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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Dual Context: Leveraging Structured Application Context for Code Generation and Runtime Feature Activation via Chat Interfaces

Integrating artificial intelligence (AI) capabilities into software applications typically involves two common paths. For developers, AI assists in generating and documenting source code and other related software engineering efforts. For users, AI assists them through question-and-answer exchanges via chatbots. Both approaches have their value, but neither effectively leverages the modularity of component-based architectures that modern web application frameworks offer. We implement a proof of concept within a centralized suite of applications used for the Atmospheric Radiation Measurement (ARM) Data Center Operational Tools, where we introduce a third integration path through the ARM Context Engine (ACE). ACE is a context driven system that uses structured contextual specifications to enable Large Language Models (LLMs) to render interactive and feature-rich user interface (UI) components directly within chat responses, alongside or in place of conventional text outputs. These specifications serve two important purposes across what we call code context and UI context. Code context provides AI-assisted development tools with structured application knowledge beyond raw code, including component relationships, architectural patterns and schematic information, enabling the generation of consistent, well-structured code. UI context defines the rules for enabling and rendering component features at runtime based on the user's natural language input, allowing end users to activate capabilities such as data export, filtering, and pagination within chat responses, without requiring code changes or redeployment. We demonstrate, through a comparative evaluation against general-purpose AI chatbots, that context-driven component rendering provides interactive capabilities that text-based responses cannot replicate, including deterministic component behavior, application-consistent design language, and on-demand feature activation. A development effort comparison further shows that features that traditionally require multi-step development cycles can be activated with a single naturallanguage request. In this ongoing work, we present ACE as an emerging approach to AI integration that positions modular, well-documented software architecture as the foundation for AI-ready applications. ACE treats context as a shared resource across both development and user-facing AI, bringing cohesion to conventionally disconnected efforts, bridging developer tooling and end-user capabilities within a single framework.

Tadimeti, Vijay [ORNL]↗
DOE OSTI2026

Development Status of the KIPM Detector Consortium

A kinetic inductance phonon-mediated detector is a calorimeter that uses kinetic inductance detectors (KIDs) to read out phonon signals from the device substrate. We have established a consortium comprising university and national lab groups dedicated to advancing the state of the art in these detectors, with the ultimate goal of designing a detector with sub-eV threshold on energy deposited in the substrate, enabling searches for both light dark matter and low-energy neutrino interactions. This consortium brings together experts in KID design, phonon and quasi-particle dynamics, and noise modeling, along with specialized fabrication facilities, test platforms, and unique calibration capabilities. Recently, our consortium has demonstrated a resolution on energy absorbed by the sensor of 2.1 eV, the current record for phonon-sensitive KIDs. However, due to ∼ 1% phonon collection efficiency, the overall detector resolution was limited to 320 eV. Finally, the current focus of the consortium is modeling and improving the phonon collection efficiency and implementing low-T c superconductors, both of which serve to improve the overall energy resolution and threshold of the detectors.

Temples, Dylan J. [Fermi National Accelerator Labo↗
DOE OSTI2024

$Z$-Pinch Interferometry Analysis With the Fourier-Based TNT Code

Here, we present the analysis of interferometry diagnostics with the user-friendly Talbot Numerical Tool (TNT), a Fourier-based postprocessing code that enables real-time assessment of plasma systems. TNT performance was explored with visible and infrared interferometry in pulsed-power-driven Z -pinch configurations to expand its capabilities beyond Talbot X-ray interferometry in the high-intensity laser environment. TNT enabled accurate electron density characterization of magnetically driven plasma flows and shocks through phase-retrieval methods that did not require data modification or masking. TNT demonstrated enhanced resolution, detecting below 4 % fringe shift, which corresponds to 8.7 × 10 15 cm –2 within 28 μ m, approaching the laser probing system limit. TNT was tested against a well-known interferometry analysis software, delivering an average resolving power nearly ten times better (~28 μ m versus ~ 210 μ m) when resolving plasma ablation features. TNT demonstrated higher sensitivity when probing sharp electron density gradients in supersonic shocks. A maximum electron areal density of 4.1 × 10 17 cm –2 was measured in the shocked plasma region, and a minimum electron density detection of ~ 1.0 × 10 15 cm –2 was achieved. When probing colliding plasma flows, the calculations of the effective adiabatic index and the associated errors were improved from γ* = 2.6 ± 1.6 – 1.4 ± 0.2 with TNT postprocessing, contributing valuable data for the interpretation of radiative transport. Additional applications of TNT in the characterization of pulsed-power plasmas and beyond are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Study of Shock Formation Parameters With Drive Conditions in Magnetically Accelerated Plasma Flows

