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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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DOE OSTI2023

A Comprehensive Analysis of Real-World Accelerometer Data Quality in a Global Smartphone-based Seismic Network

The proliferation of low-cost sensors in smartphones has facilitated numerous applications; however, large-scale deployments often encounter performance issues. Sensing heterogeneity, which refers to varying data quality due to factors such as device differences and user behaviors, presents a significant challenge. In this research, we perform an extensive analysis of 3-axis accelerometer data from the MyShake system, a global seismic network utilizing smartphones. We systematically evaluate the quality of approximately 22 million 3-axis acceleration waveforms from over 81 thousand smartphone devices worldwide, using metrics that represent sampling rate and noise level. We explore a broad range of factors influencing accelerometer data quality, including smartphone and accelerometer manufacturers, phone specifications (release year, RAM, battery), geolocation, and time. Our findings indicate that multiple factors affect data quality, with accelerometer model and smartphone specifications being the most critical. In addition, we examine the influence of data quality on earthquake parameter estimation and show that removing low-quality accelerometer data enhances the accuracy of earthquake magnitude estimation.

58 GEOSCIENCES↗

NASA NTRS1996

A convertor and user interface to import CAD files into worldtoolkit virtual reality systems

Virtual Reality (VR) is a rapidly developing human-to-computer interface technology. VR can be considered as a three-dimensional computer-generated Virtual World (VW) which can sense particular aspects of a user's behavior, allow the user to manipulate the objects interactively, and render the VW at real-time accordingly. The user is totally immersed in the virtual world and feel the sense of transforming into that VW. NASA/MSFC Computer Application Virtual Environments (CAVE) has been developing the space-related VR applications since 1990. The VR systems in CAVE lab are based on VPL RB2 system which consists of a VPL RB2 control tower, an LX eyephone, an Isotrak polhemus sensor, two Fastrak polhemus sensors, a folk of Bird sensor, and two VPL DG2 DataGloves. A dynamics animator called Body Electric from VPL is used as the control system to interface with all the input/output devices and to provide the network communications as well as VR programming environment. The RB2 Swivel 3D is used as the modelling program to construct the VW's. A severe limitation of the VPL VR system is the use of RB2 Swivel 3D, which restricts the files to a maximum of 1020 objects and doesn't have the advanced graphics texture mapping. The other limitation is that the VPL VR system is a turn-key system which does not provide the flexibility for user to add new sensors and C language interface. Recently, NASA/MSFC CAVE lab provides VR systems built on Sense8 WorldToolKit (WTK) which is a C library for creating VR development environments. WTK provides device drivers for most of the sensors and eyephones available on the VR market. WTK accepts several CAD file formats, such as Sense8 Neutral File Format, AutoCAD DXF and 3D Studio file format, Wave Front OBJ file format, VideoScape GEO file format, Intergraph EMS stereolithographics and CATIA Stereolithographics STL file formats. WTK functions are object-oriented in their naming convention, are grouped into classes, and provide easy C language interface. Using a CAD or modelling program to build a VW for WTK VR applications, we typically construct the stationary universe with all the geometric objects except the dynamic objects, and create each dynamic object in an individual file.

Wang, Peter Hor-Ching↗

DOE OSTI2021

Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach

Devising effective post-hazard recovery strategies is critical in enhancing the resilience of transportation networks (TNs). However, existing work does not consider the multiclass users’ travel behavior in network functionality quantification and the metaheuristic solution procedures often suffer from extensive computational burden due to the exploration need in large solution space and the expensive functionality quantification. Here, this study develops a bilevel decision-making framework for the resilience-based recovery scheduling of the TN in a mixed traffic environment with connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). The lower level quantifies the TN's functionality over time considering different travel behavior of CAV and HDV users arisen from their different levels of information perception. The upper level presents a novel deep-ensemble-assisted active learning approach to balance optimization performance and computational cost. This framework can help decision makers better quantify the TN's functionality to support effective recovery scheduling of TN with different mixed traffic scenarios ranging from HDV-only to future CAV-dominant traffic. The optimization approach bears the potential to be extended to solving general large-scale network recovery scheduling problems effectively and efficiently. The proposed methodology is demonstrated using a real-world traffic network in Southern California under earthquake considering deterministic and stochastic repair durations.

42 ENGINEERING↗

NASA NTRS2003

Team Oriented Robotic Exploration Task on Scorpion and K9 Platforms

This final report describes the achievements that have been made in the project over the complete period of performance. The technical progress highlights the different areas of work in terms of Progress in Mechatronics, Sensor integration, Software Development. User Interfaces, Behavior Development and Experimental Results and System Testing. The different areas are: Mechatronics, Sensor integration, Software development, Experimental results and Basic System Testing, Behaviors Development and Advanced System Testing, User Interface and Wireless Communication.

