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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Water hyacinths for upgrading sewage lagoons to meet advanced wastewater treatment standards, part 2

Field tests using water hyacinths as biological filtration agents were conducted in the Mississippi gulf coast region. The plants were installed in one single cell and one multiple cell sewage lagoon systems. Water hyacinths demonstrated the ability to maintain BOD5 and total suspended solid (TSS) levels within the Environmental Protection Agency's prescribed limits of 30 mg/lBOD5 and 30 mg/l TSS. A multiple cell sewage lagoon system consisting of two aerated and one water hyacinth covered cell connected in series demonstrated the ability to maintain BOD5 and TSS levels below 30 mg/l year-round. A water hyacinth covered lagoon with a surface area of 0.28 hectare containing a total volume of 6.8 million liters demonstrated the capacity to treat 437,000 to 1,893,000 liters of sewage influent from 2.65 hectares of aerated lagoons daily and produce an effluent that met or exceeded standards year-round.

Wolverton, B. C.↗
NASA NTRS1984

Inhibition of intracellular proteolysis in muscle cultures by multiplication-stimulating activity

The effects of the insulin-like growth factor, multiplication-stimulating activity (MSA), on chick myotube cultures are studied. The results indicate that MSA is an effective anabolic agent regulating protein metabolism and amino acid uptake, but not sugar transport. Similar size effects on protein metabolism and amino acid uptake in serum-free media were observed in parallel studies with insulin, although insulin levels well in excess of the normal physiological range are required to produce significant effects. It is suggested that there is a generally low insulin sensitivity in cultured chick myotubes relative to adult tissues.

Janeczko, Richard A.↗
NASA NTRS2002

Biosensors for Real-Time Monitoring of Radiation-Induced Biologic Effects in Space

This work seeks to develop cellular biosensors based on dendritic polymers. Nanoscale polymer structures less than 20 nm in diameter will be used as the basis of the biosensors. The structures will be designed to target into specific cells of an astronaut and be able to monitor health issues such as exposure to radiation. Multiple components can be assembled on the polymers including target directors, analytical devices (such as molecular probes), and reporting agents. The reporting will be accomplished through fluorescence signal monitoring, with the use of multispectral analysis for signal interpretation. These nanosensors could facilitate the success and increase the safety of extended space flight. The design and assembly of these devices has been pioneered at the Center for Biologic Nanotechnology in the University of Michigan. This period, synthesis of the test-bed biosensors continued. Studies were performed on the candidate fluorescent dyes to determine which might be suitable for the biosensor under development. Development continued on producing an artificial capillary bed as a tool for the use in the production of the fluorescence signal monitor. Work was also done on the in vitro multispectral analysis system, which uses the robotic microscope.

Baker, James R.↗
NASA NTRS2005

Predictive Interfaces for Long-Distance Tele-Operations

We address the development of predictive tele-operator interfaces for humanoid robots with respect to two basic challenges. Firstly, we address automating the transition from fully tele-operated systems towards degrees of autonomy. Secondly, we develop compensation for the time-delay that exists when sending telemetry data from a remote operation point to robots located at low earth orbit and beyond. Humanoid robots have a great advantage over other robotic platforms for use in space-based construction and maintenance because they can use the same tools as astronauts do. The major disadvantage is that they are difficult to control due to the large number of degrees of freedom, which makes it difficult to synthesize autonomous behaviors using conventional means. We are working with the NASA Johnson Space Center's Robonaut which is an anthropomorphic robot with fully articulated hands, arms, and neck. We have trained hidden Markov models that make use of the command data, sensory streams, and other relevant data sources to predict a tele-operator's intent. This allows us to achieve subgoal level commanding without the use of predefined command dictionaries, and to create sub-goal autonomy via sequence generation from generative models. Our method works as a means to incrementally transition from manual tele-operation to semi-autonomous, supervised operation. The multi-agent laboratory experiments conducted by Ambrose et. al. have shown that it is feasible to directly tele-operate multiple Robonauts with humans to perform complex tasks such as truss assembly. However, once a time-delay is introduced into the system, the rate of tele\ioperation slows down to mimic a bump and wait type of activity. We would like to maintain the same interface to the operator despite time-delays. To this end, we are developing an interface which will allow for us to predict the intentions of the operator while interacting with a 3D virtual representation of the expected state of the robot. The predictive interface anticipates the intention of the operator, and then uses this prediction to initiate appropriate sub-goal autonomy tasks.

