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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Translational Cellular Research on the International Space Station

The emerging field of Translational Research aims to coalesce interdisciplinary findings from basic science for biomedical applications. To complement spaceflight research using human subjects, translational studies can be designed to address aspects of space-related human health risks and help develop countermeasures to prevent or mitigate them, with therapeutical benefits for analogous conditions experienced on Earth. Translational research with cells and model organisms is being conducted onboard the International Space Station (ISS) in connection with various human systems impacted by spaceflight, such as the cardiovascular, musculoskeletal, and immune systems. Examples of recent cell-based translational investigations on the ISS include the following. The JAXA investigation Cell Mechanosensing seeks to identify gravity sensors in skeletal muscle cells to develop muscle atrophy countermeasures by analyzing tension fluctuations in the plasma membrane, which changes the expression of key proteins and genes. Earth applications of this study include therapeutic approaches for some forms of muscular dystrophy, which appear to parallel aspects of muscle wasting in space. Spheroids is an ESA investigation examining the system of endothelial cells lining the inner surface of all blood vessels in terms of vessel formation, cellular proliferation, and programmed cell death, because injury to the endothelium has been implicated as underpinning various cardiovascular and musculoskeletal problems arising during spaceflight. Since endothelial cells are involved in the functional integrity of the vascular wall, this research has applications to Earth diseases such as atherosclerosis, diabetes, and hypertension. The goal of the T-Cell Activation in Aging NASA investigation is to understand human immune system depression in microgravity by identifying gene expression patterns of candidate molecular regulators, which will provide further insight into factors that may play a critical role in immune function loss during aging. In addition, Omics investigations with cells have synergistic applications ranging from the evaluation of pharmacological countermeasures to drug discovery. Thus, cell-based translational research onboard the ISS is bidirectionally bridging cutting-edge cellular and molecular approaches with space bioastronautics and human health methodologies on Earth.

Love, John↗
NASA NTRSDate not supplied

Lunar Browser Utilization of Machine Learning for Trajectory Solution Production

This paper describes the application of machine learning tools to produce Earth-Moon spacecraft trajectories with applications to NASA’s Commercial Lunar Payload Services (CLPS) and Artemis Human Landing System (HLS) programs. Existing trajectory solutions are used to train and test machine learning models to predict essential details of a trajectory sequence from Earth-launch to Low-Lunar Orbit, populating a database of solutions with future launch dates. The machine learning model will implement hyperparameter optimization for further re-training to improve model performance. Accurate predictive models decrease the time required to produce solutions and are readily implemented in the Lunar Browser tool.

Trajectory Design↗
DOE OSTI2025

Teleoperation of Construction Robots Using Human Interaction: Case Study Examples: Preprint

The integration of advanced teleoperation methods in construction robotics has the potential to enhance productivity and safety in demanding environments. Among different methods, hand gestures have emerged as an intuitive approach, enabling seamless communication between human operators and robotic systems. This study demonstrates the use of hand gestures for controlling a model robotic excavator and a virtual robotic arm in construction applications. Two case study examples are presented. In the first case study, a series of hand gestures are performed to control a model robotic excavator's bucket and arm movements. The second case study focuses on using hand gestures to control a virtual robotic arm to perform a material handling task in a simulated environment. In both cases, the performed hand gestures are all successfully captured and interpreted by the gesture-based teleoperation method to conduct the corresponding tasks, which demonstrates the application of the gesture-based control method for various types of construction robots. Also, the limitations of the gesture-based teleoperation method (e.g., recognition delay, sensitivity to motion outliers) are discussed. Future work will focus on developing a digital twin system to support gesture-based teleoperation for a wider range of construction robots.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2016