Kirchner, Frank↗

DOE OSTI2025

Exploring Nonmotorized Travel in New York State Using 2017 National Household Travel Survey Data

This report presents systematic analysis of nonmotorized travel behavior within New York State (NYS) utilizing 2017 National Household Travel Survey data. As walking and biking assume increasingly prominent roles in advancing active transportation objectives, accessible mobility, and sustainable transportation systems, comprehensive understanding of these modal patterns becomes essential for evidence-based policy development and strategic planning initiatives. Recognizing the substantial geographic, demographic, and socioeconomic heterogeneity characterizing NYS—spanning from the concentrated urban fabric of New York City (NYC) to dispersed suburban and rural contexts—this analysis employs the structured "4Ws" analytical framework to examine participant demographics, spatial and temporal distributions, and motivational factors underlying active transportation choices relative to motorized alternatives. This methodological approach captures modal behavior patterns, user characteristics, trip purposes, and temporal variations across the state's diverse contexts. Additionally, the research examines behavioral differences among distinct user classifications, including walk-only and bike-only travelers, while conducting comparative analysis of nonmotorized travel patterns between ALICE (Asset Limited, Income Constrained, Employed) and non-ALICE household categories.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS1996

Intelligent Agent Technology

Open Sesame! is the first commercial software product that learns user's behavior, and offers automation and coaching suggestions to the user. The neural learning module looks for repetitive patterns that have not been automated; when it finds one, it creates an observation and, upon approval, automates the task. The manufacturer, Charles River Analytics, credits Langley Research Center and Johnson Space Center Small Business Innovation Research grants and the time the president and vice president spent at the two centers in the 1970s as being essential to the development of their product line.

Source record↗

NASA NTRS1990

Methodology for automating software systems

Applying ITS technology to the shuttle diagnostics would not require the rigor of the Petri Net representation, however it is important in providing the animated simulated portion of the interface and the demands placed on the system to support the training aspects to have a homogeneous and consistent underlying knowledge representation. By keeping the diagnostic rule base, the hardware description, the software description, user profiles, desired behavioral knowledge, and the user interface in the same notation, it is possible to reason about the all of the properties of petri nets, on any selected portion of the simulation. This reasoning provides foundation for utilization of intelligent tutoring systems technology.

Moseley, Warren↗

DOE OSTI2026

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗

DOE OSTI2026

Analyzing users’ preferences between personal and pooled rideshare services using a mixed logit modeling approach

Ridesharing has become an increasingly popular transportation method over the past decade. Transportation network companies such as Uber and Lyft generally provide two types of rideshare services: personal rideshare, in which users ride alone or with individuals they know, and pooled rideshare, in which users ride with passengers they do not know but share similar routes. Pooled rideshare is capable of reducing energy consumption and traffic in the transportation system in comparison to personal rideshare. Despite the growth in trip volume, ridesharing usage is still low compared to other popular transportation methods in the U.S., particularly traveling in one’s own personal vehicle. Furthermore, pooled rideshare usage is lower than personal rideshare. To understand riders’ preferences, a national survey (N = 2884) was conducted in the U.S. to investigate users’ choice behaviors in rideshare services examining personal versus pooled rideshare. Each survey respondent completed 20 stated-preference scenarios where participants choose between a personal or pooled rideshare option. Based on the responses, a mixed logit model was developed to capture the choice behavior preferences of the participants. The model unveiled the impact of demographic and trip attribute variables on users’ rideshare preferences. The discussion encompassed insights into demographic backgrounds and trip attributes, accompanied by a set of policy recommendations aimed at enhancing future pooled rideshare utilization.

Choice model↗

DOE OSTI2020

Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2017

YADA: Yet Another Distributed Architecture for Real-Time Robotic Control Systems

This paper presents YADA, a new software architecture for real-time robotic control systems that is minimal, modular, and fully transparent. YADA divides control software into decoupled behavior, user-interface, and hardware-level bus modules. This decoupling at the module level is accomplished by auto-generating human-readable message types that are tailored to the hardware topology oft he current system. These message types provide modules with a common framework for exchanging state information and relaying commands to devices while being agnostic to the communication protocol itself. We also detail how to structure behavior and bus modules to facilitate modularity and flexibility with third party software. YADA has been used with success on several technology development testbeds at JPL, an example of which is given in this paper, and has proven to provide developers a light-weight and highly reconfigurable system for efficient debugging and practical code sharing

