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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Descriptor: Infrastructure Perception and Control: Multi-Sensor Object Tracking Dataset (IPC-MSOT)

Traffic intersections are crucial and challenging nodes in transportation networks where multiple lanes of vehicles and pedestrians converge. Traffic accidents often occur at traffic intersections, including a large proportion of traffic fatalities and about one-half of all traffic injuries in the United States. Object detection data were collected in 2024 across three intersections in Colorado Springs, CO, USA, over the course of multiple days and various times to induce a heterogeneous mix of traffic conditions and behaviors. The purpose of the data collection exercises was to learn various attributes about infrastructure sensors and to build a repository of high-resolution, object-level data that can be used for research and development (e.g., to develop multisensor data fusion algorithms). The Infrastructure Perception and Control:Multi-Sensor Object tracking (IPC-MSOT) dataset was collected as part of the U.S. Department of Transportation's Strengthening Mobility and Revolutionizing Transportation (SMART) project, where the city of Colorado Springs, Colorado, and the National Renewable Energy Laboratory collaborated to collect object-level trajectory data from road users using multiple types of infrastructure sensors deployed at different intersections. This dataset allows for testing of late-stage sensor fusion algorithms and their ability to ingest multimodal sensor data, and it can be utilized by traffic engineers to design and evaluate trajectory-based signal control strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

The Navstar GPS master control station's Kalman filter experience

The Navstar Global Positioning System (GPS) is a highly accurate space based navigation system providing all weather, 24 hour a day service to both military and civilian users. The system provides a Gaussian position solution with four satellites, each providing its ephemeris and clock offset with respect to GPS time. The GPS Master Clock Station (MCS) is charged with tracking each Navstar spacecraft and precisely defining the ephemeris and clock parameters for upload into the vehicle's navigation message. Briefly described here are the Navstar system and the Kalman filter estimation process used by MCS to determine, predict, and ensure quality control for each of the satellite's ephemeris and clock states. Routine performance is shown. Kalman filter reaction and response is discussed for anomalous clock behavior and trajectory perturbations. Particular attention is given to MCS efforts to improve orbital adjust modeling. The satellite out of service time due to orbital maneuvering has been reduced in the past year from four days to under twelve hours. The planning, reference trajectory model, and Kalman filter management improvements are explained.

Scardera, Michael P.↗
NASA NTRS1999

CARES/Life Ceramics Durability Evaluation Software Enhanced for Cyclic Fatigue

The CARES/Life computer program predicts the probability of a monolithic ceramic component's failure as a function of time in service. The program has many features and options for materials evaluation and component design. It couples commercial finite element programs--which resolve a component's temperature and stress distribution--to reliability evaluation and fracture mechanics routines for modeling strength-limiting defects. The capability, flexibility, and uniqueness of CARES/Life have attracted many users representing a broad range of interests and has resulted in numerous awards for technological achievements and technology transfer. Recent work with CARES/Life was directed at enhancing the program s capabilities with regards to cyclic fatigue. Only in the last few years have ceramics been recognized to be susceptible to enhanced degradation from cyclic loading. To account for cyclic loads, researchers at the NASA Lewis Research Center developed a crack growth model that combines the Power Law (time-dependent) and the Walker Law (cycle-dependent) crack growth models. This combined model has the characteristics of Power Law behavior (decreased damage) at high R ratios (minimum load/maximum load) and of Walker law behavior (increased damage) at low R ratios. In addition, a parameter estimation methodology for constant-amplitude, steady-state cyclic fatigue experiments was developed using nonlinear least squares and a modified Levenberg-Marquardt algorithm. This methodology is used to give best estimates of parameter values from cyclic fatigue specimen rupture data (usually tensile or flexure bar specimens) for a relatively small number of specimens. Methodology to account for runout data (unfailed specimens over the duration of the experiment) was also included.