MBSE-Driven Visualization of Requirements Allocation and Traceability

In a Model Based Systems Engineering (MBSE) infusion effort, there is a usually a concerted effort to define the information architecture, ontologies, and patterns that drive the construction and architecture of MBSE models, but less attention is given to the logical follow-on of that effort: how to practically leverage the resulting semantic richness of a well-formed populated model to enable systems engineers to work more effectively, as MBSE promises. While ontologies and patterns are absolutely necessary, an MBSE effort must also design and provide practical demonstration of value (through human-understandable representations of model data that address stakeholder concerns) or it will not succeed. This paper will discuss opportunities that exist for visualization in making the richness of a well-formed model accessible to stakeholders, specifically stakeholders who rely on the model for their day-to-day work. This paper will discuss the value added by MBSE-driven visualizations in the context of a small case study of interactive visualizations created and used on NASA's proposed Europa Mission. The case study visualizations were created for the purpose of understanding and exploring targeted aspects of requirements flow, allocation, and comparing the structure of that flow-down to a conceptual project decomposition. The work presented in this paper is an example of a product that leverages the richness and formalisms of our knowledge representation while also responding to the quality attributes SEs care about.

Model Based Systems Engineering↗
NASA NTRS2016

MBSE-Driven Visualization of Requirements Allocation and Traceability

In a Model Based Systems Engineering (MBSE) infusion effort, there is a usually a concerted effort to define the information architecture, ontologies, and patterns that drive the construction and architecture of MBSE models, but less attention is given to the logical follow-on of that effort: how to practically leverage the resulting semantic richness of a well formed populated model to enable systems engineers to work more effectively, as MBSE promises. While ontologies and patterns are absolutely necessary, an MBSE effort must also design and provide practical demonstration of value (through human-understandable representations of model data that address stakeholder concerns) or it will not succeed. This paper will discuss opportunities that exist for visualization in making the richness of a well-formed model accessible to stakeholders, specifically stakeholders who rely on the model for their day-to-day work. This paper will discuss the value added by MBSE-driven visualizations in the context of a small case study of interactive visualizations created and used on NASA’s proposed Europa Mission. The case study visualizations were created for the purpose of understanding and exploring targeted aspects of requirements flow, allocation, and comparing the structure of that flow-down to a conceptual project decomposition. The work presented in this paper is an example of a product that leverages the richness and formalisms of our knowledge representation while also responding to the quality attributes SEs care about.

Model Based Systems Engineering↗
DOE OSTI2023

Load Shedding for Voltage Regulation With Probabilistic Agent Compliance

With the increased observability and controllability of distribution systems, the share of behind-the-meter systems is trending upwards rapidly. As a consequence, the impact of human behaviors on system performance can no longer be ignored and should be reflected in the energy management system models. In this paper, we discuss the problem of distribution system voltage control by active power curtailment where the agent compliance of the load curtailment signal is probabilistic. We discuss the modeling of the optimal voltage control problem with probabilistic agent compliance as a chance-constrained optimization problem, its tractable safe approximation using convex restriction, and a scenario-based mixed-integer reformulation as well as the associated solution method based on augmented Lagrangian method. The numerical simulation on IEEE test system validates the effectiveness of the proposed approach in obtaining high-quality feasible load curtailment signal with low computational cost, which makes it a viable tool for real time decision making.

augmented Lagrangian method↗
NASA NTRS2023

NAIRAS Ionizing Radiation Model: Extension from Atmosphere to Space

The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS) model is a real-time, global, physics-based model originally developed to predict exposure from cosmic radiation to air travelers from both galactic and solar sources. A prototype operational NAIRAS model has provided tabular and graphical data products via its public web site for about ten years. A new version of the NAIRAS model has been developed that incorporates an extension of the model domain from the atmospheric ionizing radiation environment to the space radiation environment, with the addition of the trapped inner belt proton source and altitude-dependent and rigidity-dependent geomagnetic shielding of the galactic cosmic rays (GCR) and solar energetic particle (SEP) protons. New output products of differential and integral particle flux have been developed for the characterization of single-event effects (SEE), expanding the application of NAIRAS from human radiation exposure assessment to allowing end-users to quantify radiation environment risks to aviation and spacecraft microelectronic systems. The NAIRAS model has transitioned to prototype operations at the Community Coordinated Modeling Center (CCMC) where the model now operates in two modes: (1) real-time global predictions of the atmospheric radiation environment and (2) a run-on-request (RoR) service allowing the user to select a specific time period for the global dosimetric calculations, or to upload an aircraft, balloon, or spaceflight trajectory file to provide predictions of the dosimetric and particle flux quantities along the flight path. The new features of NAIRAS version 3.0 are described in this paper and example results of the new output products for low-Earth orbit (LEO), medium-Earth orbit (MEO), and free-space radiation environments are presented

