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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Model Cortical Association Fields Account for the Time Course and Dependence on Target Complexity of Human Contour Perception

Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of smoothly varying, globally aligned contour fragments (amoebas) distributed among groups of randomly rotated fragments (clutter). The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time (20-200 ms), followed by an image mask optimized so as to interrupt visual processing. Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the psychophysical experiments were accounted for by a computational network model, in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images. Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least 37.5 ms of cortical processing time. Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception, with the remaining discrepancies postulated to arise from the influence of higher cortical areas.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

University of Utah, Energy Commercialization Center

During the Energy Commercialization Center’s (ECC) three years in operation, the only thing constant was change. The world of commercialization and cleantech evolved significantly during the time the ECC was formed and operating, including: the availability of cleantech funding lessoned, the growth of incubators and accelerators skyrocketed, the State of Utah created an office dedicated to energy development, the University of Utah was both praised and criticized for its success in commercialization, and the Federal government temporarily shut down. During the three-year grant there were three principle investigators on the grant, as well as three directors for the University’s Commercialization Office. Change can be hard for an organization,but as we instruct the companies we support, “Fail fast and fail often, because it is the fastest path to success.” Although there were some unanticipated challenges along the way, the local ecosystem is stronger because of the ECC’s efforts. Perhaps the greatest lesson learned was the importance of aligned incentives between key stakeholders in the commercialization process and the need for resources at the company and individual entrepreneur levels. The universities have systems and incentives to commercialize technologies, but creating value and companies generally rest with the individuals and entrepreneurs. Unfortunately the ECC was unable to create a viable mechanism to transfer the commercialization process that successfully aligned incentives and achieve a more effective ecosystem within the Rocky Mountain West. However, the ECC was successful in adding value to the individual ecosystems, and connecting national resources to regional and local needs. Regarding the ECC’s effectiveness in developing a cleantech commercialization ecosystem, initial inroads and relationships were established with key stakeholders. However, incentives, perceived or real competition, differences in commercialization processes, and culture all played a role in inhibiting the development and distribution of a regional ecosystem and commercialization process. Had the University and the ECC been able to develop a software platform, some of these challenges may have been overcome, but without the final development and release of the Western Innovation Network, the ECC realistically could not scale and distribute a commercialization platform. Further, cleantech startups need to engage in a more intensive customer validation process, and establish strong community connections if they are to succeed in commercializing their products. The university system incentivizes research and access to research funding and risk capital is competitive, so by nature collaboration on commercialization was difficult. Each of the local ecosystems within the Rocky Mountain West was unique. Utah did not, and does not, have a system outside of the universities to support entrepreneurs and cleantech commercialization. Through the ECC’s efforts developing a regional ecosystem, it became clear that successful ecosystems had a community and associated mechanisms that supported local entrepreneurs and startups. Most importantly the ECC aided in the creation of Utah’s cleantech ecosystem, one that supports entrepreneurs and startup companies that need help and support in their efforts to commercialize clean technologies. The absence of support for clean tech from state government and local organizations was a significant impediment to cleantech commercialization. To overcome this challenge, the ECC has formed Sustainable Startups. Sustainable Startups is a new non-profit organization designed to build a culture and community in Utah that supports and understands the importance of cleantech and sustainable development. While the ECC generated mixed success in building a regional commercialization ecosystem for cleantech, the organization did provide tremendous benefit to startups and the broader public. Over 60 companies were given direct business development support by the ECC, many of whom then generated direct economic development impacts. In addition, the ECC served an important role as community convener, educator and relationship builder through hosting numerous public and private events including: Energize 2013; Millennial Train whistle stop; business plan competition supporter; Clean Tech Open Accelerator organizer; Sustainable Startups Series developer, and much more. While the ECC did not fully apply, develop, and transmit the University of Utah’s TCO commercialization model to cleantech, it nevertheless assisted numerous inventors, entrepreneurs and institutions in furthering the growth of clean energy and energy efficiency technologies. The TCO’s commercialization model was not applied to regional clean tech initiatives for several main reasons. First, flaws with the commercialization model were realized after the ECC’s formation. Second, leadership changes within the TCO and ECC hampered early organizational development and implementation initiatives. Third, misaligned incentives between the ECC, regional universities, institutions, and the State of Utah resulted in a lack of collaboration and knowledge transfer regarding commercialization. In principle, everyone was aligned and willing to collaborate, but reality was much different and challenging.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Clean Energy Workforce and Employment Gap Analysis in the Hill District of Pittsburgh, PA [Slides]

