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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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NASA NTRS2006

Efficient Agent-Based Cluster Ensembles

Numerous domains ranging from distributed data acquisition to knowledge reuse need to solve the cluster ensemble problem of combining multiple clusterings into a single unified clustering. Unfortunately current non-agent-based cluster combining methods do not work in a distributed environment, are not robust to corrupted clusterings and require centralized access to all original clusterings. Overcoming these issues will allow cluster ensembles to be used in fundamentally distributed and failure-prone domains such as data acquisition from satellite constellations, in addition to domains demanding confidentiality such as combining clusterings of user profiles. This paper proposes an efficient, distributed, agent-based clustering ensemble method that addresses these issues. In this approach each agent is assigned a small subset of the data and votes on which final cluster its data points should belong to. The final clustering is then evaluated by a global utility, computed in a distributed way. This clustering is also evaluated using an agent-specific utility that is shown to be easier for the agents to maximize. Results show that agents using the agent-specific utility can achieve better performance than traditional non-agent based methods and are effective even when up to 50% of the agents fail.

Agogino, Adrian↗

DOE OSTI2025

Automatic building energy model development and debugging using large language models agentic workflow

Building energy modeling (BEM) is a complex process that demands significant time and expertise, limiting its broader application in building design and operations. While Large Language Models (LLMs) agentic workflow have facilitated complex engineering processes, their application in BEM has not been specifically explored. This paper investigates the feasibility of automating BEM using LLM agentic workflow. Here, we developed a generic LLM-planning-based workflow that takes a building description as input and generates an error-free EnergyPlus building energy model. Our robust workflow includes four core agents: 1) Building Description Pre-Processing, 2) IDF Object Information Extraction, 3) Single IDF Object Generator Suite, and 4) IDF Debugging Agent. These agents divide the complex tasks into manageable sub-steps, enabling LLMs to generate accurate and reliable results at each stage. The case study demonstrates the successful translation of a building description into an error-free EnergyPlus model for the iUnit modular building at the National Renewable Energy Laboratory. The effectiveness of our workflow surpasses: 1) naive prompt engineering, 2) other LLM-based workflows, and 3) manual modeling, in terms of accuracy, reliability, and time efficiency. The paper concludes with a discussion on the interplay between foundational models and LLM agent planning design, advocating for the use of fine-tuned, specialized models to advance this field.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2002

Nine-analyte detection using an array-based biosensor

A fluorescence-based multianalyte immunosensor has been developed for simultaneous analysis of multiple samples. While the standard 6 x 6 format of the array sensor has been used to analyze six samples for six different analytes, this same format has the potential to allow a single sample to be tested for 36 different agents. The method described herein demonstrates proof of principle that the number of analytes detectable using a single array can be increased simply by using complementary mixtures of capture and tracer antibodies. Mixtures were optimized to allow detection of closely related analytes without significant cross-reactivity. Following this facile modification of patterning and assay procedures, the following nine targets could be detected in a single 3 x 3 array: Staphylococcal enterotoxin B, ricin, cholera toxin, Bacillus anthracis Sterne, Bacillus globigii, Francisella tularensis LVS, Yersiniapestis F1 antigen, MS2 coliphage, and Salmonella typhimurium. This work maximizes the efficiency and utility of the described array technology, increasing only reagent usage and cost; production and fabrication costs are not affected.

NASA Discipline Life Sciences Technologies↗

NASA NTRS2005

Efficient Credit Assignment through Evaluation Function Decomposition

Evolutionary methods are powerful tools in discovering solutions for difficult continuous tasks. When such a solution is encoded over multiple genes, a genetic algorithm faces the difficult credit assignment problem of evaluating how a single gene in a chromosome contributes to the full solution. Typically a single evaluation function is used for the entire chromosome, implicitly giving each gene in the chromosome the same evaluation. This method is inefficient because a gene will get credit for the contribution of all the other genes as well. Accurately measuring the fitness of individual genes in such a large search space requires many trials. This paper instead proposes turning this single complex search problem into a multi-agent search problem, where each agent has the simpler task of discovering a suitable gene. Gene-specific evaluation functions can then be created that have better theoretical properties than a single evaluation function over all genes. This method is tested in the difficult double-pole balancing problem, showing that agents using gene-specific evaluation functions can create a successful control policy in 20 percent fewer trials than the best existing genetic algorithms. The method is extended to more distributed problems, achieving 95 percent performance gains over tradition methods in the multi-rover domain.