We present experimental data regarding the formation of high-energy-density shocks in magnetically accelerated plasma flows using pulsed power drivers. We quantify the flow velocity and temperature of the ablated plasma using optical Thomson scattering and gated emission imaging across two different generators. We show that, regardless of the drive parameters, the plasma flows show continuous acceleration over centimeter spatial scales, in line with trends in published simulation work. When stationary targets are placed in these supersonic flows, bow-shock formation is observed at all drive parameters in a range of materials. In the higher density flow generated on the 1-MA COBRA generator at Cornell University, heating of the upstream flow ahead of the shock is observed and quantified, which is not observed at the lower density flow on the 0.2-MA Bertha driver at UC San Diego. Here, when combined with previous work on the XP generator at Cornell, we can show that these three experimental setups allow control of the effect of radiation loss and upstream absorption on the formation of the bow shock.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A Photovoltaic MPPT Charge Controller Real-Time Testbed for Cybersecurity Applications

The increasing deployment of distributed energy resources (DER) over the last decade is a great ally to combat climate change and strengthen the grid during increasingly common extreme weather events. However, DER systems, combined with the ongoing transition to a digital power grid, also pose substantial cybersecurity threats. One of the most common communication protocols used in DER integration is the Distributed Network Protocol 3 (DNP3), which is known to have many security vulnerabilities. Thus, it is essential to investigate cyberattack behaviors and mitigation on power systems using DNP3. In this paper, we designed and implemented a cybersecurity testbed for a simulated photovoltaic (PV) maximum power point tracking (MPPT) charge controller. Our testbed uses an MPPT charge controller simulated on a Typhoon HIL602+ real-time simulator with a real DNP3 communication connection over TCP/IP, allowing for safe and efficient monitoring and manipulation of data traffic between the simulated hardware and supervisory control and data acquisition (SCADA) systems.

14 SOLAR ENERGY↗
DOE OSTI2022

Global distribution, formation and fate of mineral‐associated soil organic matter under a changing climate: A trait‐based perspective

Abstract Soil organic matter (SOM) is the largest actively cycling reservoir of terrestrial carbon (C), and the majority of SOM in Earth's mineral soils (~65%) is mineral‐associated organic matter (MAOM). Thus, the formation and fate of MAOM can exert substantial influence on the global C cycle. To predict future changes to Earth's climate, it is critical to mechanistically understand the processes by which MAOM is formed and decomposed, and to accurately represent this process‐based understanding in biogeochemical and Earth system models. In this review, we use a trait‐based framework to synthesize the interacting roles of plants, soil micro‐organisms, and the mineral matrix in regulating MAOM formation and decomposition. Our proposed framework differentiates between plant and microbial traits that influence total OM inputs to the soil (‘feedstock traits’) versus traits that influence the proportion of OM inputs that are ultimately incorporated into MAOM (‘MAOM formation traits’). We discuss how these feedstock and MAOM formation traits may be altered by warming, altered precipitation and elevated carbon dioxide. At a planetary scale, these feedstock and MAOM formation traits help shape the distribution of MAOM across Earth's biomes, and modulate biome‐specific responses of MAOM to climate change. We leverage a global synthesis of MAOM measurements to provide estimates of the total amount of MAOM‐C globally (~840–1540 Pg C; 34%–51% of total terrestrial organic C), and its distribution across Earth's biomes. We show that MAOM‐C concentration is highest in temperate forests and grasslands, and lowest in shrublands and savannas. Grasslands and croplands have the highest proportion of soil organic carbon (SOC) in the MAOM fraction (i.e. the MAOM‐C:SOC ratio), while boreal forests and tundra have the lowest MAOM‐C:SOC ratio. Drawing on our trait framework, we then review experimental data and posit the effects of climate change on MAOM pools in different biomes. We conclude by discussing how MAOM is integrated into soil C models, and how feedstock and MAOM formation traits may be included in these models. We also summarize the projected fate of MAOM under climate change scenarios (Representative Concentration Pathways 4.5 and 8.5) and discuss key model uncertainties. Read the free Plain Language Summary for this article on the Journal blog.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Testing unified theories for ozone response in C 4 species

