbed at cryogenic tempera-tures as passive probe molecules for Cu2+ sites. CO, which is commonly used as a probe molecule of both Cu2+ and Cu1+ sites in porous materials, was found to be a non-innocent probe molecule that reduces the Cu2+ and forms CO2. Isotopically labeled 13CO confirmed the Cu2+ reduction and CO oxidation. The results offer a way to tune Cu oxidation states in HKUST-1, even in the cryogenic temperature regime.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A Safety and Management Framework to Enable Automated Mobility Districts in Urban Areas

Automated mobility technology is beginning to emerge as a viable means to create sustainable and effective public mobility systems within denser urban environments. Automated mobility districts (AMDs) describe major urban districts or activity centers in which deployments of multiple automated vehicle (AV) transit and ride-hailing fleets are supported to meet public mobility needs. The authors put forward a framework to enable AMDs and their governing and management jurisdictional authorities to manage safety of AV operations based on lessons learned from the last century of automated guideway transit and roadway intersection traffic control systems. The essential concept is that of operational management and safety-critical control of multiple AV fleets using a “system-of-systems” approach to system safety analysis. The safety analysis would focus on safe passage of the AV fleet vehicles through complex roadway intersections and junctions, especially in the presence of other non-automated modes such as pedestrians and manually operated vehicles.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Fluorine spillover for ceria- vs silica-supported palladium nanoparticles: A MD study using machine learning potentials

Supported metallic nanoparticles play a central role in catalysis. However, predictive modeling is particularly challenging due to the structural and dynamic complexity of the nanoparticle and its interface with the support, given that the sizes of interest are often well beyond those accessible via traditional ab initio methods. With recent advances in machine learning, it is now feasible to perform MD simulations with potentials retaining near-density-functional theory (DFT) accuracy, which can elucidate the growth and relaxation of supported metal nanoparticles, as well as reactions on those catalysts, at temperatures and time scales approaching those relevant to experiments. Furthermore, the surfaces of the support materials can also be modeled realistically through simulated annealing to include effects such as defects and amorphous structures. We study the adsorption of fluorine atoms on ceria and silica supported palladium nanoparticles using machine learning potential trained by DFT data using the DeePMD framework. We show defects on ceria and Pd/ceria interfaces are crucial for the initial adsorption of fluorine, while the interplay between Pd and ceria and the reverse oxygen migration from ceria to Pd control spillover of fluorine from Pd to ceria at later stages. In contrast, silica supports do not induce fluorine spillover from Pd particles.

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

Pre-production results from ATLAS ITk Strip Sensors Quality Assurance Testchip

The production of strip sensors within the framework of the ATLAS Inner Tracker (ITk) development is a process which requires continuous evaluation during the full production period (about 4 years). Such an evaluation is divided into two different parts: Quality Control (QC), which focuses on the final product (the actual sensors) and tries to identify possible defects once the fabrication is completed, and Quality Assurance (QA), which aims to prevent deviations in the manufacturing process and uses specifically-designed test structures. The initial sensor pre-production consists of 5% (1041 sensors) of the total number of sensors expected during production. As part of pre-production, the collaboration has measured key parameters from miniature strip sensors (minis), monitor diodes (MD8), and the ATLAS Testchip, before and after irradiation. In this contribution we focus on the analysis of the results of the MD8 and the Testchip. All parameters have been obtained from the test structures (MD8, bias resistors, interdigitated structures, field oxide capacitors, coupling capacitors, punch-through protection structures and cross-bridge resistors) measured at the different test sites (KEK/Tsukuba, Birmingham, Toronto, Ljubljana, Valencia, Carleton, Prague, CNM-Barcelona). The results are compared to predefined pre- and post-irradiation specifications for each tested parameter.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Evolution of water structures in metal-organic frameworks for improved atmospheric water harvesting

Designing water uptake Although the locations of water molecules in some porous materials have been determined with diffraction techniques, determining the filling sequence of water sites has been challenging. Hanikel et al . used single-crystal x-ray diffraction to locate all of the water molecules in pores of the metal-organic framework MOF-303 at different water loadings (see the Perspective by Öhrström and Amombo Noa). They used this information on the water molecule adsorption sequence to modify the linkers of this MOF and control the water-harvesting properties from humid air for different temperature regimes. —PDS

