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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Attention Enabled Multi-Agent DRL for Decentralized Volt-VAR Control of Active Distribution System Using PV Inverters and SVCs

This paper proposes attention enabled multi-agent deep reinforcement learning (MADRL) framework for active distribution network decentralized Volt-VAR control. Using the unsupervised clustering, the whole distribution system can be decomposed into several sub-networks according to the voltage and reactive power sensitivity relationships. Then, the distributed control problem of each sub-network is modeled as Markov games and solved by the improved MADRL algorithm, where each sub-network is modeled as an adaptive agent. An attention mechanism is developed to help each agent focus on specific information that is mostly related to the reward. All agents are centrally trained offline to learn the optimal coordinated Volt-VAR control strategy and executed in a decentralized manner to make online decisions with only local information. Compared with other distributed control approaches, the proposed method can effectively deal with uncertainties, achieve fast decision makings, and significantly reduce the communication requirements. Comparison results with model-based and other data-driven methods on IEEE 33-bus and 123-bus systems demonstrate the benefits of the proposed approach.

distribution network↗

DOE OSTI2021

Building and experimenting with an agent-based model to study the population-level impact of CommunityRx, a clinic-based community resource referral intervention

CommunityRx (CRx), an information technology intervention, provides patients with a personalized list of healthful community resources (HealtheRx). In repeated clinical studies, nearly half of those who received clinical “doses” of the HealtheRx shared their information with others (“social doses”). Clinical trial design cannot fully capture the impact of information diffusion, which can act as a force multiplier for the intervention. Furthermore, experimentation is needed to understand how intervention delivery can optimize social spread under varying circumstances. To study information diffusion from CRx under varying conditions, we built an agent-based model (ABM). This study describes the model building process and illustrates how an ABM provides insight about information diffusion through in silico experimentation. To build the ABM, we constructed a synthetic population (“agents”) using publicly-available data sources. Using clinical trial data, we developed empirically-informed processes simulating agent activities, resource knowledge evolution and information sharing. Using RepastHPC and chiSIM software, we replicated the intervention in silico, simulated information diffusion processes, and generated emergent information diffusion networks. The CRx ABM was calibrated using empirical data to replicate the CRx intervention in silico. We used the ABM to quantify information spread via social versus clinical dosing then conducted information diffusion experiments, comparing the social dosing effect of the intervention when delivered by physicians, nurses or clinical clerks. The synthetic population (N = 802,191) exhibited diverse behavioral characteristics, including activity and knowledge evolution patterns. In silico delivery of the intervention was replicated with high fidelity. Large-scale information diffusion networks emerged among agents exchanging resource information. Varying the propensity for information exchange resulted in networks with different topological characteristics. Community resource information spread via social dosing was nearly 4 fold that from clinical dosing alone and did not vary by delivery mode. This study, using CRx as an example, demonstrates the process of building and experimenting with an ABM to study information diffusion from, and the population-level impact of, a clinical information-based intervention. While the focus of the CRx ABM is to recreate the CRx intervention in silico, the general process of model building, and computational experimentation presented is generalizable to other large-scale ABMs of information diffusion.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Multi-scale, Multi-disciplinary, and Multi-agent Explainable AI with Koopman-Undergirded Learning, Prediction, and Analysis (M3EA KULPA) (Project Closeout Report)

The goal of this project was to develop and use domain-aware machine learning formulations, based on the Koopman Operator (KO), for modelling multi-scale, multi-disciplinary (e.g., multi-physics), and/or multi-agent systems. The project developed these formulations for the following cases: • Systems with dynamics at two separate time scales, • Systems with a bi-level hierarchical control structure, • Systems with bi-level hierarchical control and dynamics at two separate time scales (the lower level controls operating at the faster time scale), and • Systems with n separate but interacting agents/disciplines (with/without control, respectively); the controls for each agent could include bi-level hierarchical control and dynamics at two separate time scales as described above. The project then defined a set of dynamical systems consisting of different nonlinear oscillators that could be used to test these different formulations and then subsequently learned the KO models for those systems. With the KO models, we were able to do the following: • Quantify system stability, including both long-term and transient behavior, • Quantify the effects of feedbacks between the different time scales and agents/disciplines in terms of those feedbacks’ effects on system stability, • Replace a standard Proportional-Integral (PI) control in the hierarchical control structure with a KO-based Linear-Quadratic Regular (LQR), a form of optimal control, • Calculate optimal supervisory control policies a) with and without time scale separated dynamics at the lower level control levels and b) with both PI and KO-based LQR lower level control policies, and • Calculate dynamic Nash equilibria for multi-agent systems where each agent makes its own control decisions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

