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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Large‐Scale Statistically Meaningful Patterns (LSMPs) Associated With Precipitation Extremes Over Northern California

Abstract We analyze large‐scale statistically meaningful patterns (LSMPs) that precede extreme precipitation (PEx) events over Northern California (NorCal). We find LSMPs by applying k‐means clustering to the two leading principal components of daily 500 hPa geopotential height anomalies two days before the onset, from October to March during 1948–2015. Statistical significance testing based on Monte Carlo simulations suggests a minimum of four statistically distinguished LSMP clusters. The four LSMP clusters are characterized as Northwest continental negative height anomaly, Eastward positive “Pacific‐North American Pattern (PNA),” Westward negative “PNA,” and Prominent Alaskan ridge. These four clusters, shown in multiple variables, evolve very differently and have differing links to the Arctic and tropical Pacific regions. Using binary forecast skill measures and a new copula‐based framework for predicting PEx events, we find LSMP indices that are useful predictors of NorCal PEx events, with moisture‐based variables being the best predictors of PEx events at least 6 days before the onset, and the lower atmospheric variables being better than their upper atmospheric counterparts any day in advance tested. To ensure statistical rigor, the LSMPs analyzed here (with the modified acronym) include local tests of both significance and consistency, which are not always featured in the literature on large‐scale meteorological patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

RouteE-Powertrain [SWR-19-19]

RouteE-Powertrain is a tool for predicting energy usage over a set of road links. RouteE-Powertrain is a Python package that allows users to work with a set of pre-trained mesoscopic vehicle energy prediction models for a varity of vehicle types. Additionally, users can train their own models if "ground truth" energy consumption and driving data are available. RouteE-Powertrain models predict vehicle energy consumption over links in a road network, so the features considered for prediction often include traffic speeds, road grade, turns, etc. The typical user will utilize RouteE's catalog of pre-trained models. Currently, the catalog consists of light-duty vehicle models, including conventional gasoline, diesel, hybrid electric (HEV), and battery electric (BEV). These models can be applied to link-level driving data (in the form of pandas dataframes) to output energy consumption predictions. Users that wish to train new RouteE models can do so. The model training function of RouteE enables users to use their own drive-cycle data, powertrain modeling system, and road network data to train custom models. https://pypi.org/project/nrel.routee.powertrain/ pip install nrel.routee.powertrain

Holden, Jacob↗
NASA NTRS2000

Trajectory Orientation: A Technology-Enabled Concept Requiring a Shift in Controller Roles and Responsibilities

The development of a decision support tool (DST) for the en-route domain with accurate conflict prediction time horizons of 20 minutes has introduced an interesting problem. A 20 minute time horizon for conflict prediction often results in the predicted conflict occurring one or more sectors downstream from the sector controller who "owns" (i-e., is responsible for the safe separation of aircraft) one or both of the aircraft in the conflict pair. Based on current roles and responsibilities of today's en route controllers, the upstream controller would not resolve this conflict. In most cases, the downstream controller would wait until the conflicting aircraft entered higher sector before resolving the conflict. This results in a delay of several minutes from the time when the conflict was initially predicted. This delay is inefficient from both a controller workload and user's cost of operations perspective. Trajectory orientation, a new concept for facilitating an efficient, conflict-free flight path across several sectors while conforming to metering or miles-in-trail spacing, is proposed as an alternative to today's sector-oriented method. This concept necessitates a fundamental shift in thinking about inter-sector coordination. Instead of operating independently, with the main focus on protecting their internal airspace, controllers would work cooperatively, depending on each other for well-planned, conflict-free flow of aircraft. To support the trajectory orientation concept, a long time horizon (15 to 20 minutes) for conflict prediction and resolution would most likely be a primary requirement. In addition, new tools, such as controller-pilot data link will be identified to determine their necessity and applicability for trajectory orientation. Finally, with significant controller participation from selected Air Route Traffic Control Centers, potential shifts in R-side/D-side roles and responsibilities as well as the creation of a new controller position for multi-sector planning will be examined to determine the most viable solutions.

