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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Design for interaction between humans and intelligent systems during real-time fault management

Initial results are reported to provide guidance and assistance for designers of intelligent systems and their human interfaces. The objective is to achieve more effective human-computer interaction (HCI) for real time fault management support systems. Studies of the development of intelligent fault management systems within NASA have resulted in a new perspective of the user. If the user is viewed as one of the subsystems in a heterogeneous, distributed system, system design becomes the design of a flexible architecture for accomplishing system tasks with both human and computer agents. HCI requirements and design should be distinguished from user interface (displays and controls) requirements and design. Effective HCI design for multi-agent systems requires explicit identification of activities and information that support coordination and communication between agents. The effects are characterized of HCI design on overall system design and approaches are identified to addressing HCI requirements in system design. The results include definition of (1) guidance based on information level requirements analysis of HCI, (2) high level requirements for a design methodology that integrates the HCI perspective into system design, and (3) requirements for embedding HCI design tools into intelligent system development environments.

Malin, Jane T.↗
NASA NTRS2019

A Planning Pipeline for Large Multi-Agent Missions

In complex multi-agent applications, human operators are often tasked with planning and managing large heterogeneous teams of humans and autonomous vehicles. Although the use of these autonomous vehicles broadens the scope of meaningful applications, many of their systems remain unintuitive and difficult to master for human operators whose expertise lies in the application domain and not at the platform level. Current research focuses on the development of individual capabilities necessary to plan multi-agent missions of this scope, placing little emphasis on the integration of these components in to a full pipeline. The work presented in this paper presents a complete and user-agnostic planning pipeline for large multiagent missions known as the HOLII GRAILLE. The system takes a holistic approach to mission planning by integrating capabilities in human machine interaction, flight path generation, and validation and verification. Components – modules – of the pipeline are explored on an individual level, as well as their integration into a whole system. Lastly, implications for future mission planning are discussed.

Chandarana, Meghan↗
NASA NTRSDate not supplied

Creating an Interface to view Multi-Spacecraft Swarm Telemetry

Distributed Spacecraft Systems are a type of multi-spacecraft mission architecture that can not only provide improved resolution, coverage, and availability of existing missions, but also enable missions that would be previously infeasible using traditional approaches. Distributed Spacecraft Autonomy (DSA) is a project developed by the National Aeronautics and Space Administration that enables distributed spacecraft systems. In previous science swarm missions, the spacecraft involved have not been able to communicate with each other without utilizing a ground station. Now that the spacecraft can perform inter-satellite communication, the spacecraft can be treated as a collective. Swarm autonomy is critical for a growing number of satellites which means novel ways of displaying swarm data needs to be implemented. Such systems introduce unique challenges to traditional approaches for command and control of these spacecraft, due to the large number of spacecraft and the complexity of the interactions between them. The ground data system for DSA addresses these challenges through the creation of a custom user interface that allows a single operator to orchestrate a multi-spacecraft swarm in a scalable way. This plenary describes the details of the autonomy demonstration being performed, the requirements of those using the interface to analyze the spacecraft telemetry to assess demonstration success, and the approach taken by the ground systems team to create an interface that satisfies these requirements. This approach involves the creation of several distinct components that correspond to the level of detail presented to the user. These components are based on conventional user roles in human-robot interaction, including supervisor, operator, and mechanic, extended to accommodate the additional overhead of coordinating actions between agents. One main feature of the interface is the listenability matrix component which will represent inter-satellite communications in a heat mapped matrix. The above work described will enable users to command and interact with the spacecraft as a collective.

