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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Agent-based simulations of shared automated vehicle operations: reflecting travel-party size, season and day-of-week demand variations

Here, this paper explores the effects of day of week and season of year demand variations for shared rides, along with realistic travel party sizes, on shared autonomous vehicle (SAV) services across the Austin, Texas region. Using the agent-based POLARIS program, synthetic person-trips that reflect travel-party size (from one to four persons) and demand variations over days and months, as evident in the National Household Travel Survey data were simulated in each scenario over a 24 h travel day. Results show that realistic party sizes can bring considerable changes to SAV fleet performance, including up to 8.5% higher service rates (number of requests accepted within 15 min), 5 min shorter journey times (wait time + travel time), 28% higher vehicle occupancies on weekends, and roughly 4% lower empty fleet VMT. Weekend travel is most impacted by season of year, with weekday travel patterns looking more uniform (thanks to work and school trips). Various performance metrics for the Austin network, like total and empty VMT, change by up to 30% when considering realistic variations in party size and time of year. This paper underscores the value of recognizing day-to-day and month-to-month variations in travel demand, and the importance of agent-based model equations to reflect travel-party size. Such realism can help quantify SAV seat occupancies more accurately, highlighting the importance of shared mobility. However, it also creates demand and supply issues for operators that now need more information on party size to manage dynamic ride-sharing, or those that may wish to shift their fleet vehicles to other regions for special events to protect profits while offering reasonable wait times to customers throughout the year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Extraction of chloride acido complexes of triply charged metal cations in water–oxyethylated nonylphenol–salting-out agent systems

The characteristic features of extraction of chloride acido complexes of iron(III), thallium(III), indium, and gallium in water–oxyethylated nonylphenol (neonol AF-9-12)–salting-out agent systems are studied. The role of temperature, nature of the salting-out agent, and its salting-out ability is shown.

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

An agent-based approach to study the diffusion rate and the effect of policies on joint placement of photovoltaic panels and green roof under climate change uncertainty

As two of the highest trending green technologies, photovoltaic panels and green roofs are proven to be effective practices for energy generation and energy saving. The achievable impact from the widespread installation of such technologies is, however, not clearly established. This is mainly because the degree of this impact highly depends on the inherently uncertain environmental and climate factors, as well as the unknown adoption rates of these technologies, which in turn depend on different characteristics of decision makers and interactions among them. To that end, this study aims to investigate the diffusion rate of these green technologies under uncertainties caused by climate change, characteristics of adopters, and their interactions. An integrated framework is developed to capture the interplay between financial and attitudinal aspects, as well as the uncertainties due to both the stochastic nature of system parameters and the interactions among agents involving human beings. Specifically, this framework consists of a integer programming model to optimize the green roof and/or photovoltaic panel installation settings for a given building under climate change uncertainty, and an agent-based model to factor in the role of human behavior and interactions. A case study for the city of Knoxville, TN, is presented to evaluate the effects of different policies on the diffusion rate of the green technologies of interest. The results show that the affordability of green technologies and public awareness are the key drivers of the adoption of these technologies, which highlight the important role of the decision makers in impacting the diffusion rate.

14 SOLAR ENERGY↗
DOE OSTI2022

Introduction to the special section on learning and security for multi-agent systems

This report features six articles that provide an overview of the emerging research topics at the intersection learning, security, and multi-agent systems. Recent years have witnessed a surge in the number of works at their intersections, and they have appeared in system and control communities as well as many other communities in artificial intelligence, cyber–physical systems, and economics. The articles in this special section give accessible and comprehensive tutorials and surveys for a broad systems and control audience, covering topics including adversarial machine learning, multi-agent reinforcement learning, cyber resilience, resilient control systems, and game design. It is hopeful that this special section will spawn future interest and cross-disciplinary collaborations in this emerging transdisciplinary research area.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Designing a transactive electric vehicle agent with customer’s participation preference

The proliferation of electric vehicles (EVs) and their inherent flexibility in charging timings make them an asset to improve grid performance. In contrast to direct control by a utility or autonomous price-based charging, the transactive control framework not only provides benefits to both grid and customers but also ensures customer autonomy. In this work, we design a transactive electric vehicle (TEV) agent that incorporates the EV owner’s willingness to trade-off between savings and amenity in form of a slider, where the EV owner’s amenity is characterized as vehicle readiness. Further, a privacy-preserving bidding formulation is proposed that also represents the customer’s transactive preference. A transactive market mechanism is discussed that integrates the TEV Agents into the local retail market and reconciles with the current day-ahead and real-time market structure. It is demonstrated that the proposed slider is able to provide a preferred trade-off between savings and amenity to individual customers. At the same time, the market mechanism is shown to successfully reduce both peak prices and peak demand. A comparative investigation of V1G and V2G technologies with respect to the battery prices is also discussed. It reveals that the V2G does not offer significant additional benefits with current battery prices, but could be promising if battery costs decline in the future.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Evaluation of various carbon blacks and dispersing agents for use in the preparation of uranium microspheres with carbon