Agogino, Adrian↗

DOE OSTI2025

Vinylsilanes as Chain-Transfer Agents in Ethylene Polymerization: Direct Synthesis of Heterotelechelic Polyolefins

A single-step route to heterotelechelic polyethylene is enabled by palladium-diimine-catalyzed polymerization of ethylene using vinylsilanes as chain-transfer agents. The reaction affords α-alkenyl-ω-silyl end-capped heterotelechelic polymers whose molecular weights are controllable over a wide range by adjusting the [ethylene]/[vinylsilane] molar ratios. Notably, highly efficient end-capping with silanes carrying a variety of functionalities can be achieved under the optimized conditions. Furthermore, the alkenyl- and silyl-terminated telechelics serve as polymer precursors for further reactions and can be converted into additional telechelic functionalized polyolefins in good yields.

Chen, Zhi-Hao [University of Houston, TX (United S↗

NASA NTRS1971

Corkscrews and singularities in fruitflies - Resetting behavior of the circadian eclosion rhythm.

Description of experiments undertaken to define the phase-resetting behavior of the circadian rhythm of pupal eclosion in populations of fruitflies. An attempt is made to determine how and why the resetting response depends on the duration of a standard perturbation as well as on the time at which it is given. Plotting a three-dimensional graph of the measured emergence centroids vs the stimulus variables, the data are found to spiral up around a vertical rotation axis. Using a computer, a smooth surface, called the resetting surface, which approximately fits the helicoidal cloud of data points, is obtained and is shown to be best described as a vertical corkscrew linking together tilted planes. This corkscrew feature of the resetting surface is taken to indicate that there is an isolated perturbation following which there is either no circadian rhythm of emergence in the steady state, or one of unpredictable phase. A hypothesis concerning the clock dynamics underlying the eclosion rhythm is briefly sketched which encompasses the main features of known resetting data using single discrete pulses of any perturbing agent.

Winfree, A. T.↗

NASA NTRS2018

Playbook for UAS: UX of Goal-Oriented Planning & Execution

We are evaluating Playbook for CASAS (Connected Autonomous Smart Aerospace Systems), a tool designed to aid first responders in disaster relief efforts. We are adapting an existing tool, Playbook, to support a future unmanned aircraft system (UAS) swarm demonstration. Playbook for CASAS will be used to plan, edit, and monitor simulated UAS swarms, and we are interested in evaluating the user experience of this prototype as well as developing recommendations for future UAS interfaces. Allocation of roles and responsibilities between human-automation systems is key to promoting productive cooperation between users and automation. Future interfaces, however, must allow for adaptive management of the swarm not a constant split in human-automation control. Our early research indicates that when a single pilot is controlling swarms of robotic agents, such as UAS or ground rovers, operators require a higher level, goal-based interface with usability at its core. Along with that high-level control, users can leverage sensors within the swarm to be notified when lower level actions must be taken by the pilot. First responders working in disaster relief efforts require a high level of situational awareness (SA) and precise control at key moments within a mission. This balance in operator workload paired with SA can lead to improved safety and mission outcomes. Our research below outlines leverage points as well as the balance between human involvement and autonomy in UAS interfaces.

UAS↗

NASA NTRS2007

Agent Based Intelligence in a Tetrahedral Rover

A tetrahedron is a 4-node 6-strut pyramid structure which is being used by the NASA - Goddard Space Flight Center as the basic building block for a new approach to robotic motion. The struts are extendable; it is by the sequence of activities: strut-extension, changing the center of gravity and falling that the tetrahedron "moves". Currently, strut-extension is handled by human remote control. There is an effort underway to make the movement of the tetrahedron autonomous, driven by an attempt to achieve a goal. The approach being taken is to associate an intelligent agent with each node. Thus, the autonomous tetrahedron is realized as a constrained multi-agent system, where the constraints arise from the fact that between any two agents there is an extendible strut. The hypothesis of this work is that, by proper composition of such automated tetrahedra, robotic structures of various levels of complexity can be developed which will support more complex dynamic motions. This is the basis of the new approach to robotic motion which is under investigation. A Java-based simulator for the single tetrahedron, realized as a constrained multi-agent system, has been developed and evaluated. This paper reports on this project and presents a discussion of the structure and dynamics of the simulator.