Wheeler, Kevin R.↗
NASA NTRS2013

Monitoring Regional Forest Disturbances across the US with near Real Time MODIS NDVI Products Resident to the ForWarn Forest Threat Early Warning System

Forest threats across the US have become increasingly evident in recent years. Sometimes these have resulted in regionally evident disturbance progressions (e.g., from drought, bark beetle outbreaks, and wildfires) that can occur across multiyear durations and have resulted in extensive forest overstory mortality. In addition to stand replacement disturbances, other forests are subject to ephemeral, sometimes yearly defoliation from various insects and varying types and intensities of ephemeral damage from storms. Sometimes, after prolonged severe disturbance, signs of recovery in terms of Normalized Difference Vegetation Index (NDVI) can occur. The growing prominence and threat of forest disturbances in part have led to the formation and implementation of the 2003 Healthy Forest Restoration Act which mandated that national forest threat early warning system be developed and deployed. In response, the US Forest Service collaborated with NASA, DOE Oakridge National Laboratory, and the USGS Eros Data Center to build and roll-out the near real time ForWarn early warning system for monitoring regionally evident forest disturbances. Given the diversity of disturbance types, severities, and durations, ForWarn employs multiple historical baselines that are used with current NDVI to derive a suite of six forest change products that are refreshed every 8 days. ForWarn employs daily quarter kilometer MODIS NDVI data from the Aqua and Terra satellites, including MOD13 data for deriving historical baseline NDVIs and eMODIS 7 NDVI for compiling current NDVI. In doing so, the Time Series Product Tool and the Phenological Parameters Estimation Tool are used to temporally de-noise, fuse, and aggregate current and historical MODIS NDVIs into 24 day composites refreshed every 8 days with 46 dates of products per year. The 24 day compositing interval enables disturbances to be detected, while minimizing the frequency of residual atmospheric contamination. Forest change products are computed versus the previous 1, previous 3, and all previous years in the MODIS record for a given 24 day interval. Other "weekly" forest change products include one computed using an adaptive length compositing method for quicker detection of disturbances, two others that adjust for seasonal fluctuations in normal vegetation phenology (e.g., early versus late springs). This overall approach enables forest disturbance dynamics from a variety of regionally evident biotic and abiotic forest disturbances to be viewed and assessed through the calendar year. The change products are also being utilized for forest change trend analysis and for developing regional forest overstory mortality products. ForWarn's forest change products are used to alert forest health specialists about new forest disturbances. Such alerts are also typically based on available Landsat, aerial, and ground data as well as communications with forest health specialists and previous experience. ForWarn products have been used to detect and track many types of regional disturbances to multiple forest types, including defoliation from caterpillars and severe storms, as well as mortality from both biotic and abiotic agents (e.g., bark beetles, drought, fire, anthropogenic clearing). ForWarn offers products that could be combined with other geospatial data on forest biomass to assess forest disturbance carbon impacts within the conterminous US.

Spruce, Joseph P.↗
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 NTRS1993

Apparatus Would Stain Microscope Slides

Proposed apparatus meters specific amounts of fluid out of containers at specific times to stain microscope slides. Intended specifically for semiautomated staining of microbiological and hematological samples in microgravity, leakproof apparatus used in other environments in which technicians have little time to allocate to staining procedures and/or exposure to toxic staining agents or to micro-organisms to be stained hazardous. Apparatus adapted to perform almost any staining procedure and accommodates multiple staining reagents, useful for small or remote clinical laboratories.

Breeding, James D.↗
NASA NTRS2004

Overview of Intelligent Systems and Operations Development

To achieve NASA's ambitious mission objectives for the future, aircraft and spacecraft will need intelligence to take the correct action in a variety of circumstances. Vehicle intelligence can be defined as the ability to "do the right thing" when faced with a complex decision-making situation. It will be necessary to implement integrated autonomous operations and low-level adaptive flight control technologies to direct actions that enhance the safety and success of complex missions despite component failures, degraded performance, operator errors, and environment uncertainty. This paper will describe the array of technologies required to meet these complex objectives. This includes the integration of high-level reasoning and autonomous capabilities with multiple subsystem controllers for robust performance. Future intelligent systems will use models of the system, its environment, and other intelligent agents with which it interacts. They will also require planners, reasoning engines, and adaptive controllers that can recommend or execute commands enabling the system to respond intelligently. The presentation will also address the development of highly dependable software, which is a key component to ensure the reliability of intelligent systems.

