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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Physics-based compact modeling of electro-thermal memristors: Negative differential resistance, local activity, and non-local dynamical bifurcations

Leon Chua's Local Activity theory quantitatively relates the compact model of an isolated nonlinear circuit element, such as a memristor, to its potential for desired dynamical behaviors when externally coupled to passive elements in a circuit. However, the theory's use has often been limited to potentially unphysical toy models and analyses of small-signal linear circuits containing pseudo-elements (resistors, capacitors, and inductors), which provide little insight into required physical, material, and device properties. Furthermore, the Local Activity concept relies on a local analysis and must be complemented by examining dynamical behavior far away from the steady-states of a circuit. In this work, we review and study a class of generic and extended one-dimensional electro-thermal memristors (i.e., temperature is the sole state variable), re-framing the analysis in terms of physically motivated definitions and visualizations to derive intuitive compact models and simulate their dynamical behavior in terms of experimentally measurable properties, such as electrical and thermal conductance and capacitance and their derivatives with respect to voltage and temperature. Within this unified framework, we connect steady-state phenomena, such as negative differential resistance, and dynamical behaviors, such as instability, oscillations, and bifurcations, through a set of dimensionless nonlinearity parameters. In particular, we reveal that the reactance associated with electro-thermal memristors is the result of a phase shift between oscillating current and voltage induced by the dynamical delay and coupling between the electrical and thermal variables. We thus, demonstrate both the utility and limitations of local analyses to understand non-local dynamical behavior. Critically for future experimentation, the analyses show that external coupling of a memristor to impedances within modern sourcing and measurement instruments can dominate the response of the total circuit, making it impossible to characterize the response of an uncoupled circuit element for which a compact model is desired. However, these effects can be minimized by proper understanding of the Local Activity theory to design and utilize purpose-built instruments.

Brown, Timothy D. (ORCID:0000000347068355)↗
DOE OSTI2025

MultiTaskDeltaNet: change detection-based image segmentation for operando ETEM with application to carbon gasification kinetics

Transforming in situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often face limitations due to the scarcity of labeled data, visually ambiguous features of interest, and scenarios involving small objects. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively leverages minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to exploit correlations between physical features of interest. In an evaluation using data from in situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.

08 HYDROGEN↗
DOE OSTI2022

High-Precision Characterization of Seismicity from the 2022 Hunga Tonga-Hunga Ha'apai Volcanic Eruption

The earthquake swarm accompanying the January 2022 Hunga Tonga-Hunga Ha'apai (HTHH) volcanic eruption includes a large number of posteruptive moderate-magnitude seismic events and presents a unique opportunity to use remote monitoring methods to characterize and compare seismic activity with other historical caldera-forming eruptions. We compute improved epicentroid locations, magnitudes, and regional moment tensors of seismic events from this earthquake swarm using regional to teleseismic surface-wave cross correlation and waveform modeling. Precise relative locations of 91 seismic events derived from 59,047 intermediate-period Rayleigh- and Love-wave cross-correlation measurements collapse into a small area surrounding the volcano and exhibit a southeastern time-dependent migration. Regional moment tensors and observed waveforms indicate that these events have a similar mechanism and exhibit a strong positive compensated linear vector dipole component. Precise relative magnitudes agree with regional moment tensor moment magnitude ($M_w$) estimates while also showing that event sizes and frequency increase during the days after the eruption followed by a period of several weeks of less frequent seismicity of a similar size. The combined information from visual observation and early geologic models indicate that the observed seismicity may be the result of a complex series of events that occurred after the explosive eruption on 15 January, possibly involving rapid resupply of the magma chamber shortly after the eruption and additional faulting and instability in the following weeks. In addition, we identify and characterize an $M_w$ 4.5 event five days before the paroxysmal explosion on 15 January, indicating that additional seismic events preceding the main eruption could have been identified with improved local monitoring. As a result, our analysis of the HTHH eruption sequence demonstrates the value of potentially utilizing teleseismic surface-wave cross correlation and waveform modeling methods to assist in the detailed analysis of remote volcanic eruption sequences.

58 GEOSCIENCES↗
DOE OSTI2022

An EvoDevo Study of Salmonid Visual Opsin Dynamics and Photopigment Spectral Sensitivity

