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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Environmental associations of Ophidiomyces ophidiicola , the causative agent of ophidiomycosis in snakes

Emerging pathogenic fungi have become a topic of conservation concern due to declines observed in several host taxa. One emerging fungal pathogen, Ophidiomyces ophidiicola, is well documented as the causative agent of ophidiomycosis, otherwise known as snake fungal disease (SFD). O. ophidiicola has been found to cause disease in a variety of snake species across the United States, including the eastern massasauga (Sistrurus catenatus), a federally threatened rattlesnake species. Most work to date has involved detecting O. ophidiicola for diagnosis of infection through direct sampling of snakes, and attempts to detect O. ophidiicola in the abiotic environment to better understand its distribution, seasonality, and habitat associations are lacking. We collected topsoil and groundwater samples from four macrohabitat types across multiple seasons in northern Michigan at a site where Ophidiomyces infection has been confirmed in eastern massasauga. Using a quantitative PCR (qPCR) assay developed for O. ophidiicola, we detected Ophidiomyces DNA in topsoil but observed minimal to no detection in groundwater samples. Detection frequency did not differ between habitats, but samples grouped seasonally showed higher detection during mid-summer. We found no relationships of detection with hypothesized environmental correlates such as soil pH, temperature, or moisture content. Furthermore, the distribution of Ophidiomyces positive samples across the site was not linked to estimated space use of massasaugas. Our data suggests that season has some effect on the presence of Ophidiomyces. Differences in presence between habitats may exist but are likely more dependent on the time of sampling and currently uninvestigated soil or biotic parameters. These findings build on our understanding of Ophidiomyces ecology and epidemiology to help inform where and when snakes may be exposed to the fungus in the environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Calibration verification for stochastic agent-based disease spread models

Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a stand-alone process evaluating the calibration procedure) and instead use overall model validation (a process comparing calibrated model results to data) to check calibration processes, which may conceal errors in calibration. In this work, we develop a stochastic agent-based disease spread model to act as a testing environment as we test two calibration methods using simulation-based calibration, which is a synthetic data calibration verification method. The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). Simulation-based calibration suggests that there are challenges with the empirical likelihood calculation used in the first calibration method in this context. These issues are alleviated in the ABC approach. Despite these challenges, we note that the first calibration method performs well in a synthetic data model validation test similar to those common in disease spread modeling literature. We conclude that stand-alone calibration verification using synthetic data may benefit epidemiological researchers in identifying model calibration challenges that may be difficult to identify with other commonly used model validation techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

SEDONA SpEctroscopic Detection Of Nerve Agents

Although the threat of a liquid explosive attack on a passenger aircraft has been mitigated by limiting the size of carry-on bottles, highly potent chemicals (e.g., nerve agents) are a very serious threat. A single travel bottle full of such chemicals would cause great harm to many people and create a national security incident disrupting civilian air travel. Current screening methods to detect highly potent chemicals require opening a closed bottle. Experts at Los Alamos National Laboratory have developed SEDONA, a low magnetic field nuclear magnetic resonance (NMR) to detect chemical threats through unopened bottles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Autonomous reinforcement learning agents for improving predictions and observations of extreme climate events

Primary Focus Area: This proposal addresses focus area 2, “Predictive modeling through the use of AI techniques.” Science Challenge: Extreme climate events associated with severe weather, coastal and inland flooding, droughts, heat waves and wildfires are expected to increase in frequency and severity in the future. Due to the complexity and chaotic behavior of the climate system, accurately predicting and observing extreme climate events requires a tremendous amount of human intervention to run predictive climate simulations and deploy measurement systems. Extreme events often unfold very quickly, leaving little time to iterate on simulations or re-position instruments. Through reinforcement learning, autonomous AI agents can be designed to make real-time decisions to characterize extreme climate events more efficiently through adaptive models and targeted observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Next Generation Chemical Agent Detection Architecture [Poster]

Sandia National Labs (SNL)-designed, portable chemical warfare agent (CWA) detection systems consist of three-stages: collection, separation, and detection. We use microfabrication technologies to miniaturize these stages and to reduce the overall size, weight, power, and (potentially) cost of the final system. Our newest system consists of a multi-dimensional separation stage and an miniature ion mobility spectrometer (IMS) detector for unprecedented system sensitivity, selectivity, and depth of target list.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Statistical Learning for Nonlinear Model Reduction from Local Simulations of Stochastic and Particle- and Agent-Based Systems