Leiden, Ken↗
DOE OSTI2025

Multi-Scale Modeling of the Evolution of Structure and Properties in Materials for Nuclear Energy Applications [Slides]

Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Multi-scale modeling of the evolution of structure and properties in materials for nuclear energy applications

Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. These modeling efforts make extensive of MOOSE (Multiphysics Object-Oriented Simulation Environment), a general-purpose open source finite element framework developed at INL. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Multi-scale modeling of the evolution of structure and properties in materials for nuclear energy applications

Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. These modeling efforts make extensive of MOOSE (Multiphysics Object-Oriented Simulation Environment), a general-purpose open source finite element framework developed at INL. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2009

Surface Map Traffic Intent Displays and Net-Centric Data-link Communications for NextGen

By 2025, U.S. air traffic is predicted to increase three fold and may strain the current air traffic management system, which may not be able to accommodate this growth. In response to this challenge, a revolutionary new concept has been proposed for U.S. aviation operations, termed the Next Generation Air Transportation System or "NextGen". Many key capabilities are being identified to enable NextGen, including the use of data-link communications. Because NextGen represents a radically different approach to air traffic management and requires a dramatic shift in the tasks, roles, and responsibilities for the flight deck, there are numerous research issues and challenges that must be overcome to ensure a safe, sustainable air transportation system. Flight deck display and crew-vehicle interaction concepts are being developed that proactively investigate and overcome potential technology and safety barriers that might otherwise constrain the full realization of NextGen. The paper describes simulation research, conducted at National Aeronautics and Space Administration (NASA) Langley Research Center, examining data-link communications and traffic intent data during envisioned four-dimensional trajectory (4DT)-based and equivalent visual (EV) surface operations. Overall, the results suggest that controller pilot data-link communications (CPDLC) with the use of mandatory pilot read-back of all clearances significantly enhanced situation awareness for 4DT and EV surface operations. The depiction of graphical traffic state and intent information on the surface map display further enhanced off-nominal detection and pilot qualitative reports of safety and awareness.

Shelton, Kevin J.↗
NASA NTRS2018

Cognitive Anti-Jamming Satellite-To-Ground Communications on NASA's SCaN Testbed

Machine learning aided cognitive anti-jamming communications is designed, developed and demonstrated on a live satellite-to-ground link. A wideband autonomous cognitive radio (WACR) is designed and implemented as a hardware-in-the- loop (HITL) prototype. The cognitive engine (CE) of the WACR is implemented on a PC while the software-defined radio (SDR) platform utilized two different radios for spectrum sensing and actual communications. The cognitive engine performs spectrum knowledge acquisition over the complete spectrum range available for the SATCOM system operation and learns an anti-jamming communications protocol to avoid both intentional jammers and inadvertent interferers using reinforcement learning. When the current satellite-to-ground link is jammed, the cognitive engine of the ground receiver directs the satellite transmitter to switch to a new channel that is predicted to be jammer-free for the longest possible duration. The end-to-end, closed-loop system was tested on the NASA Space Communications and Navigation (SCaN) Testbed on the International Space Station (ISS). The experimental results demonstrated the feasibility of satellite-to-ground cognitive anti-jamming communications along with excellent anti-jamming capability of machine learning aided cognitive protocols against several different types of jammers.

Jayaweera, Sudharman K.↗
NASA NTRS2018

Cognitive Anti-jamming Satellite-to-Ground Communications on NASA's SCaN Testbed

Machine learning aided cognitive anti-jamming communications is designed, developed and demonstrated on an experimental satellite-to-ground link. A wideband autonomous cognitive radio (WACR) is designed and implemented as a hardware-in the-loop (HITL) prototype. The cognitive engine (CE) of the WACR is implemented on a PC while the software-defined radio (SDR) platform utilized two different radios for spectrum sensing and actual communications. The cognitive engine performs spectrum knowledge acquisition over the complete spectrum range available for the SATCOM system operation and learns an anti-jamming communications protocol to avoid both intentional jammers and inadvertent interferers using reinforcement learning. When the current satellite-to-ground link is jammed, the cognitive engine of the ground receiver directs the satellite transmitter to switch to a new channel that is predicted to be jammer-free for the longest possible duration. The end-to-end, closed-loop system was tested with the NASA's Space Communications and Networking (SCaN) testbed on the International Space Station (ISS). The experimental results demonstrated the feasibility of satellite-to-ground cognitive anti-jamming communications along with excellent anti-jamming capability of machine-learning aided cognitive protocols against several different types of jammers.