A comparison study on carbon blacks and dispersing agents was performed to determine their impacts on the final properties of uranium fuel kernels with carbon. The main target compositions in this internal gelation study were 10 and 20 mol % uranium dicarbide (UC 2 ), which is UC 1.86 , with the balance uranium dioxide. After heat treatment at 1900 K in flowing carbon monoxide in argon for 12 h, the density of the kernels produced using a X-energy proprietary carbon suspension, which is commercially available, ranged from 96% to 100% of theoretical density (TD), with full conversion of UC to UC 2 at both carbon concentrations. However, higher carbon concentrations such as a 2.5 mol ratio of carbon to uranium in the feed solutions failed to produce gel spheres with the proprietary carbon suspension. The kernels using our former baseline of Mogul L carbon black and Tamol SN were 90–92% of TD with full conversion of UC to UC 2 at a variety of carbon levels. Raven 5000 carbon black and Tamol SN were used to produce 10 mol % UC2 kernels with 95% of TD. However, an increase in the Raven 5000 concentration led to a kernel density below 90% of TD. Raven 3500 carbon black and Tamol SN were used to make very dense kernels without complete conversion to UC 2 . Lastly, the selection of the carbon black and dispersing agent is highly dependent on the desired final properties of the target kernels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

An agent-based modeling tool supporting bioenergy and bio-product community communication regarding cellulosic bioeconomy development

The emergence of the cellulosic bioeconomy requires not only adequate technological, economic, and policy advances, but also effective communication and coordination among the multiple stakeholders in the bioeconomy community. Aiming to facilitate stakeholder communication and collaboration for cellulosic bioeconomy growth, an agent-based model is developed as a community communication tool in this study. The tool simulates the behaviors and interactions of key stakeholders, as well as the responses and interactions between the stakeholders and biophysical, socioeconomic, and environmental conditions. The agent behavior rules are developed and validated based on behavior and economic theories and information attained from a stakeholder focus group study and a land use survey. A pilot study in a Central Illinois watershed shows stakeholder synergies, i.e., positive feedback loops across multiple stakeholders. The modeling tool's role in facilitating community communication is demonstrated via the focus group study. A comparison of policy scenarios suggests that the cellulosic bioeconomy development is fastest under a portfolio of policies combining the views of the multiple stakeholders. Critically, subsidy for small-scale bio-facilities is essential as it rapidly initializes an early market for cellulosic feedstocks and the adoption of Miscanthus. As a result, the communication tool is applied to bringing stakeholders and researchers together to develop shared visions, assess barriers, and explore solution portfolios to initialize and speed up the regional cellulosic bioeconomy.

09 BIOMASS FUELS↗
DOE OSTI2011

Magnetic Electrochemical Sensing Platform for Biomonitoring of Exposure to Organophosphorus Pesticides and Nerve Agents Based on Simultaneous Measurement of Total Enzyme Amount and Enzyme Activity

We report a new approach for electrochemical quantification of enzymatic inhibition and phosphorylation for biomonitoring of exposure to organophosphorus (OP) pesticides and nerve agents based on a magnetic beads (MBs) immunosensing platform. The principle of this approach is based on the combination of MBs immuno-capture based enzyme activity assay and competitive immunoassay of total amount of enzyme for simultaneous detection of enzyme inhibition and phosphorylation in biological fluids. Butyrylcholinesterase (BChE) was chosen as a model enzyme. In competitive immunoassay, the target total BChE in a sample (mixture of OP-inhibited BChE and active BChE) competes with the BChE modified on the MBs to bind to the limited anti-BChE antibody labeled with quantum dots (QDs-anti-BChE), and followed by electrochemical stripping analysis of the bound QDs conjugate on the MBs. This assay shows a linear response over the total BChE concentration range of 0.1~20 nM. Simultaneously, real time BChE activity was measured on an electrochemical carbon nanotube-based sensor coupled with microflow injection system after immuno-capture by MBs-anti-BChE conjugate. Therefore, the formed phosphorylated adduct (OP-BChE) can be estimated by the difference values of the total amount BChE (including active and OP-inhibited) and active BChE from established calibration curves. This approach not only eliminates the difficulty in screening of low-dose OP exposure (less than 20% inhibition of BChE) because of individual variation of BChE values, but also avoids the drawback of the scarce availability of OP-BChE antibody. It is sensitive enough to detect 0.5 nM OP-BChE, which is less than 2% BChE inhibition. This method offers a new method for rapid, accurate, selective and inexpensive quantification of phosphorylated adducts and enzyme inhibition for biomonitoring of OP and nerve agent exposures.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Capture and Decomposition of the Nerve Agent Simulant, DMCP, Using the Zeolitic Imidazolate Framework (ZIF-8)