Stochastic physical systems across the sciences that have very high-dimensional state spaces, with a large number of fast degrees of freedom that force direct simulators to proceed by integration steps that are orders of magnitude smaller than events of interests (e.g., particle collisions). Examples range from molecular motion to dynamics of large populations of cells. A grand challenge in the simulation and understanding of such systems is the systematic construction of accurate, interpretable, reduced models, enabling faster simulations, revealing fundamental properties of the dynamics, and predicting phenomena of interest that the original simulator could not reached with sufficient accuracy or within a given computational budget. In this projected we developed novel statistical estimation/machine learning techniques for analyzing and building empirical reduced models for important families of high-dimensional stochastic systems, in particular: - we developed techniques for estimating interaction kernels in interacting particle- and agent-based systems, which are ubiquitous in Physics, Biology and many other sciences, given observed trajectories of the system; - we developed techniques for nonlinear model reduction for high-dimensional stochastic systems that have a small number of unknown, nonlinear slow variables, and a large number of fast modes, that are possibly of large magnitude, given observed short trajectories of the system in the form of bursts of trajectories from different initial conditions; - we developed novel techniques for estimating linear dynamical systems on graphs when both the dynamics and the underlying graph are unknown, and we have a sparse set of space-time observations; - we considered the problem of estimating an unknown nonlinear observation function of a standard process (e.g. Brownian motion), so that we can recognized if an observed dynamics is "just" a nonlinear version of a known dynamics; we also developed benchmarks for learning algorithms aimed at learning and classifying diffusion processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Agent-Based Models for COVID-19 [Slides]

Goals: 1) Evaluate "what if” scenarios and mitigation strategies to combat COVID-19 spread within LANL and 2) perform retrospective analysis to assess accuracy of LANL COVID-19 agent-based model (ABM).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Pedestrian-Involved Traffic Signal Optimization Using Decentralized Graph-based Multi-Agent Reinforcement Learning

Incorporating pedestrian movements in traffic signal timing optimization is essential for ensuring smooth and safe urban transportation systems. To develop an effective signal timing plan, it is crucial to comprehend how various pedestrian accommodation strategies impact both vehicle and pedestrian flow. This study explores the application of the Decentralized Graph-based Multi-Agent Reinforcement Learning (DGMARL) method for signal timing optimization. It considers both pedestrian and vehicle traffic state and assesses the effects of fixed and adaptive pedestrian request response on traffic and signal timing. The evaluation of DGMARL encompasses vehicles Eco_PI which is fuel consumption impact related to vehicle stops and stop delay, and pedestrian waiting time to ensure overall system efficiency. The proposed approach was evaluated using a Digital Twin microscopic traffic simulation model of MLK Smart Corridor in Chattanooga, Tennessee. The evaluation outcomes of vehicles Eco_PI, pedestrian waiting time and serving time are compared with the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall to determine the most suitable approaches for various traffic conditions. The results indicated that, on average, the strategy of Signal timing optimization with adaptive pedestrian request response improved the Eco_PI by 32.43%, and Signal timing optimization with both automated pedestrian traffic demand and adaptive pedestrian request response improved the Eco_PI by 31.62% compared to the actuated and DGMARL signal timing with fixed pedestrian recall and minimum recall.

K Kumarasamy, Vijayalakshmi↗
DOE OSTI2024

Organizational Resilience in the Context of Information Quality: An Agent-Based Simulation of Structural and Cognitive Influences

This study examines how organizational structure and stress levels affect decision-making with poor quality information. Using an agent-based model, it finds loosely structured organizations are timely but less effective at filtering bad information, while tightly structured ones are slower but better at filtering. The research highlights a trade-off between timeliness and robustness and suggests an optimal stress level for decision-making efficacy.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Model-form Error Correction using Universal Differential Equations for an Agent-Based Model of Infectious Disease

This report demonstrates universal differential equations (UDEs) as an approach to bridge the gap between ordinary differential equations (ODE) models and agent-based models (ABMs). Using UDE models as surrogates for ABMs allows us to preserve the foundational ODE that represents global disease dynamics while coupling it with a neural network model to approximate functions for the local behaviors of the ABM.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Verifying LLM generative agents reflect human behavior in contested information environments to effectively simulate disinformation campaigns (Proteus)

Disinformation poses a significant and evolving threat to today’s online environment. Individuals encounter challenges in detecting disinformation, subsequently influencing their behavior and decision-making processes. Our research examines the potential use of large language model (LLM) generative agents (LGAs) to replicate human behavior to better understand how disinformation is spread in online environments. Using human subjects research, we first investigate how personality traits, individual differences, and demographic factors relate to decision-making in simulated online disinformation environments. Then, we examine whether LGAs can effectively replicate human responses in the same simulated online environments when assigned personality traits, demographic characteristics and behavioral attributes. Our findings indicate that LGAs can align with human decisions in these scenarios; however, alignment is contingent upon scenario context, persona settings and LLM selection. Results provide valuable insights for methodology refinement in future research and in utilizing LGAs to model complex national security challenges such as disinformation campaigns.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Agent-based Modeling for the Multi-objective Optimization of Energy Production Pathways: Integrated Techno-Economics and Life Cycle Assessment

This project developed an integrated modeling framework to evaluate how purpose-grown bioenergy feedstocks could support large-scale sustainable aviation fuel production in the United States. Led by Colorado State University with project partners, the work combined geographically resolved analysis, techno-economic assessment, life cycle assessment, multi-objective optimization, and agent-based modeling to examine feedstock performance, deployment potential, land-use implications, and adoption dynamics. The overall goal was to identify practical and sustainable pathways for producing aviation fuel from feedstocks such as energy crops, woody crops, oilseeds, and algae while helping inform policy, investment, and research needs tied to national SAF goals. In essence, the project provides a systems-level assessment of the opportunities and constraints associated with scaling bioenergy-based aviation fuels in the U.S.

