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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Multi Agent Reward Analysis for Learning in Noisy Domains

In many multi agent learning problems, it is difficult to determine, a priori, the agent reward structure that will lead to good performance. This problem is particularly pronounced in continuous, noisy domains ill-suited to simple table backup schemes commonly used in TD(lambda)/Q-learning. In this paper, we present a new reward evaluation method that allows the tradeoff between coordination among the agents and the difficulty of the learning problem each agent faces to be visualized. This method is independent of the learning algorithm and is only a function of the problem domain and the agents reward structure. We then use this reward efficiency visualization method to determine an effective reward without performing extensive simulations. We test this method in both a static and a dynamic multi-rover learning domain where the agents have continuous state spaces and where their actions are noisy (e.g., the agents movement decisions are not always carried out properly). Our results show that in the more difficult dynamic domain, the reward efficiency visualization method provides a two order of magnitude speedup in selecting a good reward. Most importantly it allows one to quickly create and verify rewards tailored to the observational limitations of the domain.

Tumer, Kagan↗
NASA NTRS2005

Decision Facilitator for Launch Operations using Intelligent Agents

Launch operations require millions of micro-decisions which contribute to the macro decision of 'Go/No-Go' for a launch. Knowledge workers"(such as managers and technical professionals) need information in a timely precise manner as it can greatly affect mission success. The intelligent agent (web search agent) uses the words of a hypertext markup language document which is connected through the internet. The intelligent agent's actions are to determine if its goal of seeking a website containing a specified target (e.g., keyword or phrase), has been met. There are few parameters that should be defined for the keyword search like "Go" and "No-Go". Instead of visiting launch and range decision making servers individually, the decision facilitator constantly connects to all servers, accumulating decisions so the final decision can be decided in a timely manner. The facilitator agent uses the singleton design pattern, which ensures that only a single instance of the facilitator agent exists at one time. Negotiations could proceed between many agents resulting in a final decision. This paper describes details of intelligent agents and their interaction to derive an unified decision support system.

Thirumalainambi, Rajkumar↗
NASA NTRS2009

Biomorphic Multi-Agent Architecture for Persistent Computing

A multi-agent software/hardware architecture, inspired by the multicellular nature of living organisms, has been proposed as the basis of design of a robust, reliable, persistent computing system. Just as a multicellular organism can adapt to changing environmental conditions and can survive despite the failure of individual cells, a multi-agent computing system, as envisioned, could adapt to changing hardware, software, and environmental conditions. In particular, the computing system could continue to function (perhaps at a reduced but still reasonable level of performance) if one or more component( s) of the system were to fail. One of the defining characteristics of a multicellular organism is unity of purpose. In biology, the purpose is survival of the organism. The purpose of the proposed multi-agent architecture is to provide a persistent computing environment in harsh conditions in which repair is difficult or impossible. A multi-agent, organism-like computing system would be a single entity built from agents or cells. Each agent or cell would be a discrete hardware processing unit that would include a data processor with local memory, an internal clock, and a suite of communication equipment capable of both local line-of-sight communications and global broadcast communications. Some cells, denoted specialist cells, could contain such additional hardware as sensors and emitters. Each cell would be independent in the sense that there would be no global clock, no global (shared) memory, no pre-assigned cell identifiers, no pre-defined network topology, and no centralized brain or control structure. Like each cell in a living organism, each agent or cell of the computing system would contain a full description of the system encoded as genes, but in this case, the genes would be components of a software genome.

Lodding, Kenneth N.↗
NASA NTRS2003

Agent-Supported Mission Operations Teamwork

This slide presentation reviews the development of software agents to support of mission operations teamwork. The goals of the work was to make automation by agents easy to use, supervise and direct, manage information and communication to decrease distraction, interruptions, workload and errors, reduce mission impact of off-nominal situations and increase morale and decrease turnover. The accomplishments or the project are: 1. Collaborative agents - mixed initiative and creation of instructions for mediating agent 2. Methods for prototyping, evaluating and evolving socio-technical systems 3. Technology infusion: teamwork tools in mISSIons 4. Demonstrations in simulation testbed An example of the use of agent is given, the use of an agent to monitor a N2 tank leak. An incomplete instruction to the agent is handled with mediating assistants, or Intelligent Briefing and Response Assistant (IBRA). The IBRA Engine also watches data stream for triggers and executes Act-Whenever actions. There is also a Briefing and Response Instruction (BRI) which is easy for a discipline specialist to create through a BRI editor.