Nemeth, Noel N.↗
NASA NTRSDate not supplied

Hierarchical Multiscale Process Modeling of A Textile Composite Y-Joint for the Aurora D8 Aircraft

Woven polymer matrix composites (PMCs) are widely used in aerospace applications for their favorable specific properties. However, the behavior of the inter- and intra-tow matrix during manufacturing of woven PMCs is difficult to isolate experimentally due to geometric and processing complexities. Classical closed-form homogenization approaches allow for composite thermos-chemo-mechanical properties to be characterized at any fiber volume fraction. In this work, virtual curing is applied to the inter- and intra-tow matrix materials of a plain weave repeating unit cell (RUC). Thermo-chemo-mechanical property evolution within the tows is calculated using the Concentric Cylinder Model (CCM) and the Rule of Mixtures (ROM). Curing is implemented through user-written subroutines within the commercial finite element method software Abaqus. Virtual curing results are compared with those from a linear-elastic thermal cool down simulation in Abaqus. Results indicate that tow property evolution during curing has a non-negligible effect on the final stress state.

curing↗
DOE OSTI2023

Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022

Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format. Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035. The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip. These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.

Array↗
NASA NTRS1990

Foundations of measurement and instrumentation

The user of instrumentation has provided an understanding of the factors that influence instrument performance, selection, and application, and of the methods of interpreting and presenting the results of measurements. Such understanding is prerequisite to the successful attainment of the best compromise among reliability, accuracy, speed, cost, and importance of the measurement operation in achieving the ultimate goal of a project. Some subjects covered are dimensions; units; sources of measurement error; methods of describing and estimating accuracy; deduction and presentation of results through empirical equations, including the method of least squares; experimental and analytical methods of determining the static and dynamic behavior of instrumentation systems, including the use of analogs.

Warshawsky, Isidore↗
NASA NTRS2013

Deepak Condenser Model (DeCoM)

Development of the DeCoM comes from the requirement of analyzing the performance of a condenser. A component of a loop heat pipe (LHP), the condenser, is interfaced with the radiator in order to reject heat. DeCoM simulates the condenser, with certain input parameters. Systems Improved Numerical Differencing Analyzer (SINDA), a thermal analysis software, calculates the adjoining component temperatures, based on the DeCoM parameters and interface temperatures to the radiator. Application of DeCoM is (at the time of this reporting) restricted to small-scale analysis, without the need for in-depth LHP component integrations. To efficiently develop a model to simulate the LHP condenser, DeCoM was developed to meet this purpose with least complexity. DeCoM is a single-condenser, single-pass simulator for analyzing its behavior. The analysis is done based on the interactions between condenser fluid, the wall, and the interface between the wall and the radiator. DeCoM is based on conservation of energy, two-phase equations, and flow equations. For two-phase, the Lockhart- Martinelli correlation has been used in order to calculate the convection value between fluid and wall. Software such as SINDA (for thermal analysis analysis) and Thermal Desktop (for modeling) are required. DeCoM also includes the ability to implement a condenser into a thermal model with the capability of understanding the code process and being edited to user-specific needs. DeCoM requires no license, and is an open-source code. Advantages to DeCoM include time dependency, reliability, and the ability for the user to view the code process and edit to their needs.

Patel, Deepak↗
DOE OSTI2017

Identifying Effective Signals to Predict Deleted and Suspended Accounts on Twitter across Languages

Social networks have an ephemerality to them where accounts and messages are constantly being edited, deleted, or marked as private. This continuous change comes from concerns around privacy, a potential desire for deception, and spam-like behavior. In this study we analyze multiple large datasets of thousands of active and deleted Twitter accounts to produce a series of predictive features for the removal or shutdown of an account. We have selected these accounts from speakers of three languages -- Russian, Spanish, and English to evaluate if speakers of various languages behave differently with regards to deleting accounts. We find that unlike previously used profile and network features, the discourse of deleted vs. active accounts forms the basis for highly accurate account deletion prediction. More precisely, we observed that the presence of a certain set of terms in user tweets leads to a higher likelihood for that user's account deletion. We show that the predictive power of profile, language, affect, and network features is not consistent across speakers of the three evaluated languages.

account deletion prediction↗
DOE OSTI2017

Identifying Effective Signals to Predict Deleted and Suspended Accounts on Twitter across Languages

