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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Distributed Constrained Optimization with Semicoordinate Transformations

Recent work has shown how information theory extends conventional full-rationality game theory to allow bounded rational agents. The associated mathematical framework can be used to solve constrained optimization problems. This is done by translating the problem into an iterated game, where each agent controls a different variable of the problem, so that the joint probability distribution across the agents moves gives an expected value of the objective function. The dynamics of the agents is designed to minimize a Lagrangian function of that joint distribution. Here we illustrate how the updating of the Lagrange parameters in the Lagrangian is a form of automated annealing, which focuses the joint distribution more and more tightly about the joint moves that optimize the objective function. We then investigate the use of "semicoordinate" variable transformations. These separate the joint state of the agents from the variables of the optimization problem, with the two connected by an onto mapping. We present experiments illustrating the ability of such transformations to facilitate optimization. We focus on the special kind of transformation in which the statistically independent states of the agents induces a mixture distribution over the optimization variables. Computer experiment illustrate this for &sat constraint satisfaction problems and for unconstrained minimization of NK functions.

Macready, William↗
NASA NTRS2011

Method for Selective Cleaning of Mold Release from Composite Honeycomb Surfaces

Honeycomb structures are commonly employed as load- and force-bearing structures as they are structurally strong and lightweight. Manufacturing processes for heat-molded composite honeycomb structures commence with the placement of pre-impregnated composite layups over metal mandrels. To prevent permanent bonding between the composite layup and the metal mandrels, an agent, known as a mold release agent, is used. Mold release agents allow the molded composite material to be removed from mandrels after a heat-forming process. Without a specific removal process, mold release agents may continue to adhere to the surface of the composite material, thereby affecting the bonding of other materials that may come into contact with the composite surface in later stages of processing A constituent common to commercially available household cleaning agents is employed for the removal of mold release agents common to the manufacturing of heat-formed composite materials. The reliability of the solvent has been proven by the longevity and reliability of commercial household cleaners. At the time of this reporting, no one has attempted using constituent for this purpose. The material to be cleaned is immersed in the solution, vertically removed so that the solution is allowed to drain along cell walls and into a solvent bath, and then placed on a compressed airflow table for drying.

Pugel, Diane↗
NASA NTRS2003

Software for Sharing and Management of Information

DIAMS is a set of computer programs that implements a system of collaborative agents that serve multiple, geographically distributed users communicating via the Internet. DIAMS provides a user interface as a Java applet that runs on each user s computer and that works within the context of the user s Internet-browser software. DIAMS helps all its users to manage, gain access to, share, and exchange information in databases that they maintain on their computers. One of the DIAMS agents is a personal agent that helps its owner find information most relevant to current needs. It provides software tools and utilities for users to manage their information repositories with dynamic organization and virtual views. Capabilities for generating flexible hierarchical displays are integrated with capabilities for indexed- query searching to support effective access to information. Automatic indexing methods are employed to support users queries and communication between agents. The catalog of a repository is kept in object-oriented storage to facilitate sharing of information. Collaboration between users is aided by matchmaker agents and by automated exchange of information. The matchmaker agents are designed to establish connections between users who have similar interests and expertise.

Chen, James R.↗
NASA NTRS2014

CLEANing the Reward: Counterfactual Actions to Remove Exploratory Action Noise in Multiagent Learning

Learning in multiagent systems can be slow because agents must learn both how to behave in a complex environment and how to account for the actions of other agents. The inability of an agent to distinguish between the true environmental dynamics and those caused by the stochastic exploratory actions of other agents creates noise in each agent's reward signal. This learning noise can have unforeseen and often undesirable effects on the resultant system performance. We define such noise as exploratory action noise, demonstrate the critical impact it can have on the learning process in multiagent settings, and introduce a reward structure to effectively remove such noise from each agent's reward signal. In particular, we introduce Coordinated Learning without Exploratory Action Noise (CLEAN) rewards and empirically demonstrate their benefits

Reinforcement Learning↗
NASA NTRS2018

Serious Gaming for Building a Basis of Certification via Trust and Trustworthiness of Autonomous Systems

