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At least 181 records · Page 10

All sources NASA NTRS DOE OSTI

DOE OSTI2018

Biomimetic model-based advanced control strategy integrated with multi-agent optimization for nonlinear chemical processes

Here, a novel framework is proposed for integrating biomimetic-based advanced control and multi-agent optimization approaches for nonlinear chemical process applications. In particular, a Biologically-Inspired Optimal Control Strategy, denoted as BIO-CS, is combined with multi-agent optimization (MAO) techniques to provide optimal solutions for dynamic systems. In this combined framework, the BIO-CS algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower agents’ local interactions. Also, the MAO uses the capabilities of heuristic-based optimization techniques by sharing process information to obtain optimal operating setpoints for the controller considering an overall process objective. The applicability of the proposed method is demonstrated using a nonlinear, multivariable, process model of a fermentation system. Specifically, the optimal operating points are computed by the MAO implementation for setpoint tracking, trajectory tracking and plant-model mismatch scenarios for BIO-CS application. Results of the developed framework are compared to a gradient-based Sequential Quadratic Programming (SQP) technique and a classical proportional-integral (PI) controller in terms of optimization and control studies, respectively. As an additional contribution, BIO-CS is also cast as a model predictive controller (MPC) for the first time and compared to the agent-based BIO-CS approach in terms of computational time and tracking error. Closed-loop control results show up to 46% improvement in tracking performance during transient for the multi-agent BIO-CS when compared to BIO-CS as MPC for additional computational expense. The obtained results illustrate the capabilities of this novel integrated framework including BIO-CS as MPC to achieve desired nonlinear system performance for various scenarios.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

A high resolution agent-based model to support walk-bicycle infrastructure investment decisions: A case study with New York City

Active transportation modes--walk and bicycle--are central for low carbon transport, healthy living, and complete streets initiative. Building a community with amenable walk and bicycle facilities asks for smart planning and investments. It is critical to investigate the impact of infrastructure building or expansion on the overall walk and bicycle mode usage prior to making investment choices utilizing public tax money. This research developed an agent-based model to support investment decisions that allows to assess the impact of changes in walk-bike infrastructures at a high spatial resolution (e.g., block group level). The agent-based model (ABM) utilizes data from a synthetic population simulator generating agents with corresponding socio-demographic characteristics, and integrates facility attributes regarding walking and bicycling (e.g., sidewalk width, bike lane length) into the mode choice decision making process. Moreover, the ABM accounts for the effect of social interactions among agents who live and work at the same geographic locations. Finally, GIS-based maps are developed at block group resolution that allows exploring the effect of walk-bike infrastructure related investments. The results from New York City case study indicate that infrastructure investments such as widening sidewalk and increasing bike lane network can positively influence the active transportation mode choices. In addition, the level of impact varies with geographic locations--different boroughs of New York City will have different impacts. Lastly, social promotions resulting in higher social interaction among agents can reinforce the impacts of infrastructure changes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Synthesis and characterization of dihexyldithiocarbamate as a chelating agent in extraction of gold(III)

The use of dialkyldithiocarbamates as chelating agents of transition metals have been developing for decades. Many chelating agents have been synthesized and used in the extraction of the metals. Studies on particular aspects of extraction of the metals, such as the effect of increasing hydrophobicity of chelating agents on the effectiveness of the extraction, have been done. However, despite the many studies on the synthesis and applications of this type of chelating agents, interests in the aspect of molecular structure of the synthesized ligands and of their complexes, have been limited. This study aimed at synthesizing and characterizing dihexylthiocarbamate, and using the ligand for the extraction of gold III). Characterization of the ligand and of its metal complex were done by using elemental analysis, DTG, and spectroscopic methods to include NMR, ({sup 1}H, and {sup 13}C), FTIR, and MS-ESI. Data on the synthesis, characterization, and the application of the ligand as a chelating agent are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

