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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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Unlocking the Future of Aircraft Manufacturing: The Environmental Benefits of Laser Patterning for Surface Enhancement of Aircraft-Certified Alloys

Surface protection and functional modification of aircraft-certified aluminum alloys are essential for corrosion resistance, durability, and long-term airworthiness. At the same time, increasingly restrictive environmental regulations motivate the development of alternatives to legacy wet-chemical surface treatments. This study presents an integrated assessment of ultrafast femtosecond laser surface texturing as a surface functionalization approach for Aluminum 6061 alloys within an aerospace manufacturing and sustainability context. Ultrashort-pulse laser processing enables controlled micro- and nano-scale surface topographical modification with limited thermal impact, allowing adjustment of wettability and surface functionality while preserving bulk material integrity. As a dry and contactless process, femtosecond laser treatment eliminates the use of hazardous chemicals, reduces consumable inputs, and generates minimal secondary waste. A streamlined cradle-to-gate life cycle assessment conducted in accordance with ISO 14040/14044 indicates a lower global-warming potential per functional unit compared with conventional surface treatments, including anodization, plasma-assisted coatings, and organic coating systems. Complementary qualitative analyses addressing environmental health and safety, supply-chain risk, and ESG alignment indicate potential advantages related to occupational safety, regulatory compliance, waste management, and end-of-life recyclability. The investigation is performed on planar Aluminum 6061 reference surfaces with a treated area of 25 mm 2 , providing a controlled laboratory-scale basis for analyzing process behavior, functional surface modification, and associated environmental metrics. Within this defined scope, the results support further evaluation of femtosecond laser surface texturing as a surface engineering option for future aerospace manufacturing.

corrosion resistance↗
DOE OSTI2021

Nanocrystalline Materials: Synthesis, Characterization, Properties, and Applications

Nanostructuring is a commonly employed method of obtaining superior mechanical properties in metals and alloys. Compared to conventional polycrystalline counterparts, nanostructuring can provide remarkable improvements in yield strength, toughness, fatigue life, corrosion resistance, and hardness, which is attributed to the nano grain size. In this review paper, the current state-of-the-art of synthesis methods of nanocrystalline (NC) materials such as rapid solidification, chemical precipitation, chemical vapor deposition, and mechanical alloying, including high-energy ball milling (HEBM) and cryomilling was elucidated. More specifically, the effect of various process parameters on mechanical properties and microstructural features were explained for a broad range of engineering materials. This study also explains the mechanism of grain strengthening using the Hall-Petch relation and illustrates the effects of post-processing on the grain size and subsequently their properties. This review also reports the applications, challenges, and future scope for the NC materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Dioxin-Linked Covalent Organic Framework-Supported Palladium Complex for Rapid Room-Temperature Suzuki–Miyaura Coupling Reaction

Covalent organic framework (COF)-supported palladium catalysts have garnered enormous attention for cross-coupling reactions. However, the limited linkage types in COF hosts and their suboptimal catalytic performance have hindered their widespread implementation. Herein, we present the first study immobilizing palladium acetate onto a dioxin-linked COF (Pd/COF-318) through a facile solution impregnation approach. By virtue of its permanent porosity, accessible Pd sites arranged in periodic skeletons, and framework robustness, the resultant Pd/COF-318 exhibits exceptionally high activity and broad substrate scope for the Suzuki–Miyaura coupling reaction between aryl bromides and arylboronic acids at room temperature within an hour, rendering it among the most effective Pd/COF catalysts for Suzuki–Miyaura coupling reactions to date. Moreover, Pd/COF-318 demonstrates excellent recyclability, retaining high activity over five cycles without significant deactivation. The leaching test confirms the heterogeneity of the catalyst. This work uncovers the vast potential of dioxin-linked COFs as catalyst supports for highly active, selective, and durable organometallic catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Energy Efficiency Package for Tenant Fit-Out: Laboratory Testing and Validation of Energy Savings and Indoor Environmental Quality

