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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
NASA NTRS2023

High-Fidelity Simulations of a Tiltwing Vehicle for Urban Air Mobility

NASA is playing an important role in the development of the urban air mobility ecosystem by identifying key research areas and establishing a fleet of conceptual reference vehicles for urban air mobility applications. This paper delves deeper into the study of NASA’s six-passenger tiltwing air taxi aerodynamics using high-fidelity computational fluid dynamics simulations. NASA’s OVERFLOW flow solver has been loosely-coupled with the rotorcraft comprehensive analysis code CAMRAD II. Dual-time stepping, high-order accurate schemes, and the delayed detached-eddy simulation are employed. The tiltwing configuration is analyzed in airplane and helicopter mode, with flight conditions representative of an air taxi mission. Airloads, wake geometry, and performance are studied to identify the unique flow features of this configuration. NASA’s tiltwing vehicle is one of the conceptual designs intended to focus and guide NASA’s research activities in support of aircraft development for vertical take-off and landing air taxi operations.

RVLT↗
NASA NTRSDate not supplied

Applications and Development Work for Digital Image Correlation Using High Speed Cameras

NASA, Army, and industry partners are working to advance optical measurement techniques used in highly dynamic rocket engine and missile test environments. This allows for direct component data collection during test and flight environments, significantly reduced analysis time, and associated cost savings. Digital Image Correlation (DIC) uses calibrated stereo cameras to collect three-dimensional (3-D) displacements, strains, and acceleration data by referencing a stochastic speckle pattern applied to the test object. These optical measurement techniques provide significantly improved data resulting in better performance predictions, model validation, and information about components during hot-fire tests and test flights.

Rachel Paden↗
DOE OSTI2021

Estimation of Electromechanical Modes of Oscillation in the Eastern Interconnection from Ambient PMU Data

A power system's inter-area electromechanical modes of oscillation determine if the system is small-signal stable. Mode meters are tools that estimate the modes based on ambient synchrophasor measurements, allowing continuous monitoring of the system's stability margin. This paper reports on the application of mode meter algorithms to 21 months of synchrophasor measurements from the United States Eastern Interconnection (EI). Estimates for three modes were updated every minute, providing a much more comprehensive set of results than previous studies based on disturbance recordings. The detailed signal designs and mode meter configurations provided in this paper can be used to accelerate mode meter deployments in the EI. Analysis of the mode estimates reveal for the first time how seasonal load changes and system reconfiguration impact the EI system's dynamics.

Follum, James D.↗
DOE OSTI2026

MCP-eGridGPT (MCP-Enabled Chatbot with Electrical Power System Analysis and Interactive Visualization Tool) [SWR-25-126]

This software is an advanced chatbot system that integrates the Model Context Protocol (MCP) to provide intelligent electrical power system analysis and automated visualization generation. The system enables users to interact with complex electrical engineering tools through natural language, automatically analyzes power system data for voltage violations and grid health assessment, and generates professional interactive HTML dashboards and reports. Key features include dynamic tool discovery from MCP servers, multi-LLM provider support, intelligent data interpretation using large language models, automated chart generation, and a web-based interface for real-time analysis. The software bridges sophisticated electrical engineering analysis with user-friendly interfaces, making power system diagnostics accessible through conversational AI.

Choi, Seong [National Laboratory of the Rockies (N↗
DOE OSTI2024

Potential Adoption and Benefits of Co-Optimized Multimode Engines and Fuels for U.S. Light-Duty Vehicles