Through Communities LEAP, a community coalition focused on the Hill District neighborhood of Pittsburgh is working with a technical assistance provider network led by the National Renewable Energy Laboratory (NREL). The coalition includes community organizations, nonprofits, the city government, and the utility. Technical assistance provides analysis and information to support Hill District stakeholders in their goals to create informed residential energy efficiency and renewable energy transition strategies that improve housing conditions and lower energy bills, incorporate energy efficiency and renewable energy strategies into existing, community-driven development efforts; and generate quality local jobs. This presentation provides a summary of potential employment impacts of residential energy efficiency investments in the Hill District, aligned with NREL's housing stock analysis; a scan of existing energy efficiency and clean energy workforce and education stakeholders in and around the Hill District; and gaps and potential opportunities for new or expanded training to align with energy efficiency and clean energy goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Bridging the Gap between Cosmological Simulations with Graph Neural Networks and Domain Adaptation

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross dataset tasks (up to 28% better relative error and up to almost an order of magnitude better χ 2 ). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

High-throughput Single-Cell Proteomics and Transcriptomics from the Same Cells with a Nanoliter-Scale Spin-Transfer Approach

Single-cell multiomic platforms provide a comprehensive snapshot of cellular states and cell types by offering critical insights into the spatiotemporal regulation of biomolecular networks at a systems level, thereby defining the basis of multicellularity. Here, we introduce nanoSPINS, an advanced platform that enables high-throughput profiling and integrative analysis of the transcriptome and proteome from the same single cells using RNA sequencing and isobaric labeling LC-MS-based proteomics, respectively. NanoSPINS can efficiently transfer mRNA-containing droplets across two microarrays via a centrifugation-based approach, while proteins are retained on the initial platform. Benchmarking of nanoSPINS on two cell lines demonstrates its ability to generate global proteomic and transcriptomic profiles that align well with previously established methodologies/platforms. The incorporation of isobaric TMTpro labeling into this single-cell multiomics platform significantly enhances the throughput of single-cell proteomic analyses. Through the high-throughput quantification of the proteome and transcriptome, nanoSPINS not only facilitates the identification of molecular features at both mRNA and protein level but also provides larger sample sizes for improved statistical power in clustering and differential abundance. Given the broad applicability of single-cell multiomics in biological research and clinical settings, we believe nanoSPINS represents a powerful platform for the characterization of heterogeneous cell populations.

multi 'omics↗
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $\chi^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Nanostructure-enhanced Phase Change Materials (NePCM) and HRD