Autonomous systems governed by a variety of adaptive and nondeterministic algorithms are being planned for inclusion into safety-critical environments, such as unmanned aircraft and space systems in both civilian and military applications. However, until autonomous systems are proven and perceived to be capable and resilient in the face of unanticipated conditions, humans will be reluctant or unable to delegate authority, remaining in control aided by machine-based information and decision support. Proving capability, or trustworthiness, is a necessary component of certification. Perceived capability is a component of trust. Trustworthiness is an attribute of a cyber-physical system that requires context-driven metrics to prove and certify. Trust is an attribute of the agents participating in the system and is gained over time and multiple interactions through trustworthy behavior and transparency. Historically, artificial intelligence and machine learning systems provide answers without explanation - without a rationale or insight into the machine “thinking”. In order to function as trusted teammates, machines must be able to explain their decisions and actions. This transparency is a product of both content and communication. NASA’s Autonomy Teaming & TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project seeks to build a basis for certification of autonomous systems via establishing metrics for trustworthiness and trust in multi-agent team interactions, using AI (Artificial Intelligence) explainability and persistent modeling and simulation, in the context of mission planning and execution, with analyzable trajectories. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ATTRACTOR modeling and simulation environment is similar to online gaming environments in which player (aka agent) participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual agent’s active participation. This persistent simulation environment will accommodate individual agents, groups of self-organizing agents, and large-scale infrastructure behavior. The effects of the emerging adaptation and coevolution can be observed and measured to building a basis of measurable trustworthiness and trust, toward certification of safety-critical autonomous systems.

Allen, B. Danette↗
NASA NTRS2017

Distributed Fast Motion Planning for Spacecraft Swarms in Cluttered Environments using Spherical Expansions and Sequence of Convex Optimization Problems

This paper presents a novel guidance algorithm for spacecraft swarms in an environment cluttered with many obstacles like a debris field or the asteroid belt. The objective of this algorithm is to reconfigure the swarm to a desired formation in a distributed manner while minimizing fuel and avoiding collisions among themselves and with the obstacles. The agents first use a spherical-expansion-based sampling algorithm to cooperatively explore the workspace and find paths to the desired terminal positions. Using a distributed assignment algorithm, the agents converge on an optimal assignment of the target locations in the desired formation. Then each agent generates a locally optimal trajectory from its current location to its terminal position by solving a sequence of convex optimization problems. As the agent moves along this trajectory, it receives the position of other agents and updates its trajectory to avoid collisions with other agents and the obstacles. Thus the swarm achieves the desired formation in a distributed manner while avoiding collisions. Moreover, this algorithm is computationally efficient, therefore it can be implemented onboard resource-constrained spacecraft. Simulations results show that the proposed distributed algorithm can be used by a spacecraft swarm to reconfigure a desired formation around an asteroid in a collision-free manner.

Bandyopadhyay, Saptarshi↗
NASA NTRS2019

Advancing Aircraft Operations in a Net-Centric Environment with the Incorporation of Increasingly Autonomous Systems and Human Teaming

NextGen has begun the modernization of the nation’s air transportation system, with goals to improve system safety, increase operation efficiency and capacity, provide enhanced predictability, resilience and robustness. With these improvements, NextGen is poised to handle significant increases in air traffic operations, more than twice the number recorded in 2016, by 2025.1 NextGen is evolving toward collaborative decision-making across many agents, including automation, by use of a Net-Centric architecture, which in itself creates a very complex environment in which the navigation and operation of aircraft are to take place. An intricate environment such as this, coupled with the expected upsurge of air traffic operations generates concern respecting the ability of the human-agent to both fly and manage aircraft within. Therefore, it is both necessary and practical to begin the process of increasingly autonomous systems within the cockpit that will act independently to assist the human-agent achieve the overall goal of NextGen. However, the straightforward technological development and implementation of intelligent machines into the cockpit is only part of what is necessary to maintain, at minimum, or improve human-agent functionality, as desired, while operating in NextGen. The full integration of Increasingly Autonomous Systems (IAS) within the cockpit can only be accomplished when the IAS works in concert with the human, formulating trust between the two, thereby establishing a team atmosphere. Imperative to cockpit implementation is ensuring the proper performance of the IAS by the development team and the human-agent with which it will be paired when given a specific piloting, navigation, or observational task. Described in this paper are the steps taken, at NASA Langley Research Center, during the second and third phases of the development of an IAS, the Traffic Data Manager (TDM), its verification and validation by human-agents, and the foundational development of Human Autonomy Teaming (HAT) between the two.