Approximately 40% of the total U.S. office floor space of 1.5 billion sq.m (16 billion sq.ft.) is leased space occupied by tenants. Tenant fit-out presents a key opportunity to incorporate energy efficiency within the real estate business cycle. We designed a package of energy efficiency measures tailored to the scope of a tenant fit-out. This tenant fit-out package (TFP) includes advanced lighting and heating, ventilating and air-conditioning (HVAC) controls as core measures, with ceiling fans, automated shading, and plug load controls as additional optional measures. We conducted laboratory testing of six configurations of the package to evaluate energy savings, indoor environmental quality, and identify installation, commissioning, and operational issues. Combined savings for HVAC, lighting, and plug loads ranged from 33–40%. Lighting savings ranged from 69–83%, and HVAC savings from 20–40%. The laboratory testing also revealed some minor but tractable challenges with installation and commissioning of HVAC controls. Overall, the results demonstrate that significant savings can be realized in existing office buildings by incorporating relatively low-risk, proven measures at the time of a tenant fit-out.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Biological Consequences of Marine Energy Development on Marine Animals

Marine energy devices harness power from attributes of ocean water to form a sustainable energy source. Knowledge gaps remain about whether marine energy systems can affect the environment, adding another threat to animal populations and habitats already under pressure from climate change and anthropogenic activities. To date, potential environmental effects have been studied under the scope of stressor–receptor interactions, where moving parts of, or emissions from, a system could harm the animals, habitats, and natural processes. While crucial for understanding effects and identifying knowledge gaps, this approach misses a holistic view of what animals may experience in the presence of marine energy systems. We look at six biological consequences and forces that drive the health of an animal population and the effects expected from marine energy development: success of early life stages; changes in competitive capabilities; growth and survival based on food availability; susceptibility to predators; injury or death; and reproductive success. We use case studies to develop this approach, focusing on a variety of marine animals. An approximate level of risk is assigned for each interaction based on the biological consequences. This work highlights the need to examine the effects of marine energy development on animal populations within their natural habitats.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Effects of Coating on the Electrochemical Performance of a Nickel-Rich Cathode Active Material

Due to their safety and high power density, one of the most promising types of all-solid-state lithium batteries is the one made with the argyrodite solid electrolyte (ASE). Although substantial efforts have been made toward the commercialization of this battery, it is still challenged by some technical issues. One of these issues is to prevent the side reactions at the interface of the ASE and the cathode active material (CAM). A solution to address this issue is to coat the CAM particles with a material that is compatible with both ASE and CAM. Prior studies show that the lithium niobate, LiNbO 3 , (LNO) is a promising material for coating CAM particles to reduce the interfacial side reactions. However, no systematic study is available in the literature to show the effect of coating LNO on CAM performance. This paper aims to quantify the effect of LNO coating on the electrochemical performance of a nickel-rich CAM. The electrochemical performance parameters that are studied are the capacity, cycling performance, and rate performance of the coated-CAM; and the effectiveness of the coating to prevent the side reactions at the ASE and CAM interface is out of the scope of this study. To eliminate the effect of side reactions at the ASE and CAM interface, we conduct all tests in the organic liquid electrolyte (OLE) cells to solely present the effect of coating on the CAM performance. For this purpose, 0.5 wt.% and 1 wt.% LNO are used to coat the LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC-60) CAM through two synthesizing methods. Consequently, the effects of the synthesizing method and the coating weight percentage on the NMC-60 performance are presented.