Pallix, Joan↗
NASA NTRS2011

Examining the Relationships Between Education, Social Networks and Democratic Support With ABM

This paper introduces an agent-based model that explores the relationships between education, social networks, and support for democratic ideals. This study examines two factors thai affect democratic support, education, and social networks. Current theory concerning these two variables suggests that positive relationships exist between education and democratic support and between social networks and the spread of ideas. The model contains multiple variables of democratic support, two of which are evaluated through experimentation. The model allows individual entities within the system to make "decisions" about their democratic support independent of one another. The agent based approach also allows entities to utilize their social networks to spread ideas. Current theory supports experimentation results. In add~ion , these results show the model is capable of reproducing real world outcomes. This paper addresses the model creation process and the experimentation procedure, as well as future research avenues and potential shortcomings of the model

Drucker, Nick↗
NASA NTRS2009

Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 5 - Phase 2 Implementation of the Concept Demonstrator

This report describes the second phase of the implementation of the Concept Demonstrator experimental test-bed system containing sensors and processing hardware distributed throughout the structure, which uses multi-agent algorithms to characterize impacts and determine a suitable response to these impacts. This report expands and adds to the report of the first phase implementation. The current status of the system hardware is that all 192 physical cells (32 on each of the 6 hexagonal prism faces) have been constructed, although only four of these presently contain data-acquisition sub-modules to allow them to acquire sensor data. Impact detection.. location and severity have been successfully demonstrated. The software modules for simulating cells and controlling the test-bed are fully operational. although additional functionality will be added over time. The visualization workstation displays additional diagnostic information about the array of cells (both real and simulated) and additional damage information. Local agent algorithms have been developed that demonstrate emergent behavior of the complex multi-agent system, through the formation of impact damage boundaries and impact networks. The system has been shown to operate well for multiple impacts. and to demonstrate robust reconfiguration in the presence of damage to numbers of cells.

Batten, Adam↗
DOE OSTI2024

SEAS Communication Engine: An Extensible, Flexible Wrapper for Co-Simulation Agents

When modeling and analyzing the power grid and other large scale systems, researchers often express scenarios as optimization problems and feed them into advanced software solvers. In order to allow multiple solvers to communicate with each other and share data from different domains, the National Renewable Energy Laboratory (NREL) and associated Department of Energy (DOE) labs have developed a software framework called the Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS). HELICS allows cosimulation via a collection of client libraries for different languages that can be called from the appropriate optimization software. However, these client libraries do not provide a higher level of abstraction beyond reading and writing data off of the shared HELICS bus. In this paper, we describe a new software library called the SEAS Communication Engine that exposes a higher-level API for running cosimulation problems. The SEAS Engine provides a class-based abstraction on top of the Python HELICS client, in order to allow users to implement their domain-specific cosimulations without needing to interact with core HELICS primitives. This will make adoption of HELICS and cosimulation in general easier, by exposing a simpler API. In the second part of the paper, we validate our library on a collection of different simulation examples, including the canonical IEEE 13 Bus Feeder. Lastly, we demonstrate using the SEAS Engine to directly call domain-specific code written in the Julia programming language. Our hope is that this will serve as a template for easily calling software in different programming languages via the SEAS Engine, thereby avoiding code duplication and complexity.

co-simulation↗
NASA NTRS2005

Why Moldavites are Reduced

Moldavite tektites are colored a bright bottle green by reduced Fe, despite the originating sediments being completely oxidized. The high temperature processing of silicates often results in very reduced products, such as Si-bearing Fe metal in type-1 chondrules [1], lunar regolith agglutinates [2], and multiple Si-Fe metal phases in fulgurites [3,4]. Previous work on silicate reduction has often focused on the need for reducing agents such as carbon or hydrogen; we show that for a moldavite composition these are not necessary. The isentropic cooling path is inherently reducing.