Social networks have an ephemerality to them where accounts and messages are constantly being edited, deleted, or marked as private. This continuous change comes from concerns around privacy, a potential desire for deception, and spam-like behavior. In this study we analyze multiple large datasets of thousands of active and deleted Twitter accounts to produce a series of predictive features for the removal or shutdown of an account. We have selected these accounts from speakers of three languages -- Russian, Spanish, and English to evaluate if speakers of various languages behave differently with regards to deleting accounts. We find that unlike previously used profile and network features, the discourse of deleted vs. active accounts forms the basis for highly accurate account deletion prediction. More precisely, we observed that the presence of a certain set of terms in user tweets leads to a higher likelihood for that user's account deletion. We show that the predictive power of profile, language, affect, and network features is not consistent across speakers of the three evaluated languages.

social media, machine learning, natural language p↗
DOE OSTI2017

Time-resolved chemical imaging based on stimulated TERS (Final Scientific-Technical Report-Phase-II-DE-SC0013172)

This is the Final Scientific/Technical Report for the DOE FastTrack SBIR Grant from Fiscal Year 2015. The report details the work pursued to scientifically demonstrate ultrahigh spatial resolution in ultrafast spectroscopy and commercially develop a product offering that provides the similar capability to other researchers. Chemical and electronic information associated with molecular-scale processes can be interrogated via optical spectroscopy. By utilizing two ultrafast lasers, one to “pump” and the other to “probe” with time delay between them, researchers are able to study various molecular and electronic dynamics with time resolution in femtosecond range. Due to the diffraction limit associated with these optical techniques, these techniques measure average behavior of ensemble of molecules since individual molecules are typically much smaller than the wavelengths of the lasers being utilized. This report presents the successful demonstration of ultrafast spectroscopy (temporal resolution of 150 femtosecond limited by the laser pulse width) of electronic recovery in carbon nanotubes, revealing different recovery dynamics for individual carbon nanotubes with spatial resolution of ~ 10 nm, more than 50X smaller than the wavelengths used to study them. The report also details the required instrumentation for such measurements and how it plans to offer a turnkey solution to the research community to provide such capability in a user-friendly and low maintenance package.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

MSD CoP Webinar: "Generative agents: A new frontier for representing human actors and their behavior in MSD models"

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Talk #1: Behavioral Generative Agents for Energy Operations Presenter: Dr. Cong Chen (Thayer School of Engineering, Dartmouth College) Abstract: Accurately modeling consumer behavior in energy operations remains challenging due to inherent uncertainties, behavioral complexities, and limited empirical data. This talk introduces a novel approach leveraging generative agents--artificial agents powered by large language models--to realistically simulate customer decision-making in dynamic energy operations. Talk #2: Simulating multiple human perspectives in socio-ecological systems using large language models Presenter: Dr. Yongchao Zeng (Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU) of the Karlsruhe Institute of Technology in Germany) Abstract: Understanding socio-ecological systems requires insights from diverse stakeholder perspectives. This talk describes a novel simulation system called HoPeS (Human-oriented Perspective Shifting). HoPeS enables model users to not only explore simulated socio-ecological systems (SESs) from a third-person observer's perspective but also take any of the simulated stakeholder roles, like playing an RPG game. By shifting multiple perspectives, model users can reflect and integrate the situated knowledge learned through the participatory simulation, approximating a more holistic and less biased understanding of SESs. Moderators: Jim Yoon (MSD CoP Human Systems Modeling Working Group Co-Chair); Stefano Galelli (MSD CoP Using AI to Enhance MSD Research Working Group Co-Chair); Patrick M. Reed (MSD CoP Facilitation Team) This webinar was held on: November 13th, 2025 from 12-1 PM EST.

