Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “agent based”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Spectroscopic Studies of Charge-Transfer Character and Photoresponses of F 4 TCNQ-Based Donor–Acceptor Complexes

F 4 TCNQ (2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane) is used widely as a hole-doping agent in photoresponsive organic semiconducting materials, yet relatively little is known about the photoresponses of the F 4 TCNQ· - anion generated via doping. Furthermore, there is still relatively little systematic exploration of how the properties of the local material or chemical environment impacts the driving force for generating these charge-transfer complexes. Here we present spectroscopic and photophysical studies of F 4 TCNQ in charge-transfer complexes (CTCs) with the electron donor N,N’-Diphenyl-N-N’-di-p-tolylbenzene-1,4-diamine (MPDA) both in dichloroethane solution and polystyrene matrices. Integer charge transfer (ICT) between donor and acceptor occurs readily in dichloroethane solvent to form F 4 TCNQ· - :MPDA + CTCs, due to a ~150 mV difference in MPDA + /MPDA and F 4 TCNQ/F 4 TCNQ· - reduction potentials. Ultrafast spectroscopic studies of the CTC as well as electrochemically generated F 4 TCNQ· - and MDPA + reveal that the photoresponses of these CTCs are dominated by that of the dopant anion, including rapid deactivation (800 fs) after excitation to the anion D 1 excited state, followed by slower (~10 ps) vibrational cooling in the anion D 0 state. Excitation to the higher-lying D 2 state results in a rapid relaxation to the D 1 state, in contrast to direct D 2 -D 0 relaxation previously observed for F 4 TCNQ· - in gas phase. CTCs embedded in polystyrene (PS) matrices are observed to lose their integer charge-transfer character upon evaporation of solvent, as evidenced by changes to electronic and vibrational absorption features associated with F 4 TCNQ· - . This change is attributed to the loss of solvent stabilization of the ion pair formed through the charge-transfer reaction. Ultrafast spectral measurements reveal that the photoresponses of the partial charge-transfer (PCT) species embedded in PS are still highly similar to those of the ICT species and unlike that of neutral F 4 TCNQ, implying the electronic properties of the PCT state are likewise dominated by properties of the reduced acceptor molecule. We conclude that excitation of ICT or PCT states introduces optical losses for photoresponses of doped organic semiconductor materials due to the large anion absorption cross section and its rapid, dissipative deactivation dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Decentralised demand response market model based on reinforcement learning

A new decentralised demand response (DR) model relying on bi‐directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters defined by a reinforcement learning algorithm called Q‐learning. The bids are sent to a local DR market, which is responsible for communicating all bids to the wholesale market and the system operator (SO), reporting to the customers after determining the local DR market clearing price. From local markets’ viewpoint, the goal is to maximise social welfare. Four DR levels are considered to evaluate the effect of different DR portions in the cost of the electricity purchase. The outcomes are compared with the ones achieved from a centralised approach (aggregation‐based model) as well as an uncontrolled method. Numerical studies prove that the proposed decentralised model remarkably drops the electricity cost compare to the uncontrolled method, being nearly as optimal as a centralised approach.

Shafie‐Khah, Miadreza↗
DOE OSTI2020

Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems

This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (DRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions in the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DRL environment. The proposed method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.

41 EE - Solar Energy Technologies Office (EE-4S)↗
NASA NTRS1989

Microcomputer-based tests for repeated-measures: Metric properties and predictive validities

A menu of psychomotor and mental acuity tests were refined. Field applications of such a battery are, for example, a study of the effects of toxic agents or exotic environments on performance readiness, or the determination of fitness for duty. The key requirement of these tasks is that they be suitable for repeated-measures applications, and so questions of stability and reliability are a continuing, central focus of this work. After the initial (practice) session, seven replications of 14 microcomputer-based performance tests (32 measures) were completed by 37 subjects. Each test in the battery had previously been shown to stabilize in less than five 90-second administrations and to possess retest reliabilities greater than r = 0.707 for three minutes of testing. However, all the tests had never been administered together as a battery and they had never been self-administered. In order to provide predictive validity for intelligence measurement, the Wechsler Adult Intelligence Scale-Revised and the Wonderlic Personnel Test were obtained on the same subjects.

