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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 361 records · Page 20

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NASA NTRS1990

Definition of experiments to investigate fire suppressants in microgravity

Defined and justified here are the conceptual design and operation of a critical set of experiments expected to yield information on suppressants and on suppressant delivery systems under realistic spacecraft-fire conditions (smoldering). Specific experiment parameters are provided on the solid fuel (carbon), oxidants (habitable spacecraft atmospheres), fuel/oxidant supply, mixing mode, and rate (quiescent and finite; ventilated and replenishable), ignition mode, event, and reignition tendency, fire-zone size, fire conditions, lifetime, and consequences (toxicity), suppressants (CO2, H2O, N2) and suppressant delivery systems, and diagnostics. Candidate suppressants were identified after an analysis of how reduced gravity alters combustion, and how these alterations may influence the modes, mechanisms, and capacities of terrestrial agents to suppress unwanted combustion, or fire. Preferred spacecraft suppression concepts included the local, near-quiescent application of a gas, vapor, or mist that has thermophysical fire-suppression activity and is chemically inert under terrestrial (normal gravity) combustion conditions. The scale, number, and duration (about 1 hour) of the proposed low-gravity experiments were estimated using data not only on the limitations imposed by spacecraft-carrier (Shuttle or Space Station Freedom) accommodations, but also data on the details and experience of standardized smolder-suppression experiments at normal gravity. Deliberately incorporated into the conceptual design was sufficient interchangeability for the prototype experimental package to fly either on Shuttle now or Freedom later. This flexibility is provided by the design concept of up to 25 modular fuel canisters within a containment vessel, which permits both integration into existing low-gravity in-space combustion experiments and simultaneous testing of separate experiments to conserve utilities and time.

Reuther, James J.↗

NASA NTRS2013

Alternative to Nitric Acid for Passivation of Stainless Steel Alloys

Corrosion is an extensive problem that affects the Department of Defense (DoD) and National Aeronautics and Space Administration (NASA). The deleterious effects of corrosion result in steep costs, asset downtime affecting mission readiness, and safety risks to personnel. Consequently, it is vital to reduce corrosion costs and risks in a sustainable manner. The DoD and NASA have numerous structures and equipment that are fabricated from stainless steel. The standard practice for protection of stainless steel is a process called passivation. Typical passivation procedures call for the use of nitric acid; however, there are a number of environmental, worker safety, and operational issues associated with its use. Citric acid offers a variety of benefits including increased safety for personnel, reduced environmental impact, and reduced operational cost. DoD and NASA agreed to collaborate to validate citric acid as an acceptable passivating agent for stainless steel. This paper details our investigation of prior work developing the citric acid passivation process, development of the test plan, optimization of the process for specific stainless steel alloys, ongoing and planned testing to elucidate the process' resistance to corrosion in comparison to nitric acid, and preliminary results.

Lewis, Pattie L.↗

NASA NTRS2022

Towards Run-Time Search for Real-World Multi-Agent Systems

Multi-agent systems (MAS) may encounter uncertainties in the form of unexpected environmental conditions, sub-optimal system configurations, and unplanned interactions between autonomous agents. The number of combinations of such uncertainties may be innumerable, however run-time testing may reduce the issues impacting such a system. We posit that search heuristics can augment a run-time testing process, in-situ, for a MAS. To support our position we discuss our in-progress experimental testbed to realize this goal and highlight challenges we anticipate for this domain.

Abigail C. Diller↗

DOE OSTI2024

Identifying Molecular Scale Interactions of Nucleating Agents with Salt Hydrates for Thermal Energy Storage Applications