human-swarm interaction↗
DOE OSTI2020

Influence of the Turing instability on the motion of domain boundaries

Turing's theory of pattern formation has provided crucial insights into the behavior of various biological, geographical, and chemical systems over the last few decades. Existing studies have focused on moving-boundary Turing systems for which the motion of the boundary is prescribed by an external agent. In this paper, we present an extension of this theory to a class of systems in which the front motion is governed by the physical processes that occur within the domain. Biological systems exhibiting apically dominant growth and corrosion of metals and alloys highlight some of the noteworthy examples of such systems. In this study, we characterize the nature of interaction between the moving front and the Turing-instability for both an activator-inhibitor and an activator-substrate model. Behavioral regimes of periodic, as well as nonperiodic (nonconstant), growth rates are obtained. Furthermore, the trends in the first show striking similarities with the cyclic-boundary-kinetics observed in experimental systems. In general, a stationary, periodic structure is also left behind the moving front. If the periodicity of the boundary kinetics agrees with the allowed range of the stable-periodic solutions, the pattern formed tends to persist. Lastly, it evolves to a nearby energy-minimum either by peak-splitting, peak-decay, or by settling down to a spatially homogeneous state.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Eating the brain - A multidisciplinary study provides new insights into the mechanisms underlying the cytopathogenicity of Naegleria fowleri

Naegleria fowleri , the causative agent of primary amoebic meningoencephalitis (PAM), requires increased research attention due to its high lethality and the potential for increased incidence as a result of global warming. The aim of this study was to investigate the interactions between N. fowleri and host cells in order to elucidate the mechanisms underlying the pathogenicity of this amoeba. A co-culture system comprising human fibrosarcoma cells was established to study both contact-dependent and contact-independent cytopathogenicity. Proteomic analyses of the amoebas exposed to human cell cultures or passaged through mouse brain were used to identify novel virulence factors. Our results indicate that actin dynamics, regulated by Arp2/3 and Src kinase, play a considerable role in ingestion of host cells by amoebae. We have identified three promising candidate virulence factors, namely lysozyme, cystatin and hemerythrin, which may be critical in facilitating N. fowleri evasion of host defenses, migration to the brain and induction of a lethal infection. Long-term co-culture secretome analysis revealed an increase in protease secretion, which enhances N. fowleri cytopathogenicity. Raman microspectroscopy revealed significant metabolic differences between axenic and brain-isolated amoebae, particularly in lipid storage and utilization. Taken together, our findings provide important new insights into the pathogenic mechanisms of N. fowleri and highlight potential targets for therapeutic intervention against PAM.

Malych, Ronald↗
NASA NTRS2006

Adjustably Autonomous Multi-agent Plan Execution with an Internal Spacecraft Free-Flying Robot Prototype

We present an multi-agent model-based autonomy architecture with monitoring, planning, diagnosis, and execution elements. We discuss an internal spacecraft free-flying robot prototype controlled by an implementation of this architecture and a ground test facility used for development. In addition, we discuss a simplified environment control life support system for the spacecraft domain also controlled by an implementation of this architecture. We discuss adjustable autonomy and how it applies to this architecture. We describe an interface that provides the user situation awareness of both autonomous systems and enables the user to dynamically edit the plans prior to and during execution as well as control these agents at various levels of autonomy. This interface also permits the agents to query the user or request the user to perform tasks to help achieve the commanded goals. We conclude by describing a scenario where these two agents and a human interact to cooperatively detect, diagnose and recover from a simulated spacecraft fault.

Dorais, Gregory A.↗
DOE OSTI2020

An intelligent energy router for managing behind-the-meter resources and assets

With increase in distributed energy resources (DERs) and smart loads, each energy resource and load need a separate power conversion system leading to complex coordination and interaction, reduced energy conversion efficiency, coordinating compliance to grid standards (IEEE 1547) from multiple sources, reduced security. Also, multiple vendors with legacy system designs and proprietary communications interfaces result in redundancy and increase in cost of power electronics systems. This paper presents an energy router concept for buildings applications which provides autonomous power flow between sources and loads with a novel agent-based software interface.

Chinthavali, Madhu Sudhan↗
DOE OSTI2022

DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance Computing

Cluster schedulers are crucial in high-performance computing (HPC). They determine when and which user jobs should be allocated to available system resources. Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads. However, the increasing complexity of computing systems and the highly dynamic nature of application workloads have placed tremendous burden on manually designed and tuned scheduling heuristics. More aggressive optimization and automation are needed for cluster scheduling in HPC. In this work, we present an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for Scheduling) by leveraging deep reinforcement learning. DRAS is built on a hierarchical neural network incorporating special HPC scheduling features such as resource reservation and backfilling. An efficient training strategy is presented to enable DRAS to rapidly learn the target environment. Once being provided a specific scheduling objective given by the system manager, DRAS automatically learns to improve its policy through interaction with the scheduling environment and dynamically adjusts its policy as workload changes. We implement DRAS into a HPC scheduling platform called CQGym. CQGym provides a common platform allowing users to flexibly evaluate DRAS and other scheduling methods such as heuristic and optimization methods. Furthermore, the experiments using CQGym with different production workloads demonstrate that DRAS outperforms the existing heuristic and optimization approaches by up to 50%.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