The Role of Process-Directing Agents on Enamel Lesion Remineralization: Fluoride Boosters

The aim of this study was to investigate the effects of two process-directing agents (polyaspartic acid and osteopontin) used in a polymer-induced liquid-precursor (PILP) process on the remineralization of bacteria-induced enamel demineralization. Enamel demineralization lesions (depths of about 180–200 µm) were created and exposed to Streptococcus mutans, cultured with a 10% sucrose solution for 21 days, and remineralized using a PILP process (pH = 7.4, 14 days) with a calcium phosphate solution containing either polyaspartic acid or osteopontin in the presence or absence of fluoride (0.5 ppm). The specimens were examined under scanning electron microscopy. The fluoride was successfully incorporated into the PILP remineralization process for both polyaspartic acid and osteopontin. When the fluoride was added to the PILP remineralization solution, there was more uniform remineralization throughout the lesion than with either polyaspartic acid or osteopontin alone. However, in the absence of these process-directing agents, fluoride alone showed less remineralization with the formation of a predominantly surface-only layer. The PILP remineralization process relies on the ability of process-directing agents to stabilize calcium phosphate ions and holds promise for enamel lesion remineralization, and these agents, in the presence of fluoride, seem to play an important role as a booster or supplement in the continuation of remineralization by reducing the mineral gains at the surface layer.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Optimal Equilibrium Selection of Price-Maker Agents in Performance-Based Regulation Market

This paper analyzes the oligopolistic equilibrium of multiple price-maker agents in performance-based regulation (PBR) markets. In these markets, there are price-maker agents representing some frequency regulation (FR) providers and a number of independent price-taker FR providers. An equilibrium problem with equilibrium constraints (EPEC) model is employed in this paper to study the equilibria of a PBR market in the presence of price-maker agents and pricetaker FR providers. Due to the incorporation of the FR providers' dynamics, the proposed model is reformulated as a mixed-integer linear programming (MILP) problem over innovative mathematical techniques. An optimal equilibrium point is also selected for the market, where none of the agents is unique deviator and the dynamic performance of power system is improved simultaneously. The effectiveness of proposed optimal equilibrium point is evaluated in the numerical results section by comparing the outputs with conventional optimal dispatches of the FR providers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Enhancing Autonomous Control of Microreactors Using Multi-Agent Reinforcement Learning

In order for microreactors to be economically competitive, operation costs will need to be minimized through some degree of autonomous control. Previous work has demonstrated the effectiveness of reinforcement learning (RL) for load-following control in a drum-controlled microreactor. This study extends that work by exploring the potential of RL to independently control each of the reactor’s drums. We compare a single-agent RL approach with a multi-agent RL (MARL) framework, testing them for generalization across different load-following power profiles and control timescales, and for robustness in cases of randomly disabled control drums. Since the point kinetics simulation environment used in this study cannot resolve spatial effects, we assume that in the absence of spatially localized disturbances, optimal drum movements should be symmetrical. We demonstrate that single-agent RL is able to achieve accurate performance only when symmetric actions are ignored; otherwise, it fails to train a useful controller. Meanwhile, the MARL framework performs symmetric actions by design and trains a robust, accurate agent, as evidenced by mean absolute errors in power matching of 0.41% for the training power profile, 0.68% for a profile with half the drums disabled, and 0.21% for a profile on a realistic load-following time horizon.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Implanting Transition Metal into Li 2 O-Based Cathode Prelithiation Agent for High-Energy-Density and Long-Life Li-Ion Batteries