Sheffer, A.↗
DOE OSTI2025

Data Assimilation for Robust UQ Within Agent-Based Simulation on HPC Systems

Agent-based simulation provides a powerful tool for in silico system modeling. However, these simulations do not provide built-in methods for uncertainty quantification (UQ). Within these types of models a typical approach to UQ is to run multiple realizations of the model then compute aggregate statistics. This approach is limited due to the compute time required for a solution. When faced with an emerging biothreat, public health decisions need to be made quickly and solutions for integrating near real-time data with analytic tools are needed. We propose an integrated Bayesian UQ framework for agent-based models based on sequential Monte Carlo sampling. Given streaming or static data about the evolution of an emerging pathogen this Bayesian framework provides a distribution over the parameters governing the spread of a disease through a population. These estimates of the spread of a disease may be provided to public health agencies seeking to abate the spread. By coupling agent-based simulations with Bayesian modeling in a data assimilation, our proposed framework provides a powerful tool for modeling dynamical systems in silico. We propose a method which reduces model error and provides a range of realistic possible outcomes. Moreover, our method addresses two primary limitations of ABMs: the lack of UQ and an inability to assimilate data. Our proposed framework combines the flexibility of an agent-based model with UQ provided by the Bayesian paradigm in a workflow which scales well to HPC systems. We provide algorithmic details and results on a simulated outbreak with both static and streaming data.

Spannaus, Adam [ORNL] (ORCID:0000000225213657)↗
NASA NTRS1998

Contrasting actions of pressor agents in severe autonomic failure

BACKGROUND: Orthostatic hypotension is the most disabling symptom of autonomic failure. The choice of a pressor agent is largely empiric, and it would be of great value to define predictors of a response. PATIENTS AND METHODS: In 35 patients with severe orthostatic hypotension due to multiple system atrophy or pure autonomic failure, we determined the effect on seated systolic blood pressure (SBP) of placebo, phenylpropanolamine (12.5 mg and 25 mg), yohimbine (5.4 mg), indomethacin (50 mg), ibuprofen (600 mg), caffeine (250 mg), and methylphenidate (5 mg). In a subgroup of patients, we compared the pressor effect of midodrine (5 mg) with the effect of phenylpropanolamine (12.5 mg). RESULTS: There were no significant differences in the pressor responses between patients with multiple system atrophy or pure autonomic failure. When compared with placebo, the pressor response was significant for phenylpropanolamine, yohimbine, and indomethacin. In a subgroup of patients, we confirmed that this pressor effect of phenylpropanolamine, yohimbine, and indomethacin corresponded to a significant increase in standing SBP. The pressor responses to ibuprofen, caffeine, and methylphenidate were not significantly different from placebo. Phenylpropanolamine and midodrine elicited similar pressor responses. There were no significant associations between drug response and autonomic function testing, postprandial hypotension, or plasma catecholamine levels. CONCLUSIONS: We conclude that significant increases in systolic blood pressure can be obtained in patients with orthostatic hypotension due to primary autonomic failure with phenylpropanolamine in low doses or yohimbine or indomethacin in moderate doses. The response to a pressor agent cannot be predicted by autonomic function testing or plasma catecholamines. Therefore, empiric testing with a sequence of medications, based on the risk of side effects in the individual patient and the probability of a response, is a useful approach.

Clinical Trial↗
DOE OSTI2026

Data for Filling the Cellulosic Bio-economy Gap by Utilizing a Wedge Approach Combined with Stakeholder Collaboration

The price gap between the market and breakeven prices of cellulosic biomass for farmers represents a significant barrier to the development of a low-carbon cellulosic bioeconomy. Using a bottom-up, agent-based modeling tool that replicates the behaviors and interactions of key stakeholders, this study analyzes the emergence of a cellulosic bioeconomy at the local scale through a wedge approach that examines an integrated portfolio of multiple policy options, including subsidies for small-scale bioproducts and environmental credits. The role of collaboration among multiple stakeholders, such as biomass producers (farmers), bio-refinery industry, government, and society, is assessed for filling the price gap. Using the Sangamon River Basin as a case study site, we evaluate the effectiveness of the wedge approach by comparing simulation results from multiple scenarios, each incorporating different combinations of bioeconomy wedges, with and without stakeholder collaboration. Results underscore that active collaboration among stakeholders acts as a catalyst enlarging the effectiveness of bioeconomy wedges. Including the carbon credits and environmental value in the policy portfolio is found to bridge the price gap through collective contributions from diverse stakeholders, where the cellulosic biofuel and bioproduct industry plays a pivotal role. Although this study is conducted at the local watershed scale, the methodology and findings offer valuable insights for market development in other watersheds and the potential scaling of local markets to regional and national levels.