Artificial Intelligence↗
NASA NTRS2013

Develop a Model Component

During my internship at NASA, I was a model developer for Ground Support Equipment (GSE). The purpose of a model developer is to develop and unit test model component libraries (fluid, electrical, gas, etc.). The models are designed to simulate software for GSE (Ground Special Power, Crew Access Arm, Cryo, Fire and Leak Detection System, Environmental Control System (ECS), etc. ~.) before they are implemented into hardware. These models support verifying local control and remote software for End-Item Software Under Test (SUT). The model simulates the physical behavior (function, state, limits and 110) of each end-item and it's dependencies as defined in the Subsystem Interface Table, Software Requirements & Design Specification (SRDS), Ground Integrated Schematic (GIS), and System Mechanical Schematic.(SMS). The software of each specific model component is simulated through MATLAB's Simulink program. The intensiv~ model development life cycle is a.s follows: Identify source documents; identify model scope; update schedule; preliminary design review; develop model requirements; update model.. scope; update schedule; detailed design review; create/modify library component; implement library components reference; implement subsystem components; develop a test script; run the test script; develop users guide; send model out for peer review; the model is sent out for verific~tionlvalidation; if there is empirical data, a validation data package is generated; if there is not empirical data, a verification package is generated; the test results are then reviewed; and finally, the user. requests accreditation, and a statement of accreditation is prepared. Once each component model is reviewed and approved, they are intertwined together into one integrated model. This integrated model is then tested itself, through a test script and autotest, so that it can be concluded that all models work conjointly, for a single purpose. The component I was assigned, specifically, was a fluid component, a discrete pressure switch. The switch takes a fluid pressure input, and if the pressure is greater than a designated cutoff pressure, the switch would stop fluid flow.

Ensey, Tyler S.↗
NASA NTRS1983

An overview of artificial intelligence and robotics. Volume 1: Artificial intelligence. Part A: The core ingredients

Artificial Intelligence (AI) is an emerging technology that has recently attracted considerable attention. Many applications are now under development. The goal of Artificial Intelligence is focused on developing computational approaches to intelligent behavior. This goal is so broad - covering virtually all aspects of human cognitive activity - that substantial confusion has arisen as to the actual nature of AI, its current status and its future capability. This volume, the first in a series of NBS/NASA reports on the subject, attempts to address these concerns. Thus, this report endeavors to clarify what AI is, the foundations on which it rests, the techniques utilized, applications, the participants and, finally, AI's state-of-the-art and future trends. It is anticipated that this report will prove useful to government and private engineering and research managers, potential users, and others who will be affected by this field as it unfolds.

Gevarter, W. B.↗
NASA NTRS2026

Mechanical Design and Operation of a Novel Lunar Environment Structural Test Rig (LESTR)

The Lunar Environment Structural Test Rig (LESTR) was developed to address a critical gap in mechanical property data for metal alloy wire materials under lunar-relevant conditions down to 40 K. Conventional aerospace material databases provide thermophysical properties for bulk metals over a wide temperature range, but validated mechanical and physical property data below 77 K, particularly for small-diameter wires, remain limited and are generally the exception rather than the rule. These conditions are essential for Artemis mission hardware such as shape memory alloy (SMA) rover tires. LESTR’s design requirements were to combine a high-stiffness electrodynamic load frame with closed- cycle cryogenic cooling, high-vacuum capability (10–6 torr), and noncontact optical strain measurement to enable tensile, four-point bend, and fatigue testing of wires or other materials and geometries at temperatures from 40 to 125 K. These considerable requirements were merged with the need to lower the barrier of testing for the end user as measured in terms of cost-per-test cycle, safety improvement, and reduction in upkeep costs associated with state-of-the-art solutions associated with cryomechanical material characterization. The system incorporates modular gripping and alignment fixtures; precision thermal management using cryocoolers and embedded heaters; and integrated instrumentation for displacement, load, temperature, and vacuum control. Calibration procedures establish correlations between tooling and specimen temperature, ensuring accurate thermal conditions across the design envelope unbound by cryofluid conditions in heritage immersion-based test systems. Initial validation tests using Inconel (Special Metals Corp.) wire demonstrated accurate ultimate strength and post-yield behavior. The load frame operation was also verified under representative service conditions, including thermal cycling and prolonged fatigue loading. By generating mechanical property data at ultralow temperatures, LESTR fills a critical gap in existing materials databases and provides a scalable platform for iterative alloy development and durability assessment for planetary hardware. This capability supports NASA’s long-term objectives for surface exploration by enabling design confidence for components operating in extreme cryogenic environments.