Development and characterization of high aspect-ratio nanostructures tailored for dramatic improvements of thermal conductivity of phase change materials is proposed. This DOE renewal project builds upon a proven research record on development of a number of stable and functionality-proven colloids of metal oxide and metallic nanoparticles (avg. diameters less than 10 nm). These efforts followed original conceptualization and synergistic activities on promoting nanostructure-enhanced phase change materials (NePCM) and their utilization for thermal energy storage, waste heat recovery, thermal comfort, etc. Given this background, availability of promising preliminary results for the proposed target materials and formation of a new team of researchers, the investigators will focus on development of nanoadditives of metallic nanorods, functionalized carbon nanotubes, Graphene, graphite nanoplatelets, nanocomposites combining graphene with metal/metal oxide nanostructures and self-assembling carbon nanofiber-based nanomaterials. Specifically, the chemical synthesis group will identify procedures leading to nanorods of copper, copper oxide, silver and graphitic carbon exhibiting long-term stability against precipitation when dispersed in hydrocarbon solvents suitable for PCM applications. Important goals are to achieve nanorods with dimensions, including aspect ratio, that induce significant enhancements in thermal conductivity and also to control instabilities against rod precipitation during solvent freezing and melting. Another group will focus on developing complex nanoarchitectured nanocomposites through utilizing heterostructures that combine graphene with metal or metal oxide nanostructures. These heterostructures will be mixed with paraffin, fatty acids, or stearyl alcohol to realize novel NePCM. Fundamental studies will be performed by conducting materials and thermal characterization methods and structure-processing-properties relationships will be derived. In addition, growth mechanisms for the proposed heterostructures will be evaluated. Another research team will undertake a variety of approaches to functionalize the surfaces of nanostructured carbon-based materials, i.e. carbon nanotubes, Graphene and graphite nanoplatelets. The proposed techniques include both surfactant- and polymer-assisted dispersion of carbon nanotubes, Graphene and graphite nanoplatelets, in addition to microwave-initiated carbon nanotube growth to produce 3-D nanocarbon fillers for NePCM. Functionalized and pristine carbon nanotubes will also be subjected to a strong magnetic field to explore the effect of controlled alignment on the improved thermal conductivity. Microwave-initiated nanotube growth on the surfaces of the voids of graphite foam will be attempted to extend the previous work on thermal conductivity improvement of PCM that are impregnated into porous highly-conductive structures. Refinements of continuum modeling of colloids and most importantly, molecular dynamics-based mechanistic studies for carbon nanotubes and Graphene-based multi-component systems will be pursued. Finally, self-assembling carbon nanofiber-based NePCM will be investigated by another group. A highly-conductive carbon nanofiber network, which is created by a self-assembling approach, will host the PCM and enhance the thermal conductivity by several folds. Furthermore, the inert and relatively stable carbon nanofiber will be further utilized for creating low-mid range temperature NePCM and high-temperature NePCM by filling the porous space inside the self-assembling carbon nanofiber network with paraffin-based or salt-based PCM, respectively. Improved thermal energy charge/discharge performance and stable phase change temperature control features exhibited by this new generation of self-assembling carbon nanofiber-based NePCM will potentially impact many applications including solar energy storage systems.

25 ENERGY STORAGE↗
NASA NTRS2018

Project-domain Science Traceability and Alignment Framework (P-STAF): Analysis of a Payload Architecture

Large science-focused space missions often have multiple instruments working together to address broad science goals. Systems engineers on these types of projects must work with the project scientists to evaluate trades and make decisions that result in a system that efficiently serves the mission science goals. This collaboration is more effective if the systems engineers understand both the traceability from the L1 customer requirements to the selected instruments and the contributions of each instrument in the context of the whole payload suite. These relationships might be understood implicitly by the science team on a project, but there is value in formally codifying them so this understanding can be accessed and formally analyzed by a broader systems engineering effort. We first described a framework for this communication, called the Project-domain Science Traceability and Alignment Framework (P-STAF), in the IEEE 2017 paper “A Framework for Extending the Science Traceability Matrix: Application to the Planned Europa Mission.” This paper shows how that basic framework can be leveraged to not only formally capture these relationships between the instruments and the customer needs, but also how that information can be codified in an analyzable graph that can be queried to provide a better understanding of mission risks and scope. This work was drawn from the application of P-STAF to the Europa Clipper mission, but generic example networks are used to illustrate the power of this technique.

Reinholtz, Kirk↗
DOE OSTI2026

Optimizing HAARP Beam Pattern for Generation of Strong F‐Region Field‐Aligned Irregularities

Field-aligned irregularities (FAIs) are the signatures of plasma turbulence and convection in the mid- and high-latitude F-region ionosphere, and also provide coherent backscatter targets for HF radars. To serve irregularity generation and characterization studies, we conducted experiments at the High-frequency Active Auroral Research Program (HAARP) in August 2023 with the goal of identifying the optimal HAARP beam pattern for reliable generation of intense FAIs over a large geographic region. The HAARP beam patterns we tested were the commonly-used narrow beam, also known as L0, as well as the wider L1 and L2 “twisted” beam patterns. The size and intensity of the FAI region generated by each HAARP beam pattern was quantified using the Kodiak Island Super Dual Auroral Radar Network (SuperDARN) radar. Stimulated electromagnetic emissions (SEE) from heater wave-FAI scattering were also recorded using a receiver located near HAARP. The L1 beam pattern was found to produce the strongest SuperDARN backscatter over the largest region. Although the heater frequency was intended to be tuned a few hundred kHz below the F-region critical frequency (foF2) during each experiment, difficulty in estimating foF2 during the campaign likely resulted in HAARP heating at significantly different frequency ranges around foF2 during each experiment. Although this additional free parameter complicated data analysis for this study, the SuperDARN and SEE measurements have led to further inquiry into the role heater frequency plays in the artificial generation of FAIs.