A Solar System Perspective on Laboratory Astrophysics

Planetary science deals with a wide variety of natural materials in a wide variety of environments. These materials include metals, minerals, ices, gases, plasmas, and organic chemicals. In addition, the newly defined discipline of astrobiology introduces biological materials to planetary science. The environments range from the interiors of planets with megapascal pressures to planetary magnetospheres, encompassing planetary mantles, surfaces, atmospheres, and ionospheres. The interplanetary environment includes magnetic and electrical fields, plasma, and dust. In order to understand planetary processes over these vast ranges, the properties of materials must be known, and most of the necessary information comes from the laboratory. Observations of the bodies and materials in the Solar System are accomplished over the full range of the electromagnetic spectrum by remote sensing from Earth or spacecraft. Comets exemplify this; molecular and atomic identifications are made from the hard ultraviolet to radio wavelengths, while X-rays are emitted as comets interact with the solar wind. Gamma rays from the surfaces of the Moon and asteroids are diagnostic of the mineral and ice content of those bodies; eventually, gamma rays will also be observed by probes to comets. A number of planetary materials are available in the laboratory for extensive Study: rocks from the Moon, Mars, several asteroids, as well as dust from comets (and perhaps the Kuiper Belt) are closely studied at every level, including atomic (isotopic). Even pre-solar interstellar grains isolated from meteorites are scrutinized for composition and crystalline structure. Beyond the materials themselves, various agents and processes have altered them over the 4.6-Gy age of the Solar System. Solar radiation, solar wind particles, trapped magnetospheric particles, cosmic rays, and micrometeoroid impacts have produced chemical, physical, and morphological changes in the atmospheres and on the surfaces of all planetary bodies. These processes are not well understood, so studies in a laboratory setting are especially needed.

Cruikshank, Dale P.↗
NASA NTRSDate not supplied

Enabling Advanced Air Mobility Operations through Appropriate Trust in Human-Autonomy Teaming: Foundational Research Approaches and Applications

Emerging Advanced Air Mobility(AAM)operations will be enabled by increasingly autonomous systems, requiring technologies to take on more responsibilities and fundamentally altering traditional human-automation interaction paradigms. The growing reliance on higher levels of automation will necessitate research to identify capabilities and principles that facilitate humans and machines working and thinking better together, i.e., human-autonomy teaming (HAT). Trust is an inherent requirement in effective teams because when members work interdependently, those agents (human, automation) must be willing to accept a level of risk to rely upon each other to reach goals and contribute to team tasks. This work provides an initial approach to enabling AAM operations through appropriate trust within HAT. The main contributions of this approach resides in connecting the construct of trust to mental models. Using the outlined mental model approach, we propose novel HAT strategies, such as Adaptive Trust Calibration, and preview planned research activities derived from this approach. Additionally, we propose several practical applications that can currently be employed by AAM development communities.

Eric T Chancey↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗
NASA NTRS2013

Aviation Safety: Modeling and Analyzing Complex Interactions between Humans and Automated Systems

The on-going transformation from the current US Air Traffic System (ATS) to the Next Generation Air Traffic System (NextGen) will force the introduction of new automated systems and most likely will cause automation to migrate from ground to air. This will yield new function allocations between humans and automation and therefore change the roles and responsibilities in the ATS. Yet, safety in NextGen is required to be at least as good as in the current system. We therefore need techniques to evaluate the safety of the interactions between humans and automation. We think that current human factor studies and simulation-based techniques will fall short in front of the ATS complexity, and that we need to add more automated techniques to simulations, such as model checking, which offers exhaustive coverage of the non-deterministic behaviors in nominal and off-nominal scenarios. In this work, we present a verification approach based both on simulations and on model checking for evaluating the roles and responsibilities of humans and automation. Models are created using Brahms (a multi-agent framework) and we show that the traditional Brahms simulations can be integrated with automated exploration techniques based on model checking, thus offering a complete exploration of the behavioral space of the scenario. Our formal analysis supports the notion of beliefs and probabilities to reason about human behavior. We demonstrate the technique with the Ueberligen accident since it exemplifies authority problems when receiving conflicting advices from human and automated systems.