Understanding mechanisms of decontamination of chemical warfare agents (CWA) is an area of intense research aimed at developing new filtration materials to protect soldiers and civilians in case of state-sponsored or terrorist attack. In this study, we employed complementary structural, chemical, and dynamic probes and in situ data collection, to elucidate the complex chemistry, capture, and decomposition of the CWA simulant, dimethyl chlorophosphonate (DMCP). Our work reveals key details of the reactive adsorption of DMCP and demonstrates the versatility of zeolitic imidazolate framework (ZIF-8) as a plausible material for CWA capture and decomposition. Here, the in situ synchrotron-based powder X-ray diffraction (PXRD) and pair distribution function (PDF) studies, combined with Fourier transform infrared (FT-IR) spectroscopy, X-ray photoelectron spectroscopy (XPS), zinc K-edge X-ray absorption near edge structure (XANES), and Raman spectroscopies, showed that the unique structure, chemical state, and topology of ZIF-8 enable accessibility, adsorption, and hydrolysis of DMCP into the pores and revealed the importance of linker chemistry and Zn 2+ sites for nerve agent decomposition. DMCP decontamination and decomposition product(s) formation were observed by thermogravimetric analysis, FT-IR spectroscopy, and phosphorus (P) K-edge XANES studies. Differential PDF analysis indicated that the average structure of ZIF-8 (at the 30 Å scale) remains unchanged after DMCP dosing and provided information on the dynamics of interactions of DMCP with the ZIF-8 framework. Using in situ PXRD and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), we showed that nearly 90% regeneration of the ZIF-8 structure and complete liberation of DMCP and decomposition products occur upon heating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

X-ray scatter imaging of hepatocellular carcinoma in a mouse model using nanoparticle contrast agents

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide and is almost uniformly fatal. Current methods of detection include ultrasound examination and imaging by CT scan or MRI; however, these techniques are problematic in terms of sensitivity and specificity, and the detection of early tumors (<1 cm diameter) has proven elusive. Better, more specific, and more sensitive detection methods are therefore urgently needed. Here we discuss the application of a newly developed x-ray imaging technique called Spatial Frequency Heterodyne Imaging (SFHI) for the early detection of HCC. SFHI uses x-rays scattered by an object to form an image and is more sensitive than conventional absorption-based x-radiography. We show that tissues labeled in vivo with gold nanoparticle contrast agents can be detected using SFHI. We also demonstrate that directed targeting and SFHI of HCC tumors in a mouse model is possible through the use of HCC-specific antibodies. As a result, the enhanced sensitivity of SFHI relative to currently available techniques enables the x-ray imaging of tumors that are just a few millimeters in diameter and substantially reduces the amount of nanoparticle contrast agent required for intravenous injection relative to absorption-based x-ray imaging.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Laser-induced chemical liquid phase deposition of copper from aqueous solutions without reducing agents

Laser-induced chemical liquid phase deposition of copper without a traditional reducing agent has been used for the first time to obtain conductive patterns on a dielectric surface having a reducing ability. It is shown that phenol-formaldehyde binder of the dielectric (glass fibre) can successfully play the role of a reducing agent in this process. The resulting copper sediments have low electrical resistance and good topology. (interaction of laser radiation with matter. laser plasmas)

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Towards philosophical reasoning with agentic LLMs: Socratic method for scientific assistance

As large language models (LLMs) become central tools in science, improving their reasoning capabilities is critical for meaningful and trustworthy applications. We introduce a Socratic agent for scientific reasoning, implemented through a structured system prompt that guides LLMs via classical principles of inquiry. Unlike typical prompt engineering or retrieval-based methods, our approach leverages definition, analogy, hypothesis elimination, and other Socratic techniques to generate more coherent, critical, and domain-aware responses. We evaluate the agent across diverse scientific domains and benchmark it on the abstraction and reasoning corpus challenge dataset, achieving 97.15% under a fixed prompting protocol and without fine-tuning or external tools. Expert evaluation shows improved reasoning depth, clarity, and adaptability over conventional LLM outputs, suggesting that structured prompting rooted in philosophical reasoning can improve the scientific utility of language models.

