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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Bootstrapping Multi-Agent Unmanned Aerial Vehicle (UAV) System Integration Using Ground-Based Assets: Lessons Learned

The highly dynamic nature of UAVs imposes significant challenges when conducting initial testing ranging from safety risks posed by high-capacity lithium batteries and spinning propellers to rigorous timing demands on controllers and the consequences of failures mid-air. Flight testing of a single vehicle is time and labor intensive due to these challenges and more, and the complexity increases exponentially with the number of vehicles. While simulations and hardware-in-the-loop bench testing can provide adequate environments for preliminary validation, differences in system deployment architecture, software interfaces, and hardware infrastructure between simulation and a fleet of real UAVs create a sizable gap that must be navigated carefully during system integration. In support of the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project, which had the goal of establishing a basis of certification of trust and trustworthiness in multi-agent autonomous systems, this gap was tackled from two directions. First, a novel mixed-reality simulation environment was engineered to blur the transition from simulation to flight hardware. Second, a fleet of Unmanned Surface Vehicles (USVs) was developed as a test and evaluation platform that more closely represented the final aerial fleet while eliminating many of the risks associated with air vehicles. This paper delves into the second element, analyzing the efficacy of the USV platform in performing system integration testing for the UAV system. In this paper we present the USV fleet and its role in reducing the aforementioned gaps in deployment architecture, software interfaces, and hardware infrastructure when moving from simulation to flight. An overview of the hardware and software onboard the vehicles will be provided along with supporting infrastructure. The system integration process will be documented including results in supporting both the overarching design reference mission (DRM) of ATTRACTOR and individual research efforts conducted during the project. Finally, we will discuss some of the practical lessons learned regarding the testing, deployment, and operation of multi-agent autonomous systems.

Matthew P Vaughan↗
NASA NTRSDate not supplied

Bootstrapping Multi-Agent Unmanned Aerial Vehicle (UAV) System Integration Using Ground-Based Assets: Lessons Learned

In support of the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project, a fleet of unmanned ground vehicles (UGVs) was developed as a test and evaluation (T\&E) platform to reduce system integration gaps between simulation and live flight hardware. While simulation and hardware-in-the-loop bench testing provide adequate environments for preliminary validation, differences in system deployment architecture, software interfaces, and hardware infrastructure increase the risks to safety, property, and the project. Given ATTRACTOR’s goal of establishing a basis of certification of trust and trustworthiness in multi-agent autonomous systems, bridging these gaps was critical to successful project execution and feasibility assessment. In this paper we present the UGV fleet and its role in speeding up system integration, smoothing the transition from simulation to flight, and providing researchers an easy-to-use hardware test bed. An overview of the hardware and software on-board the vehicles is provided along with supporting infrastructure. The system integration process is documented including results in supporting both the overarching design reference mission (DRM) of ATTRACTOR and individual research efforts conducted since the creation of the fleet. Finally, we discuss the practical lessons learned regarding the testing, deployment, and operation of multi-agent autonomous systems.

Matthew P. Vaughan↗
NASA NTRS2018

Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy

In this paper, we review multi-agent collective behavior algorithms in the literature and classify them according to their underlying mathematical structure. For each mathematical technique, we identify the multi-agent coordination tasks it can be applied to, and we analyze its scalability, bandwidth use, and demonstrated maturity. We highlight how versatile techniques such as artificial potential functions can be used for applications ranging from low-level position control to high-level coordination and task allocation, we discuss possible reasons for the slow adoption of complex distributed coordination algorithms in the field, and we highlight areas for further research and development.

Pavone, Marco↗
NASA NTRSDate not supplied

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Rachel Dudukovich↗
NASA NTRSDate not supplied