Houston, Vincent E.↗
NASA NTRSDate not supplied

Collision Avoidance Approach Using Deep Reinforcement Learning

A method to enable autonomous robots moving in a 2D space collision free motivates the purposed approach for collision avoidance for autonomous UAM vehicles. Challenges of autonomous collision free navigation for both problems are similar. Agents in each environment do not know the intent, or goal, of the other. Finding the time efficient paths require some level of anticipation with neighboring agents which is computationally expensive. In the original work, these obstacles were overcome with a novel application of deep reinforcement learning which offloads the online computation to an offline learning algorithm. A value network that encodes the estimated time to the goal given the agent’s state and the observable portion of the other agent’s state is trained on a baseline policy and further refined with reinforcement learning to promote time efficient collision free navigation. Online, the value network efficiently informs the agent’s decision making in the face of uncertainty of the other agent’s next move. In this paper, challenges extending this methodology to the 3D environment of autonomous UAM vehicles with kinematic constraints are discussed and initial results shown.

Collision Avoidance↗
NASA NTRSDate not supplied

Collision Avoidance Approach Using Deep Reinforcement Learning

A method to enable autonomous robots moving in a 2D space collision free motivates the purposed approach for collision avoidance for autonomous UAM vehicles. Challenges of autonomous collision free navigation for both problems are similar. Agents in each environment do not know the intent, or goal, of the other. Finding the time efficient paths require some level of anticipation with neighboring agents which is computationally expensive. In the original work, these obstacles were overcome with a novel application of deep reinforcement learning which offloads the online computation to an offline learning algorithm. A value network that encodes the estimated time to the goal given the agent’s state and the observable portion of the other agent’s state is trained on a baseline policy and further refined with reinforcement learning to promote time efficient collision free navigation. Online, the value network efficiently informs the agent’s decision making in the face of uncertainty of the other agent’s next move. In this paper, challenges extending this methodology to the 3D environment of autonomous UAM vehicles with kinematic constraints are discussed and initial results shown.

Collision Avoidance↗
DOE OSTI2020

Transactive Energy Systems: The Market-based Coordination of Distributed Energy Resources

This paper synthesizes a unifying framework that captures a large class of transactive energy systems studied in the literature. The framework consists of four key elements: agent preferences, control decisions, information structure, and solution concept. Among them, agent preference specifies the preferred outcome by each agent and models the conflict of interest in decision making. Control decision specifies the set of decisions for each agent to maximize its payoff. Information structure describes the information available to each agent before it makes a decision. Solution concept encodes the rationality assumptions of each agent in the system. These elements are important in identifying and distinguishing different transactive energy systems. In this paper, we use them to analyze a number of important ones, including the competitive equilibrium, Stackelberg game, reverse Stackelberg game, and mechanism design. Each class of these problems is formulated as a special case of the proposed transactive energy systems. Their connections and differences are discussed. Available tools and results for each category of problems are surveyed.

Li, Sen↗
DOE OSTI2023

Data exchange and processing synchronization in distributed systems

Systems, methods, techniques and apparatuses of asynchronous communication is distributed systems are disclosed. One exemplary embodiment is a method determining, with a plurality of agent nodes structured to communicate asynchronously in a distributed system, a first set of iterations including an iteration determined by each of the plurality of agent nodes; determining, with a first agent node of the plurality of agent nodes, a local vector clock; receiving, with the first agent node, a first iteration of the first set of iterations and a remote vector clock determined based on the first iteration; updating, with the first agent node, the local vector clock based on the received remote vector clock; and determining a first iteration of a second set of iterations based on the first set of iterations after determining all iterations of the first set of iterations have been received based on the local vector clock.