58 GEOSCIENCES↗
DOE OSTI2021

Seamless Hybrid-integrated Interconnect NEtwork (SHINE)

Large-scale date centers are becoming increasingly limited by the capacity of interconnects due to the rapid increasing power consumption and bandwidth demands driven by the rigorous down scaling of CMOS critical dimensions in according with the Moore’s Law. As a result of the continuing bandwidth scaling, optical interconnects based on parallel optics, where data are simultaneously transmitted along more than one lane, have becoming a preferred interconnected solution over copper cables. To building an efficient optical interconnect system, optical coupling between different elements, such as optical fibers, photonics chips, is a critical step, which is challenging due to the mode mismatch of varies optical ports. In this project, we proposed and demonstrated an optical interfacing scheme, which provided a light coupling approach with low energy loss, broadband operation, high alignment tolerance and compatible with scalable fabrication and testing. Here, the mode transformation is achieved by a total internal reflection from a chip integrated microscale freefrom surface. Due to the enormous light wavefront manipulating ability associated with the gigantic design space, the freeform couplers platform is universally applicable to a wide range of optical coupling scenarios, such as the interfacing between a waveguide to a fiber, another waveguide, surface normal device, or free space.

42 ENGINEERING↗
DOE OSTI2025

Mechanistic Insights into the Demethylation of Lignin‐Derived Structures Using Protic Ionic Liquids: A Density Functional Theory Study

Lignin valorization is restricted by the stability of its methoxy groups, creating a critical need for efficient demethylation strategies. Here, in this study, density functional theory (DFT) is employed to dissect the mechanistic pathways of demethylation in lignin model compounds, guaiacol and syringol, using protic ionic liquids (PILs) that act as both solvent and catalyst. Conductor‐like Screening Model for Real Solvents (COSMO‐RS) analysis identifies monoethanolammonium acetate ([MEOA][Ace]) as the most promising medium, attributed to its strong hydrogen bonding network and solvation ability. By integrating implicit and explicit solvation models, it is revealed that an acid‐catalyzed hydrolytic mechanism governs demethylation, with PILs stabilizing crucial transition states and intermediates. Complementary electronic structure evaluations, including highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO‐LUMO) gap analysis, charge distribution, and electrostatic potential mapping, demonstrate how PILs lower energetic barriers and enhance reactivity. To simulate realistic environments, this study is extended to lignin dimer complexes with varying water content, uncovering how water‐bridged solvation reverses demethylation preference from guaiacyl to syringyl units. This mechanistic shift aligns with experimental observations showing faster S‐unit reactivity in hydrated systems. Together, these findings provide atomic‐level insight into lignin demethylation dynamics and highlight how tuning acid concentration in PILs can accelerate kinetics, enabling a rational pathway toward next‐generation biomass conversion technologies.