Salmonids are ideal models as many species follow a distinct developmental program from demersal eggs and a large yolk sac to hatching at an advanced developmental stage. Further, these economically important teleosts inhabit both marine- and freshwaters and experience diverse light environments during their life histories. At a genome level, salmonids have undergone a salmonid-specific fourth whole genome duplication event (Ss4R) compared to other teleosts that are already more genetically diverse compared to many non-teleost vertebrates. Thus, salmonids display phenotypically plastic visual systems that appear to be closely related to their anadromous migration patterns. This is most likely due to a complex interplay between their larger, more gene-rich genomes and broad spectrally enriched habitats; however, the molecular basis and functional consequences for such diversity is not fully understood. This study used advances in genome sequencing to identify the repertoire and genome organization of visual opsin genes (those primarily expressed in retinal photoreceptors) from six different salmonids [Atlantic salmon ( Salmo salar ), brown trout ( Salmo trutta ), Chinook salmon ( Oncorhynchus tshawytcha ), coho salmon ( Oncorhynchus kisutch ), rainbow trout ( Oncorhynchus mykiss ), and sockeye salmon ( Oncorhynchus nerka )] compared to the northern pike ( Esox lucius ), a closely related non-salmonid species. Results identified multiple orthologues for all five visual opsin classes, except for presence of a single short-wavelength-sensitive-2 opsin gene. Several visual opsin genes were not retained after the Ss4R duplication event, which is consistent with the concept of salmonid rediploidization. Developmentally, transcriptomic analyzes of Atlantic salmon revealed differential expression within each opsin class, with two of the long-wavelength-sensitive opsins not being expressed before first feeding. Also, early opsin expression in the retina was located centrally, expanding dorsally and ventrally as eye development progressed, with rod opsin being the dominant visual opsin post-hatching. Modeling by spectral tuning analysis and atomistic molecular simulation, predicted the greatest variation in the spectral peak of absorbance to be within the Rh2 class, with a ∼40 nm difference in λ max values between the four medium-wavelength-sensitive photopigments. Overall, it appears that opsin duplication and expression, and their respective spectral tuning profiles, evolved to maximize specialist color vision throughout an anadromous lifecycle, with some visual opsin genes being lost to tailor marine-based vision.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Visualizing strange metallic correlations in the two-dimensional Fermi-Hubbard model with artificial intelligence

Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm for phases with subtle, or even unknown, patterns. Long- and short-range spin correlations spontaneously emerge in filters of a convolutional neural network trained on snapshots of single atomic species. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi-liquid behavior. We report our technique can be employed to characterize correlations unique to other phases with no obvious order parameters or signatures in projective measurements, and has implications for science discovery through AI beyond strongly correlated systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation

Trust calibration for a human–machine team is the process by which a human adjusts their expectations of the automation’s reliability and trustworthiness; adaptive support for trust calibration is needed to engender appropriate reliance on automation. Herein, we leverage an instance-based learning ACT-R cognitive model of decisions to obtain and rely on an automated assistant for visual search in a UAV interface. This cognitive model matches well with the human predictive power statistics measuring reliance decisions; we obtain from the model an internal estimate of automation reliability that mirrors human subjective ratings. The model is able to predict the effect of various potential disruptions, such as environmental changes or particular classes of adversarial intrusions on human trust in automation. Finally, we consider the use of model predictions to improve automation transparency that account for human cognitive biases in order to optimize the bidirectional interaction between human and machine through supporting trust calibration. The implications of our findings for the design of reliable and trustworthy automation are discussed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Engineering and characterization of carbohydrate‐binding modules for imaging cellulose fibrils biosynthesis in plant protoplasts

Abstract Carbohydrate binding modules (CBMs) are noncatalytic domains that assist tethered catalytic domains in substrate targeting. CBMs have therefore been used to visualize distinct polysaccharides present in the cell wall of plant cells and tissues. However, most previous studies provide a qualitative analysis of CBM‐polysaccharide interactions, with limited characterization of engineered tandem CBM designs for recognizing polysaccharides like cellulose and limited application of CBM‐based probes to visualize cellulose fibrils synthesis in model plant protoplasts with regenerating cell walls. Here, we examine the dynamic interactions of engineered type‐A CBMs from families 3a and 64 with crystalline cellulose‐I and phosphoric acid swollen cellulose. We generated tandem CBM designs to determine various characteristic properties including binding reversibility toward cellulose‐I using equilibrium binding assays. To compute the adsorption ( nk on ) and desorption ( k off ) rate constants of single versus tandem CBM designs toward nanocrystalline cellulose, we employed dynamic kinetic binding assays using quartz crystal microbalance with dissipation. Our results indicate that tandem CBM3a exhibited the highest adsorption rate to cellulose and displayed reversible binding to both crystalline/amorphous cellulose, unlike other CBM designs, making tandem CBM3a better suited for live plant cell wall biosynthesis imaging applications. We used several engineered CBMs to visualize Arabidopsis thaliana protoplasts with regenerated cell walls using confocal laser scanning microscopy and wide‐field fluorescence microscopy. Lastly, we also demonstrated how CBMs as probe reagents can enable in situ visualization of cellulose fibrils during cell wall regeneration in Arabidopsis protoplasts.