09 BIOMASS FUELS↗
DOE OSTI2020

Multi-Agent Control of Lane-Switching Automated Vehicles for Energy Efficiency

The proliferation of automatic control systems and their connectivity accents the performance of their interactions. In particular, connected and automated vehicles could become high-impact examples thanks to their energy use and productivity effects. Ideally, a controller might collectively optimize all agents' control moves for a given objective. However, limits on computational complexity, incomplete knowledge of the central planner, and risks of a single point of failure make distributed control attractive as well. This paper proposes a collaborative heuristic to approach the performance of centralized control with decentralized-like computational effort. The related centralized controller is also described in detail and evaluated as a baseline. A collaboration-intensive obstacle avoidance scenario involving electric vehicles is simulated to demonstrate benefits over fully decentralized control. While centralized optimization performed best with an 8.6 % energy reduction, the collaborative decentralized scheme reached a favorable computation-performance tradeoff with a 6.7 % energy reduction.

Dollar, R. Austin↗
DOE OSTI2024

Design, Selection, and Evaluation of Reinforcement Learning Single Agents for Ground Target Tracking

Previous approaches for small fixed-wing unmanned air systems that carry strapdown rather than gimbaled cameras achieved satisfactory ground target tracking performance using both standard and deep reinforcement learning algorithms. However, these approaches have significant restrictions and abstractions to the dynamics of the vehicle, such as constant airspeed and constant altitude, because the number of states and actions was necessarily limited. Thus, extensive tuning was required to obtain good tracking performance. The expansion from 4 state–action degrees of freedom to 15 enabled the agent to exploit previous reward functions that produced novel yet undesirable emergent behavior. This paper investigates the causes of and various potential solutions to undesirable emergent behavior in the ground target tracking problem. A combination of changes to the environment, reward structure, action space simplification, command rate, and controller implementation provides insight into obtaining stable tracking results. Consideration is given to reward structure selection and refinement to mitigate undesirable emergent behavior. Results presented in the paper for a simulated environment of a single unmanned air system tracking a randomly moving single ground target show that a soft actor–critic algorithm can produce feasible tracking trajectories without limiting the state space and action space, provided that the environment is properly posed.

Engineering↗
DOE OSTI2022

Optimization of Preparation Method, Nucleating Agent, and Stabilizers for Synthesizing Calcium Chloride Hexahydrate (CaCl2.6H2O) Phase Change Material

This study investigates improvements in low-cost latent heat storage material calcium chloride hexahydrate (CaCl2.6H2O). Its melting point is between 25 and 28 °C, with relatively high enthalpy (170–190 J/g); however, this phase change material (PCM) shows supercooling and phase separation. In CaCl2.6H2O incongruent melting causes lower hydrates of CaCl2 to form, which affects the overall energy storage capacity and long-term durability. In this work, PCM performance enhancement was achieved by adding SrCl2.6H2O as a nucleating agent and NaCl/KCl as a stabilizer to prevent supercooling and phase separation, respectively. We investigated the PCM preparation method and optimized the proportions of SrCl2.6H2O and NaCl/KCl. Thermal testing for 25 cycles combined with DSC and T-history testing was performed to observe changes in enthalpy, phase transitions and supercooling over the extended period of usage. X-ray diffraction was used to verify crystalline structure in the compounds. It was found that the addition of 2 wt.% of SrCl2.6H2O reduced supercooling from 12 °C to 0 °C compared to unmodified CaCl2.6H2O. The addition of 5 wt.% NaCl or KCl proved to effectively suppress separation and the melting enthalpy achieved was 169 J/g–178 J/g with congruent melting over 25 cycles, with no supercooling and almost no reduction in the latent heat.

42 ENGINEERING↗
DOE OSTI2022

The Fight against Plant-Parasitic Nematodes: Current Status of Bacterial and Fungal Biocontrol Agents

Plant-parasitic nematodes (PPNs) are among the most notorious and underrated threats to food security and plant health worldwide, compromising crop yields and causing billions of dollars of losses annually. Chemical control strategies rely heavily on synthetic chemical nematicides to reduce PPN population densities, but their use is being progressively restricted due to environmental and human health concerns, so alternative control methods are urgently needed. Here, we review the potential of bacterial and fungal agents to suppress the most important PPNs, namely Aphelenchoides besseyi, Bursaphelenchus xylophilus, Ditylenchus dipsaci, Globodera spp., Heterodera spp., Meloidogyne spp., Nacobbus aberrans, Pratylenchus spp., Radopholus similis, Rotylenchulus reniformis, and Xiphinema index.

Pires, David (ORCID:0000000230383319)↗