Compensating the irreversible loss of limited active lithium (Li) is essentially important for improving the energy-density and cycle-life of practical Li-ion battery full-cell, especially after employing high-capacity but low initial coulombic efficiency anode candidates. Introducing prelithiation agent can provide additional Li source for such compensation. Herein, we precisely implant trace Co (extracted from transition metal oxide) into the Li site of Li 2 O, obtaining (Li 0.66 Co 0.11$\square$0.23 ) 2 O (CLO) cathode prelithiation agent. Further, the synergistic formation of Li vacancies and Co-derived catalysis efficiently enhance the inherent conductivity and weaken the Li-O interaction of Li 2 O, which facilitates its anionic oxidation to peroxo/superoxo species and gaseous O 2 , achieving 1642.7 mAh/g ~Li2O prelithiation capacity (≈980 mAh/g for prelithiation agent). Coupled 6.5 wt % CLO-based prelithiation agent with LiCoO 2 cathode, substantial additional Li source stored within CLO is efficiently released to compensate the Li consumption on the SiO/C anode, achieving 270 Wh/kg pouch-type full-cell with 92 % capacity retention after 1000 cycles.

25 ENERGY STORAGE↗

DOE OSTI2021

Tonal Emergence: An agent-based model of tonal coordination

Humans have a remarkable capacity for coordination. Our ability to interact and act jointly in groups is crucial to our success as a species. Joint Action (JA) research has often concerned itself with simplistic behaviors in highly constrained laboratory tasks. But there has been a growing interest in understanding complex coordination in more open-ended contexts. In this regard, collective music improvisation has emerged as a fascinating model domain for studying basic JA mechanisms in an unconstrained and highly sophisticated setting. A number of empirical studies have begun to elucidate coordination mechanisms underlying joint musical improvisation, but these empirical findings have yet to be cached out in a working computational model. The present work fills this gap by presenting TonalEmergence, an idealized agent-based model of improvised musical coordination. TonalEmergence models the coordination of notes played by improvisers to generate harmony (i.e., tonality), by simulating agents that stochastically generate notes biased towards maximizing harmonic consonance given their partner’s previous notes. Here, the model replicates an interesting empirical result from a previous study of professional jazz pianists: feedback loops of mutual adaptation between interacting agents support the production of consonant harmony. The model is further explored to show how complex tonal dynamics, such as the production and dissolution of stable tonal centers, are supported by agents that are characterized by (i) a tendency to strive toward consonance, (ii) stochasticity, and (iii) a limited memory for previously played notes. TonalEmergence thus provides a grounded computational model to simulate and probe the coordination mechanisms underpinning one of the more remarkable feats of human cognition: collective music improvisation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Improved adhesion of polyurethane-based nanocomposite coatings to tin surface through silane coupling agents

Conformal coatings with higher adhesion strength is critical for mitigating the growth of whiskers from the pure tin (Sn) or Sn-rich surfaces of the Pb-free electronics. Silane coupling agents have the ability to form a durable bond between polymeric coatings and metallic materials. The main goal of this paper is to investigate the effect of silane agents on the adhesion behavior of polyurethane (PU), polyurethane acrylate (PUA) and both coatings filled with silicone rubber (SR) nanoparticles on the tin solder pads. It was found that an isocyanate-based silane provided excellent adhesion for PU and PU + SR coatings and an acrylic-based silane for PUA and PUA + SR coatings on the tin surface. This was due to the reaction bond formation between the silane coupling agent and the coating. Hence, the selection of silane coupling agents with the organofunctional group similar to the coating material is crucial to improve the adhesion properties of the coating.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Desorption Electrospray Ionization–Mass Spectrometry Imaging Provides Spatiochemical Information on Potential Biocontrol Agents against Phytophthora capsici Infection in Tomato Plants