Jayaweera, Sudharman K.↗
NASA NTRS2010

Evaluation of Mixed-Mode Data-Link Communications for NextGen 4DT and Equivalent Visual Surface Operations

By 2025, U.S. air traffic is predicted to increase 3-fold and may strain the current air traffic management system, which may not be able to accommodate this growth. In response to this challenge, a revolutionary new concept has been proposed for U.S. aviation operations, termed the Next Generation Air Transportation System or NextGen. Many key capabilities are being identified to enable NextGen, including the use of data-link communications. Because NextGen represents a radically different approach to air traffic management and requires a dramatic shift in the tasks, roles, and responsibilities for the flight deck, there are numerous research issues and challenges that must be overcome to ensure a safe, sustainable air transportation system. Flight deck display and crew-vehicle interaction concepts are being developed that proactively investigate and overcome potential technology and safety barriers that might otherwise constrain the full realization of NextGen. The paper describes simulation research examining data-link communications during 4DT and equivalent visual surface operations.

Prinzel, Lawrence J., III↗
DOE OSTI2021

Nerpa: A Tool for Discovering Biosynthetic Gene Clusters of Bacterial Nonribosomal Peptides

Microbial natural products are a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class of natural products that include antibiotics, immunosuppressants, and anticancer agents. Recent breakthroughs in natural product discovery have revealed the chemical structure of several thousand NRPs. However, biosynthetic gene clusters (BGCs) encoding them are known only for a few hundred compounds. Here, we developed Nerpa, a computational method for the high-throughput discovery of novel BGCs responsible for producing known NRPs. After searching 13,399 representative bacterial genomes from the RefSeq repository against 8368 known NRPs, Nerpa linked 117 BGCs to their products. We further experimentally validated the predicted BGC of ngercheumicin from Photobacterium galatheae via mass spectrometry. Nerpa supports searching new genomes against thousands of known NRP structures, and novel molecular structures against tens of thousands of bacterial genomes. The availability of these tools can enhance our understanding of NRP synthesis and the function of their biosynthetic enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Tri-Port Current-Source Soft-Switching Medium-Voltage String Inverter for Large-Scale Solar-Plus-Storage Farms

This article presents a tri-port current-source soft-switching medium-voltage string inverter (TMVSI) to reduce the Levelized cost of energy (LCOE) of large-scale solar-plus-storage (SPS) farms. Throughout this work, the topology, operating principle, circuit simulation, control, and hardware design of the TMVSI are introduced. In addition, three key challenges compromising the performance of the TMVSI are addressed in this article. First, a new feed-forward compensation for model predictive control of the TMVSI is proposed to compensate for sampling and computational delay and high dc-link ripple with low computational cost and high scalability. Second, the use of laminated permanent magnets is proposed to reduce the losses of the MV medium-frequency transformer (MFT) in the TMVSI while increasing its saturation current. Finally, an improved U-shape winding pattern is proposed to decrease the leakage inductance of the MV MFT and reduce the voltage stress across semiconductor switches without adding cost or complexity. Here, the effectiveness of the TMVSI is validated by experiments at up to 20kW/500V in different test cases with custom-built prototypes. The contributions of this work make the TMVSI a viable solution to SPS farms and unleash its potential for LCOE reduction.