Malin, Jane T.↗
NASA NTRS2005

Monitoring Agents for Assisting NASA Engineers with Shuttle Ground Processing

The Spaceport Processing Systems Branch at NASA Kennedy Space Center has designed, developed, and deployed a rule-based agent to monitor the Space Shuttle's ground processing telemetry stream. The NASA Engineering Shuttle Telemetry Agent increases situational awareness for system and hardware engineers during ground processing of the Shuttle's subsystems. The agent provides autonomous monitoring of the telemetry stream and automatically alerts system engineers when user defined conditions are satisfied. Efficiency and safety are improved through increased automation. Sandia National Labs' Java Expert System Shell is employed as the agent's rule engine. The shell's predicate logic lends itself well to capturing the heuristics and specifying the engineering rules within this domain. The declarative paradigm of the rule-based agent yields a highly modular and scalable design spanning multiple subsystems of the Shuttle. Several hundred monitoring rules have been written thus far with corresponding notifications sent to Shuttle engineers. This chapter discusses the rule-based telemetry agent used for Space Shuttle ground processing. We present the problem domain along with design and development considerations such as information modeling, knowledge capture, and the deployment of the product. We also present ongoing work with other condition monitoring agents.

Semmel, Glenn S.↗
NASA NTRS2012

Optimal Routing and Control of Multiple Agents Moving in a Transportation Network and Subject to an Arrival Schedule and Separation Constraints

We address the problem of navigating a set of moving agents, e.g. automated guided vehicles, through a transportation network so as to bring each agent to its destination at a specified time. Each pair of agents is required to be separated by a minimal distance, generally agent-dependent, at all times. The speed range, initial position, required destination, and required time of arrival at destination for each agent are assumed provided. The movement of each agent is governed by a controlled differential equation (state equation). The problem consists in choosing for each agent a path and a control strategy so as to meet the constraints and reach the destination at the required time. This problem arises in various fields of transportation, including Air Traffic Management and train coordination, and in robotics. The main contribution of the paper is a model that allows to recast this problem as a decoupled collection of problems in classical optimal control and is easily generalized to the case when inertia cannot be neglected. Some qualitative insight into solution behavior is obtained using the Pontryagin Maximum Principle. Sample numerical solutions are computed using a numerical optimal control solver.

Sadovsky, A. V.↗
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↗
DOE OSTI2026

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Heterogeneous Catalyst Discovery

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We demonstrate that hierarchical agentic large language model reasoning can efficiently drive simulation and scientific exploration. Across two chemical applications, CO adsorption on Cu surface transition metal adatoms and on M–N–C catalysts, reasoning-guided exploration reduces required atomistic simulations by up to 90% relative to heuristic or random selection. Comparisons across single-agent, multi-agent, and stochastic baselines show that hierarchical strategies yield more coherent and information-efficient search trajectories. Reasoning traces reveal chemically grounded decisions that cannot be explained by semantic bias or stochastic sampling. We realize these agentic reasoning strategies in Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), a multimodal system that translates natural language into density functional theory workflows. Altogether, multi-agent collaboration accelerates heterogeneous catalyst discovery and marks a step toward more autonomous, reasoning-guided scientific exploration.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS2026

Multi-Agent Swarm State of the Art Report

The Next-Generation Multi-Agent Swarm (NGS) Study conducted by NASA’s Ames Research Center for NASA’s Space Technology Mission Directorate (STMD) will develop a comprehensive understanding of emerging multi-agent swarm capabilities. The study aims to identify existing swarm capabilities and asses their potential for persistent lunar space situational awareness, surface monitoring, and distributed autonomy demonstrations. A key objective is to inform the design of a next-generation multi-agent swarm that can perform autonomous distributed remote sensing, position, navigation, and timing (PNT) services, automated deployment that leverages autonomy, edge computing, and interoperable networking to enable cooperative operations without the need for immediate human operation. This study will address specific shortfalls identified by STMD, including intelligent multi-agent constellations, autonomy, edge computation, position, navigation, and timing for small spacecraft, small spacecraft propulsion, and space situational awareness (1625, 1438, 1433, 1557, 1431, 1430, 1589). The NASA Ames Mission Design Center (MDC) will provide subject matter expertise to support systems engineering trades, while experts in autonomy and spacecraft swarms in NASA’s Intelligent Systems Division will lead the study and focus on identifying emerging next-generation swarm capabilities. The study objectives include: capturing the current state-of-the-art for multi-agent swarm capabilities, evaluating technologies and creating technology roadmaps, and developing at least one new technology demonstration mission concept. This initial NGS study report surveys the current state of the art in technology areas relevant for the next-generation multi-agent swarm design. Our primary focus is on surveying relevant deployed space systems1, supplemented with selective analysis of relevant proposed missions and technology developments that have yet to fly.