10 SYNTHETIC FUELS↗
DOE OSTI2022

A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection

To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. Empirical methods are usually based on pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The presented strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability, and suitability for training and testing, cause-effect, entropy inference, and model fitting. The strategy is demonstrated through a pilot use case for the application of anomaly detection to capture a valve packing leak at the high-pressure coolant injection system of a nuclear power plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Reduced-Order Model of a Nuclear Power Plant with Thermal Power Dispatch

This paper presents reduced-order modeling of thermal power dispatch (TPD) from a pressurized water reactor (PWR) for providing heat to nearby heat consuming industrial processes that seek to take advantage of nuclear heat to reduce carbon emissions. The reactor model includes the neutronics of the reactor core, thermal–hydraulics of the primary coolant cycle, and a three-lump model of the steam generator (SG). The secondary coolant cycle is represented with quasi-steady state mass and energy balance equations. The secondary cycle consists of a steam extraction system, high-pressure and low-pressure turbines, moisture separator and reheater, high-pressure and low-pressure feedwater heaters, deaerator, feedwater and condensate pumps, and a condenser. The steam produced by the SG is distributed between the turbines and the extraction steam line (XSL) that delivers steam to nearby industrial processes, such as production of clean hydrogen. The reduced-order simulator is verified by comparing predictions with results from separate validated steady-state and transient full-scope PWR simulators for TPD levels between 0% and 70% of the rated reactor power. All simulators indicate that the flow rate of steam in the main steam line and turbine systems decrease with increasing TPD, which causes a reduction in PWR electric power generation. The results are analyzed to assess the impact of TPD on system efficiency and feedwater flow control. Due to the simplicity of the proposed reduced-order model, it can be scaled to represent a PWR of any size with a few parametric changes. In the future, the proposed reduced-order model will be integrated into a power system model in a digital real-time simulator (DRTS) and physical hardware-in-the-loop simulations.

08 HYDROGEN↗
DOE OSTI2024

The Role of Low-Carbon Fuels and Carbon Capture in Decarbonizing the U.S. Clinker Manufacturing for Cement Production: CO2 Emissions Reduction Potentials

Low-carbon fuels, feedstocks, and energy sources can play a vital role in the decarbonization of clinker production in cement manufacturing. Fuel switching with renewable natural gas, green hydrogen, and biomass can provide a low-carbon energy source for the high-temperature process heat during the pyroprocessing steps of clinker production. However, up to 60% of CO2 emissions from clinker production are attributable to process-related CO2 emissions, which will need the simultaneous implementation of other decarbonization technologies, such as carbon capture. To evaluate the potential of fuel switching and carbon capture technologies in decarbonizing the cement industry, a study of the facility-level CO2 emissions is necessary. This study evaluates the potential for using a single low-carbon fuel as an energy source in clinker production for cement manufacturing compared to conventional clinker production (which uses a range of fuel mixes). In addition, conventional carbon capture (operated with natural gas-based steam for solvent regeneration) and electrified carbon capture configurations were designed and assessed for net-zero emission targets. Carbon emissions reductions with and without biogenic emissions credits were analyzed to ascertain their impact on the overall carbon accounting. Results show that carbon emissions intensity of cement can vary from 571 to 784 kgCO2eq/metric ton of cement without carbon capture and from 166.33 to 438.66 kgCO2eq/metric ton of cement with carbon capture. We find that when biogenic carbon credits are considered, cement production with a sustainably grown biomass as fuel source coupled with conventional carbon capture can lead to a net-negative emission cement (−271 kgCO2eq/metric ton of cement), outperforming an electrified capture design (35 kgCO2eq/metric ton of cement). The carbon accounting for the Scope 1, 2, and biogenic emissions conducted in this study is aimed at helping researchers and industry partners in the cement and concrete sector make an informed decision on the choice of fuel and decarbonization strategy to adopt.