Biological control agents can offer an eco-friendly and more sustainable alternative to conventional chemical pesticides, providing protection against destructive pathogens, such as Phytophthora capsici, while reducing potential environmental harm associated with synthetic pesticide use in agricultural systems. This work evaluates the biocontrol effectiveness of Bacillus vallismortis, Bacillus amyloliquefaciens, Bacillus thuringiensis, and Bacillus subtilis, against the widespread plant pathogen Phytophthora capsici. Our studies showed that Bacillus thuringiensis and Bacillus subtilis promote plant growth and provide protection against Phytophthora capsici in both in vitro and in vivo greenhouse studies, while Bacillus vallismortis and Bacillus amyloliquefaciens were effective in vitro but not in vivo. Specifically, Bacillus thuringiensis was observed to both hinder the growth of Phytophthora capsici and enhance plant resilience to this pathogen, with B. thuringiensis-treated, pathogen-exposed plants displaying a 94.4% increase in root length and a 74.0% increase in shoot height compared to plants with only oomycete exposure. To probe the molecular interactions between the biocontrol agent and pathogen, a dual culture of Bacillus thuringiensis and Phytophthora capsici was analyzed in situ using a desorption electrospray ionization–mass spectrometry imaging (DESI-MSI) workflow. This approach interrogated the spatially oriented biochemical interactions that may serve as the molecular foundation for the effectiveness of these biological control agents in crop protection, identifying seven unique phenotypic regions within the dual culture. Herein, we demonstrate the benefits of biological control agent application in tomato cultivation and showcase the strengths of desorption electrospray ionization–mass spectrometry imaging when applied to the spatially resolved molecular characterization of agriculturally relevant microorganisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Ultra-high SERS detection of consumable coloring agents using plasmonic gold nanostars with high aspect-ratio spikes

Solution-based SERS detection by using a portable Raman instrument has emerged as an important tool due to its simplicity, and flexibility for rapid and on-site screening of analyte molecules. However, this method has several shortcomings, including poor sensitivity especially for weak-affinity analyte molecules, where there is no close contact between the plasmonic metal surface and analyte molecule. Examples of weak-affinity molecules include pigment molecules that are commonly used as a consumable coloring agent, such as allura red (AR), and sunset yellow (SY). As high consumption of colorant agents has been shown to cause adverse effects on human health, there is a strong need to develop a simple and practical sensing system with high sensitivity for these agents. Here we present a novel, highly sensitive solution-based SERS detection method for AR, and SY by using CTAC capped gold nanostars (GNS) having different aspect ratios (GNS-2, GNS-4, and GNS-5) without utilizing any aggregating agents which can enhance SERS signal however it reduces batch to batch reproducibility. The influence of the aspect ratio of GNS on SERS properties was investigated. We have achieved a limit of detection (LOD) of AR and SY as low as 0.5 and 1 ppb, respectively by using GNS-5 with the advantages of minimal sample preparation by just mixing the analyte solution into a well plate containing GNS solution. In addition, excellent colloidal stability and reproducibility have further enhanced the applicability in real-world samples. Altogether, our results evidence that the solution-based SERS detection platform using high aspect-ratio GNS can be applied for practical application to detect pigment molecules in real samples with satisfactory results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

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

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

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

DOE OSTI2025

Foveal vision reduces neural resources in agent-based game learning

Efficient processing of information is crucial for the optimization of neural resources in both biological and artificial visual systems. In this paper, we study the efficiency that may be obtained via the use of a fovea. Using biologically-motivated agents, we study visual information processing, learning, and decision making in a controlled artificial environment, namely the Atari Pong video game. We compare the resources necessary to play Pong between agents with and without a fovea. Our study shows that a fovea can significantly reduce the neural resources, in the form of number of neurons, number of synapses, and number of computations, while at the same time maintaining performance at playing Pong. To our knowledge, this is the first study in which an agent must simultaneously optimize its visual system, along with its decision making and action generation capabilities. That is, the visual system is integral to a complete agent.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1975