agent↗
DOE OSTI2024

Operando visualization of porous metal additive manufacturing with foaming agents through high-speed x-ray imaging

Porous metals find extensive applications in soundproofing, filtration, catalysis, and energy-absorbing structures, thanks to their unique internal pore structure and high specific strength. In recent years, there has been an increasing interest in fabricating porous metals using additive manufacturing (AM), leveraging its unique advantages, including improved design freedom, spatial material control, and cost-effective small-batch production. In this study, we conducted pioneering operando visualization of AM porous metal using a laser powder bed fusion (L-PBF) setup combined with a high-speed synchrotron x-ray imaging system. Single track printing experiments using Ti6Al4V (Ti64) combined with titanium hydride (TiH 2 ) and sodium carbonate (Na 2 CO 3 ) as foaming agents, with varying mixing ratios were performed under different processing conditions. Here. the results elucidate the dynamic development of porosity formation. The average pore size is significantly influenced by the particle size of foaming agents when pore coalescence is absent. For all foaming agent content tested in the current study, the number of pores is found to be more sensitive to changes in laser power than in laser scanning speed. Increasing linear energy density (increasing laser power or reducing laser scanning speed) promotes the foaming agent activation thereby porosity formation. However, high linear energy density skews pore distribution towards the surface despite forming deeper melt pools. In addition, the impact of additional factors including foaming agent's laser absorptivity and decomposition kinetics with respect to AM time scales should be carefully considered to avoid ineffective activation of foaming agents during the AM of porous metals.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Design and performance of AI agents interfacing with an atomic layer deposition tool

In this work, we introduce the design of an atomic layer deposition (ALD) reactor augmented with an AI interface for autonomous materials synthesis. Our modular design encapsulates the particularities of the hardware behind a Python interface that communicates with the ALD control software via transmission control protocol. This interface is compatible with model context protocol interfaces used in agentic frameworks. We have integrated our tool with a simple AI agent that leverages a large language model to transform user-supplied queries into ALD processes that are then run in our reactor. Our approach uses a JavaScript object notation schema to encode ALD processes. Our experimental results show that the AI interface does not impose a significant overhead to our control software, at least within our fastest 10 ms scale. We also carried out a detailed evaluation of the agent performance using leading models in two classes of tasks: basic instruction and process discovery tasks, where the agent is presented with a target material and needs to identify the correct ALD process compatible with the reactor configuration. Despite the simplicity of our agent design, we observed that most of the advanced models excelled at the instruction tasks. However, only recent models, such as o1, o3, GPT-5, and Claude Opus 4, performed well in process discovery tasks. We also observed significant variability in the response for the hardest challenges. While the results obtained are promising, we identify areas where AI research could improve the performance of agents for ALD.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Multi-Agent Hierarchical Deep Reinforcement Learning for HVAC Control With Flexible DERs

As electricity consumption in commercial and residential buildings continues to rise, reducing energy costs presents an increasing challenge. Heating, ventilating, and air-conditioning (HVAC) systems, which typically account for 40%-50% of a building's energy use, are prime targets for energy savings. Intelligent control of HVAC temperature through the exploitation of HVAC load flexibility brings significant potential to reduce energy consumption and electricity expenses. The nonlinear models of HVAC systems challenge traditional control methods, while the uncertainty introduced by HVAC load flexibility complicates distributed energy resource (DER) management using conventional optimal dispatch techniques. In response to these challenges, we propose a hierarchical multi-agent deep reinforcement learning (DRL) approach. The lower-level agents focus on balancing comfort and energy conservation, while the upper-level DRL agents optimize the use of DERs to reduce peak demand based on the control outcomes of the HVAC by the lower-level agents. Here, in the upper-level agents, we incorporate a multi-agent structure based on ensemble learning, which acts based on historical and current data without relying on precise load forecasting to address the delayed rewarding issue in DRL. This allows for the effective reduction of energy costs. The proposed method is tested using a real-world microgrid comprising 413 buildings in Southern California, and the results demonstrate that our approach can significantly reduce overall electricity bills while ensuring the comfort of consumers and residents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