42 ENGINEERING↗
DOE OSTI2025

PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Aging of Limestones and Silane–Siloxane-Based Protective Hydrophobics: The Impact of Heating–Cooling and Freeze–Thaw Cycles

Stones are traditionally used in construction and architectural applications as building elements due to their aesthetic and technical/structural performance. Like other environmental factors (rain, humidity, moisture, salt presence, biological activity, etc.), heating–cooling and freeze–thaw cycles significantly threaten the longevity of stone materials. Hence, considering the socio-economic and cultural value of stones, preventive actions such as hydrophobic coatings are applied to prevent or mitigate damage. The scope of this study is the performance assessment of limestones with different characteristics and the efficiency of various commercial silane/siloxane-based hydrophobic coatings when exposed to thermal variation and freeze–thaw. For that purpose, the standards EN 14066:2013 (determination of resistance to aging by thermal shock) and EN 12371:2010 (determination of frost resistance) were followed. Open porosity and static contact angles were estimated to assess the stone durability and water protection capabilities of the hydrophobics. Additionally, sound speed propagation velocity, quality of building material index, elastic modulus and flexural strength were measured to evaluate the variation of mechanical properties. Static contact angle revealed that the coatings maintained an efficient level of hydrophobicity even after thermal-shock and freeze–thaw weathering tests. The study also revealed a critical interaction between freeze–thaw cycles, hydrophobic coatings and structural integrity of the stones, mostly on more porous ones. When they are subjected to harsh environmental conditions, untreated porous limestones keep structural cohesion, allowing for the natural absorption and release of water during freezing and thawing. On the contrary, when limestones are treated, the hydrophobic coatings can moderately obstruct the water release due to the partial saturation of the porous framework by the products. It also probably resulted from the different mechanical behavior between the inner matrix and layer of stone coated, resulting in a premature breakout and mechanical damage of the stone.

Lisci, Carla (ORCID:000000032179130X)↗
DOE OSTI2020

A Meta-Analysis of Stressors from the Total Environment Associated with Children’s General Cognitive Ability

General cognitive ability, often referred to as ‘general intelligence’, comprises a variety of correlated abilities. Childhood general cognitive ability is a well-studied area of research and can be used to predict social outcomes and perceived success. Early life stage (e.g., prenatal, postnatal, toddler) exposures to stressors (i.e., chemical and non-chemical stressors from the total (built, natural, social) environment) can impact the development of childhood cognitive ability. Building from our systematic scoping review (Ruiz et al., 2016), we conducted a meta-analysis to evaluate more than 100 stressors related to cognitive development. Our meta-analysis identified 23 stressors with a significant increase in their likelihood to influence childhood cognitive ability by 10% or more, and 80 stressors were observed to have a statistically significant effect on cognitive ability. Stressors most impactful to cognition during the prenatal period were related to maternal health and the mother’s ability to access information relevant to a healthy pregnancy (e.g., diet, lifestyle). Stressors most impactful to cognition during the early childhood period were dietary nutrients (infancy), quality of social interaction (toddler), and exposure to toxic substances (throughout early childhood). In conducting this analysis, we examined the relative impact of real-world exposures on cognitive development to attempt to understand the inter-relationships between exposures to both chemical and non-chemical stressors and early developmental life stages. Our findings suggest that the stressors observed to be the most influential to childhood cognitive ability are not permanent and can be broadly categorized as activities/behaviors which can be modified to improve childhood cognition. This meta-analysis supports the idea that there are complex relationships between a child’s total environment and early cognitive development.

Nilsen, Frances M. (ORCID:0000000241936951)↗
DOE OSTI2022

A Summary of Environmental Monitoring Recommendations for Marine Energy Development That Considers Life Cycle Sustainability

Recommendations derived from papers documenting the Triton Field Trials (TFiT) study of marine energy environmental monitoring technology and methods under the Triton Initiative (Triton), as reported in this Special Issue, are summarized here. Additionally, a brief synopsis describes how to apply the TFiT recommendations to establish an environmental monitoring campaign, and provides an overview describing the importance of identifying the optimal time to perform such campaigns. The approaches for tracking and measuring the effectiveness of recommendations produced from large environmental monitoring campaigns among the stakeholder community are discussed. The discussion extends beyond the initial scope of TFiT to encourage discussion regarding marine energy sustainability that includes life cycle assessment and other life cycle sustainability methodologies. The goal is to inspire stakeholder collaboration across topics associated with the marine energy industry, including diversity and inclusion, energy equity, and how Triton’s work connects within the context of the three pillars of energy sustainability: environment, economy, and society.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Thermodynamics and Magnetism of SmFe 12 Compound Doped with Zr, Ce, Co and Ni: An Ab Initio Study