Christopher J. Mertens↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
NASA NTRS1989

Constructor selection system

Future space construction missions will involve both human and machine constructors. Selection of the optimum constructor mix requires a model of constructor capabilities and requirements. The database for that model is developed via extrapolation from current literature. Optimization is done via minimization of total mission cost using a linear programming approach. This prototype is the first cut at producing a general tool for choosing a near-optimum constructor mix for any space construction mission. It illuminates some significant representational and data-gathering problems with the modelling approach.

Johnson, Richard↗
NASA NTRS2012

Modeling of Atmosphere Revitalization

NASA's AES is pioneering new approaches for future human missions beyond Earth orbit. All spacecraft systems must be minimized with respect to mass, power, and volume. Here, we show work related to improving system efficiency and reliability for water separation systems on crewed vehicles and the initial development of COMSOL simulations in support of the Atmosphere Revitalization Recovery and Environmental Monitoring (ARREM) project

Coker, Robert↗
DOE OSTI2020

Data analysis and modeling pipelines for controlled networked social science experiments

There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2013

Human and Natural Influences on the Changing Thermal Structure of the Atmosphere

Since the late 1970s, satellite-based instruments have monitored global changes in atmospheric temperature. These measurements reveal multidecadal tropospheric warming and stratospheric cooling, punctuated by short-term volcanic signals of reverse sign. Similar long- and short-term temperature signals occur in model simulations driven by human-caused changes in atmospheric composition and natural variations in volcanic aerosols. Most previous comparisons of modeled and observed atmospheric temperature changes have used results from individual models and individual observational records. In contrast, we rely on a large multimodel archive and multiple observational datasets. We show that a human-caused latitude/altitude pattern of atmospheric temperature change can be identified with high statistical confidence in satellite data. Results are robust to current uncertainties in models and observations. Virtually all previous research in this area has attempted to discriminate an anthropogenic signal from internal variability. Here, we present evidence that a human-caused signal can also be identified relative to the larger "total" natural variability arising from sources internal to the climate system, solar irradiance changes, and volcanic forcing. Consistent signal identification occurs because both internal and total natural variability (as simulated by state-of-the-art models) cannot produce sustained global-scale tropospheric warming and stratospheric cooling. Our results provide clear evidence for a discernible human influence on the thermal structure of the atmosphere.

Santer, Benjamin D.↗
NASA NTRS1980

Model development for automatic guidance of a VTOL aircraft to a small aviation ship

This paper describes a detailed mathematical model which has been assembled to study automatic approach and landing guidance concepts to bring a VTOL aircraft onto a small aviation ship. The model is used to formulate system simulations which in turn are used to evaluate different guidance concepts. Ship motion (Sea State 5), wind-over-deck turbulence, MLS-based navigation, implicit model following flight control, lift fan V/STOL aircraft, ship and aircraft instrumentation errors, various steering laws, and appropriate environmental and human factor constraints are included in the model. Results are given to demonstrate use of the model and simulation to evaluate performance of the flight system and to choose appropriate guidance techniques for further cockpit simulator study.

Goka, T.↗
NASA NTRS1991

A proven knowledge-based approach to prioritizing process information

Many space-related processes are highly complex systems subject to sudden, major transients. In any complex process control system, a critical aspect is rapid analysis of the changing process information. During a disturbance, this task can overwhelm humans as well as computers. Humans deal with this by applying heuristics in determining significant information. A simple, knowledge-based approach to prioritizing information is described. The approach models those heuristics that humans would use in similar circumstances. The approach described has received two patents and was implemented in the Alarm Filtering System (AFS) at the Idaho National Engineering Laboratory (INEL). AFS was first developed for application in a nuclear reactor control room. It has since been used in chemical processing applications, where it has had a significant impact on control room environments. The approach uses knowledge-based heuristics to analyze data from process instrumentation and respond to that data according to knowledge encapsulated in objects and rules. While AFS cannot perform the complete diagnosis and control task, it has proven to be extremely effective at filtering and prioritizing information. AFS was used for over two years as a first level of analysis for human diagnosticians. Given the approach's proven track record in a wide variety of practical applications, it should be useful in both ground- and space-based systems.