14 SOLAR ENERGY↗
DOE OSTI2025

Tetranucleotide frequencies differentiate genomic boundaries and metabolic strategies across environmental microbiomes

Microbiomes are constrained by physicochemical conditions, nutrient regimes, and community interactions across diverse environments, yet genomic signatures of this adaptation remain unclear. Metagenome sequencing is a powerful technique to analyze genomic content in the context of natural environments, establishing concepts of microbial ecological trends. Here, we developed a data discovery tool-a tetranucleotide-informed metagenome stability diagram-that is publicly available in the integrated microbial genomes and microbiomes (IMG/M) platform for metagenome ecosystem analyses. We analyzed the tetranucleotide frequencies from quality-filtered and unassembled sequence data of over 12,000 metagenomes to assess ecosystem-specific microbial community composition and function. We found that tetranucleotide frequencies can differentiate communities across various natural environments and that specific functional and metabolic trends can be observed in this structuring. Our tool places metagenomes sampled from diverse environments into clusters and along gradients of tetranucleotide frequency similarity, suggesting microbiome community compositions specific to gradient conditions. Within the resulting metagenome clusters, we identify protein-coding gene identifiers that are most differentiated between ecosystem classifications. We plan for annual updates to the metagenome stability diagram in IMG/M with new data, allowing for refinement of the ecosystem classifications delineated here. This framework has the potential to inform future studies on microbiome engineering, bioremediation, and the prediction of microbial community responses to environmental change. IMPORTANCE: Microbes adapt to diverse environments influenced by factors like temperature, acidity, and nutrient availability. We developed a new tool to analyze and visualize the genetic makeup of over 12,000 microbial communities, revealing patterns linked to specific functions and metabolic processes. This tool groups similar microbial communities and identifies characteristic genes within environments. By continually updating this tool, we aim to advance our understanding of microbial ecology, enabling applications like microbial engineering, bioremediation, and predicting responses to environmental change.

Kellom, Matthew↗
NASA NTRS1990

Nonlinear Modeling of Joint Dominated Structures

The development and verification of an accurate structural model of the nonlinear joint-dominated NASA Langley Mini-Mast truss are described. The approach is to characterize the structural behavior of the Mini-Mast joints and struts using a test configuration that can directly measure the struts' overall stiffness and damping properties, incorporate this data into the structural model using the residual force technique, and then compare the predicted response with empirical data taken by NASA/LaRC during the modal survey tests of the Mini-Mast. A new testing technique, referred to as 'link' testing, was developed and used to test prototype struts of the Mini-Masts. Appreciable nonlinearities including the free-play and hysteresis were demonstrated. Since static and dynamic tests performed on the Mini-Mast also exhibited behavior consistent with joints having free-play and hysteresis, nonlinear models of the Mini-Mast were constructed and analyzed. The Residual Force Technique was used to analyze the nonlinear model of the Mini-Mast having joint free-play and hysteresis.

Chapman, J. M.↗
DOE OSTI2024

Self-Consistent Theory for Structural Relaxation, Dynamic Bond Exchange Times, and the Glass Transition in Polymeric Vitrimers

Here, we formulate a statistical mechanical theory for how dynamic bond exchange influences the activated hopping-driven relaxation of Kuhn segments in dynamically cross-linked networks or vitrimers over a wide range of temperatures and cross-link densities. The key new methodological aspect is to address in a self-consistent manner the dynamic consequences of bond exchange on the Kuhn segmental alpha relaxation, and vice versa. The predicted temperature dependence of the segmental alpha time of vitrimers at high temperatures remains the same as that of permanent networks, but at lower temperatures, a significant acceleration of relaxation occurs due to bond exchanges. From a mechanistic perspective, the vitrimer local cage barrier is very weakly affected by bond exchange, while the collective elastic barrier contribution decreases significantly in the deeply supercooled regime. The vitrimer glass transition temperature is predicted to grow linearly with the square root of the cross-link density, as previously found for permanent networks. Material-specific chemical effects such as cross-linker size relative to that of the normal Kuhn segment or special attraction of a cross-linker with polymers are crudely considered based on model calculations. The theory is quantitatively applied to recent experiments on dry ethylene vitrimers. Good agreements are found including that the bond exchange time follows an Arrhenius law at high enough temperatures but upward non-Arrhenius deviations emerge in the deeply supercooled regime, and a collapsed master curve of the segment alpha time exists over a wide range of cross-link densities and temperatures. Possible extensions to treat dynamic heterogeneity effects and penetrant transport in vitrimers are briefly discussed.