Science & Technology - Other Topics↗
DOE OSTI2024

Jackson, L., Johnson, M.B., Latrach, A., Grimes, D., Martinez, C., and Mclaughlin, J.F., 2024, Multidisciplinary geotechnical data collection, curation, and analysis for conformity with the regulatory framework for geologic carbon storage in Wyoming, USA: Geological Society of America Abstracts with Programs. Vol. 56, No. 5, 2024, doi: 10.1130/abs/2024AM-405024

Title: Multidisciplinary Geotechnical Data Collection, Curation, and Analysis for Conformity with the Regulatory Framework for Geologic Carbon Storage in Wyoming, USA. Text: Construction and operation of wells for geologic sequestration of carbon dioxide necessitate that they are permitted under the Environmental Protection Agency’s Underground Injection Control Class VI requirements. Class VI wells conform to stringent requirements to ensure long-term safety and integrity of the storage site and the protection of Underground Sources of Drinking Water. Entities pursuing Class VI permitting must provide comprehensive geologic site characterization, including regional geologic structure and stratigraphy, aquifer information, reservoir and confining unit geomechanical properties, geochemical analyses, assessment of trapping capacity and mechanisms, and a variety of other of multidisciplinary geotechnical data. The Wyoming Class VI Site Characterization Database Project is focused on developing a geologic site characterization database of geotechnical information, which has been compiled and verified from established, public databases/entities and scientific literature to expedite Class VI permitting in Sweetwater County within the Greater Green River Basin of southern Wyoming. The preliminary suite of compiled data from 14,000 wells includes 8,000 wells with logs and 7,250 wells with formation tops, ~70 wells with core data (e.g., X-Ray diffraction, petrographic, and petrophysical data), ~2,500 water analyses, ~740 seismic events data, and ~520 bottom-hole temperature measurements. Future work on—and stemming from—this project will include new core analyses, calculation and interpolation of subsurface temperature gradients, mechanical earth models, geochemical simulations, storage capacity estimation, stratigraphic column generation and correlation, and construction of subsurface maps. Finally, this work will help to inspire and facilitate subsurface data compilation and curation beyond Sweetwater County, Wyoming.

42 ENGINEERING↗
DOE OSTI2025

Utility-Scale Operational Consequences for Solar Grid Services

This report delves into the critical aspects of grid services provided by solar inverter-based resources (IBRs), with an emphasis on the evolving landscape of microgrids, virtual power plants (VPPs), aggregators, and distributed energy resource management systems (DERMS). As the energy sector undergoes a transformative shift towards more decentralized and resilient grid architectures, understanding the multifaceted risks associated with these technologies becomes paramount. The report categorizes these risks into organizational, technical, and procedural domains, providing a thorough risk assessment framework that stakeholders can utilize to anticipate and mitigate potential issues. In addressing the increasing complexity of grid interconnections, the report highlights the importance of Cyber-Informed Engineering (CIE). By embedding engineering controls and cybersecurity measures into the early stages of system design, this approach aims to fortify grid infrastructure against emerging cyber threats. The analysis includes an exploration of best practices and strategies for integrating CIE principles to enhance grid security and resilience. To provide practical insights, the report conducts a detailed consequence analysis of various grid services and cyber mitigations that can be applied through the interconnection process. This analysis evaluates the potential impacts of different failure modes and vulnerabilities, offering a clear understanding of the consequences that could arise from disruptions within the energy grid. The findings are further enriched by a series of case studies that illustrate real-world scenarios and lessons learned from past incidents. Through this comprehensive examination of grid services and their criticality, the report aims to prepare industry professionals with the knowledge and tools necessary to navigate the complexities of modern energy systems. By providing a comprehensive approach that includes risk assessment, cybersecurity, and consequence analysis, solar stakeholders can more effectively guarantee the reliability, efficiency, and security of the energy grid.

14 SOLAR ENERGY↗