Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “behavioral sciences”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Metal forming and working of stabilized nanocrystalline Cu-Ta for electrical contacts

The commercialization of nanocrystalline metals and alloys is currently entering a renaissance period. Many of the processing and consolidation challenges that have haunted them are now more fully understood, opening the doors for stabilized nanocrystalline metals to be produced on a bulk scale. While challenges remain, the increased volume at which these materials are being supplied is for the first time allowing for investigations into more traditional methods of metal working, such as extruding, rolling, forming, and forging. Recently, the manufacturing science has been developed to allow nanocrystalline Cu-Ta alloys to progress to this point. This article therefore builds upon the last decade of evolutionary progression within the family of stabilized nanocrystalline Cu-Ta alloys by presenting some of the first findings related to scaled powder synthesis, production of billets, thin sheets, and foils. Here, the mechanical performance and physical properties relevant to forming electrical contacts and pins including, tensile, J-integral fracture toughness, Charpy Impact, bi-axial tension, and conductivity are reported. This introductory investigation into forming such a novel material, provides evidence to these alloys potential at bridging the gap between being a scientific curiosity to that of a real engineering material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

On the role of geometrically necessary dislocations in void formation and growth in response to shock loading conditions in wrought and additively manufactured Ta

This study investigates the role of geometrically necessary dislocations (GNDs) and microstructure on void nucleation and growth in wrought and additively manufactured (AM) tantalum subjected to high-strain rate loading. Multi-modal 3D data was collected using TriBeam tomography to calculate GND densities and their spatial relationship to voids. A microstructural comparison between the wrought and AM samples identified distinct void shapes and locations, with intragranular voids and more spherical voids frequently observed in the AM dataset. Results indicate that voids preferentially form at both high-angle grain boundaries and low-angle subgrain boundaries, the latter of which are frequently observed in the AM material. Through a radial distribution analysis of all voids in the datasets, significant GND localization to near-void-surface regions was observed in both samples. 3D crystal plasticity simulations were employed to extend the experimental observations, revealing higher void growth rates in [111] oriented grains when compared to [001] grains. The simulations also suggest that GNDs can be generated as part of the void growth process, with more GND accumulation for growth in a [111] grain than a [001] grain. These findings provide valuable insights into the links between nanoscale void nucleation, mesoscale void growth, and microstructural effects in dynamically loaded tantalum.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effect of Anisotropic Confinement on Electronic Structure and Dynamics of Band Edge Excitons in Inorganic Perovskite Nanowires

Inorganic lead halide perovskite nanostructures show promise as the active layers in photovoltaics, light emitting diodes, and other optoelectronic devices. They are robust in the presence of oxygen and water, and the electronic structure and dynamics of these nanostructures can be tuned through quantum confinement. Here we create aligned bundles of CsPbBr3 nanowires with widths resulting in quantum confinement of the electronic wave functions and subject them to ultrafast microscopy. We directly image rapid one-dimensional exciton diffusion along the nanowires, and we measure an exciton trap density of roughly one per nanowire. Using transient absorption microscopy, we observe a polarization-dependent splitting of the band edge exciton line, and from the polarized fluorescence of nanowires in solution, we determine that the exciton transition dipole moments are anisotropic in strength. Furthermore, our observations are consistent with a model in which splitting is driven by shape anisotropy in conjunction with long-range exchange.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

In situ observation of the atomic shuffles during the {${{11}}\bar{{{2}}}{{1}}$} twinning in hexagonal close-packed rhenium

Twinning, on par with dislocations, is critically required in plastic deformation of hexagonal close-packed crystals at low temperatures. In contrast to that in cubic-structured crystals, twinning in hexagonal close-packed crystals requires atomic shuffles in addition to shear. Though the twinning shear that is carried by twinning dislocations has been captured for decades, direct experimental observation of the atomic shuffles, especially when the shuffling mode is not unique and does not confine to the plane of shear, remains a formidable challenge to date. Here, by using in-situ transmission electron microscopy, we directly capture the atomic mechanism of the {${{11}}\bar{{{2}}}{{1}}$} twinning in hexagonal close packed rhenium nanocrystals. Results show that the {${{11}}\bar{{{2}}}{{1}}$} twinning is dominated by the ($\textbf{b}$ 1/2 , h 1/2 ) twinning disconnections. In contrast to conventional expectations, the atomic shuffles accompanying the twinning disconnections proceed on alternative basal planes along 1/6 {${{1\bar1}}{{{0}}}{{0}}$} , which may be attributed to the free surface in nanocrystal samples, leading to a lack of mirror symmetry across the {${{11}}\bar{{{2}}}{{1}}$} twin boundary.