Kennedy, Robert S.↗
DOE OSTI2026

Optimal CO 2 storage management considering safety constraints in multi-stakeholder multi-site GCS projects: A Markov game perspective

Geological carbon storage (GCS) projects could involve a diverse array of stakeholders or players from public, private, and regulatory sectors, each with different objectives and responsibilities. Given the complexity, scale, and long-term nature of GCS operations, determining whether individual stakeholders can independently optimize their interests — or whether collaborative coalition agreements are needed — remains a central question for effective GCS project planning and management. To access large, high-quality storage resources, future GCS deployment may increasingly occur in geologically connected sites, where shared geological features such as pressure space and reservoir pore capacity can lead to competitive behavior among stakeholders. In this work, we propose a paradigm based on Markov games to quantitatively investigate how different coalition structures affect the goals of stakeholders. We frame this multi-stakeholder multi-site problem as a multi-agent reinforcement learning problem with safety constraints. Our approach enables agents to learn optimal strategies while complying with safety regulations. We present an example where multiple operators are injecting CO 2 into their respective project areas in a geologically connected basin. To address the high computational cost of repeated simulations of high fidelity models, a previously developed surrogate model based on the Embed-to-Control (E2C) framework is employed. Our results demonstrate the effectiveness of the proposed framework in addressing optimal management of CO 2 storage when multiple stakeholders with different objectives and goals are involved.

58 GEOSCIENCES↗
NASA NTRS2001

Human Centered Autonomous and Assistant Systems Testbed for Exploration Operations

The Engineering and Mission Operations Directorates at NASA Johnson Space Center are combining laboratories and expertise to establish the Human Centered Autonomous and Assistant Systems Testbed for Exploration Operations. This is a testbed for human centered design, development and evaluation of intelligent autonomous and assistant systems that will be needed for human exploration and development of space. This project will improve human-centered analysis, design and evaluation methods for developing intelligent software. This software will support human-machine cognitive and collaborative activities in future interplanetary work environments where distributed computer and human agents cooperate. We are developing and evaluating prototype intelligent systems for distributed multi-agent mixed-initiative operations. The primary target domain is control of life support systems in a planetary base. Technical approaches will be evaluated for use during extended manned tests in the target domain, the Bioregenerative Advanced Life Support Systems Test Complex (BIO-Plex). A spinoff target domain is the International Space Station (ISS) Mission Control Center (MCC). Prodl}cts of this project include human-centered intelligent software technology, innovative human interface designs, and human-centered software development processes, methods and products. The testbed uses adjustable autonomy software and life support systems simulation models from the Adjustable Autonomy Testbed, to represent operations on the remote planet. Ground operations prototypes and concepts will be evaluated in the Exploration Planning and Operations Center (ExPOC) and Jupiter Facility.

Malin, Jane T.↗
DOE OSTI2020

A Distributed Reinforcement Learning Yaw Control Approach for Wind Farm Energy Capture Maximization: Preprint

In this paper, we present a reinforcement-learning based distributed approach to wind farm energy capture maximization using yaw control, also known as wake steering. In order to maximize the power output of a wind farm, it is often necessary for individual turbines to decrease their own power output through yaw misalignment so as to deflect their wakes away from downstream turbines. Although using model-based methods to achieve yaw misalignment is one option, a model-free method might be better suited to incorporate factors that are difficult to model or changing conditions. We propose an algorithm that adapts concepts of temporal difference reinforcement learning distributed to a multi-agent environment that allows individual turbines to act so as to optimize overall wind farm output and react to unforeseen disturbances.

controls↗
NASA NTRS2006

Screening and Identification of Cryopreservative Agents for Human Cellular Biotechnology Experiments in Microgravity