Agent-based modeling of microbes in space

Space is tough on organisms. Microorganisms traveling to space experience stress from environmental features such as ionizing radiation and lack of normal microgravity; however, much remains unknown about the mechanisms by which those environmental features affect microbial physiology. Microbes experience changes in gravity not directly but rather through changes in their fluid environment, and deep-space particle radiation causes cell damage that is complex but rare. Computational modeling at the single-cell level (agent-based modeling) can allow us to probe the spatially heterogeneous processes that characterize space stresses, to gain insight into the relationships of microbial cells with their environments and with each other. Here we present two software packages for simulating microbial population dynamics in space conditions: CAMDLES and AMMPER. Microbes growing in liquid culture medium in the microgravity of an orbital space station experience a quiescent, poorly-mixed fluid environment. CAMDLES (CFD-DEM Artificial Microgravity Developments for Living Ecosystem Simulation) simultaneously simulates biological, chemical, and mechanical processes to predict microbial ecological dynamics in microgravity, and in the rotating culture vessels used to create an artificial microgravity environment in the lab. Initial results demonstrate that the growth of a cross-feeding microbial consortium, dependent on the exchange of soluble metabolites, is sensitive to the initial spatial distribution of cells, and grows differently in real versus artificial microgravity. Microbial populations exposed to deep-space radiation experience spatially and temporally heterogeneous damage from the traversal of high-energy particles. AMMPER (Agent-Based Model for Microbial Populations Exposed to Radiation) pairs a 3d model of energy deposition along a radiation particle track with a microbial population growth and damage model to predict the effects of localized radiation damage on population-level responses. It includes a user-friendly graphical interface. AMMPER results agree with experimental data indicating that indirect effects of radiation (reactive oxygen species generation, metabolic impairment) have a greater impact on microorganisms than direct effects (DNA damage).

Jessica A Lee↗
NASA NTRS2021

Detection of Transient Instabilities in Multi-Agent Systems and Swarms

In this paper, we introduce the novel concept of transient instabilities in multi-agent systems and swarms, i.e., a small disturbance leads to increasing-amplitude oscillations throughout the swarm, which results in a large number of interagent collisions. This instability is dominant in the transient phase of the system and it does not appear in the steady-state behavior of the system, as each agent uses Lyapunov-stable feedback control laws. We present a rigorous definition of transient instability in swarms, and discuss its key properties. We also present a sufficient condition to check if a swarm will be transient stable. Using both theoretical techniques and numerical results, we show that a simple Proportional-Integral-Derivative (PID) feedback-tracking control law is not suitable for these applications. We also present a robust control law for Euler- Lagrangian systems that maintains transient stability across static swarms or time-varying reconfiguration in swarms. Both theoretical results and numerical simulations are presented to demonstrate the effectiveness of our proposed approach.

Quadrelli, Marco B.↗
NASA NTRS2022

Agent-Based Modeling of Microbes in Space

Space is tough on organisms. Microorganisms traveling to space experience stress from environmental features such as ionizing radiation and lack of normal gravity, and much remains unknown about the mechanisms by which those environmental features affect microbial physiology. Microbes experience changes in gravity not directly but rather through changes in their fluid environment, and deep-space particle radiation causes cell damage that is complex but rare. Computational modeling at the single-cell level (agent-based modeling) can allow us to probe the spatially heterogeneous processes that characterize space stresses, to gain insight into the relationships of microbial cells with their environments and with each other. Here we present two software packages for simulating microbial population dynamics in space conditions: CAMDLES and AMMPER. Microbes growing in liquid culture medium in the microgravity of an orbital space station experience a quiescent, poorly-mixed fluid environment. CAMDLES (CFD-DEM Artificial Microgravity Developments for Living Ecosystem Simulation) simultaneously simulates biological, chemical, and mechanical processes to predict microbial ecological dynamics in microgravity, and in the rotating culture vessels used to create an artificial microgravity environment in the lab. Initial results demonstrate that the growth of a cross-feeding microbial consortium, dependent on the exchange of soluble metabolites, is sensitive to the initial spatial distribution of cells, and grows differently in real versus artificial microgravity. Microbial populations exposed to deep-space radiation experience spatially and temporally heterogeneous damage from the traversal of high-energy particles. AMMPER (Agent-Based Model for Microbial Populations Exposed to Radiation) pairs a 3d model of energy deposition along a radiation particle track with a microbial population growth and damage model to predict the effects of localized radiation damage on population-level responses. It includes a user-friendly graphical interface. AMMPER growth curves recapitulate experimental results, and allow comparison between direct effects (DNA damage) and indirect effects (reactive oxygen species generation, metabolic impairment) of radiation.

microbiology↗
NASA NTRS2022

Towards Run-Time Search for Real-World Multi-Agent Systems

Multi-agent systems (MAS) may encounter uncertainties in the form of unexpected environmental conditions, sub-optimal system configurations, and unplanned interactions between autonomous agents. The number of combinations of such uncertainties may be innumerable, however run-time testing may reduce the issues impacting such a system. We posit that search heuristics can augment a run-time testing process, in-situ, for a MAS. To support our position we discuss our in-progress experimental testbed to realize this goal and highlight challenges we anticipate for this domain.