Alloys that are Ni-doped, such as the (Sm 1—y Zr y )(Fe 1—x Co x ) 12 and (Ce 0.5 Sm 0.5 )Fe 10 Co 2 systems, are studied because of their magnetic properties. The (Sm 1—y Zr y )(Fe 1—x Co x ) 11—z Ti z and (Ce. 1—x Sm x )Fe 9 Co 2 Ti alloys are considered contenders for vastly effective permanent magnets because of their anisotropy field and Curie temperature. Ti can act as a stabilizer for the SmFe 12 compound but substantially suppresses saturation magnetization. To maintain the saturation magnetization in the scope of 1.3–1.5 T, we propose substituting a particular quantity of Fe and Co in the (Sm 1—y Zr y )(Fe 1—x Co x ) 12 and (Ce 0.5 Sm 0.5 )Fe 10 Co 2 alloys with Ni. By performing ab initio calculations, we show that Ni incorporation results in increased thermodynamic stability and, in contrast to Ti, has a parallel spin moment aligned to the moment of the SmFe 12 compound and improves its saturation magnetization without affecting the anisotropy field or Curie temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Microwave Synthetic Routes for Shape-Controlled Catalyst Nanoparticles and Nanocomposites

The use of microwave irradiation for the synthesis of inorganic nanomaterials has recently become a widespread area of research that continues to expand in scope and specialization. The growing demand for nanoscale materials with composition and morphology tailored to specific applications requires the development of facile, repeatable, and scalable synthetic routes that offer a high degree of control over the reaction environment. Microwave irradiation provides unique advantages for developing such routes through its direct interaction with active reaction species, which promotes homogeneous heat distribution, increased reaction rates, greater product quality and yield, and use of mild reaction conditions. Many catalytic nanomaterials such as noble metal nanoparticles and intricate nanocomposites have very limited synthetic routes due to their extreme temperature sensitivity and difficulty achieving homogeneous growth. This work presents recent advances in the use of MW irradiation methods to produce high-quality nanoscale composites with controlled size, morphology, and architecture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses

This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Best Practices for Measuring the Modulation Transfer Function of Video Endoscopes

Endoscopes are crucial for assisting in surgery and disease diagnosis, including the early detection of cancer. The effective use of endoscopes relies on their optical performance, which can be characterized with a series of metrics such as resolution, vital for revealing anatomical details. The modulation transfer function (MTF) is a key metric for evaluating endoscope resolution. However, the 2020 version of the ISO 8600-5 standard, while introducing an endoscope MTF measurement method, lacks empirical validation and excludes opto-electronic video endoscopes, the largest family of endoscopes. Measuring the MTF of video endoscopes requires tailored standards that address their unique characteristics. This paper aims to expand the scope of ISO 8600-5:2020 to include video endoscopes, by optimizing the MTF test method and addressing parameters affecting measurement accuracy. We studied the effects of intensity and uniformity of image luminance, chart modulation compensation, linearity of image digital values, auto gain control, image enhancement, image compression and the region of interest dimensions on images of slanted-edge test charts, and thus the MTF based on these images. By analyzing these effects, we provided recommendations for setting and controlling these factors to obtain accurate MTF curves. Our goal is to enhance the standard’s relevance and effectiveness for measuring the MTF of a broader range of endoscopic devices, with potential applications in the MTF measurement of other digital imaging devices.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

photovoltaics↗