Dimethyl sulfoxide (DMSO) has been used as a standard cryopreservative agent for mammalian cell culture; however, prolonged exposure of thawed cells to DMSO can alter cell growth. While DMSO is easily eliminated in ground-based experiments, removal of DMSO in flight-based experiments is more difficult due to various on-orbit constraints. Failure of cryopreservation is due to a number of factors, including intracellular ice formation, solute effect, and apoptotic cell death following thawing. One objective of this study is to identify and characterize an alternative cryopreservative that could be used on the International Space Station (ISS). We systematically screened for potential permeating and non-permeating agents using a human colorectal carcinoma cell line, MIP-101. Cells were suspended in cryopreservation solution and frozen either following a two-step procedure involving initial cooling at -1 C/min overnight followed by storage in liquid nitrogen (LN2) vapor, or by freezing cells directly in the LN2 vapor phase at -10 C/min. Ability to preserve cellular function after one cycle of freeze-thawing was assessed by the recovery of viable cells in short and long-term cell culture experiments. Results showed that permeating preservatives glycerol (G) and ethylene glycol (EG) had an efficacy (80-110%) comparable to, if not better than, 7.5% DMSO; but, propylene glycol (PG) had a somewhat lesser efficacy. Among the non-permeating preservatives, trehalose, raffinose, and dextran exhibited significant protective effect (50-80%) relative to that offered by 7.5% DMSO, but at -10 C and not at -1 C/min cooling rate. Preliminary data thus suggest that a combination of permeating and non-permeating agents may have improved efficacy as a cryoprotectant and serve as an alternate to DMSO for experimentation on ISS.

Love,J.↗
DOE OSTI2020

From cellulose to 1,2,4-benzenetriol via catalytic degradation over a wood-based activated carbon catalyst

In this study, spherical P-doped activated carbon (CSP) was obtained from poplar bark by microwave carbonization with phosphoric acid as the dopant and activating agent. Phosphorus bonded with four oxygen atoms to form a phosphate-like structure rather than directly bonding with C atoms in CSP; such groups can provide Lewis acid active sites with moderately strong acidity. The Lewis acid and total acid content increased with increasing phosphoric acid doping ratio. The use of phosphoric acid as an activating agent promotes the formation of a developed porous structure. CSP 1 (mass ratio of raw material to acid = 1 : 1) had a specific surface area of 1115 m 2 g –1 with developed micro–mesoporous structures, mainly micropores. Investigation of the activity of CSP in catalytic cellulose hydrolysis during hydrothermal degradation showed that Brønsted acid sites promote hydrolysis and dehydration of cellulose, Lewis acid sites promote subsequent reactions such as isomerization, rearrangement, and oligomerization, and the developed pores can provide reaction sites and facilitate reactant transportation. The optimal reaction conditions, i.e. , 1 h at 230 °C and 1 MPa with CSP 1 (0.4 g) as the catalyst, gave a cellulose conversion of >99.9%, with 42.30% selectivity for 1,2,4-benzenetriol. This study provides an effective method for preparing 1,2,4-benzenetriol from cellulose with biomass-based activated carbon as the catalyst, which enables green synthesis of 1,2,4-benzenetriol and promotes the use of biomass waste resources.

1,2,4-benzenetriol↗
NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight methods is paramount for establishing an effective architecture for autonomous systems. A fundamental objective of the ATTRACTOR (Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability) project was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation environment for test and evaluation of autonomous systems. The Autonomous Entity Operations Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single- and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. Together AEON and BEAM enable sim-to-flight with minimal configuration changes. By using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the lab and in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗
NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight methods is paramount for establishing an effective architecture for autonomous systems. A fundamental objective of the ATTRACTOR (Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability) project was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation environment for test and evaluation of autonomous systems. The Autonomous Entity Operations Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single- and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. Together AEON and BEAM enable sim-to-flight with minimal configuration changes. By using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the lab and in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗
DOE OSTI2024

Platform for Integrated Land use And Transportation Experiments and Simulation (PILATES) v1.0