An agent-based blackboard system for multi-objective optimization

In the field of multi-objective optimization, there are a multitude of algorithms from which to choose. Each algorithm has strengths and weaknesses associated with the mechanics for finding the Pareto front. Recently, researchers have begun to examine how multi-agent environments can be used to help solve multi-objective optimization problems. In this work, we propose a multi-objective optimization algorithm based on a multi-agent blackboard system (MABS). The MABS framework allows for multiple agents to read and write pertinent optimization problem data to a central blackboard agent. Agents can stochastically search the design space, use previously discovered solutions to explore local optima, or update and prune the Pareto front. A centralized blackboard framework allows the optimization problem to be solved in a cohesive manner and permits stopping, restarting, or updating the optimization problem. The MABS framework is tested against three alternative optimization algorithms across a suite of engineering design problems and typically outperforms the other algorithms in discovering the Pareto front. A parallelizability study is performed where we find that the MABS is able to evaluate a set number of designs, which require an evaluation time ranging from 0 to 300 seconds, quicker than a traditional optimization algorithm: this fact becomes more apparent the longer it takes to evaluate a design. To provide context for the benefits provided by MABS, a real-world nuclear engineering design problem is examined. MABS is used to examine the placement of experiments in a nuclear reactor, where we are able to evaluate hundreds of configurations for experimental placement while maintaining a strict set of safety constraints.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning

In multi-timescale multi-agent reinforcement learning (MARL), agents interact across different timescales. In general, policies for time-dependent behaviors, such as those induced by multiple timescales, are non-stationary. Learning non-stationary policies is challenging and typically requires sophisticated or inefficient algorithms. Motivated by the prevalence of this control problem in real-world complex systems, we introduce a simple framework for learning non-stationary policies for multi-timescale MARL. Our approach uses available information about agent timescales to define and learn periodic multi-agent policies. In detail, we theoretically demonstrate that the effects of non-stationarity introduced by multiple timescales can be learned by a periodic multi-agent policy. To learn such policies, we propose a policy gradient algorithm that parameterizes the actor and critic with phase-functioned neural networks, which provide an inductive bias for periodicity. The framework's ability to effectively learn multi-timescale policies is validated on a gridworld and building energy management environment.

control↗

DOE OSTI2023

Control of multi-agent systems: Results, open problems, and applications

The purpose of this review article is to present some recent results on the modeling and control of large systems of agents. We focus on particular applications where the agents are capable of independent actions instead of simply reacting to external forces. In the literature, such agents were referred to as autonomous, intelligent, self-propelled, greedy, and others. The main applications we have in mind are social systems (as opinion dynamics), pedestrians’ movements (also called crowd dynamics), animal groups, and vehicular traffic. We note that the last three examples include physical constraints; however, the agents are able to inject energy into the system, thus preventing the typical conservation of momentum and energy. In addition, the control problems posed by such systems are new and require innovative methods. We illustrate some ideas developed recently, including the use of sparse controls, limiting the total variation of controls, and defining new control problems for measures. After reviewing various approaches, we discuss some future research directions of potential interest. The latter encompasses both new types of equations and new types of limiting procedures to connect several scales at which a system can be represented. We conclude by illustrating a recent real-life experiment using autonomous vehicles on an open highway to smooth traffic waves. This opens the door to a new era of interventions to control real-time multi-agent systems and to increase the societal impact of such interventions guided by control research.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Agent Based Modeling for Multi-threat Environments [Slides]

Agent Based Modeling is a simulation method that models individual ‘agents’ instead of solving equations. Agents can be any object in a simulation including a person, an environment, a particle, or a virus. Agents follow rules to update their state. They move to a random cell, keeping a certain distance from other agents.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Application of Gaussian Bayes classifier to differentiate chlorine-based chemical agents

The Portable Isotopic Neutron Spectroscopy (PINS) is a commercialized system developed by Idaho National Laboratory (INL) to examine chemical warfare agents (CWA) non-destructively, utilizing Prompt Gamma Neutron Activation Analysis (PGNAA) techniques. The PINS system takes advantage of a high-resolution gamma-ray spectrum from a mechanically-cooled high-purity germanium (HPGe) detector. One of the difficult technical challenges is to discriminate the chlorine-based chemical agents. Especially, CN, CNB, CNS and CG have similar chemical compositions to make it hard to discriminate them with a higher confidence. Current identification algorithms for PINS systems with 252-Cf sources have been improved and updated continuously as more field data became available, and new algorithms was studied to complement the current algorithms by adopting the Gaussian Bayes classifier. These new algorithms were intended to be applied to a subset of chlorine-based chemical agents, and their main goal is discriminate CN, CNB, CNS and CG with their ratios of the chlorine neutron inelastic 1763keV peak to the chlorine thermal neutron capture 1959keV peak, which is referred to as the “Cl i/c” or “clic” ratio in this study. The Cl i/c ratios were assumed to follow Gaussian distributions with the means and the standard deviations unique to their corresponding chemical agents. The prior probabilities of these four chemical agents were optimized with a collection of field data to achieve the best performance in terms of precision or positive predictive value (PPV). Finally, their posterior probabilities as functions of the Cl i/c ratio were implemented in the current version of PINS analysis software in order to be tested with more field data.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2020