chemical calculations↗
DOE OSTI2022

Towards Automatic Mapping of Vulnerabilities to Attack Patterns using Large Language Models

With the advent of new devices and applications, cyber attack surface is continuously evolving due to the emergence of new attack techniques and vulnerabilities. Hence, security management tool must assess the cyber risk of an enterprise at regular interval basis through comprehensively identifying associations among attack techniques, weakness, and vulnerabilities. However, existing repositories providing such associations are incomplete (i.e., missing associations), inducing the likelihood of undermining the risk of particular set of attack techniques. Moreover, such associations still rely on manual interpretation, which is slow compared to attack speed and ineffective for the increasing list of vulnerabilities and attack actions. Therefore, there is an urge to develop methodologies for automatically associating vulnerabilities to all relevant attack techniques. In this paper, we present a framework, named VWC-MAP, that can automatically identify all relevant attack techniques of a vulnerability via weakness based on their text descriptions, applying natural language process (NLP) techniques. To achieve that, we present a novel two-tiered classification approach, where the first tier classifies vulnerabilities to weakness, and the second tier classifies weakness to attack techniques. This research has improved the scalability of the current state-of-the-art tool to make vulnerability to weakness mapping significantly faster. Moreover, this paper presents two novel approaches for weakness to attack technique mapping applying Text-to-Text and link prediction techniques. Our experiment results cross-validated through cyber-security experts show that VWC-MAP can associate vulnerabilities to weakness types with 87% accuracy and to new attack patterns with 80% accuracy.

Das, Siddhartha Shankar↗
NASA NTRS1983

Potential for monitoring soil erosion features and soil erosion modeling components from remotely sensed data

Accurate estimates of soil erosion and its effects on soil productivity are essential in agricultural decision making and planning from the field scale to the national level. Erosion models have been primarily developed for designing erosion control systems, predicting sediment yield for reservoir design, predicting sediment transport, and simulating water quality. New models proposed are more comprehensive in that the necessary components (hydrology, erosion-sedimentation, nutrient cycling, tillage, etc.) are linked in a model appropriate for studying the erosion-productivity problem. Recent developments in remote sensing systems, such as Landsat Thematic Mapper, Shuttle Imaging Radar (SIR-B), etc., can contribute significantly to the future development and operational use of these models.

Langran, K. J.↗
DOE OSTI2026

Isolation of genome-predicted Caldatribacterium ( Atribacterota ) reveals pervasive microbial cultivation problem due to folate precipitation

Most bacterial phyla have few or no pure cultures, including Atribacterota , comprised of ubiquitous anaerobes. Here, we report genome-guided enrichment and isolation of two Atribacterota species representing a new family, Caldatribacterium saccharofermentans from a hot spring, and Caldatribacterium inferamans from a deep aquifer. Both were co-enriched with sulfate-reducing bacteria and initially resisted isolation, which we link to inadvertent removal of precipitated folic acid by filter-sterilization of unbuffered Wolin’s vitamin solution. We then predict folate auxotrophy across the Atribacterota and ~29% of all bacteria, with extensive auxotrophy in 27% of phyla. Since ≥604 of 791 ( ≥ 76%) media with folic acid additions in the MediaDive database use unbuffered vitamin solutions in which folic acid is likely removed during filter-sterilization, we propose that folate auxotrophy limits culturability in defined media en masse. We also uncover unusual features of Caldatribacterium , including three lipid membrane-like layers (LMLs), with the inner LML surrounding the nucleoid, and a high percentage of secreted proteins, supporting a unique cell biology of Atribacterota .

Biological and medical sciences↗