Abstract There is tremendous interspecific variability in O 3 sensitivity among C 3 species, but variation among C 4 species has been less clearly documented. It is also unclear whether stomatal conductance and leaf structure such as leaf mass per area (LMA) determine the variation in sensitivity to O 3 across species. In this study, we investigated leaf morphological, chemical, and photosynthetic responses of 22 genotypes of four C 4 bioenergy species (switchgrass, sorghum, maize, and miscanthus) to elevated O 3 in side‐by‐side field experiments using free‐air O 3 concentration enrichment (FACE). The C 4 species varied largely in leaf morphology, physiology, and nutrient composition. Elevated O 3 did not alter leaf morphology, nutrient content, stomatal conductance, chlorophyll fluorescence, and respiration in most genotypes but reduced net CO 2 assimilation in maize and photosynthetic capacity in sorghum and maize. Species with lower LMA and higher stomatal conductance tended to show greater losses in photosynthetic rate and capacity in elevated O 3 compared with species with higher LMA and lower stomatal conductance. Stomatal conductance was the strongest determinant of leaf photosynthetic rate and capacity. The response of both area‐ and mass‐based leaf photosynthetic rate and capacity to elevated O 3 were not affected by LMA directly but negatively influenced by LMA indirectly through stomatal conductance. These results demonstrate that there is significant variation in O 3 sensitivity among C 4 species with maize and sorghum showing greater sensitivity of photosynthesis to O 3 than switchgrass and miscanthus. Interspecific variation in O 3 sensitivity was determined by direct effects of stomatal conductance and indirect effects of LMA. This is the first study to provide a test of unifying theories explaining variation in O 3 sensitivity in C 4 bioenergy grasses. These findings advance understanding of O 3 tolerance in C 4 grasses and could aid in optimal placement of diverse C 4 bioenergy feedstock across a polluted landscape.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Drought Reduces Formation, but Enhances Persistence, of Mineral‐Associated Organic Matter in a Grassland Soil

Drought effects are pervasive in terrestrial ecosystems, yet there is limited understanding of how drought impacts the transformation of plant carbon (C) inputs to mineral-associated organic matter (MAOM)—the largest and slowest-cycling pool of soil organic carbon (SOC). In a 12-week 13 C-CO 2 greenhouse labeling experiment, we tracked the formation of MAOM derived from the two dominant sources of plant C input to the mineral soil—living root inputs ( 13 C-rhizodeposits) and decaying root inputs ( 13 C-root detritus)—under normal moisture and droughted conditions in a semiarid grassland soil. At the end of the 12-week period, we also measured the persistence of 13 C-MAOM formed from rhizodeposits versus root detritus via a subsequent persistence assay. Drought reduced the formation of MAOM derived from living roots by decreasing rhizodeposits, reducing microbial growth rates, and altering the composition of organic matter, lipids, and metabolites. Drought initially delayed the formation of MAOM derived from root detritus by slowing the early stages of root litter decomposition (week 4–8), but did not decrease total MAOM formation by the end of the 12-week period. Notably, drought enhanced the persistence of MAOM derived from root detritus, but did not influence the persistence of MAOM derived from rhizodeposits. Our results provide some of the first direct evidence that drought can reduce the formation of MAOM in a grassland soil, but may enhance its persistence, based on the source of plant input from which MAOM is derived.