09 BIOMASS FUELS↗
DOE OSTI2025

Adaptive stretching of representations across brain regions and deep learning model layers

Prefrontal cortex (PFC) is known to modulate the visual system to favor goal-relevant information by accentuating task-relevant stimulus dimensions. Does the brain broadly re-configures itself to optimize performance by stretching visual representations along task-relevant dimensions? We considered a task that required monkeys to selectively attend on a trial-by-trial basis to one of two dimensions (color or motion direction) to make a decision. Although effects were most prominent in frontal areas, representations stretched along task-relevant dimensions in all sites considered: V4, MT, lateral PFC, frontal eye fields (FEF), lateral intraparietal cortex (LIP), and inferotemporal cortex (IT). Spike timing was crucial to this code. A deep learning model was trained on the same visual input and rewards as the monkeys. Despite lacking an explicit selective attention or other control mechanism, by minimizing error during learning, the model’s representations stretched along task-relevant dimensions, indicating that stretching is an adaptive strategy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

How Can Construction Process Simulation Modeling Aid the Integration of Lean Principles in the Factory-Built Housing Industry?

New and existing factories that produce and deliver factory-built housing can benefit from construction process simulation modeling to explore the integration of Lean principles in their operations. Construction process simulation modeling provides digital or virtual recreations of the real-world factory environments to visualize, quantify, analyze, and optimize their underlying behavior, including factory productivity, material flow, labor dynamics, bottlenecks, and work scope. One of the key benefits of process simulation modeling is the ability to create and compare "what-if" scenarios, including integrating Lean principles such as reducing waste (for example, transportation, waiting), line balancing, and just-in-time concepts. In general, three process simulation methods are widely used: discrete event simulation (DES), agentbased modeling (ABM), and system dynamics (SD). Myriad process simulation software also is available, but depending on the industry, complexity of the system, and purposes of the simulation, some software might be more appropriate. Similar to how computer-aided design (CAD) software such as AutoCAD and Rhinoceros enable building design of modular or factory-built housing, process simulation modeling software such as jStrobe, ProModel, and AnyLogic can enable factory design of new and existing factories to deliver modular affordable housing at scale, as opposed to traditional site-built construction. Software with DES capabilities can help generate a process model that is a logical representation of resources and activities in a factory. Software with CAD-DES integration can leverage product-process data integration to help spatially visualize a DES model of the factory in the CAD environment. Software with multimethod simulation capabilities, widely used in the manufacturing industry, brings together DES, ABM, and SD in a single platform that allows visualization, quantification, analyses, and optimization at varying data fidelities. Near-real-time data from an existing factory can be directly plugged into multimethod simulation software so that the construction process simulation model is a near-accurate representation of the real-world factory conditions. This report provides insights into the use of simulation as an aid to integrate Lean concepts in factories, including guidelines for selecting the appropriate process simulation modeling method and software. These insights have been developed as part of ongoing process simulation modeling research, development, and demonstration projects at the U.S. Department of Housing and Urban Development, the U.S. Department of Energy, and the National Renewable Energy Laboratory focused on how process simulation models can enable better integration of resilience, energy efficiency, and low-carbon design strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Evaluating long-term model-based scenarios of the energy system

Energy-economic models are used to provide science-based decision support in a variety of contexts. Analyses using these tools often involve defining a “reference” scenario, which serves as a counterfactual against which alternative scenarios are compared. Evaluating scenarios, including reference scenarios, is critical for establishing the credibility of the analyses these models support. We propose a framework for evaluating energy system scenarios which consists of three parts – a qualitative storyline, quantitative metrics, and evaluation criteria. We apply this framework to the reference scenario for GCAM-USA, a version of the global human-Earth system model GCAM (Global Change Assessment Model) with state-level detail in the United States, focusing on the evolution of the electric power sector. We develop new visual analytic tools to facilitate the evaluation of model outcomes in 51 sub-national regions, and demonstrate how scenario performance can be tracked and compared across four quantifications of the GCAM-USA reference scenario.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Visualizing Temporal Topic Embeddings with a Compass

—Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.

Cluster analysis↗
DOE OSTI2023

Spatial Bayesian models project shifts in suitable habitat for Pacific Northwest tree species under climate change