Abigail C. Diller↗
NASA NTRSDate not supplied

Retrieval-Augmented Generation and LLM Agents for Biomimicry Design Solutions

We present BIDARA, a Bio-Inspired Design And Research Assistant, to address the complexity of biomimicry – the practice of designing modern-day engineering solutions inspired by biological phenomena. Large Language Models (LLMs) have been shown to act as sufficient general purpose task solvers, but they often hallucinate and fail in regimes that require domain-specific and up-to-date knowledge. We integrate Retrieval-Augmented Generation (RAG) and Reasoning-and-Action agents to aid LLMs in avoiding hallucination and utilizing updated knowledge during generation of biomimetic design solutions. We find that incorporating RAG increases the feasibility of the design solutions in both prompting and agent settings, and we use these findings to guide our ongoing work. To the extent of our knowledge, this is the first work that integrates and evaluates Retrieval-Augmented Generation within LLM-generated biomimetic design solutions.

biomimicry↗
NASA NTRSDate not supplied

Retrieval-Augmented Generation and LLM Agents for Biomimicry Design Solutions

We present BIDARA, a Bio-Inspired Design And Research Assistant, to address the complexity of biomimicry – the practice of designing modern-day engineering solutions inspired by biological phenomena. Large Language Models (LLMs) have been shown to act as sufficient general purpose task solvers, but they often hallucinate and fail in regimes that require domain-specific and up-to-date knowledge. We integrate Retrieval-Augmented Generation (RAG) and Reasoning-and-Action agents to aid LLMs in avoiding hallucination and utilizing updated knowledge during generation of biomimetic design solutions. We find that incorporating RAG increases the feasibility of the design solutions in both prompting and agent settings, and we use these findings to guide our ongoing work. To the extent of our knowledge, this is the first work that integrates and evaluates Retrieval-Augmented Generation within LLM-generated biomimetic design solutions.

biomimicry↗
DOE OSTI2020

Sorption and Desorption Study of a Nerve-Agent Simulant from Office Materials for Forensic Applications

Dimethyl methylphosphonate (DMMP) was used as a simulant to investigate the sorption and desorption of G-series nerve agents such as sarin (GB) released into an office space. DMMP was disseminated systematically as an aerosol and vapor on three common office materials, cardboard, polyurethane foam, and cured acrylic-based paint. The amount of DMMP initially captured on the office materials and the amount retained over a period of 10 hours was tracked gravimetrically and by solvent extraction and gas chromatography/mass spectrometry (GC/MS). Physicochemical properties of the office media including the specific surface area per mass, polarity, and Henry’s constants were determined to help explain the capture and release of DMMP that was observed. Polyurethane foam was found to have the largest sorption capacity for DMMP in a low-humidity setting, however, cardboard was found to have the largest sorption capacity at higher humidity. A fraction of the DMMP collected desorbed with first-order kinetics from all of the office materials in an open-air atmosphere over 8 hours. However, importantly for forensic purposes relevant to an indoor attack, a significant quantity (most notably on cardboard) of the nerve agent simulant was retained after 6 weeks and could be analyzed efficiently using solvent extraction followed by GC/MS.

chemical forensics↗
DOE OSTI2026

Natural Language Processing to Inform Agent-Based Modeling: With Application to Modeling Adoption of Medium-Duty Electric Vehicles

Agent-based socio-technical modeling of medium- and heavy-duty (MDHD) electric vehicle (EV) adoption has the potential to provide analysis, prediction, and gui. This paper describes new applications of text analysis developed through machine learning (ML) to build and understand relevant topics and their saliency in the published discourse on adoption of MDHD EVs. This work contributes to the state of the art in topic mining models by defining a new metric of topic ranking (START) that quantifies the importance of predefined topics within the corpus using weighted results for predefined topics from two topic modeling approaches: Latent Dirichlet Allocation (LDA) and BERTopic. The START metric is then demonstrated in practice to model how academia and industry view the EV adoption process based on the respective texts published by these groups. Results show that academic literature places more emphasis on categories of interests such as norms/attitudes and adopter knowledge, while trade journals tend to emphasize long-term cost more than academia. The two bodies of literature agree on the importance of policy and incentives in MDHD EV adoption. Together these results illustrate the potential to use ML-based text analysis to populate the characteristics of agent-based socio-technical models.