LESTR↗
DOE OSTI2026

Long-Range Fit: A Software Package for the Representation and Study of Long-Range Molecular Interactions

Describing intermolecular forces is fundamental to modeling and predicting the behavior of molecular systems. In particular, long-range molecular interactions─with electrostatic, induction, and dispersion as the main components─play a critical role, especially for low-temperature and low-density regimes. Long-range interactions are often described through perturbation theory, representing the electronic charge distribution via a multipolar series of the moments and polarizability tensors corresponding to each molecule. However, while the theory is well established, obtaining the resulting analytical expressions (and their practical implementation) constitutes a highly complex and system-dependent task. To address this challenge, we developed long-range-fit (LRF), an interactive and user-friendly software package designed to automate the generation and fitting of long-range interaction terms for arbitrary molecules in nondegenerate (ground or excited) electronic states. We have derived and implemented all terms up to 15th order, without approximations, via a spherical tensor representation, with symmetry adaptation to all molecular point-group symmetries. The resulting potential energy surface is compatible with most representations of the close interaction region.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Ecobee Donate Your Data 1,000 homes in 2017

This dataset is a subset of the Ecobee Donate Your Data (DYD) dataset. The Ecobee DYD data comprises user-reported metadata (of home and occupant characteristics), and data collected by Ecobee thermostats (reported in 5-minute intervals). Participant data are pulled from the Ecobee servers, and then anonymized to remove any personally identifiable information. This subset selects 1,000 single family homes in four states - California, Texas, New York, and Illinois, and span the entire year of 2017. In addition to the measurements, a metadata JSON file is included to illustrate the high-level contextual information of the dataset. The dataset can be analyzed to understand how a single-family heating, ventilation, and air-conditioning (HVAC) system operates, occupant behavior, and building thermal dynamics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1984

Some methods of computing platform transmitter terminal location estimates

A position estimation algorithm was developed to track a humpback whale tagged with an ARGOS platform after a transmitter deployment failure and the whale's diving behavior precluded standard methods. The algorithm is especially useful where a transmitter location program exists; it determines the classical keplarian elements from the ARGOS spacecraft position vectors included with the probationary file messages. A minimum of three distinct messages are required. Once the spacecraft orbit is determined, the whale is located using standard least squares regression techniques. Experience suggests that in instances where circumstances inherent in the experiment yield message data unsuitable for the standard ARGOS reduction, (message data may be too sparse, span an insufficient period, or include variable-length messages). System ARGOS can still provide much valuable location information if the user is willing to accept the increased location uncertainties.

Hoisington, C. M.↗
NASA NTRSDate not supplied

Web-based Visualization and Analytics of Petascale Data: Equity as a Tide that Lifts All Boats

Scientists generate petabytes of data daily to help uncover environmental trends or behaviors that are hard to predict. For example, understanding climate simulations based on the long-term average of temperature, precipitation, and other environmental variables is essential to predicting and establishing root causes of future undesirable scenarios and assessing possible mitigation strategies. While supercomputer centers provide a powerful infrastructure for generating petabytes of simulation output, accessing and analyzing these datasets interactively remains challenging on multiple fronts. This paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hid-ing the complexities of dealing with file systems or cloud services.We also optimize network utilization while streaming from petas-cale repositories through state-of-the-art progressive compression algorithms. Based on this abstraction, we provide customizable dashboards that can be accessed from any device with any inter-net connection, enabling interactive visual analysis of vast amounts of data to a wide range of users - from top scientists with access to leadership-class computing environments to undergraduate students of disadvantaged backgrounds from minority-serving institutions. We focus on NASA’s use of petascale climate datasets as an example of particular societal impact and, therefore, a case where achieving equity in science participation is critical. We validate our approach by improving the ability of climate scientists to visually explore their data via two fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution.These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Data visualization↗