Abstract We developed spatial Bayesian hierarchical models to assess potential climate change impacts on suitable habitat for five important tree species in the Pacific northwestern United States (California, Oregon, and Washington). Individual‐species models were fit with presence–absence data from forest inventory field plots and spatial relationships were specified through a conditional autoregressive model. This modeling approach allowed us to visualize uncertainty in response curves, map current and future prediction uncertainty, and provide interval estimates for change. Upward elevational or northward latitudinal shifts in climatically suitable habitat were projected for all species. Climate change impacts were the most damaging for noble fir ( Abies procera ), for which 79%–100% of the current range was projected to become climatically unsuitable by the 2080s. Although coastal Douglas‐fir ( Pseudotsuga menziesii var. menziesii ) has been projected by others to gain habitat in Canada, within our study area we projected a net loss of climatically suitable habitat (ca. 8000–31,400 km 2 ) under three of four future climate scenarios. A net loss in habitat was also projected for Oregon white oak ( Quercus garryana ) under three of four scenarios, with 40%–60% of the current range becoming unsuitable. Although there was no net loss of habitat for forest land blue oak under any scenario, other factors like competition may inhibit blue oak ( Quercus douglasii ) and white oak from occupying areas projected to increase in climatic suitability. Additionally, between 13% and 32% of blue oak's current range was projected to become unsuitable; some of these areas aligned with dieback following the 2012–2015 California drought, which our data set predates. Unlike the other four species, we projected a 17%–25% increase in climatically suitable habitat for California black oak ( Quercus kelloggii ), although 1%–20% of the current range was still projected to become unsuitable. Our findings indicate that, although some species will face more pressure in tracking climatically suitable habitat than others, climate change will impact the location of suitable habitat for many species.

Kralicek, Karin↗
DOE OSTI2025

Developing a digital twin framework for remotely monitoring nuclear reactor facilities

A digital twin must seek to represent all applicable functional components of the system of interest. Different expertise is required for understanding the physical system being modeled than the skills needed for transforming those models into a functional digital twin through physics modeling, machine learning analysis, and visualization. The diversity of knowledge requires a multi-disciplinary team to ensure all system details are captured. Team members also need a method to verify that the data they generate within their domain can be effectively communicated to professionals in other fields. To address this challenge, this work provides an approach for developing a digital twin framework to remotely monitoring nuclear facilities. Through this, general knowledge of the framework is presented along with two examples to solidify the process. The AGN-201 digital twin and microreactor digital twins provide varying levels of complexity in a potential nuclear facility, where common threads are identified and lessons learned are provided. The goal of this research is to aid future researchers by providing a formula for a successful digital twin and in turn reducing the development time of nuclear system digital twins, specifically for remote monitoring.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Standardized nomenclature for photovoltaic connectors

Photovoltaic (PV) systems rely on discrete connectors for the efficient and safe flow of power from module to module and from strings to combiner boxes and inverters. Despite their functional importance, no common nomenclature for PV connectors currently exists, resulting in confusion and miscommunication. Misunderstood terms like "MC4 compatible", "cross-mating", "intermating", "female", and "male" can lead to installation and maintenance errors and compromise system reliability. We believe a standardized terminology will reduce confusion, help support installation best practices, aid in maintenance and repair, inform next-generation designs, and provide a technical basis for improved codes and standards. To that end, we are proposing a standardized glossary for 4 mm PV connectors (the most common type of connector used in PV applications) based on, and validated by, a Sandia National Laboratories' investigation that included the following sources: 1) a comprehensive review of official documents from 20 connector manufacturers, including schematics, datasheets, installation manuals, and catalogs, as well as relevant patents; 2) two rounds of surveys distributed to stakeholders, including connector manufacturers, engineers, asset owners, test labs, and researchers; and 3) visual examination of 25 different models of 4 mm single-pole DC PV connectors to document variations in design and functionality. This work provides a foundation for establishing a clear and consistent terminology for PV connectors that will in turn enable progress toward greater reliability and collaboration across the industry.

14 SOLAR ENERGY↗
DOE OSTI2020

Mechanical Regulation of Photosynthesis in Cyanobacteria

Photosynthetic organisms regulate their responses to many diverse stimuli in an effort to balance light harvesting with utilizable light energy for carbon fixation and growth (source–sink regulation). This balance is critical to prevent the formation of reactive oxygen species that can lead to cell death. However, investigating the molecular mechanisms that underlie the regulation of photosynthesis in cyanobacteria using ensemble-based measurements remains a challenge due to population heterogeneity. Here, to address this problem, we used long-term quantitative time-lapse fluorescence microscopy, transmission electron microscopy, mathematical modelling and genetic manipulation to visualize and analyse the growth and subcellular dynamics of individual wild-type and mutant cyanobacterial cells over multiple generations. We reveal that mechanical confinement of actively growing Synechococcus sp. PCC 7002 cells leads to the physical disassociation of phycobilisomes and energetic decoupling from the photosynthetic reaction centres. We suggest that the mechanical regulation of photosynthesis is a critical failsafe that prevents cell expansion when light and nutrients are plentiful, but when space is limiting. These results imply that cyanobacteria must convert a fraction of the available light energy into mechanical energy to overcome frictional forces in the environment, providing insight into the regulation of photosynthesis and how microorganisms navigate their physical environment.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗
DOE OSTI2022

Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR

The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Marmot

Marmot is a data formatting and visualization tool for production cost modelling results. It provides an efficient way to view PLEXOS production cost modeling results quickly, while also creating publication ready figures and data tables.

Levie, Daniel↗