(S)TEM↗
DOE OSTI2020

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Robust errant beam prognostics with conditional modeling for particle accelerators

Abstract Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data’s variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Oak Ridge National Laboratory's Strategic Research and Development Insights for Digital Twins

Oak Ridge National Laboratory (ORNL) is pleased to provide our response to the NITRD RFI on Digital Twins Research and Development. Digital twins are virtual representations of physical systems, leveraging real-time data to simulate and predict behaviors. ORNL is advancing digital twin technology across various disciplines, including neutron scattering, networking, science ecosystems, supercomputing, secure facilities, mobility technologies, materials design and discovery, power systems, fusion reactors, biological sciences, and earth observation. These efforts aim to enhance scientific research, operational efficiency, and decision-making processes. ORNL facilities, such as the High Flux Isotope Reactor (HFIR), Grid-C, Spallation Neutron Source (SNS), and Oak Ridge Leadership Computing Facility (OLCF), provide the infrastructure to develop and demonstrate these digital twin technologies. In this document, we lay out key challenges, research gaps, and future opportunities based on our experience with digital twins that aim to serve as useful contributions towards a National Digital Twins R&D Strategic Plan. In the remaining document, we address nine of the thirteen topic areas specified in the RFI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

AWAKEN

The American WAKE experimeNt (AWAKEN) is a landmark collaborative international wake observation and validation campaign. Wake interactions are among the least understood and most impactful physical interactions in wind plants today, leading to unexpected power losses and increased operations and maintenance costs. The AWAKEN campaign is designed to gather observational data to address the most pressing science questions about wind turbine wake interactions and aerodynamics and to further understand wake behavior and validate wind plant models. Simultaneously, the AWAKEN campaign will also focus on testing of wind farm control strategies that have been shown to increase wind plant power production. Leveraging the expertise and resources of a large body of National Laboratories, academic institutions, and industry partners will lead to improved wind farm layout with greater power production and improved reliability, ultimately leading to lower wind energy costs.

17 WIND ENERGY↗
DOE OSTI2023

Advanced monitoring of soil-vegetation co-dynamics reveals the successive controls of snowmelt on soil moisture and on plant seasonal dynamics in a mountainous watershed

Evaluating the interactions between above- and below-ground processes is important to understand and quantify how ecosystems respond differently to atmospheric forcings and/or perturbations and how this depends on their intrinsic characteristics and heterogeneity. Improving such understanding is particularly needed in snow-impacted mountainous systems where the complexity in water and carbon storage and release arises from strong heterogeneity in meteorological forcing and terrain, vegetation and soil characteristics. This study investigates spatial and temporal interactions between terrain, soil moisture, and plant seasonal dynamics at the intra- and inter-annual scale along a 160 m long mountainous, non-forested hillslope-to-floodplain system in the upper East River Watershed in the upper Colorado River Basin. To this end, repeated UAV-based multi-spectral aerial imaging, ground-based soil electrical resistivity imaging, and soil moisture sensors were used to quantify the interactions between above and below-ground compartments. Results reveal significant soil-plant co-dynamics. The spatial variation and dynamics of soil water content and electrical conductivity, driven by topographic and soil intrinsic characteristics, correspond to distinct plant types, with highest plant productivity in convergent areas. Plant productivity in heavy snow years benefited from more water infiltration as well as a shallow groundwater table depth. In comparison, low snowpack years with an early first bare-ground date, which are linked to an early increase in plant greenness, imply a short period of saturated conditions that leads to lower average and maximum greenness values during the growing season. Overall, these results emphasize the strong impact of snowpack dynamics, and terrain and subsurface characteristics on the heterogeneity in plant type and seasonal dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Atmospheric Modeling to Enable Prediction of Golden Eagle Interactions with Wind Power Plants