Cintuglu, Mehmet H.↗
DOE OSTI2020

Novel Foaming Solutions for High Level Waste (HLW) Processing - 20195

Foaming occurs during treatment of high level waste (HLW) at Savannah River Site's Defense Waste Processing Facility (DWPF) in the Chemical Process Cell (CPC) due to high gas generation from process steam and chemical offgas products [1]. The presence of amphiphilic particles in the waste slurry stabilizes the foam [2], [3]. Efficient processing of HLW requires foam control, as foam-overs lead to lower productivity and potential radioactive contamination of condensate streams. DWPF currently employs Siovation Antifoam 747, a surfactant produced by Momentive Performance Materials, as an antifoaming agent during waste treatment. Despite its ability to control foam generation, processing issues have arisen from its use [4]. During DWPF chemical processing, a chemical antifoam agent must be effective at temperatures up to 103 deg. C between pH 3-13. Antifoam 747 is effective at pH 7 but degrades as pH deviates [5]. Antifoam 747 is 90% by weight Momentive{sup TM} L77 and 10% by weight Momentive{sup TM} Y-17580. These ingredients are trisiloxane products, which are especially vulnerable to attack in acidic and caustic environments. Since Antifoam 747 readily degrades, periodic addition is required. Several processing concerns related to Antifoam 747 have affected DWPF operation. Its overuse warranted concern of potential flammable melter offgas under upset conditions due to the generation of carbon dioxide and hydrogen. As a result, a Potential Inadequacy in the Safety Analysis (PISA) was declared [6]. Antifoam contribution has been included in the melter offgas flammability strategy and the quantity of antifoam is monitored to ensure limits are not exceeded. Testing at Savannah River National Laboratory (SRNL) determined that propanal, tri-methylsilanol, and hexamethyldisiloxane are generated as Antifoam 747 degrades, leading to a second PISA [7]. The formation of these byproducts poses additional flammability risks. Furthermore, the methyl and ethyl groups of these compounds may be responsible for the formation of the methyl and ethyl mercury discovered in DWPF [8], [9]. The formation of organomercury has specifically been called out by DOE as a need for increased research. The effectiveness of non-chemical methods for foam control and the efficiency of alternative antifoam agents were evaluated as part of an effort to improve HLW treatment operations at DWPF and to reduce, or eliminate, the flammability hazards associated with Antifoam 747 currently in use. Foaming of high-level waste (HLW) slurries is an issue at the Defense Waste Processing Facility (DWPF) Chemical Process Cell (CPC) which is currently mitigated with a chemical antifoam agent. The effectiveness of non-chemical methods for foam control and the efficiency of alternative antifoams were examined to improve HLW treatment at DWPF and to eliminate the flammability hazards associated with Siovation Antifoam 747 currently in use. Non-chemical foam control methods were deemed unviable for chemical processing at DWPF, as the spray/mist technique requires too much water and space limitations make installation and implementation of headspace agitators unfeasible. The use of ultrasonic energy thickened and further stabilized the foam. Two suitable alternative defoaming agents, Momentive{sup TM} Y- 17112 and Evonik Surfynol{sup R} MD-20, were identified. Both defoamers control foam over a pH range of 4- 13 at boiling, with no flammable degradation products, and only minimal quantities (100-200 mg/kg) are required under glycolic acid conditions. In order to validate the effectiveness of these defoaming agents, SRNL recommends irradiation testing and laboratory scale SRAT/SME experiments with actual radioactive waste. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Investigating uncertainties in human adaptation and their impacts on water scarcity in the Colorado river Basin, United States

The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer’s water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019–2060 simulation period, depending on the paths of farmers’ adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision-making models require further investigation and the parameters with the higher uncertainty reduction potentials. Here, by conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers’ adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling.

Agent-based modeling↗
NASA NTRSDate not supplied

APIS: Honeybee Foraging Task Assignment for Use in Uncertain and Unreliable Environments

Multiagent Cyber-Physical-Human (CPH) systems in realistic environments operate under uncertain conditions. Communication among agents, aimed at reducing the uncertainty, is itself subject to uncertainty. We propose to manage uncertainties in autonomous, long-duration operations of multiagent systems via a modified Honeybee Foraging (HBF) behavioral scheme. The resulting system, Autonomous Persistent Intelligent Swarm (APIS),incorporates two new behaviors to ameliorate informational uncertainty. When “scouting”, agents are tasked based on informational quality and reliability rather than solely on priorities. When “dancing”, agents are tasked to rendezvous with other dancing agents to exchange information at close range, where successful communication is guaranteed. When coupled with uncertainty-aware modeling across agents, these behaviors improve situational awareness and resilience of the system, enabling it to function under more uncertain conditions arising during long-duration missions.