Muti-agent System↗
DOE OSTI2021

Neutron Radiography and Computed Tomography of Biological Systems at the Oak Ridge National Laboratory's High Flux Isotope Reactor

Neutrons have historically been used for a broad range of biological applications employing techniques such as small-angle neutron scattering, neutron spin echo, diffraction, and inelastic scattering. Unlike neutron scattering techniques that obtain information in reciprocal space, attenuation-based neutron imaging measures a signal in real space that is resolved on the order of tens of micrometers. The principle of neutron imaging follows the Beer-Lambert law and is based on the measurement of the bulk neutron attenuation through a sample. Greater attenuation is exhibited by some light elements (most notably, hydrogen), which are major components of biological samples. Contrast agents such as deuterium, gadolinium, or lithium compounds can be used to enhance contrast in a similar fashion as it is done in medical imaging, including techniques such as optical imaging, magnetic resonance imaging, X-ray, and positron emission tomography. For biological systems, neutron radiography and computed tomography have increasingly been used to investigate the complexity of the underground plant root network, its interaction with soils, and the dynamics of water flux in situ. Moreover, efforts to understand contrast details in animal samples, such as soft tissues and bones, have been explored. This manuscript focuses on the advances in neutron bioimaging such as sample preparation, instrumentation, data acquisition strategy, and data analysis using the High Flux Isotope Reactor CG-1D neutron imaging beamline. The aforementioned capabilities will be illustrated using a selection of examples in plant physiology (herbaceous plant/root/soil system) and biomedical applications (rat femur and mouse lung).

Bilheux, Hassina↗
NASA NTRS1991

Who or what saved the day? A comparison of traditional and glass cockpits

This study examined incidents reported to NASAs Aviation Safety Reporting System from a different perspective: rather than focusing on the factors contributing to or causing incidents, this study concentrated on who and what (subsystems and information) enabled the recovery from an anomaly. Incident reports describing altitude deviations were classified as to cockpit type (glass or traditional), flight phase, agent restoring safety, and cockpit subsystems providing specific information that helped restore safety. The data revealed and quantified the agents, information, and factors that 'saved the day'. The flight crews used many sources of information to detect the altitude deviations: altimeter, outside scene, altitude alert, kinesthesia, attitude and communications monitoring. In the glass cockpits the crews also used the map display and autothrottles to detect deviations from assigned altitudes. There was an interaction between the person detecting the anomaly (controller/flight crew) and the type of cockpit. Glass cockpit flight crews detect proportionally more deviations than their counterparts in traditional cockpits, while controllers tend to detect more deviations involving traditional cockpits. There was no effect of cockpit position (captain/first officer). A model that details the flow of altitude information between air traffic control, flight crews, and cockpit subsystems, was developed and validated. This model identifies strengths and weaknesses in the flow of altitude information within the current ground/air system.

Degani, Asaf↗
DOE OSTI2024

Investigation of the Potential of Repurposing Medium-Density Fiberboard Waste as an Adsorbent for Heavy Metal Ion Removal

Medium-density fiberboard (MDF) waste generation has increased steadily over the past decades, and therefore, the investigation of novel methods to recycle this waste is very important. The potential of repurposing MDF waste as an adsorbent for the treatment of Cd(II), Cu(II), Pb(II), and Zn(II) ions in water was investigated using MDF offcuts. The highest adsorption potential in single-metal ion solution systems was observed for Pb(II) ions. The experimental data of Pb(II) ions fit well with the Freundlich isotherm and pseudo-second-order kinetic models. Complexation and electrostatic interactions were identified as the adsorption mechanisms. The adsorption behavior of multi-metal ion adsorption systems was investigated by introducing Cd(II) ions as a competitive metal ion. The presence of the Cd(II) ions reduced the adsorption potential of Pb(II) ions, yet the preference for the Pb(II) ions remained. Regeneration studies were performed by using 0.1 M HCl as a regeneration agent for both systems. Even though a significant amount of adsorbed metal ions were recovered, the adsorption potential of the MDF was reduced in the subsequent adsorption cycles. Based on these results, MDF fines have the potential to be used as an economical adsorbent for remediation of wastewater containing heavy metal ions.