This panel will explore the use of technologies and methods that enhance our understanding of eagle behavior in and near wind energy facilities. The decreasing costs of tracking technologies and substantial computational capabilities are allowing for finer resolution and confidence of eagle observations and the modeling of such datasets increase our understanding of their movements and use of airspace and underlying landscapes. Behavior ecologists' intrinsic knowledge of eagle behavior can augment and further enhance inferences from technology-derived information alone, e.g., GPS, camera, radar, etc. The panel will discuss the state of science and, given the cost-effective tools available, what research objectives are emerging to better assess and reduce wind-eagle risk. The discussion will focus on the following areas: Integrating Behavioral and Quantitative Ecologist perspectives of bald eagle and golden eagle behavior and wind energy risk; Regional-scale vs. facility-scale investigations of bald eagle and golden eagle use of airspace. For example, avoidance behavior to assess risk at facility-scale, and eagle use of landscapes, e.g., territories, nesting, and life-cycle behavior; Bald eagle and golden eagle use of airspace and nexus with atmospheric flow dynamics of wind energy generation; GPS, biomonitoring, tracking camera, radar, and other tools/techniques for deepening the knowledge of eagle behavior in and around wind farms.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2024

FIU Project 2: Environmental Remediation Science & Technology [Slides]

FIU’s research under this project involves conducting basic and applied science to fill knowledge gaps and validate potential remediation technologies for contaminated soil and groundwater and the assessment of the fate and transport of contaminants in the environment. The aim of FIU’s research is to reduce the potential for contaminant mobility or toxicity in the surface and subsurface through the development and application of state-of-the-art scientific and environmental remediation technologies at the Hanford Site, Savannah River Site (SRS), and the Waste Isolation Pilot Plant (WIPP), which is the Nation’s only mined geologic repository for permanent disposal of transuranic waste. FIU collaborates with scientists from Pacific Northwest National Laboratory (PNNL), Savannah River National Laboratory (SRNL), Savannah River Ecology Laboratory (SREL), Los Alamos National Laboratory (LANL) and the DOE Carlsbad Field Office (CBFO) in order to plan and execute research that is synergistic with the work being conducted at the sites, and that supports the resolution of critical science and engineering needs which leads to a better understanding of the long-term behavior of subsurface contaminants. The knowledge gained through this research will be used to transform experimental and modeling innovations into practical applications deployed at the sites to support EM’s primary goal of expediting the closure of major contaminated soil and groundwater sites and waste units. Collaborative relationships between FIU and the national laboratories have provided large benefits over the years to FIU, the national laboratories, the DOE complex, and the DOE EM mission. By working closely with the national laboratories, FIU’s research is not only closely aligned with the cleanup mission priorities at the DOE sites, but complements and supports ongoing work at the national laboratories for screening of new remedial technologies. This coordination and leveraging of research efforts results in time- and cost-savings, and will accelerate progress of the DOE EM environmental restoration mission.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Exceptional Alignment in a Donor–Acceptor Conjugated Polymer via a Previously Unobserved Liquid Crystal Mesophase

Abstract Orienting polymer semiconductors is desirable to optimize device characteristics, provide insight into microstructure, and magnify subtle phase behavior. Here, a combination of uniaxial strain and subsequent heating of the donor–acceptor (DA) polymer PBnDT‐FTAZ is discovered to lead to exceptional optical dichroic ratios of up to 38 (and close to 50 near the polymer's absorption edge). This alignment is achieved due to the existence of a previously undetected thermotropic liquid crystal (LC) mesophase. The LC transition, not discernable through calorimetry, is uncovered through a combination of in situ UV–vis spectroscopy, X‐ray scattering, and dynamic mechanical analysis (DMA). Comparing PBnDT‐FTAZ to the non‐fluorinated PBnDT‐HTAZ and the homo polymer PBnDT, all of which show similar thermal transitions, reveals that exceptional alignment is only found in PBnDT‐FTAZ. This is attributed to the PBnDT‐FTAZ film having two distinct liquid crystal populations, and the polymer templating to a highly aligned, high‐clearing temperature population when heated. The DMA thermal relaxation behavior observed here is also seen in other DA conjugated polymers suggesting that such thermotropic LC mesophases may be common in these materials. These findings demonstrate a polymer semiconductor with remarkable alignment and uncover phase behavior with broad implications for process‐structure‐property relationships in polymer semiconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Roadmap for the future of extreme wildfire events