matsim-agents v1.0

matsim-agents is a multi-agent AI framework for atomistic materials simulation and discovery. It orchestrates large language models (LLMs), machine-learned interatomic potentials (MLIPs), and DFT codes into a single agentic loop running on laptops and DOE leadership-class supercomputers. MULTI-AGENT ORCHESTRATION A LangGraph state machine with three nodes: a Planner that converts a natural-language research objective into structured tasks; an Executor that dispatches atomistic tools and loops until the queue is empty; and an Analyst that summarizes results into a human-readable report. State is checkpointed after every step and human-in-the-loop gates can be inserted at any edge. HYPOTHESIS-DRIVEN DISCOVERY CHAT An interactive REPL (matsim-agents chat) that couples LLM dialogue with atomistic simulation. Chemical formulas are automatically detected in conversation turns and trigger a full crystal-phase exploration: structure generation → relaxation → stability scoring → result injection back into the conversation, creating a closed hypothesis-refinement loop. CRYSTAL PHASE ENUMERATION Given a composition, the phase explorer enumerates prototypes by stoichiometry: elemental (fcc/bcc/hcp/sc/diamond), binary 1:1 (rocksalt/CsCl/zincblende/ wurtzite/fluorite/rutile), ternary 1:1:3 (cubic perovskite), ternary 1:2:4 (perovskite + spinel), quaternary 1:1:2:6 (Fm-3m double perovskite). 2-D prototypes (graphene, h-BN, MoS2 2H/1T) and multilayer stacking are also supported via --include-2d and --num-layers. SUPERCELL GENERATION AND SITE DECORATION Auto-tiling to a minimum atom count (--min-atoms), explicit NxNxN tiling (--supercell), symmetry-distinct site decorations (--n-orderings), and isotropic lattice-scale sweeps (--lattice-scales) for volume bracketing. MLFF RELAXATION AND STABILITY SCORING HydraGNN (multi-headed GNN) drives structure relaxation via ASE with FIRE, BFGS, or BFGSLineSearch. Stability output: delta-E/atom ranking across phases and a max-residual-force dynamical-stability proxy. Other MLIPs (MACE, NequIP, Orb) can be plugged in through the same interface. DFT BACKENDS Quantum ESPRESSO pw.x and VASP 6.6 are first-class labellers. Both have validated GPU builds and SLURM/PBS launchers for three DOE platforms: Frontier (AMD MI250X, ROCm), Aurora (Intel PVC, oneAPI), Perlmutter (NVIDIA A100, CUDA). QE produces ~100 binaries (pw.x, ph.x, epw.x, ...). VASP supports scf, relax, vc-relax, and vc-relax-shape run types. ACTIVE-LEARNING LOOP matsim-agents al run CONFIG.yaml drives an iterative HydraGNN-DFT loop: MD generates candidates → ensemble/MC-dropout uncertainty selects the most informative → DFT labels them in parallel inside one allocation → dataset grows → HydraGNN retrains → repeat. DFT backend is a single YAML toggle (dft.backend: vasp | qe). LLM-generated seed structures are supported (no curated POSCAR library needed). Config uses ${VAR}, ${VAR:-default}, ${VAR:?msg} shell-style substitution for cross-user/cross-site portability. LLM BACKENDS Ollama (local, default), vLLM (HPC multi-GPU serving), OpenAI, Anthropic, HuggingFace Transformers+Accelerate. Selected at runtime via flag or env var with no code changes. HPC PORTABILITY Same Python entry points run on Frontier (ROCm 7.2), Aurora (oneAPI), and Perlmutter (CUDA 12). DFT and ML stacks are never co-loaded in the same shell; they couple through the scheduler and filesystem. Advanced multi-node launchers (serve, discovery-chat, single-relaxation, active-learning, QE warm-start) are provided for all three platforms. CODABENCH COMPETITION BUNDLE A self-contained benchmark: 159 atomistic test structures across 11 material classes, 5 tasks (formation energy, forces, ML relaxation, AI-DFT relaxation, phase stability ranking), public/private leaderboard split (30/70), and four ready-to-run baselines: MACE-MP-0, HydraGNN, UMA, AllScAIP.

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
NASA NTRS1983

Ultrasonic mixing of epoxy curing agents

A new technique for mixing solid curing agents into liquid epoxy resins using ultrasonic energy was developed. This procedure allows standard curing agents such as 4,4 prime-diaminodiphenyl sulfone (4,4 prime-DDS) and its 3,3 prime-isomer, (3,3 prime-DDS) to be mixed without prior melting of the curing agent. It also allows curing agents such as 4,4 prime-diaminodiphenyl sulfone (4,4 prime-DDS) and its 3,3 prime-isomer, (3,3 prime-DDS) to be mixed without prior melting of the curing agent. It also allows curing agents with very high melt temperatures such as 4,4 prime-diaminobenzophenone (4,4 prime-DABP) (242 C) to be mixed without premature curing. Four aromatic diamines were ultrasonically blended into MY-720 epoxy resin. These were 4,4 prime-DDS; 3,3 prime-DDA; 4,4 prime-DABP and 3,3 prime-DABP. Unfilled moldings were cast and cured for each system and their physical and mechanical properties compared.