13C- labeling↗
DOE OSTI2021

Bioenergy sorghum maintains photosynthetic capacity in elevated ozone concentrations

Abstract Elevated tropospheric ozone concentration (O 3 ) significantly reduces photosynthesis and productivity in several C 4 crops including maize, switchgrass and sugarcane. However, it is unknown how O 3 affects plant growth, development and productivity in sorghum ( Sorghum bicolor L.), an emerging C 4 bioenergy crop. Here, we investigated the effects of elevated O 3 on photosynthesis, biomass and nutrient composition of a number of sorghum genotypes over two seasons in the field using free‐air concentration enrichment (FACE), and in growth chambers. We also tested if elevated O 3 altered the relationship between stomatal conductance and environmental conditions using two common stomatal conductance models. Sorghum genotypes showed significant variability in plant functional traits, including photosynthetic capacity, leaf N content and specific leaf area, but responded similarly to O 3 . At the FACE experiment, elevated O 3 did not alter net CO 2 assimilation ( A ), stomatal conductance ( g s ), stomatal sensitivity to the environment, chlorophyll fluorescence and plant biomass, but led to reductions in the maximum carboxylation capacity of phosphoenolpyruvate and increased stomatal limitation to A in both years. These findings suggest that bioenergy sorghum is tolerant to O 3 and could be used to enhance biomass productivity in O 3 polluted regions.

09 BIOMASS FUELS↗
DOE OSTI2022

The leaf economics spectrum of triploid and tetraploid C 4 grass Miscanthus x giganteus

Abstract The leaf economics spectrum (LES) describes multivariate correlations in leaf structural, physiological and chemical traits, originally based on diverse C 3 species grown under natural ecosystems. However, the specific contribution of C 4 species to the global LES is studied less widely. C 4 species have a CO 2 concentrating mechanism which drives high rates of photosynthesis and improves resource use efficiency, thus potentially pushing them towards the edge of the LES. Here, we measured foliage morphology, structure, photosynthesis, and nutrient content for hundreds of genotypes of the C 4 grass Miscanthus × giganteus grown in two common gardens over two seasons. We show substantial trait variations across M .× giganteus genotypes and robust genotypic trait relationships. Compared to the global LES, M .× giganteus genotypes had higher photosynthetic rates, lower stomatal conductance, and less nitrogen content, indicating greater water and photosynthetic nitrogen use efficiency in the C 4 species. Additionally, tetraploid genotypes produced thicker leaves with greater leaf mass per area and lower leaf density than triploid genotypes. By expanding the LES relationships across C 3 species to include C 4 crops, these findings highlight that M .× giganteus occupies the boundary of the global LES and suggest the potential for ploidy to alter LES traits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Natural variation in Brachypodium distachyon responses to combined abiotic stresses

SUMMARY The demand for agricultural production is becoming more challenging as climate change increases global temperature and the frequency of extreme weather events. This study examines the phenotypic variation of 149 accessions of Brachypodium distachyon under drought, heat, and the combination of stresses. Heat alone causes the largest amounts of tissue damage while the combination of stresses causes the largest decrease in biomass compared to other treatments. Notably, Bd21‐0, the reference line for B. distachyon , did not have robust growth under stress conditions, especially the heat and combined drought and heat treatments. The climate of origin was significantly associated with B. distachyon responses to the assessed stress conditions. Additionally, a GWAS found loci associated with changes in plant height and the amount of damaged tissue under stress. Some of these SNPs were closely located to genes known to be involved in responses to abiotic stresses and point to potential causative loci in plant stress response. However, SNPs found to be significantly associated with a response to heat or drought individually are not also significantly associated with the combination of stresses. This, with the phenotypic data, suggests that the effects of these abiotic stresses are not simply additive, and the responses to the combined stresses differ from drought and heat alone.

Plant Sciences↗
DOE OSTI2020

Numerical Investigation of Fuel Property Effects on Mixed-Mode Combustion in a Spark-Ignition Engine