Janus: A Python Package for Agent-Based Modeling of Land Use and Land Cover Change

Janus is an open source Python package for agent-based modeling (ABM) of land use and land cover change (LULCC). Many ABMs of LULCC have been created across platforms, some of which are not ideal for large scale, high resolution scenarios. This model provides a simple object-oriented framework for creating ABMs specific to LULCC. The organizational philosophy of the modeling framework is to create software objects (agents) that are associated with specific and contextual attributes which are isolated from where those agents exist in the spatial setting of the model, yet provide clear linkages between the agent, their environment, and other agents in the simulation. In this way, the framework allows for assembly of LULCC ABMs with low (programmatic) overhead, making the models extensible and providing clear mechanisms for integrating them with process-oriented biophysical models. Provided with Janus is a suite of geospatial data preprocessing tools that can use arbitrary land cover products as an input. Crop choice decisions are based on potential crop prices, these can be created synthetically, or drawn from integrated human-Earth systems models such as the GCAM. Janus is publicly accessible through GitHub and provides an example dataset for testing.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Challenges to detection: Humidity as a spur to chemical agent change

Detecting chemical agents in outdoor environments such as a battlefield is made challenging by not only the spurious signatures from background chemicals and surfaces (e.g. asphalt, dirt, concrete), but also by the chemical transformation of the actual agents. The change of CW agents to other species can be catalyzed by other chemicals present in the scene, by different substrates, as well as by local weather conditions. Some of the final environmental transformation products are known (e.g. for the G agents methylphosphonic acid), but many of the intermediate chemical states are not, nor are the rates of transformation to the other intermediates or the end products. In this study we have made preliminary optical investigations into the degradation products of a G-agent intermediate, namely methylphosphonic anhydride and its rate of conversion to the more stable methylphosphonic acid. Using infrared and Raman spectroscopies, we have found that the relative humidity (RH) greatly affects the rate of change and we report first results from these studies.

: chemical warfare agent detection, Methylphosphon↗

DOE OSTI2017

Preliminary Evaluation of Decontamination Agent and Ion Exchange Resin Requirements for System Decontamination of a Nuclear Power Plant

Generally, the oxide film formed on the surface of the structure materials in Light-Water-Reactor is a metal oxide such as iron, nickel and chromium. Iron and nickel in these metal oxides are removed in reduction process and chromium is removed in oxidation process. The amount of the oxidizing and reducing agent for effectively removing the oxide film through system decontamination depends on the properties of the oxide film. In this paper, the amount of decontamination agent required for WH 2-Loop PWR (KORI-1 unit) system decontamination using permanganic acid as the oxidizing agent and oxalic acid as the reducing agent, and ion exchange resin required to finally remove dissolved metal ions and decontamination agent were preliminarily evaluated.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

Effects of Compounded Curing Agents on Properties and Performance of Urea Formaldehyde Resin

The effects of three compounded curing agents on the properties and performance of the urea-formaldehyde (UF) resin were investigated in this study. The compounded curing agents were prepared by mixing ammonium chloride with hexamethylenetetramine, citric acid, and oxalic acid respectively at a ratio of 1:1, named N-H, N–CA, and N–OA, respectively. The curing process, crystallinity, and physical properties were measured, and the three-ply plywood was fabricated to measure its prepress strength, wet shear strength, and formaldehyde emission. Results showed that the compounded curing agents N–CA and N–OA enhanced the initial viscosity, crosslinking density and thermal stability of UF resin. Additionally, the prepress strength of the plywood bonded by UF resin with N–CA and N–OA increased by 82 and 111% respectively compared to the UF resin with NH{sub 4}Cl, and the wet shear strength increased by 14 and 16%, the formaldehyde emission decreased by 19 and 42% respectively. However, owing to the short pot-life of these curing agent limited their storage time, the curing agents N–CA and N–OA should be applied to fabricate plywood in winter for obtaining a better bond strength and a lower formaldehyde emission. While the UF resin with N–HT showed a suitable pot-life, so it could be applied to fabricate plywood in summer for long time storage and avoiding procuring problem.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2016

Influence of quenching agent on microstructure, properties and thermal stress of SiC{sub p}/2009 composites