36 MATERIALS SCIENCE↗
NASA NTRS2017

Effects of Contamination and Cleaning on Parachute Structural Textile Elements

Throughout their lifecycle, parachute textiles come into contact with various other substances. This contact may occur during manufacturing and repair, storage and transportation, packing, or actual use. While this interaction does not always result in negative repercussions, it may cause a loss in material strength. This paper examines the strength degradation due to several contaminants as well as the effects of cleaning agents on common parachute materials. Materials tested were: Kevlar cord and webbing, Nylon broadcloth and webbing, and Vectran cord; all of these constitute the major structural elements for CPAS (Capsule Parachute Assembly System), the parachute system for the NASA Orion Crew Module. Contaminants tested were: sewing machine oil, dried stamping ink, dirt, basting glue, Sergene, and rust. Recommendations for cleaning (or not cleaning) these materials with respect to each of the contaminants are given in this paper, as well as recommendations for future tests.

Mollmann, Catherine↗
DOE OSTI2025

Adsorptive behavior of poly (vinylidene fluoride) membranes for the recovery of lignin-derived hydrophobic deep eutectic solvents

Abstract Recently, membrane technology has gained significant traction as an energy-efficient alternative to traditional thermal processes for solvent recovery. Deep eutectic solvents (DESs) have emerged as sustainable alternatives to conventional organic solvents, yet a systematic methodology for selecting compatible membrane materials for their recovery remains underdeveloped. This study established a predictive framework for membrane material selection in hydrophobic DES applications using Hansen Solubility Parameters (HSP) with inverted criteria targeting materials with relative energy difference (RED) values greater than 1.0. Flat sheet membranes were fabricated via the non-solvent induced phase separation (NIPS) technique. Four NIPS fabricated polymer membranes were evaluated: polysulfone, cellulose acetate, polyvinylidene fluoride (PVDF) fabricated with polyethylene glycol (PEG) as a pore-forming agent, and polybenzimidazole (PBI). The HSP approach successfully predicted membrane-solvent compatibility, with polysulfone (RED = 0.6) and cellulose acetate (RED = 0.9) dissolving completely within 24 h, while PVDF (RED = 1.9) and PBI (RED = 1.1) maintained structural integrity throughout a 7-day exposure period. Furthermore, PVDF demonstrated superior performance with minimal weight gain (3.0%), hydrophobic surface characteristics (122° water contact angle), and enhanced mechanical properties following DES exposure. Comprehensive chemical and morphological characterization confirmed PVDF’s chemical stability and revealed a surface-selective interaction mechanism involving simultaneous PEG (pore-forming agent) extraction and DES component adsorption. Adsorption kinetics followed pseudo-first-order behavior with reversible characteristics, best described by the Temkin isotherm model (R² = 0.9987). PVDF membranes-maintained separation functionality with average lignin rejection (75.2 ± 7.69%) and demonstrated filtration permeability of 2.0 ± 0.34 LMH/bar. This methodology provides a rational approach for membrane selection in emerging solvent systems, contributing to the advancement of sustainable separation technologies for DES-based biomass processing applications.

Science & Technology - Other Topics↗
NASA NTRS1994

Integrating deliberative planning in a robot architecture

The role of planning and reactive control in an architecture for autonomous agents is discussed. The postulated architecture seperates the general robot intelligence problem into three interacting pieces: (1) robot reactive skills, i.e., grasping, object tracking, etc.; (2) a sequencing capability to differentially ativate the reactive skills; and (3) a delibrative planning capability to reason in depth about goals, preconditions, resources, and timing constraints. Within the sequencing module, caching techniques are used for handling routine activities. The planning system then builds on these cached solutions to routine tasks to build larger grain sized primitives. This eliminates large numbers of essentially linear planning problems. The architecture will be used in the future to incorporate in robots cognitive capabilites normally associated with intelligent behavior.

Elsaesser, Chris↗