Exploring a diverse portfolio of technologies for decarbonization is crucial to understanding the potential impacts of different technological solutions and their associated environmental implications. Using high-octane, high-sensitivity biofuel blends in co-optimized multimode engines can increase engine efficiency and reduce vehicle emissions. Here, the multimode engine research focuses on the benefits of light-duty vehicle engines, which can operate in multiple modes depending on the vehicle's load. Low-temperature combustion can improve efficiency and reduce emissions (such as those from oxides of nitrogen and particulate matter) during low-load operation, while spark ignition performance is maintained in high-load operation. These advanced engines can be optimized to run on blends of biobased fuels. This analysis models scenarios for potential market adoption of co-optimized multimode vehicles fueled by three different bioblendstocks: ethanol, isopropanol, and isobutanol. An integrated modeling approach is used to forecast the energy and environmental impacts of the deployment of co-optimized multimode vehicles and fuels in the light-duty sector over the 2020-to-2050 time horizon. The multidisciplinary approach combines vehicle sales modeling, system dynamics modeling of the biorefining industry, and life cycle assessment to estimate the emissions and energy benefits. The models consider market forces such as consumer preferences for vehicle attributes, biofuel supply and demand dynamics subject to biorefinery capacity build-out and bioresource constraints, and forecasted changes to the U.S. bulk energy system over time. Market adoption of co-optimized vehicles is evaluated across a wide parameter space for incremental vehicle cost and engine efficiency improvement. This analysis reveals that the deployment of co-optimized multimode fuels and vehicles results in up to a 5% reduction in annual sector-wide life cycle greenhouse gas (GHG) emissions by 2050, relative to a business-as-usual scenario, but is also indicates environmental trade-offs, such as higher life cycle water-use. Emission benefits could potentially increase beyond 2050, as the new technologies penetrate the market and gain a foothold. Results also show that, under certain circumstances, vehicles with engines co-optimized for use with high-octane, high-sensitivity biofuel blends can be cost-competitive with conventional gasoline, while reducing GHG emissions. Our modeling results indicate that co-optimized multimode fuels and engines can be strategically leveraged in tandem with electrification to decarbonize the light-duty sector. Co-optimized vehicles could play a role in the early years of the time horizon, while electric vehicles (EVs) could become more competitive in the later years, highlighting the complementary benefits of these technologies for GHG reductions.

Oke, Doris↗
DOE OSTI2024

Altered excitation energy transfer between phycobilisome and photosystems in the absence of ApcG, a small linker peptide, in Synechocystis sp. PCC 6803, a cyanobacterium

Phycobilisome (PBS) is a large pigment-protein complex in cyanobacteria and red algae responsible for capturing sunlight and transferring its energy to photosystems (PS). Spectroscopic and structural properties of various PBSs have been widely studied, however, the nature of so-called complex-complex interactions between PBS and PSs remains much less explored. In this work, we have investigated the function of a newly identified PBS linker protein, ApcG, some domain of which, together with a loop region (PB-loop in ApcE), is possibly located near the PBS-PS interface. Using Synechocystis sp. PCC 6803, we generated an ApcG deletion mutant and probed its deletion effect on the energetic coupling between PBS and photosystems. Steady-state and time-resolved spectroscopic characterization of the purified ΔApcG-PBS demonstrated that ApcG removal weakly affects the photophysical properties of PBS that the spectroscopic properties of terminal energy emitters are comparable to those of PBS from wild-type. However, analysis of fluorescence decay imaging datasets reveals that ApcG deletion induces disruptions within the allophycocyanin (APC) core, resulting in the emergence (splitting) of two spectrally diverse subgroups with some short-lived APC. Profound spectroscopic changes of the whole ΔApcG mutant cell, however, emerge during state transition, a dynamic process of light scheme adaptation. The mutant cells in State I show a substantial increase in PBS-related fluorescence. On the other hand, global analysis of time-resolved fluorescence demonstrates that in general ApcG deletion does not alter or inhibit state transitions if it is interpreted only in terms of the changes of the PSII and PSI fluorescence emission intensity. Furthermore, the results revealed yet–to–be discovered mechanism of ApcG-docking induced excitation energy transfer regulation within PBS or to Photosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Hyperchaotic Analysis and Adaptive Projective Synchronization of Nonlinear Dynamical System

In this paper, a new nonlinear dynamical system has been studied which is obtained from the 3D chaotic system. The hyperchaotic analysis of the new system is checked in terms of dissipation, equilibrium points and their stability, Lyapunov exponent, time series, phase portraits, Poincaré section and bifurcation diagram. Furthermore, the adaptive projective synchronization technique is used to synchronize the novel hyperchaotic system. A brief theoretical analysis and simulation results are presented to prove the behavior of the novel hyperchaotic system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Assessing changes in eco-productivity of wastewater treatment plants: The role of costs, pollutant removal efficiency, and greenhouse gas emissions