Phelps, Peter↗

DOE OSTI2025

Spin State Modulation via Magnetic Fields in Fe Single Atom Catalysts for High-Performance Aqueous Zinc–Sulfur Batteries

Aqueous zinc–sulfur battery has garnered significant attention as a high-energy, low-cost, and safe energy storage system. However, the multielectron transfer kinetics of sulfur cathodes are relatively slow, presenting challenges such as limited sulfur utilization and lower discharge voltage, which significantly hinder their practical applications. Here, in this study, we explored a comprehensive design approach for high-performance, long-cycle aqueous zinc–sulfur batteries. The simultaneous introduction of ZnI 2 and Fe single atoms (Fe-SAs) as catalytically active agents decouples the redox reactions, effectively facilitating ZnS oxidation and S reduction separately. The application of an external magnetic field regulates the spin state of Fe-SAs, further enhancing their catalytic activity and electron transfer capability. Electrochemical tests demonstrate that the S@Fe-NC HS/ZnI 2 cathode assembled under a magnetic field exhibits excellent rate performance, achieving an impressive specific capacity of 1399 mAh g –1 at a high current density of 5 A g –1 and good cycling stability over 300 cycles, representing the highest reported high-current discharge capacity to date. This study provides a comprehensive design framework for optimizing zinc–sulfur (Zn–S) battery performance and elucidates the influence of magnetic field-induced spin state modulation on catalytic behavior.

36 MATERIALS SCIENCE↗

NASA NTRS1989

Coordinating the activities of a planner and an execution agent

A research program was defined that will explore the link between planning and execution systems. A simple scenario was defined in which a very capable off-line planning system interacts with the user and a smaller, less capable, on-line real-time system executing plans and reacting to faults. However, the on-line execution system may have a more flexible representation of the plans it is executing. This imbalance in the capabilities of the two agents involved should clarify some of the research objectives and give an experimental framework for the work. The task is to investigate the knowledge representations and communication protocols needed to link a user stating some requirements for a task to be carried out through a planning system to the (remote) execution agent that can carry out the user's wishes. The notion that a single representation can encapsulate the expression of the user's requirements, the capabilities for action, the communication to the execution agent, the successful or faulty response from the execution agent and the means of keeping the user informed, is examined. Methods of creating plan patches to update the plans separately held by each of the parties involved to keep them in step as they each react to changing circumstances in real-time is investigated. This involves the specification of plan patch attachment points that can be understood by the recipient. Transaction based methods are also investigated for coordinating the activities of the planner with those of the execution agent and user. The trial application area for the research is in the command and control of an advanced Earth Observation Space Platform.

Tate, Austin↗

DOE OSTI2026

Cross-Domain Reasoning for Neuromorphic Model Design

Designing performant neuromorphic models requires reasoning across neuroscience, neuromorphic computing, and machine learning, making it a natural target for cross-domain hypothesis generation. Our primary contribution is a multi-corpus knowledge graph spanning all three domains, which we show substantially increases cross-domain retrieval novelty over single-corpus baselines. We additionally introduce NeuKReAct, an agentic reasoning framework that iteratively retrieves from this graph and synthesizes design hypotheses via a step-by-step blackboard architecture, enabling structured compartmentalization of design decisions. Lastly, we introduce an execution head that translates hypotheses into structured design documents and runnable code. We evaluate novelty using a combinatorial creativity metric that measures cross-domain retrieval distance across the citation graph. Our results confirm that corpus breadth is the dominant driver of novelty. Moreover, we highlight a concrete instance of the novelty-utility tradeoff within NeuKReAct, underscoring a need for joint creativity evaluation, balancing both novelty and utility.

Ramavarapu, Vikram [ORNL] (ORCID:0009000188757213)↗

NASA NTRS2006

QUICR-learning for Multi-Agent Coordination

Coordinating multiple agents that need to perform a sequence of actions to maximize a system level reward requires solving two distinct credit assignment problems. First, credit must be assigned for an action taken at time step t that results in a reward at time step t > t. Second, credit must be assigned for the contribution of agent i to the overall system performance. The first credit assignment problem is typically addressed with temporal difference methods such as Q-learning. The second credit assignment problem is typically addressed by creating custom reward functions. To address both credit assignment problems simultaneously, we propose the "Q Updates with Immediate Counterfactual Rewards-learning" (QUICR-learning) designed to improve both the convergence properties and performance of Q-learning in large multi-agent problems. QUICR-learning is based on previous work on single-time-step counterfactual rewards described by the collectives framework. Results on a traffic congestion problem shows that QUICR-learning is significantly better than a Q-learner using collectives-based (single-time-step counterfactual) rewards. In addition QUICR-learning provides significant gains over conventional and local Q-learning. Additional results on a multi-agent grid-world problem show that the improvements due to QUICR-learning are not domain specific and can provide up to a ten fold increase in performance over existing methods.