Electric vehicle adoption, fleet electrification, ↗
DOE OSTI2026

LeWRON: Agentic Analysis of Electroweak Phase Transitions

The electroweak phase transition (EWPT) is a central topic in particle physics and cosmology, connecting collider phenomenology, baryogenesis, and gravitational-wave observatories. Its analysis requires a technically demanding, convention-sensitive, and model-dependent pipeline, from constructing the finite-temperature effective potential to tracking thermal histories, computing bubble nucleation rates, and predicting gravitational-wave spectra. We present LeWRON (Learning ElectroWeak phase tRansitiON), an agentic framework that orchestrates this pipeline starting from an input Lagrangian. LeWRON combines audited toolbox construction with an Explorer module that uses the generated model-specific code for further analysis, including scans and plots. Intermediate analytic outputs are checked by auditor agents and stored as structured artifacts, enabling reproducible human inspection and downstream use through both a command-line interface and a public Python API. The framework supports a reproduction mode, which infers conventions from the literature and reproduces published results, and a discovery mode, which guides users through structured checkpoints for new models. We demonstrate LeWRON across representative beyond-the-Standard-Model scenarios and release the code on GitHub.

Wang, Isaac R. [Fermilab] (ORCID:000000030789218X)↗
DOE OSTI2026

Archi: Agentic Operations at the CMS Experiment

We present Archi, an open-source, end-to-end framework for scientific collaborations that combines the systematic ingestion and organization of heterogeneous data sources with the deployment of configurable, private, and extensible agents that retrieve and reason over them. An instance of Archi has been deployed for the Computing Operations team of the CMS experiment at CERN's LHC since February 2026 as a support agent for technical operators, offering retrieval and analysis capabilities by combining documentation, historical data, and live monitoring systems. We evaluate the system on operator feedback and a question set collected from production usage, graded by human and automated panels. The system proves effective at operational tasks, resolving real-world queries posed by CMS operators. We also observe that locally-hosted, open-weight models perform competitively, enabling fully private management of sensitive data.

Lugato, Pietro [MIT; CERN]↗
DOE OSTI2026

Reining in an Agentic Harness for High Energy Physics

Agentic systems now address tasks across theoretical, phenomenological, and experimental high energy physics (HEP), but their scientific capabilities remain difficult to reuse across different large language models, providers, and harnesses. We argue that stable parts of these workflows should be promoted into versioned scientific operations and exposed through common protocols. Existing general-purpose harnesses can then be specialized for HEP through task-specific sets of tools and skills, while community-maintained registries would make these capabilities discoverable and citable. We identify mismatches in conventions, assumptions, and domains of validity among independently developed operations as a potential obstacle to their composition, and discuss machine-readable scientific contracts as one possible solution. These design principles and evaluation guidelines provide a near-term path toward a portable and community-maintained agentic harness for HEP.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗
DOE OSTI2020

Analyzing the co-evolution of green technology diffusion and consumers’ pro-environmental attitudes: An agent-based model

Massive diffusion of green technologies is significant for building a cleaner world. However, the process of technology diffusion is usually slow and complex. An in-depth understanding of the mechanism regarding green technology diffusion is an essential precondition for effectively stimulating this process. Green technology diffusion heavily involves both social and technological changes. Although existing studies have provided rich knowledge about identifying critical drivers and barriers affecting green technology diffusion, research that considers consumers’ pro-environmental attitudes and green technology diffusion as an evolving system is still sparse. Aiming at exploring the co-evolution of consumers’ pro-environmental attitudes and green technology diffusion, this paper builds an agent-based model that integrates the relative agreement model with technology diffusion theories to conduct a sequence of controlled numerical experiments, which progressively unveil how attitudinal and technological factors impact green technology diffusion. The main findings include that (1) improving consumers’ pro-environmental attitudes is prominently beneficial to green technology diffusion and maturation; (2) technology maturity has very limited impact on consumers’ first-time purchases but significantly affects consumers’ satisfaction, which would further impact consumers’ repeat purchases; (3) consumers that do not support green technologies frequently emerge during the evolution of attitudes (despite the high technology maturity), which corresponds to the emergence of the anti-environmental groups observed in the real world; (4) active interactions between non-adopters enable their attitudes to converge, which results in the polarization of consumers’ attitudes.

ABM↗