Economics↗
NASA NTRS2005

An Ecological Forecasting Agent

The project goals are: Make data analysis faster and cheaper. Increase use of NASA data by removing barriers to data access. Cope with data heterogeneity. Support code reuse and rapid application development. Support multiple applications, users. Including fire and health domains. Improve QOS. Always provide an answer. Tell user how good it is, where it come from.

Golden, Keith↗
NASA NTRS2020

Mineralogy of Vera Rubin Ridge from the Mars Science Laboratory CheMin Instrument

Vera Rubin ridge (VRR) is an erosion-resistant feature on the northwestern slope of Mount Sharp in Gale crater, Mars, and orbital visible/short-wave infrared measurements indicate it contains red-colored hematite. The Mars Science Laboratory Curiosity rover performed an extensive campaign on VRR to study its mineralogy, geochemistry, and sedimentology to determine the depositional and diagenetic history of the ridge and constrain the processes by which the hematite could have formed. X-ray diffraction (XRD) data from the CheMin instrument of four samples drilled on and below VRR demonstrate differences in iron, phyllosilicate, and sulfate mineralogy and hematite grain size. Hematite is common across the ridge, and its detection in a gray-colored outcrop suggested localized regions with coarse-grained hematite, which commonly forms from warm fluids. Broad XRD peaks for hematite in one sample below VRR and the abundance of FeOT in the amorphous component suggest the presence of nano-crystalline hematite and amorphous Fe oxides/oxyhydroxides. Well-crystalline akaganeite and jarosite are present in two samples drilled from VRR, indicating at least limited alteration by acid-saline fluids. Collapsed nontronite is present below VRR, but samples from VRR contain phyllosilicate with d(001) = 9.6 Å, possibly from ferripyrophyllite or an acid-altered smectite. The most likely cementing agents creating the ridge are hematite and opaline silica. We hypothesize late diagenesis can explain much of the mineralogical variation on the ridge, where multiple fluid episodes with variable pH, salinity, and temperature altered the rocks, causing the precipitation and crystallization of phases that are not otherwise in equilibrium.

E. B. Rampe↗
NASA NTRS2014

Agent Based Modeling of Air Carrier Behavior for Evaluation of Technology Equipage and Adoption

As part of ongoing research, the National Aeronautics and Space Administration (NASA) and LMI developed a research framework to assist policymakers in identifying impacts on the U.S. air transportation system (ATS) of potential policies and technology related to the implementation of the Next Generation Air Transportation System (NextGen). This framework, called the Air Transportation System Evolutionary Simulation (ATS-EVOS), integrates multiple models into a single process flow to best simulate responses by U.S. commercial airlines and other ATS stakeholders to NextGen-related policies, and in turn, how those responses impact the ATS. Development of this framework required NASA and LMI to create an agent-based model of airline and passenger behavior. This Airline Evolutionary Simulation (AIRLINE-EVOS) models airline decisions about tactical airfare and schedule adjustments, and strategic decisions related to fleet assignments, market prices, and equipage. AIRLINE-EVOS models its own heterogeneous population of passenger agents that interact with airlines; this interaction allows the model to simulate the cycle of action-reaction as airlines compete with each other and engage passengers. We validated a baseline configuration of AIRLINE-EVOS against Airline Origin and Destination Survey (DB1B) data and subject matter expert opinion, and we verified the ATS-EVOS framework and agent behavior logic through scenario-based experiments. These experiments demonstrated AIRLINE-EVOS's capabilities in responding to an input price shock in fuel prices, and to equipage challenges in a series of analyses based on potential incentive policies for best equipped best served, optimal-wind routing, and traffic management initiative exemption concepts..

Horio, Brant M.↗