Development of high temperature resistant graphite fiber coupling agents

Surface treatments were investigated as potential coupling agents to improve the elevated temperature shear strength retention of polyimide/graphite and polyphenylquinoxaline/graphite composites. The potential coupling agents were evaluated by fiber strand tensile tests, fiber and composite weight losses at 533 and 588K, and by interlaminar shear strength retention at 533 and 588K. The two surface treatments selected for more extensive evaluation were a coating of Ventromer T-1, a complex organometallic reaction product of titanium tetrachloride and trimethyl borate, and a polyphenylquinoxaline (PPQ) sizing which was pyrolyzed in nitrogen to form a carbonaceous layer on the fiber. Pyrolyzed polyphenylquinoxaline is a satisfactory coupling agent for polyimide/Thornel 300 graphite fiber composites. During 1000 hours aging at 588K such composites lose a little over half their transverse tensile strength, and suffer a slight loss in flexural modulus. No degradation of flexural strength or interlaminar shear strength occured during 1000 hours aging at 588K. None of the coupling agents examined had a markedly beneficial effect with polyphenylquinoxaline composites.

Griffin, R. N.↗

NASA NTRS1977

Spacecraft disinfectant/cleansing agent development

The sanitation agent container, which was selected in a trade off study, employs two chambers, a rigid outer container and an inner flexible bladder. The bladder contains the sanitation agent formulation and its release is controlled by a manually operated valve. The outer container holds a high pressure vapor. There is no void in the bladder which makes the package operation independent of orientation and therefore usable in zero gravity. Foam is developed by a low boiling point fluid or dissolved in the product. When the product is dispensed at atmospheric presure, the evolved gas or vapor whips up a goam in the valve. The sanitation agents were initially formulated with freons which produces an excellent foam. However freon is incompatible with the life support system and was replaced with carbon dioxide dissolved at high pressure. The CO2 system may limit high temperature exposure to prevent leakage or package distortion. The sanitation agents have been shown to be effective in cleaning soils from personnel and material.

Abbate, M.↗

NASA NTRS1994

Agent oriented programming

The goal of our research is a methodology for creating robust software in distributed and dynamic environments. The approach taken is to endow software objects with explicit information about one another, to have them interact through a commitment mechanism, and to equip them with a speech-acty communication language. System-level applications include software interoperation and compositionality. A government application of specific interest is an infrastructure for coordination among multiple planners. Daily activity applications include personal software assistants, such as programmable email, scheduling, and new group agents. Research topics include definition of mental state of agents, design of agent languages as well as interpreters for those languages, and mechanisms for coordination within agent societies such as artificial social laws and conventions.

Shoham, Yoav↗

NASA NTRS1998

Research and Development for an Operational Information Ecology: The User-System Interface Agent Project

The User System Interface Agent (USIA) is a special type of software agent which acts as the "middle man" between a human user and an information processing environment. USIA consists of a group of cooperating agents which are responsible for assisting users in obtaining information processing services intuitively and efficiently. Some of the main features of USIA include: (1) multiple interaction modes and (2) user-specific and stereotype modeling and adaptation. This prototype system provides us with a development platform towards the realization of an operational information ecology. In the first phase of this project we focus on the design and implementation of prototype system of the User-System Interface Agent (USIA). The second face of USIA allows user interaction via a restricted query language as well as through a taxonomy of windows. In third phase the USIA system architecture was revised.

Srivastava, Sadanand↗

NASA NTRS2000

Emergent Aerospace Designs Using Negotiating Autonomous Agents

This paper presents a distributed design methodology where designs emerge as a result of the negotiations between different stake holders in the process, such as cost, performance, reliability, etc. The proposed methodology uses autonomous agents to represent design decision makers. Each agent influences specific design parameters in order to maximize their utility. Since the design parameters depend on the aggregate demand of all the agents in the system, design agents need to negotiate with others in the market economy in order to reach an acceptable utility value. This paper addresses several interesting research issues related to distributed design architectures. First, we present a flexible framework which facilitates decomposition of the design problem. Second, we present overview of a market mechanism for generating acceptable design configurations. Finally, we integrate learning mechanisms in the design process to reduce the computational overhead.

Deshmukh, Abhijit↗