PILATES allows for flexibly and at-scale coupling of multiple models to allow for multi-scale and multi-resolution simulation of regional-scale transport networks. In particular, it couples the MATSim-derived transportation modeling framework for Behavior, Energy, Autonomy and Mobility (BEAM) with other models operating at different time scales. Rather than tightly coupling supply and demand models using shared agents and memory within the same software process, PILATES orchestrates different model runs in a containerized framework. This structure requires passing information from the demand models to BEAM in the format of a synthetic population and agent plans, and from BEAM to the demand models in terms or origin/destination tables (also known as "skims"). This allows it to take advantage of the behavioral sophistication of existing activity-based models as well as the reinforcement learning structure of MATSim replanning and adopted by BEAM, in a way that requires minimal changes to existing models. It also takes advantage of the computational performance of BEAM, which allows for simulations with millions of agents to complete in reasonable time as well as allowing for detailed mechanistic simulation of the operation of on-demand modes.

Needell, Zachary↗
DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗
NASA NTRS1980

Spacecraft surgical scrub system

Ease of handling and control in zero gravity and minimizing the quantity of water required were prime considerations. The program tasks include the selection of biocidal agent from among the variety used for surgical scrub, formulation of a dispensing system, test, and delivery of flight dispensers. The choice of an iodophore was based on effectiveness on single applications, general familiarity among surgeons, and previous qualification for space use. The delivery system was a choice between the squeeze foamer system and impregnated polyurethane foam pads. The impregnated foam pad was recommended because it is a simpler system since the squeeze foamer requires some applicator to effectively clean the skin surfaces, whereas the form pad is the applicator and agent combined. Testing demonstrated that both systems are effective for use as surgical scrubs.

Abbate, M.↗
DOE OSTI2022

Unique W-Shape Y6 isomer as effective solid additive for High-Performance PM6:Y6 polymer solar cells

The current top-performing polymer solar cell (PSC) systems are mostly based on PM6:Y6 host blend. To date, numerous materials have been explored as the third component for these systems to form ternary blends or as additives. Vitrification agents are a group of additives proved to be useful in affecting the morphology of organic semiconductors. Here to design a suitable vitrification agent for non-fullerene electron acceptor Y6, an isomer strategy was explored where the thienothiophene wings of the Y6 molecule were inversed to form a W-shaped Y6 isomer of i-Y6. It was found that i-Y6 crystallized poorly with a different packing style than Y6 and could blend well in amorphous phase of Y6. These properties enabled i-Y6 to finely tune the morphology of PM6:Y6 blend at low additive dosages. The power conversion efficiency (PCE) of PM6:Y6 based PSC was raised from 16.827% to 17.433% at a dosage of Y6:i-Y6 ratio of 24:1. This work demonstrated isomerization as a viable strategy for developing solid additives for high performance PSC blends and vitrification agents as effective additives for tuning morphology and improving performances of PSCs.

36 MATERIALS SCIENCE↗
NASA NTRS1978

Growth of single crystals by vapor transport in zero-gravity environment, ground-based experiments

Mass and heat transfer phenomena associated with the growth of single crystals by chemical vapor transport reactions were investigated. In this technique, a gaseous transport agent reacts with the solid source material to form exclusively gaseous products. The gas phase species migrate from the source to the condensation zone of the closed reaction ampoule where the reverse reaction occurs with formation of single crystals. The necessary concentration gradient is achieved by means of a temperature gradient.

Wiedemeier, H.↗
NASA NTRS1992

Toward Agent Programs with Circuit Semantics

New ideas are presented for computing and organizing actions for autonomous agents in dynamic environments-environments in which the agent's current situation cannot always be accurately discerned and in which the effects of actions cannot always be reliably predicted. The notion of 'circuit semantics' for programs based on 'teleo-reactive trees' is introduced. Program execution builds a combinational circuit which receives sensory inputs and controls actions. These formalisms embody a high degree of inherent conditionality and thus yield programs that are suitably reactive to their environments. At the same time, the actions computed by the programs are guided by the overall goals of the agent. The paper also speculates about how programs using these ideas could be automatically generated by artificial intelligence planning systems and adapted by learning methods.

ELECTRICAL CIRCUITS↗