Agogino, Adrian K.↗

NASA NTRS2017

Flight Deck Surface Trajectory-Based Operations (STBO): A Four-Dimensional Trajectory (4DT) Simulation

Within human factors there is burgeoning interest in the Human-Autonomy Teaming (HAT) concept as away to address the challenges of interacting with complex, increasingly autonomous systems. The HAT concept comes out of an aspiration to interact with increasingly autonomous automation as a team member, rather than simply use automation as a tool. The authors, and others, have proposed core tenets for HAT that include bi-directional communication, automation and system transparency, and advanced coordination between human and automated teammates via predefined, dynamic task sequences known as plays (Shively et al., 2017). It is believed that, with proper implementation, HAT should foster appropriate teamwork, thus increasing trust and reliance on the system, which in turn will reduce workload, increase situation awareness, and improve performance. To this end, HAT has been demonstrated and/or studied in multiple applications including search and rescue operations (Nourbakhsh et al., 2005), healthcare and medicine (Tsui Yanco, 2007), autonomous vehicles (Parasuraman, Barnes, Cosenzo, Mulgund, 2007), photography (Lachter, Brandt, Sadler, Shively, in press), and aviation (Shively et al., in press). The current paper presents one such effort to apply HAT. It details the design of a R-HAT Agent developed as part of a NASA Research Agreement awarded to Human-Autonomy Teaming Solutions Inc. (HATS Inc), and developed in collaboration with the Human-Autonomy Teaming Laboratory at NASA Ames Research Center. The role of this Agent is to mediate interaction between the automation and the human operator of an advanced ground dispatch station, with this mediation based upon previously mentioned core tenets for HAT and the many lessons learned from the HAT research literature. This dispatch station was developed to support a NASA project investigating a concept called Reduced Crew Operations (RCO; Lachter, Brandt, Battiste, Matessa, Johnson, in press). Part of the RCO concept involves a ground operator providing enhanced support to a large number of aircraft with a single pilot on the flight deck. When assisted by the Agent, operators can monitor and support or manage a large number of aircraft and use plays to respond in real-time to complicated, workload-intensive events (e.g., an airport closure). A play is a plan that encapsulates goals, tasks, and a task allocation strategy appropriate for a particular situation. In the current implementation, when a play is initiated by a user, the Agent determines what tasks need to be done and has the ability to autonomously execute them (e.g., determining diversion options and uplinking new routes to aircraft) when it is safe and appropriate. The R-HAT Agent has been designed to both support end users and research in RCO and HAT. Additionally, the Agent and its underlying architecture were developed with generalizability in mind as a modular piece of software applicable outside of RCO aviation in domains such as those mentioned above. This paper will also discuss future further development and testing of the R-HAT Agent.

Bakowski, Deborah L.↗

NASA NTRS2004

Spacecraft Fire Suppression: Testing and Evaluation

The objective of this project is the testing and evaluation of the effectiveness of a variety of fire suppressants and fire-response techniques that will be used in the next generation of spacecraft (Crew Exploration Vehicle, CEV) and planetary habitats. From the many lessons learned in the last 40 years of space travel, there is common agreement in the spacecraft fire safety community that a new fire suppression system will be needed for the various types of fire threats anticipated in new space vehicles and habitats. To date, there is no single fire extinguishing system that can address all possible fire situations in a spacecraft in an effective, reliable, clean, and safe way. The testing conducted under this investigation will not only validate the various numerical models that are currently being developed, but it will provide new design standards on fire suppression that can then be applied to the next generation of spacecraft extinguishment systems. The test program will provide validation of scaling methods by conducting small, medium, and large scale fires. A variety of suppression methods will be tested, such as water mist, carbon dioxide, and nitrogen with single and multiple injection points and direct or distributed agent deployment. These injection methods cover the current ISS fire suppression method of a portable hand-held fire extinguisher spraying through a port in a rack and also next generation spacecraft units that may have a multi-point suppression delivery system built into the design. Consideration will be given to the need of a crew to clean-up the agent and recharge the extinguishers in flight in a long-duration mission. The fire suppression methods mentioned above will be used to extinguish several fire scenarios that have been identified as the most relevant to spaceflight, such as overheated wires, cable bundles, and circuit boards, as well as burning cloth and paper. Further testing will be conducted in which obstructions and ventilation will be added to represent actual spacecraft conditions (e.g., a series of cards in a card rack).