Corsberg, Daniel R.↗
NASA NTRSDate not supplied

Extreme Problem Solving: The New Challenges of Deep Space Exploration

On the International Space Station today, the crew has the near real-time support of a large group of system experts on the ground when dealing with problems on-board. For exploration beyond Low Earth Orbit, however, intermittent and delayed communication with ground will force small crews to take the lead in responding to vehicle anomalies. Enabling a flight crew of roughly four astronauts to perform the job that has traditionally been done by a ground crew of over 80 experts will require a fundamental rethinking of human-systems integration. Through observations of anomaly resolution processes, interviews with system experts and astronauts, and analyses of problem-solving models, we have identified the capabilities that are not currently available on-board but will be needed to enable safe exploration further away from Earth. These include increased data access, just-in-time training tools and technologies, and troubleshooting decision support. Important questions remain on how these technologies can be designed and implemented for increased crew autonomy. We present this critical challenge for deep space exploration to the human-computer interaction research community to reflect on the areas identified by our needs analysis and contemplate how they might be manifested as solutions.

autonomy↗
NASA NTRS1999

Development of the ECLSS Sizing Analysis Tool and ARS Mass Balance Model Using Microsoft Excel

The development of a Microsoft Excel-compatible Environmental Control and Life Support System (ECLSS) sizing analysis "tool" for conceptual design of Mars human exploration missions makes it possible for a user to choose a certain technology in the corresponding subsystem. This tool estimates the mass, volume, and power requirements of every technology in a subsystem and the system as a whole. Furthermore, to verify that a design sized by the ECLSS Sizing Tool meets the mission requirements and integrates properly, mass balance models that solve for component throughputs of such ECLSS systems as the Water Recovery System (WRS) and Air Revitalization System (ARS) must be developed. The ARS Mass Balance Model will be discussed in this paper.

McGlothlin, E. P.↗
DOE OSTI2026

SIMBER: the Simula Berkeley Education and Research Collaboration (CRADA Final Report)

The SIMBER project is centered around advancing the state-of-the-art in the science of modelling of the human heart and leveraging the collaborations between leading research groups at the University of California Berkeley (UC Berkeley), the Lawrence Berkeley National Laboratory (Berkeley Lab), and Simula Research Laboratory (Simula). In particular, the following areas of expertise are shared and expanded through this project: “heart-on-chip” experimental systems from UC Berkeley, mathematical modelling of the heart from Simula, and supercomputing software from Simula and Berkeley Lab. This intersection of expertise is already enabling the development of novel tools and knowledge that produce more accurate models of the human heart in both health and disease, which in turn are leading to drug screening technologies that make cardiac drug development faster, cheaper and more humane.

Li, Xiaoye Sherry [Lawrence Berkeley National Labo↗
NASA NTRS2014

Is Model-Based Development a Favorable Approach for Complex and Safety-Critical Computer Systems on Commercial Aircraft?

A system is safety-critical if its failure can endanger human life or cause significant damage to property or the environment. State-of-the-art computer systems on commercial aircraft are highly complex, software-intensive, functionally integrated, and network-centric systems of systems. Ensuring that such systems are safe and comply with existing safety regulations is costly and time-consuming as the level of rigor in the development process, especially the validation and verification activities, is determined by considerations of system complexity and safety criticality. A significant degree of care and deep insight into the operational principles of these systems is required to ensure adequate coverage of all design implications relevant to system safety. Model-based development methodologies, methods, tools, and techniques facilitate collaboration and enable the use of common design artifacts among groups dealing with different aspects of the development of a system. This paper examines the application of model-based development to complex and safety-critical aircraft computer systems. Benefits and detriments are identified and an overall assessment of the approach is given.

Torres-Pomales, Wilfredo↗