15% vol. SiC{sub p}/2009 composites prepared by powder metallurgy were quenched in room temperature water and 20% polyethylene glycol (PEG) solution respectively, then aged naturally. The influence of quenching agent on microstructure, properties and thermal stress of SiC{sub p}/2009 composites were investigated by means of scanning/transmission electron microscope, hardness and tensile test. The results showed that the number of precipitated phase in water quenched composites increased, with much finer in size and more homogeneous in distribution compared with 20% PEG quenched one. Meanwhile, the density of dislocation in composites by water quenching was also much higher. Intergranular corrosion did not occur with the two quenching agents. The 20% PEG quenched composites exhibited slight lower hardness and higher electrical conductivity than that of water quenched one. The two quenched composites showed same level in tensile strength, but the yield strength of water-quenched composites was higher (8 MPa, 3%). The usage of 20% PEG reduced thermal stress and minimized warping deformation of the parts, it is a more suitable quenching agent for SiC{sub p}/2009 composites in engineering application fields. - Highlights: •SiC{sub p}/2009 composites quenched by water and 20% PEG solution were investigated. •Aging precipitation behavior of SiC{sub p}/2009 composites is sensitive to quenchant. •Influence of quenching agent on properties of SiC{sub p}/2009 composites are minimal. •Quenching with 20% PEG reduces thermal stress of SiC{sub p}/2009 composites remarkably. •20% PEG is a more suitable quenching agent for SiC{sub p}/2009 composites than water.

36 MATERIALS SCIENCE↗

DOE OSTI2011

Nerve agent intoxication: Recent neuropathophysiological findings and subsequent impact on medical management prospects

This manuscript provides a survey of research findings catered to the development of effective countermeasures against nerve agent poisoning over the past decade. New neuropathophysiological distinctive features as regards organophosphate (OP) intoxication are presented. Such leading neuropathophysiological features include recent data on nerve agent-induced neuropathology, related peripheral or central nervous system inflammation and subsequent angiogenesis process. Hence, leading countermeasures against OP exposure are down-listed in terms of pre-treatment, protection or decontamination and emergency treatments. The final chapter focuses on the description of the self-repair attempt encountered in lesioned rodent brains, up to 3 months after soman poisoning. Indeed, an increased proliferation of neuronal progenitors was recently observed in injured brains of mice subjected to soman exposure. Subsequently, the latter experienced a neuronal regeneration in damaged brain regions such as the hippocampus and amygdala. The positive effect of a cytokine treatment on the neuronal regeneration and subsequent cognitive behavioral recovery are also discussed in this review. For the first time, brain cell therapy and neuronal regeneration are considered as a valuable contribution towards delayed treatment against OP intoxication. To date, efficient delayed treatment was lacking in the therapeutic resources administered to patients contaminated by nerve agents. - Highlights: > This review focuses on neuropathophysiology following nerve agent poisoning in mice. > Extensive data on long-term neuropathology and related inflammation are provided here. > Delayed self-repair attempts encountered in lesioned rodent brains are also described. > Cell therapy is considered as a valuable treatment against nerve agent intoxication.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2012

Silibinin, dexamethasone, and doxycycline as potential therapeutic agents for treating vesicant-inflicted ocular injuries

There are no effective and approved therapies against devastating ocular injuries caused by vesicating chemical agents sulfur mustard (SM) and nitrogen mustard (NM). Herein, studies were carried out in rabbit corneal cultures to establish relevant ocular injury biomarkers with NM for screening potential efficacious agents in laboratory settings. NM (100 nmol) exposure of the corneas for 2 h (cultured for 24 h), showed increases in epithelial thickness, ulceration, apoptotic cell death, epithelial detachment microbullae formation, and the levels of VEGF, cyclooxygenase-2 (COX-2) and matrix metalloproteinase-9 (MMP-9). Employing these biomarkers, efficacy studies were performed with agent treatments 2 h and every 4 h thereafter, for 24 h following NM exposure. Three agents were evaluated, including prescription drugs dexamethasone (0.1%; anti-inflammatory steroid) and doxycycline (100 nmol; antibiotic and MMP inhibitor) that have been studied earlier for treating vesicant-induced eye injuries. We also examined silibinin (100 μg), a non-toxic natural flavanone found to be effective in treating SM analog-induced skin injuries in our earlier studies. Treatments of doxycycline + dexamethasone, and silibinin were more effective than doxycycline or dexamethasone alone in reversing NM-induced epithelial thickening, microbullae formation, apoptotic cell death, and MMP-9 elevation. However, dexamethasone and silibinin alone were more effective in reversing NM-induced VEGF levels. Doxycycline, dexamethasone and silibinin were all effective in reversing NM-induced COX-2 levels. Apart from therapeutic efficacy of doxycycline and dexamethasone, these results show strong multifunctional efficacy of silibinin in reversing NM-induced ocular injuries, which could help develop effective and safe therapeutics against ocular injuries by vesicants. -- Highlights: ► Established injury biomarkers in rabbit corneal culture with nitrogen mustard (NM) ► This NM model is a cost effective system to evaluate and optimize therapeutics. ► Show that doxycycline and dexamethasone reduce NM-caused ocular injuries ► Demonstrate that silibinin effectively reverses NM-caused ocular injury endpoints ► Suggest optimization of identified agents against ocular injuries by vesicants.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Role of Capping Agents in the Synthesis of Salicylate-Capped Zinc Oxide Nanoparticles