Hodges, W. T.↗
NASA NTRS1983

Ultrasonic mixing of epoxy curing agents

A new technique for mixing solid curing agents into liquid epoxy resins using ultrasonic energy was developed. This procedure allows standard curing agents such as 4,4 prime-diaminodiphenyl sulfone (4,4 prime-DDS) and its 3,3 prime-isomer, (3,3 prime-DDS) to be mixed without prior melting of the curing agent. It also allows curing agents with very high melt temperatures such as 4,4 prime-diaminodiphenyl sulfone (4,4 prime-DDS) and its 3,3 prime-isomer, (3,3 prime-DDS) to be mixed without prior melting of the curing agent. It also allows curing agents with very high melt temperatures such as 4,4 prime-diaminobenzophenone (4,4 prime-DABP) (242 C) to be mixed without premature curing. Four aromatic diamines were ultrasonically blended into MY-720 epoxy resin. These were 4, 4 prime-DDS; 3,3 prime-DDA; 4, 4 prime-DABP and 3,3 prime-DABP. Unfilled moldings were cast and cured for each system and their physical and mechanical properties compared. Previously announced in STAR as N83-27018

Hodges, W. T.↗
NASA NTRS1994

An agent-oriented approach to automated mission operations

As we plan for the next generation of Mission Operations Control Center (MOCC) systems, there are many opportunities for the increased utilization of innovative knowledge-based technologies. The innovative technology discussed is an advanced use of agent-oriented approaches to the automation of mission operations. The paper presents an overview of this technology and discusses applied operational scenarios currently being investigated and prototyped. A major focus of the current work is the development of a simple user mechanism that would empower operations staff members to create, in real time, software agents to assist them in common, labor intensive operations tasks. These operational tasks would include: handling routine data and information management functions; amplifying the capabilities of a spacecraft analyst/operator to rapidly identify, analyze, and correct spacecraft anomalies by correlating complex data/information sets and filtering error messages; improving routine monitoring and trend analysis by detecting common failure signatures; and serving as a sentinel for spacecraft changes during critical maneuvers enhancing the system's capabilities to support nonroutine operational conditions with minimum additional staff. An agent-based testbed is under development. This testbed will allow us to: (1) more clearly understand the intricacies of applying agent-based technology in support of the advanced automation of mission operations and (2) access the full set of benefits that can be realized by the proper application of agent-oriented technology in a mission operations environment. The testbed under development addresses some of the data management and report generation functions for the Explorer Platform (EP)/Extreme UltraViolet Explorer (EUVE) Flight Operations Team (FOT). We present an overview of agent-oriented technology and a detailed report on the operation's concept for the testbed.

Truszkowski, Walt↗
NASA NTRS1995

Building an adaptive agent to monitor and repair the electrical power system of an orbital satellite

Over several years we have developed a multistrategy apprenticeship learning methodology for building knowledge-based systems. Recently we have developed and applied our methodology to building intelligent agents. This methodology allows a subject matter expert to build an agent in the same way in which the expert would teach a human apprentice. The expert will give the agent specific examples of problems and solutions, explanations of these solutions, or supervise the agent as it solves new problems. During such interactions, the agent learns general rules and concepts, continuously extending and improving its knowledge base. In this paper we present initial results on applying this methodology to build an intelligent adaptive agent for monitoring and repair of the electrical power system of an orbital satellite, stressing the interaction with the expert during apprenticeship learning.

Tecuci, Gheorghe↗
NASA NTRS1993

Virtual agents in a simulated virtual training environment

A drawback to live-action training simulations is the need to gather a large group of participants in order to train a few individuals. One solution to this difficulty is the use of computer-controlled agents in a virtual training environment. This allows a human participant to be replaced by a virtual, or simulated, agent when only limited responses are needed. Each agent possesses a specified set of behaviors and is capable of limited autonomous action in response to its environment or the direction of a human trainee. The paper describes these agents in the context of a simulated hostage rescue training session, involving two human rescuers assisted by three virtual (computer-controlled) agents and opposed by three other virtual agents.

Achorn, Brett↗