Highlights: • Temporal dynamics of eco-efficiency were measured for wastewater treatment plants. • Individual drivers (costs, pollutants, GHG) of eco-productivity were quantified. • Most of the plants evaluated worsened its eco-efficiency over time. • Operational costs were the main responsible of negative eco-technical change. - Abstract: Improving eco-efficiency of wastewater treatment plants (WWTPs) has been identified as being essential for achieving urban sustainability. Several previous papers have evaluated the eco-efficiency of WWTPs using data envelopment analysis (DEA) models. However, those models provided only a static assessment in that they ignored possible fluctuations over time within each plant. To overcome this temporal limitation, this paper evaluates dynamic eco-efficiency (changes in eco-productivity over time) of WWTPs using the dynamic weighted Russell directional distance model (WRDDM). This approach allows one to obtain an eco-productivity change index for each major component of the WRDDM model (costs, pollutants removal, and greenhouse gas emissions). Our results illustrate that although eco-productivity improved in half of the WWTPs we assessed, there was still potential for improving some eco-efficiency components. Moreover, operational costs and greenhouse gases emissions were the main drivers reducing eco-productivity. This paper demonstrates the importance of evaluating change in eco-productivity over time and in identifying the drivers associated with those changes, both of which can be used to support decision-making focused on the sustainability of WWTPs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Characterizing patterns and variability of building electric load profiles in time and frequency domains

The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. Furthermore, the commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Dynamics of the transition from the metastable tetragonal st12 to the diamond-cubic phase in germanium characterized by neutron spectroscopy

The complex phase diagram of germanium allows for exotic crystalline phases recoverable from high pressure. Among these, the tetragonal st12 phase is unusual as it shares characteristics with amorphous Ge resulting in desirable band-gap characteristics. Here, we leverage a high neutron flux and large volume pressure capabilities to characterize st12-Ge and its dynamics upon conversion to diamond-cubic Ge with inelastic neutron scattering. Here, we exploit the characteristics of time-of-flight neutron scattering for time-resolved investigations. This analysis may suggest the existence of a transient disordered state upon annealing befitting the known structural relationship between st12-Ge and amorphous Ge, a relationship that can open the potential for band-gap tuning.

Elemental semiconductors↗
DOE OSTI2021

Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series. Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam G CC (70.4 days, p adj < 0.0001) and EC GPP (79.6 days, p adj < 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integrated estimates of productivity from EC GPP and PhenoCam G CC diverged from those estimated by Landsat GPP and NDVI at sites where annual production exceeds 1000 gC/m –2 yr –1 . Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework was designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Integrating Learning and Physics based Computation for Fast Online Transient Analysis

In this work, a novel method that integrates learning and physics based computation is developed for greatly accelerating the simulation of full power system transient trajectories. To solve the dynamic algebraic equations, the method replaces the time-consuming dynamic computation for generator dynamics with trained predictors, while retaining the time-efficient algebraic computation of solving AC-power flow (PF) for power systems. In particular, a predictor is trained for each generator, and the system trajectories are computed by alternating steps of calling the predictors and solving AC-PF. The proposed method also allows fully parallelizable training strategies and a flexible trade-off between training time and testing accuracy. Comprehensive evaluations of the proposed method for transient/dynamic contingency analysis of the New York/New England 16-machine 68-bus power systems demonstrate excellent performance and significant acceleration of computation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2013

Human and Robotic Mission to Small Bodies: Mapping, Planning and Exploration

This study investigates the requirements, performs a gap analysis and makes a set of recommendations for mapping products and exploration tools required to support operations and scientific discovery for near- term and future NASA missions to small bodies. The mapping products and their requirements are based on the analysis of current mission scenarios (rendezvous, docking, and sample return) and recommendations made by the NEA Users Team (NUT) in the framework of human exploration. The mapping products that sat- isfy operational, scienti c, and public outreach goals include topography, images, albedo, gravity, mass, density, subsurface radar, mineralogical and thermal maps. The gap analysis points to a need for incremental generation of mapping products from low (flyby) to high-resolution data needed for anchoring and docking, real-time spatial data processing for hazard avoidance and astronaut or robot localization in low gravity, high dynamic environments, and motivates a standard for coordinate reference systems capable of describing irregular body shapes. Another aspect investigated in this study is the set of requirements and the gap analysis for exploration tools that support visualization and simulation of operational conditions including soil interactions, environment dynamics, and communications coverage. Building robust, usable data sets and visualisation/simulation tools is the best way for mission designers and simulators to make correct decisions for future missions. In the near term, it is the most useful way to begin building capabilities for small body exploration without needing to commit to specific mission architectures.