COSMO-RS↗
NASA NTRS2003

NASA Tech Briefs, January 2003

Topics covered include: Optoelectronic Tool Adds Scale Marks to Photographic Images; Compact Interconnection Networks Based on Quantum Dots; Laterally Coupled Quantum-Dot Distributed-Feedback Lasers; Bit-Serial Adder Based on Quantum Dots; Stabilized Fiber-Optic Distribution of Reference Frequency; Delay/Doppler-Mapping GPS-Reflection Remote-Sensing System; Ladar System Identifies Obstacles Partly Hidden by Grass; Survivable Failure Data Recorders for Spacecraft; Fiber-Optic Ammonia Sensors; Silicon Membrane Mirrors with Electrostatic Shape Actuators; Nanoscale Hot-Wire Probes for Boundary-Layer Flows; Theodolite with CCD Camera for Safe Measurement of Laser-Beam Pointing; Efficient Coupling of Lasers to Telescopes with Obscuration; Aligning Three Off-Axis Mirrors with Help of a DOE; Calibrating Laser Gas Measurements by Use of Natural CO2; Laser Ranging Simulation Program; Micro-Ball-Lens Optical Switch Driven by SMA Actuator; Evaluation of Charge Storage and Decay in Spacecraft Insulators; Alkaline Capacitors Based on Nitride Nanoparticles; Low-EC-Content Electrolytes for Low-Temperature Li-Ion Cells; Software for a GPS-Reflection Remote-Sensing System; Software for Building Models of 3D Objects via the Internet; "Virtual Cockpit Window" for a Windowless Aerospacecraft; CLARAty Functional-Layer Software; Java Library for Input and Output of Image Data and Metadata; Software for Estimating Costs of Testing Rocket Engines; Energy-Absorbing, Lightweight Wheels; Viscoelastic Vibration Dampers for Turbomachine Blades; Soft Landing of Spacecraft on Energy-Absorbing Self-Deployable Cushions; Pneumatically Actuated Miniature Peristaltic Vacuum Pumps; Miniature Gas-Turbine Power Generator; Pressure-Sensor Assembly Technique; Wafer-Level Membrane-Transfer Process for Fabricating MEMS; A Reactive-Ion Etch for Patterning Piezoelectric Thin Film; Wavelet-Based Real-Time Diagnosis of Complex Systems; Quantum Search in Hilbert Space; Analytic Method for Computing Instrument Pointing Jitter; and Semiselective Optoelectronic Sensors for Monitoring Microbes.

Source record↗
DOE OSTI2022

AI for Experimental Controls at Jefferson Lab

We report the AI for Experimental Controls project is developing an AI system to control and calibrate detector systems located at Jefferson Laboratory. Currently, calibrations are performed offline and require significant time and attention from experts. This work would reduce the amount of data and the amount of time spent calibrating in an offline setting. The first use case involves the Central Drift Chamber (CDC) located inside the GlueX spectrometer in Hall D. We use a combination of environmental and experimental data, such as atmospheric pressure, gas temperature, and the flux of incident particles as inputs to a sequential Neural Network (NN) to recommend a high voltage setting and the corresponding calibration constants in order to maintain consistent gain and optimal resolution throughout the experiment. Utilizing AI in this manner represents an initial shift from offline calibration towards near real time calibrations performed at Jefferson Laboratory.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Pathway-based analyses of gene expression profiles at low doses of ionizing radiation

Radiation exposure poses a significant threat to human health. Emerging research indicates that even low-dose radiation once believed to be safe, may have harmful effects. This perception has spurred a growing interest in investigating the potential risks associated with low-dose radiation exposure across various scenarios. To comprehensively explore the health consequences of low-dose radiation, our study employs a robust statistical framework that examines whether specific groups of genes, belonging to known pathways, exhibit coordinated expression patterns that align with the radiation levels. Notably, our findings reveal the existence of intricate yet consistent signatures that reflect the molecular response to radiation exposure, distinguishing between low-dose and high-dose radiation. Moreover, we leverage a pathway-constrained variational autoencoder to capture the nonlinear interactions within gene expression data. By comparing these two analytical approaches, our study aims to gain valuable insights into the impact of low-dose radiation on gene expression patterns, identify pathways that are differentially affected, and harness the potential of machine learning to uncover hidden activity within biological networks. This comparative analysis contributes to a deeper understanding of the molecular consequences of low-dose radiation exposure.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRS2015

Mini-Stamp as a Micro-Display for At-A-Glance Subsystem Information for DSN Links