Autonomous systems↗
NASA NTRSDate not supplied

APIS: Honeybee Foraging Task Assignment for Use in Uncertain and Unreliable Environments

Multiagent Cyber-Physical-Human (CPH) systems in realistic environments operate under uncertain conditions. Communication among agents, aimed at reducing the uncertainty, is itself subject to uncertainty. We propose to manage uncertainties in autonomous, long-duration operations of multiagent systems via a modified Honeybee Foraging (HBF) behavioral scheme. The resulting system, Autonomous Persistent Intelligent Swarm (APIS),incorporates two new behaviors to ameliorate informational uncertainty. When “scouting”, agents are tasked based on informational quality and reliability rather than solely on priorities. When “dancing”, agents are tasked to rendezvous with other dancing agents to exchange information at close range, where successful communication is guaranteed. When coupled with uncertainty-aware modeling across agents, these behaviors improve situational awareness and resilience of the system, enabling it to function under more uncertain conditions arising during long-duration missions.

Autonomous systems↗
DOE OSTI2022

Quinoa protein extract: an effective alternative for the fining of wine phenolics

Abstract BACKGROUND Lately, there has been an increasing interest in using plant‐derived proteins for wine phenolic fining. Proteins extracted from cereals, potatoes, and legumes have been proposed as effective fining agents, but only those from pea, wheat, and potatoes have been approved for their use in wine. This work aimed at determining the fining ability of quinoa ( Chenopodium quinoa Willd.) protein extracts (QP), compared to commercial fining agents, on red wines. RESULTS The trials compared the performance of QP (30 and 50 g/hL), two potato protein extracts and gelatin, at two different contact times (48 and 96 h), using Petit Verdot, Malbec, and Cabernet Sauvignon wines. Turbidity, total phenolics, precipitable tannins, catechins, and color characteristics were determined. QP reduced the turbidity of all wines in a similar way to commercial fining agents. Both doses of QP significantly reduced tannins and other phenolic measures, including color intensity reductions, in a similar way to commercial fining agents. CONCLUSION QP behaved as an effective fining agent that deserves further studies in order to improve its performance and advance its characterization. © 2022 Society of Chemical Industry.

Pino‐Ramos, Liudis L.↗
DOE OSTI2023

A Simplified Approach Based on Cellular Automata for Describing Direct Reduced Iron Production in Different Reducing Conditions

A quick computation approach based on cellular automata is developed and implemented to describe the reduction of iron ore pellets by a mixture of reducing agents featured by different H 2 /CO ratios. The evolution of oxygen concentration inside the pellet is followed from the beginning to the end of contact between reducing agent and pellet. The variation of thermal state of pellets and gas mixture is computed based on their initial temperature, considering the heat involved and the convective heat exchange between pellet and gas mixture. The use of cellular automata and finite‐difference method to solve the diffusion equation point out the absence of any diffusion coefficient value, allowing to make the model fit the experimental trial, because the problem is that it is not ruled just by diffusion but also by the concentration variation of reducing agent inside the pellet due to porosity increasing during reduction. The updating of the reducing agents concentration implies a sharp decrease of oxygen concentration that the cellular automata model considers. The developed model is able to provide the in‐line control of reduction process and could be used to adjust the chemical concentration and temperature of injected reducing agents.

Metallurgy & Metallurgical Engineering↗
DOE OSTI2025

Unsupported and carbon-supported silver catalysts for oxygen reduction reaction in alkaline media

Quick and easy Ag catalysts preparation via wet chemical synthesis method using only reducing agent (pure-Ag); reducing agent and citric acid as the capping agent (Ag-CA); and carbon support (KetjenBlack 600J), capping agent, and the reducing agent (Ag/C) is demonstrated. The Ag-based electrocatalysts are characterized by high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) with energy-dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), X-ray diffraction (XRD) analysis, and X-ray photoelectron spectroscopy (XPS). The electrocatalytic activity of Ag catalysts for O 2 reduction reaction (ORR) in 1 M KOH is evaluated using the rotating (ring)-disc electrode method. SEM and HAADF-STEM results show that the unsupported pure-Ag and Ag-CA catalysts consist mainly of big agglomerates, and Ag/C has the smallest agglomerates and some sub-3 nm Ag nanoparticles. The XPS results reveal that Ag in all the catalysts is in the metallic form (Ag 0 ). Despite consisting of big agglomerates, the Ag-CA catalyst exhibits similar ORR electrocatalytic activity to that of Ag/C. Ag-CA (unsupported) shows the lowest hydrogen peroxide yield. These results are of great importance for the development of Ag-based catalysts that can be prepared in a fast, simple and easily up scalable fashion, for anion exchange membrane fuel cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