Capping agents are often used for controlling the size, aggregation, and properties of nanoparticles. To guide the design of improved nanomaterials for targeted performance, one can use mechanistic insights into the interactions between capping agents and nanoparticles. Here, we employ density functional theory (DFT), reactive force-field molecular dynamics (ReaxFF MD) simulations, and optical spectroscopy to study the interactions between salicylate, as a model capping agent, and zinc oxide (ZnO) nanoparticles. In this work, we find that salicylate strongly interacts with the nanoparticle via the formation of O–Zn bonds in a distorted six-membered coordination ring structure. We describe the mechanisms of capping of ZnO nanoparticles by salicylate via three different binding modes. Simulations indicate that salicylate undergoes dissociative adsorption at the highly active surface Zn sites via a hydrogen-transfer process, thereby forming a tridentate configuration. The water-mediated interaction also facilitates the dissociative adsorption, leading to two salicylate O atoms coordinating with a surface Zn atom, while the other salicylate O atom bonds with another surface Zn atom. For molecular adsorption, binding free energies indicate that salicylate binds more strongly to ZnO, often in a bidentate configuration, than water does. The formation of the salicylate–ZnO complex is substantiated by UV–visible and Fourier transform infrared spectra. We find that the C=O stretching mode of salicylate becomes softened when it interacts with the nanoparticle, suggesting chemisorption of salicylate on ZnO. Although DFT predicts strong interaction between salicylate and ZnO, ReaxFF MD simulation indicates the moderate interaction between these two components in aqueous solution. Water molecules in close contact with the nanoparticle surface undergo dissociation, thus resulting in a surface hydroxyl, a reactive oxygen species that may influence the nanoparticle’s catalytic properties. Overall, the atomic-level information provided here can guide the selection process of salicylate as an appropriate agent for ZnO nanoparticle synthesis when strong interactions of particles with capping agents are required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Assessing Adaptive Irrigation Impacts on Water Scarcity in Nonstationary Environments—A Multi‐Agent Reinforcement Learning Approach

Abstract One major challenge in water resource management is to balance the uncertain and nonstationary water demands and supplies caused by the changing anthropogenic and hydroclimate conditions. To address this issue, we developed a reinforcement learning agent‐based modeling (RL‐ABM) framework where agents (agriculture water users) are able to learn and adjust water demands based on their interactions with the water systems. The intelligent agents are created by a reinforcement learning algorithm adapted from the Q‐learning algorithm. We illustrated this framework in a case study where the RL‐ABM is two‐way coupled with the Colorado River Simulation System (CRSS), a long‐term planning model used for the administration of the Colorado River Basin, for assessing agriculture water uses impacts on water scarcity. Seventy‐eight intelligent agents are simulated, which can be grouped into three categories based on their parameter values: the “aggressive” (swift actions; low regrets), the “forward‐looking conservative” (mild actions; high regrets; fast learning), and the “myopic conservative” (mild actions; median regrets; slow learning). The ABM‐CRSS results showed that the major reservoirs in the Upper Colorado Basin might experience more frequent water shortages due to the increasing water uses compared to the original CRSS results. If the drought continues, the case study also demonstrates that agents can learn and adjust their demands.

Hung, Fengwei↗

DOE OSTI2013

Complex dynamics of a nonlinear voter model with contrarian agents

We investigate mean-field dynamics of a nonlinear opinion formation model with congregator and contrarian agents. Each agent assumes one of the two possible states. Congregators imitate the state of other agents with a rate that increases with the number of other agents in the opposite state, as in the linear voter model and nonlinear majority voting models. Contrarians flip the state with a rate that increases with the number of other agents in the same state. The nonlinearity controls the strength of the majority voting and is used as a main bifurcation parameter. We show that the model undergoes a rich bifurcation scenario comprising the egalitarian equilibrium, two symmetric lopsided equilibria, limit cycle, and coexistence of different types of stable equilibria with intertwining attractive basins.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