Operators of the Deep Space Network (DSN) attend to numerous tasks with the overall goal of providing continuous support for the world's deep space missions. This high-stakes operations environment requires operators to understand the state of the Deep Space Network and predict what will happen next. Under the Follow-the-Sun initiative which requires remote operations of the highly complex telecommunications equipment, operators will need to remain aware of the state of the entire network rather than just their own facility, and transitioning fluidly between periods of low activity and periods of high demand. I designed a micro-display for operators to see, at a glance, the state of a Deep Space Network support including its subsystems. Using in-depth user-centered and participatory design techniques to identify information requirements, I designed what I called a Postage Stamp (NTR-49720) for individual operators to be able to maintain awareness of their own assigned supports. However, under Follow the Sun, operators must remain aware of all supports. The area occupied by the Postage Stamp must shrink to allow operators to see the state of the entire system, e.g., via a Big Board posted prominently in the operations room. Micro-displays are tools for mental model re-alignment, helping operators to keep their mental models of how the system works and behaves aligned with the changing state of the complex system. Data-driven micro-displays such as the Postage Stamp and Mini-Stamp display information about the system in a consistent way. Like a traffic light, the format of the micro-display never changes: the operator always knows where to look to find a specific piece of information. The Mini-Stamp always looks like the Mini-Stamp, and all of its data fields always lie in the same place on the micro-display. Real-time data flows through the Mini-Stamp to provide information to the operator.

Holloway, Alexandra↗
DOE OSTI2026

Capturing the fractocohesive length scale in elastomers through a statistical mechanics-based gradient enhanced damage model

Here, this study aims to examine modeling flaw sensitivity in elastomers. The direct incorporation of polymer chain statistical mechanics considerations into a continuum stretch-based gradient-enhanced damage formulation, in turn, allows a representation of diffuse chain damage and fracture events that align with known micromechanical mechanisms. Through a series of numerical experiments, we simulate crack propagation and extract the fracture energy as an output of the model, while keeping track of the micromechanical signatures of diffuse chain damage that accommodate fracture propagation and eventually influence flaw sensitivity. Finally, by combining the fracture toughness and the work to rupture, we identify a fractocohesive length of the material, corresponding to the full width of the damage process zone. As the damage-to-fracture cascade in the proposed GED model is influenced by the introduction of a length scale associated with network imperfection and long-range load transfer, the emerging relationship of the two length scales is discussed, providing a potential link between microscopic damage mechanisms and the observed macroscopic fracture response.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Real World Use Case Evaluation of Radar Retro-reflectors for Autonomous Vehicle Lane Detection Applications

Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors. This evaluation is performed using ground truth data, obtained by measuring the lane positions and transforming them into pixel coordinates. The performance of each method is assessed using the statistical R2 score, indicating the correlation between the detected lane lines and the ground truth. The results show that the U-Net camera-based method exhibits limitations in accurately detecting and aligning the lane lines, particularly in challenging scenarios. However, the radar-based lane detection method demonstrates a strong correlation with the ground truth which implies that the use of this sensor may improve current reliability issues from conventional camera lane detection approach. Furthermore, the study highlights the limitations of the U-Net model for camera lane detection, especially in scenarios with sun glare. This study shows that infrastructure-based radar retro-reflectors can improve autonomous vehicle lane detection reliability. The integration of different sensor modalities and the development of advanced computer vision algorithms are crucial for improving the accuracy, reliability, and energy efficiency of lane detection systems. Addressing these challenges contributes to the advancement of autonomous vehicles and the realization of safer and more efficient transportation systems.

Brown, Nicolas E.↗
DOE OSTI2018

Local structure orders and glass forming ability of Ni-Nb liquids

In this paper, ab initio molecular dynamics simulations were performed to study the atomistic structures of Ni 48 Nb 52 , Ni 59.5 Nb 40.5 and Ni 75 Nb 25 liquids near their melting temperatures. It is found that the total coordination number only changes slightly across the compositions. However, there is significant difference in the chemical ordering. The Cluster Alignment method was used to identify the dominant local structure motifs. It is found that distorted and perfect icosahedral clusters are the most popular motifs around Ni atoms, while Z14, Z15, and Z16 Frank-Kasper polyhedra are dominant around Nb atoms. Among the three compositions, the eutectic composition (Ni 59.5 Nb 40.5 ) has the largest population of these dominant motifs and the highest degree of five-fold local symmetry. Chemical order in the distorted icosahedral motif is studied and the occupation probability of Nb in the distorted region is found to be much different from other sites. The network formed by distorted and perfect icosahedral clusters and that by the interconnection of the perfect icosahedra, Z14, Z15, and Z16 motifs are the strongest at the eutectic composition. Finally, the high percentage of perfect and distorted icosahedral clusters and their strong network structures in this system (especially around the eutectic composition) correlate well with the excellent glass forming ability in this system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