Background Extreme wildfire events (EWEs) represent a growing threat globally, posing substantial risks to ecosystems, human communities, and infrastructure. Despite increased recognition of their ecological, social, and economic significance, current definitions of EWEs vary widely, reflecting disciplinary biases and regional contexts. This article emerges from an interdisciplinary workshop convened to reassess and refine the definition of EWEs, examine their impacts across ecological and social dimensions, and identify critical knowledge gaps impeding our understanding of these infrequent but important events. Results Our synthesis highlights significant limitations with existing definitions, particularly their reliance on subjective thresholds and their emphasis on extreme fire behavior alone. EWEs encompass a spectrum of complex, multi-dimensional phenomena that extend beyond immediate biophysical characteristics to include cumulative social, economic, and ecological impacts. These impacts often manifest over extended timeframes and include hazardous environmental contamination, severe geomorphic disturbances, ecosystem transformations, and unintended consequences of post-fire management actions. Current wildfire modeling frameworks inadequately capture these compounding factors, particularly the interactions among social systems, ecological conditions, and extreme fire behavior. To overcome these issues, we advocate for an interdisciplinary and context-sensitive approach to defining and studying EWEs. This revised definition emphasizes wildfires exhibiting anomalies in fire behavior, ecological outcomes, or social impacts relative to historically observed baselines, accommodating variability across different geographic regions and ecological settings. Conclusions Adopting an interdisciplinary framework that integrates biophysical and social sciences will enhance the predictive capability of wildfire models and improve resilience planning and response strategies. Filling identified knowledge gaps—such as limited high-quality empirical fire behavior data and insufficient integration of social dynamics into modeling—will better prepare communities and ecosystems to cope with and adapt to EWEs. This inclusive approach underscores the necessity for collaboration across disciplines and sectors, essential to managing extreme wildfires in an era of increasing climatic and ecological uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Semiconducting to Metallic Electronic Landscapes in Defects-Controlled 2D π-d Conjugated Coordination Polymer Thin Films

Two-dimensional coordination polymers (2DCPs) have been predicted to exhibit exotic properties such as superconductivity, topological insulating behavior, catalytic activity, and superior ion transport for energy applications; experimentally, these materials have fallen short of their expectation due to the lack of synthesis protocols that yield continuous, large crystallite domains, and highly ordered thin films with controllable physical and chemical properties. Herein, the fabrication of large-area, highly ordered 2DCP thin films with large crystallite domains using chemical vapor deposition (CVD) approaches is described. It is demonstrated that defects and the packing motifs of 2DCP thin films may be controlled by adjusting the vapor–vapor and vapor–solid interactions of the metal and organic linker precursors during the CVD fabrication process. Such control allows for the fabrication of defects-controlled 2DCP thin films that show either semiconducting or metallic behavior. The findings provide the first demonstration of tuning the electrical properties of sub 100 nm-thick continuous 2DCP thin films by controlling their electronic landscape through defect engineering. As such, it is determined that large-area, highly ordered 2DCP thin films may undergo a semiconducting to metallic transition that is correlated to changes in morphology, crystalline domain sizes, crystallite orientation, defect interactions, and electronic structure.

2D coordination polymers↗
DOE OSTI2023

Role of surface wetting on tribological behavior for laser nanotextured steel using ionic liquid lubricants

Here, this research evaluates the effect of surface wettability on the tribological performance through ball-on-flat tribology testing. The substrate material, M2 tool steel, is laser processed and then functionalized with fluorocarbon and nitrile chemistry to achieve distinct oleophobicity and oleophilicity, respectively, but with a similar nanoscale surface texture. The baseline lubricant is poly-alpha-olefin (PAO) oil, and ionic liquids (ILs) are used as additives for this study. The interaction between the nanoscale textured steel surface and ionic liquid-based oils is investigated. A set of reciprocating wear tests are performed to investigate the tribological behavior of the tribo-system consisting of the surface-engineered, flat M2 tool steel specimen and a standard, surface-polished steel ball. Results show that the oleophobic flat surface results in a lower friction, while the oleophilic surface modification leads to a better wear protection to the flat surface. Ammonium-based IL provides the highest friction reduction, while the phosphonium-based ILs provide an improved wear protection.

36 MATERIALS SCIENCE↗
DOE OSTI2025

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