Abbud-Madrid, Angel↗

NASA NTRS2013

The Regolith Biters: A Divide-And-Conquer Architecture for Sample-Return Missions

The collective interaction of simple systems can be leveraged to attain complex goals. Based on this principle, we envision space system architectures where the core functional components are decoupled, autonomous, and cooperative. We aim to pursue this vision in the context of small-body sample-return missions. After all, no experimental study sheds more light into our understanding of the origin and evolution of the Solar System than the analysis of samples from asteroids and comets. We also believe that their study is important from a strategic perspective: meteorite impacts pose a direct and credible threat to life on Earth, and the development of contingency small-body deflection missions presupposes some knowledge of the target body. The current architectural paradigm for sample-return missions is centered around a design where spacecraft and sampling device are merged into a single, complex system. We argue that this monolithic approach couples the navigation and sample-collection problems, making both more difficult. In contrast, we propose a decoupled system based on the coordinated interaction between a spacecraft and a collective of small, simple devices - the Regolith Biters (RBs). A spacecraft carrying a number of RBs would travel to the vicinity of a small body. From a favorable vantage point, and while remaining within a safe distance in a non-colliding trajectory, it would release the RBs towards the target body. Upon encountering the body, they would bite the regolith (thus retaining a sample), and eject back to orbit. The spacecraft, being endowed with appropriate navigation and tracking capabilities, would rendezvous with and collect those RBs within its reach, and bring them back to Earth. Separating the navigation and sampling concerns removes the need for proximity operations with the small body-the stage in current architectures that carries the most challenges and risks. Eliminating the need for proximity operations brings back to the discussion the exploration of exciting prospects, like highly active comets, fast-rotating bodies, and binary systems. Distributing the sampling problem among a collective of agents provides the opportunity to sample multiple regions in a single mission. It also provides robustness to various environmental conditions, and may enable the distributed, in situ characterization of the body. In the search for reliability, current architectures rely on complexity: an elaborate system should succeed at once. We rely on numbers: a given agent may fail at any stage, but success is attained by the collective.

Asteroids↗

DOE OSTI2026

SetGo: Metadata Readiness for Scientific AI Datasets

Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse. The Readiness Engine for Data Integration (REDI) addresses computational readiness, but no corresponding tool evaluates whether a dataset’s metadata are sufficiently complete, governed, and standards-compliant for publication and agent-based consumption. Existing FAIR assessors operate only on published repository records, and no single system covers FAIR compliance, licensing, provenance, governance, reproducibility, and catalog readiness together. We present SetGo, an open-source Python toolkit that assesses and repairs metadata readiness across these six dimensions before a dataset is published or archived. Applied to four scientific corpora, SetGo surfaces deficiencies that general-purpose tools do not detect: ERA5 climate metadata scores 4% on ACDD 1.3 compliance; materials datasets fail OPTIMADE species-definition requirements; and PDB-derived proteomics data carries licensing terms incompatible with standard SPDX identifiers. Guided enrichment raises overall FAIR scores from 52–57% to 81–91%, and a single setgo publish command pushes to Hugging Face Hub, CKAN, or OpenMetadata with ML Commons Croissant 1.0 metadata sidecars. To support interactive and automated workflows, SetGo integrates with coding agents powered by large language models (LLMs) through a /setgo skill that enables natural-language execution of the full assess–enrich–publish loop, with user involvement limited to supplying missing metadata values.

Wilkinson, Sean [ORNL] (ORCID:0000000214437479)↗

NASA NTRS1969

Improved cure method for single component silicone rubber

Water is incorporated in a carrier and then thoroughly mixed with the single component silicone rubber containing acetic anhydride as a curing agent. Because curing occurs with the water supplied internally, controlled curing is possible within a reasonable period of time, regardless of the thickness of the material.

Lippitt, M. W.↗

NASA NTRS2005

Solution Concepts for Distributed Decision-Making without Coordination

Consider a single-stage problem in which we have a group N agents who are attempting to minimize the expected cost of their joint actions, without the benefit of communication or a pre-established protocol but with complete knowledge of the expected cost of any joint set of actions for the group. We call this situation a static coordination problem. The central issue in defining an appropriate solution concept for static coordination problems is considering how to deal with the fact that if the agents axe faced with a set of multiple (mixed) strategies that are equally attractive in terms of cost, a failure of coordination may lead to an expected cost value that is worse than that of any of the strategies in the set. In this proposal, we describe the notion of a general coordination problem, describe initial efforts at developing a solution concept for static coordination problems, and then outline a research agenda that centers on activities that will be basis for obtaining a complete understanding of solutions to static coordination problems.

Beling, Peter A.↗