Godart, Peter↗

NASA NTRS2014

SSL Freeform Generator v1.00

The Orion spacecraft utilizes a system of Compact Unique Identifiers (CUIs) to identify telemetry measurements in the process of communicating with the ground. While avionic systems are still in development, simulation of telemetric data must be used for the development of dependent systems. The SuperScript Language (SSL) developed by Honeywell Aerospace provides users a programming language for simulation of this data. The use of tools that provide SSL generation capabilities at a higher level allows for faster and more efficient generation of simulation waveforms. Previous SSL Generator implementations such as Orion Scripted Interface Generator (OrionSIG) relied on software supported routines to generate simulation points for the SSL scripts. This provided relevant simulation capabilities, but was reduced in flexibility by the available software routines available. For this approach, software supported routines have been entirely dropped. Rather, the user is required to enter their own simulation waveform input. This allows arbitrarily complex functions to be utilized simulated with maximum degree of freedom for the user.: The idea for this version of the SSL Generator was a mapping of arbitrary waveforms to Orion CUI telemetry data. The SSL Generator implemented offers a higher degree of freedom to the user than previous versions while attempting to maintain abstraction from the underlying SSL script and the Orion CUI data types. As such, many mappings of waveforms to CUIs will simply not work. Certain implementation decisions were made to minimize the damage caused by these unintended side-effects, such as saturation of values exceeding the maximum or minimum bounds of the data type. Other details, such as signed values being represented in unsigned data types, present problems which cant be fixed to an acceptable degree of intended behavior without user input. Awareness of the limitations of all layers of the software stack will increase the productivity of the end-user.

telemetry↗

DOE OSTI2023

Factors Influencing Adoption of Pooled Rideshare An Explorative Study on User-Centered Design and Services

Gangadharaiah, Rakesh↗

DOE OSTI2023

A User-Centered Design Exploration of Factors That Influence the Rideshare Experience

The rise of real-time information communication through smartphones and wireless networks enabled the growth of ridesharing services. While personal rideshare services (individuals riding alone or with acquaintances) initially dominated the market, the popularity of pooled ridesharing (individuals sharing rides with people they do not know) has grown globally. However, pooled ridesharing remains less common in the U.S., where personal vehicle usage is still the norm. Vehicle design and rideshare services may need to be tailored to user preferences to increase pooled rideshare adoption. Based on a large, national U.S. survey (N = 5385), the results of exploratory and confirmatory factor analyses suggested that four key factors influence riders’ willingness to consider pooled ridesharing: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. A binomial logistic regression was conducted to determine how the four factors influence one’s willingness to consider pooled ridesharing. The two factors that positively influence riders’ willingness to consider pooled ridesharing are vehicle technology/accessibility (B = 1.10) and convenience (B = 0.94), while lack of passenger safety (B = –0.63) and comfort/ease of use (B = –0.17) are pooled ridesharing deterrents. Understanding user-centered design and service factors are critical to increase the use of pooled ridesharing services in the future.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1976

The timing and sources of information for the adoption and implementation of production innovations

Two dimensions (personal-impersonal and internal-external) are used to characterize information sources as they become important during the interorganizational transfer of production innovations. The results of three studies are reviewed for the purpose of deriving a model of the timing and importance of different information sources and the utilization of new technology. Based on the findings of two retrospective studies, it was concluded that the pattern of information seeking behavior in user organizations during the awareness stage of adoption is not a reliable predictor of the eventual utilization rate. Using the additional findings of a real-time study, an empirical model of the relative importance of information sources for successful user organizations is presented. These results are extended and integrated into a theoretical model consisting of a time-profile of successful implementations and the relative importance of four types of information sources during seven stages of the adoption-implementation process.

Ettlie, J. E.↗

NASA NTRS2011

Automated Test Case Generation for an Autopilot Requirement Prototype

Designing safety-critical automation with robust human interaction is a difficult task that is susceptible to a number of known Human-Automation Interaction (HAI) vulnerabilities. It is therefore essential to develop automated tools that provide support both in the design and rapid evaluation of such automation. The Automation Design and Evaluation Prototyping Toolset (ADEPT) enables the rapid development of an executable specification for automation behavior and user interaction. ADEPT supports a number of analysis capabilities, thus enabling the detection of HAI vulnerabilities early in the design process, when modifications are less costly. In this paper, we advocate the introduction of a new capability to model-based prototyping tools such as ADEPT. The new capability is based on symbolic execution that allows us to automatically generate quality test suites based on the system design. Symbolic execution is used to generate both user input and test oracles user input drives the testing of the system implementation, and test oracles ensure that the system behaves as designed. We present early results in the context of a component in the Autopilot system modeled in ADEPT, and discuss the challenges of test case generation in the HAI domain.

Giannakopoulou, Dimitra↗

DOE OSTI2024

Distribution Grid Incentive Design with Unknown Agent Behavior

Motivation: During extreme events, traditional grid regulation methods (e.g., energy prices, net power injection limits) may be insufficient. While system operators typically lack control over end-user grid interactions, (e.g., energy demand), incentives can influence behavior - for example, a user that receives a grid-driven incentive may adjust their consumption or expose relevant control variables in response. Problem: Optimize for the best incentive subject to system stability constraints. However, user behavior is unknown to the SO - i.e., for a given incentive, the amount of curtailed load or control variables exposed is unknown.

feedback based control↗

NASA NTRS1992

Establishing a research agenda for scientific and technical information (STI) - Focus on the user

This report addresses the relationship between library science and information science theory and practice, between the development of conceptual understanding, and the practical competence of information professionals. Consideration is given to the concept of research, linking theory with practice, and the reality of theory based practice. Attention is given to the need for research and research priorities, focus on the user and information-seeking behavior, and a user-oriented research agenda for STI.

Pinelli, Thomas E.↗