Neffian, Ara V.↗
DOE OSTI2017

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization: Human Factors in Streaming Data Analysis

State-of-the-art visual analytics models and frameworks mostly assume a static snapshot of the data, while in many cases it is a stream with constant updates and changes. Exploration of streaming data poses unique challenges as machine-level computations and abstractions need to be synchronized with the visual representation of the data and the temporally evolving human insights. In the visual analytics literature, we lack a thorough characterization of streaming data and analysis of the challenges associated with task abstraction, visualization design, and adaptation of the role of human-in-the-loop for exploration of data streams. We aim to fill this gap by conducting a survey of the state-of-the-art in visual analytics of streaming data for systematically describing the contributions and shortcomings of current techniques and analyzing the research gaps that need to be addressed in the future. Our contributions are: i) problem characterization for identifying challenges that are unique to streaming data analysis tasks, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space for dynamic data and the role of the human-in-the-loop, and iii) reflections on the design-trade-offs for streaming visual analytics techniques and their practical applicability in real-world application scenarios.

streaming data↗
DOE OSTI2019

Identification of Performance Shaping Factor Lag and Linger Effects for Dynamic Human Reliability Analysis: Application of Experimental Data

Many researchers have emphasized the importance of dynamic probabilistic safety assessment (PSA), which highlights the necessity of studies for dynamic human reliability analysis (HRA). Dynamic HRA concentrates on a dynamic account of human actions or how performance shaping factors (PSFs) can dynamically modify the human error probability (HEP) over time. In contrast, existing static HRA does not consider human actions as changes over time or event progressions. Accordingly, previous research on dynamic HRA conceptually suggested PSF lag and linger effects to treat dependence between operator actions in dynamic HRA versus existing approaches. As follow-up research, this study attempted to identify PSF lag and linger effects for dynamic HRA on the basis of experimental data. Firstly, mathematical models for PSF lag and linger effects were developed based on a literature survey in the field of biology. Secondly, we analyzed experimental scenarios from a control room study to identify operator actions considered in PSA. Lastly, based on the experimental data, the effect of the stress PSF with lag and linger effects was compared to that without PSF lag and linger effects. This paper concludes with an overall discussion of PSF lag and linger effects based on the relation between the stress PSF and error occurrence.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Using Stage Level Microseismic Analysis to Gain Insight into Fracture Efficiency and Completion Effectiveness

Microseismic event density heat maps at sequential time intervals during the treatment of individual completion stages have been developed to better understand fracture behavior on a stage level. Animating these heat maps through time on a single stage provides fascinating visual insights into the dynamic sequencing of populations of events, event timing and their likely genesis, providing a new way of visualizing and interpreting microseismic data sets. This technique leverages heat-map benefits from URTEC 2674376, excluding microseismic event clusters that may obscure or skew statistical analysis of the event densities, thus allowing for more accurate assessment of geologic features, stimulated rock volume, near-field concentration of events due to varying cluster spacing, fracture efficiency and stress shadowing. Far-field microseismic events prior to ramp up of treatment pressure are clearly indicated from this technique that cannot be observed with standard heat maps with certain completion designs. Unambiguous changes in microseismic event character from these far-field pre-ramp up events to those near-field events occurring at the onset of pressure are also observed. Applying a stage by stage approach on packages of wells and different locations provides robust statistical conclusions pertaining to reservoir behavior during well completion.

Wang, Iris↗
DOE OSTI2020

Memory Analysis Tool

This tool traces all memory accesses to stack (static allocation) and heap (dynamic allocation) on a trace run in a particular hardware and then estimates execution time on given arbitrary hardware configurations for hardware design space exploration. It can also compute memory access statistics, such as reuse distance.

Sato, Kento↗