LLM reasoning↗
DOE OSTI2011

Using Swarming Agents for Scalable Security in Large Network Environments

The difficulty of securing computer infrastructures increases as they grow in size and complexity. Network-based security solutions such as IDS and firewalls cannot scale because of exponentially increasing computational costs inherent in detecting the rapidly growing number of threat signatures. Hostbased solutions like virus scanners and IDS suffer similar issues, and these are compounded when enterprises try to monitor these in a centralized manner. Swarm-based autonomous agent systems like digital ants and artificial immune systems can provide a scalable security solution for large network environments. The digital ants approach offers a biologically inspired design where each ant in the virtual colony can detect atoms of evidence that may help identify a possible threat. By assembling the atomic evidences from different ant types the colony may detect the threat. This decentralized approach can require, on average, fewer computational resources than traditional centralized solutions; however there are limits to its scalability. This paper describes how dividing a large infrastructure into smaller managed enclaves allows the digital ant framework to effectively operate in larger environments. Experimental results will show that using smaller enclaves allows for more consistent distribution of agents and results in faster response times.

agents↗
DOE OSTI2022

Multi-Agent Deep Reinforcement Learning for Realistic Distribution System Voltage Control Using PV Inverters

Over the last few decades, the deployment of distributed solar photovoltaic (PV) systems has increased consistently. High PV penetration could cause adverse effects on the grid, such as voltage violations. This paper proposes a new distributed soft actor-critic based multi-agent deep reinforcement learning (SAC-MADRL) control solution to minimize the PV real power curtailment while keeping the grid voltage in an acceptable range. New reward functions have been designed to coordinate different agents during the learning process, yielding improved convergence. Comparison results with other control methods on a real feeder in western Colorado U.S. with 80% penetration of PVs demonstrate that the proposed method has better capability of effectively regulating voltage while minimizing the PV real power curtailment.

distribution system↗
DOE OSTI2023

Multi-Agent Reinforcement Learning for Distribution System Critical Load Restoration

Grid resilience has become a critical topic recently because of the increasing occurrence of extreme events and the growing integration of intermittent renewable energy sources. To build a resilient distribution system, this paper develops a multiagent reinforcement learning-based (MARL) method to coordinate distribution energy resources (DERs) dispatch, load pickup, and network reconfiguration for load restoration after a system outage. With the help of two types of control agents, namely critical load restoration (CLR) and coordination (COR) agents, system loads can be restored efficiently, given available resources. The effectiveness and superiority of the proposed algorithm are demonstrated through simulations and comparative studies on a real distribution feeder in Western Colorado.

distribution system↗
DOE OSTI2021

Unbundling Smart Meter Services Through Spatio-Temporal Decomposition Agents in DER-rich Environment

Smart meters and the advanced metering infrastructure (AMI) facilitate distribution system operators (DSOs) to gather information on energy consumption at the customer level. With the increasing penetration of building-level intermittent distributed energy resources (DERs) behind the meter, DER information is not available to DSOs. At the same time, smart meter enables users to participate in grid, with real-time information. Information for behind the meter is needed by user to coordinate building level assets for maximum benefits. The concept of unbundled smart meter (USM) needs agents to decompose smart meter measurements to provide service to DSO as well as customers. In this paper, we propose a Spatio-Temporal Decomposition Agent (STDA) for USM based on Artificial Intelligence (AI). STDA can help users optimize their energy usage, help DSO to utilize building assets for the grid operation. The energy usage strategy developed by STDA is suitable for different users, and can be customized by deep learning (DL) models according to the different energy consumption habits of each user. The power prediction performance results of various DL models and evaluation using a set of data from a Hawaii utility is presented. Furthermore, STDA integration with Home Energy management Systems (HEMS) to manage resources is presented and validated. STDA pre-processes the measurements before model training, and provides the spatio-temporal decomposed forecasting.

42 ENGINEERING↗
DOE OSTI2020

Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids

This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Lastly, numerical experiments have been devised to verify the performance of the proposed method.

, policy gradient↗
DOE OSTI2021

Additive Manufacturing of Porous Ceramics With Foaming Agent

The macro-porous ceramics has promising durability and thermal insulation performance. As porous ceramics find more and more applications across many industries, a cost-effective and scalable additive manufacturing technique for fabricating macro-porous ceramics is highly desirable. Herein, we reported a facile additive manufacturing approach to fabricate porous ceramics and control the printed porosity. Several printable ceramic inks were prepared, and the foaming agent was added to generate gaseous bubbles in the ink, followed by the direct ink writing and the ambient-pressure and room-temperature drying to create the three-dimensional geometries. In this work, a set of experimental studies were performed to optimize the printing quality. The results revealed the optimal process parameters for printing the foamed ceramic ink with a high spatial resolution and fine surface quality. Varying the concentration of the foaming agent enables the controllability of the structural porosity. The maximum porosity can reach 85%, with a crack-free internal porous structure. The tensile tests showed that the printed macro-porous ceramics possessed enhanced durability with the addition of fiber. With a high-fidelity three-dimensional (3D) printing process and the precise controllability of the porosity, we showed that the printed samples exhibited a remarkably low thermal conductivity and durable mechanical strength.

36 MATERIALS SCIENCE↗