In this research, lean mixed-mode combustion is numerically investigated using computational fluid dynamics (CFD) in a spark-ignition engine. A new E30 fuel surrogate is developed using a neural network model with matched octane numbers. A skeletal mechanism is also developed by automated mechanism reduction and by incorporating a NO x submechanism. A hybrid approach that couples the G-equation model and the well-stirred reactor model is employed for turbulent combustion modeling. The developed CFD model is shown to well predict pressure and apparent heat release rate (AHRR) traces compared with experiment. Two types of combustion cycles (deflagration-only and mixed-mode cycles) are observed. The mixed-mode cycles feature early flame propagation and subsequent end-gas auto-ignition, leading to two distinctive AHRR peaks. The validated CFD model is then employed to investigate the effects of NO x chemistry. The NO x chemistry is found to promote auto-ignition through the residual gas, while the deflagration phase remains largely unaffected. Sensitivity analysis is finally performed to understand effects of fuel properties, including heat of vaporization (HoV) and laminar flame speed (S L ). An increased HoV tends to suppress auto-ignition through charge cooling, while the impact of HoV on flame propagation is insignificant. In contrast, an increased S L is found to significantly promote both flame propagation and end-gas auto-ignition. The promoting effect of S L on auto-ignition is not a direct chemical effect; it is rather caused by an advancement of the combustion phasing, which increases compression heating of the end-gas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Influence of Fuel Properties on Gasoline Direct Injection Particulate Matter Emissions Over First 200 s of World-Harmonized Light-Duty Test Procedure Using an Engine Dynamometer and Novel “Virtual Drivetrain” Software

Abstract The influence of fuel properties on particulate matter (PM) emissions from a catalytic gasoline particulate filter (GPF) equipped gasoline direct injection (GDI) engine was investigated using novel “virtual drivetrain” software and an engine mated to an engine dynamometer. The virtual drivetrain software was developed in labview to operate the engine on an engine dynamometer as if it were in a vehicle undergoing a driving cycle. The software uses a physics-based approach to determine vehicle acceleration and speed based on engine load and a programed “shift” schedule to control engine speed. The software uses a control algorithm to modulate engine load and braking to match a calculated vehicle speed with the prescribed speed trace of the driving cycle of choice. The first 200 s of the World-harmonized Light-duty Test Procedure (WLTP) driving cycle was tested using six different fuel formulations of varying volatility, aromaticity, and ethanol concentration. The first 200 s of the WLTP was chosen as the test condition because it is the most problematic section of the driving cycle for controlling PM emissions due to the cold start and cold drive off. It was found that there was a strong correlation between aromaticity of the fuel and the engine-out PM emissions, with the highest emitting fuel producing more than double the mass emissions of the low PM production fuel. However, the post-GPF PM emissions depended greatly on the soot loading state of the GPF. The fuel with the highest engine-out PM emissions produced comparable post-GPF emissions to the lowest PM producing fuel over the driving cycle when the GPF was loaded over three cycles with the respective fuels. These results demonstrate the importance of GPF loading state when aftertreatment systems are used for PM reduction. It also shows that GPF control may be more important than fuel properties, and that regulatory compliance for PM can be achieved with proper GPF control calibration irrespective of fuel type.

Energy & Fuels↗
DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗
DOE OSTI2024

Thinking Beyond the Default User: The Impact of Gender, Stereotypes, and Modality on Interpretation of User Needs

Throughout the mechanical design process, designers, the majority of whom are men, often fail to consider the needs of women, resulting in consequences ranging from inconvenience to increased risk of serious injury or death. Although these biases are well studied in other fields of research, the mechanical design field lacks formal investigation into this phenomenon. In this study, engineering students (n = 301) took a survey in which they read a Persona describing a student makerspace user and a Walkthrough describing the user’s interaction with the makerspace while completing a project. During the Walkthrough, the user encountered various obstacles or Pain Points. Participants were asked to recall and evaluate the Pain Points that the user encountered and then evaluated their perceptions of the makerspace and user. Furthermore, the independent variables under investigation were the gender of the user Persona (woman, gender-neutral, or man), the Walkthrough room case (crafting or woodworking makerspace), and the modality of the Persona and Walkthrough (text- or audio-based). Results showed that participants from the Text-based modality were better able to recall Pain Points compared to participants from the Audio-based modality. Pain Points were assessed as more severe when they impacted women users, potentially stemming from protective paternalism. In addition to finding that the gender of a user impacted the way a task environment was perceived, results confirmed the presence of androcentrism, or “default man” assumptions, in the way designers view end users of unknown gender. Promisingly, providing user Persona information in an audio modality significantly reduced this bias compared to text-based modalities, indicating that providing richer detail in user personas has the capability to reduce gender bias in designers.

cognitive-based design↗