Latent heat based thermal energy storage is of interest as a method to mitigate and time-shift thermal load peaks and hence reduce energy demands for heating and cooling buildings. Notably, Glauber’s salt (GS) presents a good choice as a phase change material (PCM) for building applications because it has a melting point near room temperature (i.e., 32.4 °C), a large heat of fusion and energy density, and is low cost (~ $100/ton). However, there are several known limitations to using GS as a PCM, such as incongruent melting, high degree of supercooling, and formation of other hydration states, which renders GS unsuitable for practical use. Inorganic crystals acting as nucleating agents can avoid some of these issues with GS [1]. Understanding nucleation behavior through typical calorimetry experiments offers limited insight into the molecular-scale mechanisms. Here, we report isothermal titration calorimetry [2] to investigate the interactions between sodium sulfate salt and an organic nucleating agent - glycine. Our experiments provide qualitative and quantitative understanding of how glycine interacts with sodium sulfate across various temperatures. The findings offer insights into the nucleation onset temperature (24°C) and underscore the pivotal role of glycine's zwitterionic structure in facilitating nucleation. This preliminary work provides a foundation for subsequent exploration of nucleating agents that would be incorporated in different salt hydrate systems. 1. Purohit, B. K., and V. S. Sistla. "Inorganic salt hydrate for thermal energy storage application: A review." Energy Storage 3, no. 2 (2021): e212. 2. Velázquez-Campoy, Adrián, Hiroyasu Ohtaka, Azin Nezami, Salman Muzammil, and Ernesto Freire. "Isothermal titration calorimetry." Current protocols in cell biology 23, no. 1 (2004): 17-8.

calorimetry, nucleation & growth↗

DOE OSTI2023

Impact of carbamate formation on the surface tension of epoxy‐amine curing systems

Abstract Epoxy‐amine reactive systems, including their surface tension, have been studied due to their use and importance in composites and adhesives in laboratory and industrial applications. Understanding how the system's surface tension changes during curing could help target applications for tailored composites. Using the Wilhelmy plate method, surface tensions for epoxy‐curing agent systems were investigated under different environments. While the epoxy exhibited an expected negative temperature coefficient of −0.096 mN/m/°C, the curing agent exhibited a positive temperature coefficient of 0.073 mN/m/°C, which is due to the curing agent reacting with atmospheric CO 2 to form carbamate through a zwitterion pathway and functional groups accumulating at the surface of the reacting systems. Carbamate formation results in a surface tension below that of the individual constituents in the epoxy‐amine curing agent system. Fourier transform infrared (FTIR) spectroscopy was used to verify in situ formation of carbamate, and by reducing the system's temperature, it was demonstrated that the system surface tension profiles displayed micellar surfactant‐like characteristics. FTIR was used to explore the difference in composition between the surface and the bulk, in particular carbamate formation at the surface, resulting in a lowering of the surface tension of reacting systems.

Shenk, Timothy M.↗

DOE OSTI2021

Silica-modifying chemical admixtures for directed zeolitization of metakaolin-based alkali-activated materials

The effect of using trimethyladamantyl-ammonium hydroxide (TMAAOH) as a silica-modifying admixture to induce early-age mineralization during alkali-activation of metakaolin was investigated and reported herein. In all material formulations, the use of TMAAOH induced early-age mineralization, increased mixture stiffening in the fresh state, and lowered total heat of reaction. In activating solutions with silica moduli above unity (Ms >1.0), mineralogy results demonstrate that TMAAOH induces the nucleation and growth of metastable zeolitic phases, which correlated with increased permeability and increased plastic shrinkage of the paste. When TMAAOH was added to activating solutions with silica moduli near unity (Ms ~1.0), the controlled formation of crystalline, silica-rich faujasite was observed, which correlated with reduced permeability and lower plastic shrinkage. Together, these results demonstrate for the first time that silica-templating agents such as TMAAOH can be exploited in the design of new chemical admixtures that directly influence the dynamics of zeolitization in alkali-activated materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Multi-stage charging and discharging of electric vehicle fleets

Fleets of electric vehicles will likely shift electricity demand, and the effect of upstream charging emissions will come from generation sources that are dispatched in response. This study proposes a multi-stage charging and discharging problem to translate low-cost energy transactions into vehicle dispatch decisions. A day-ahead charging optimization problem minimizes electricity purchases and marginal emissions damages, with energy transactions becoming targets in an optimization-based dispatch strategy for an on-demand shared autonomous electric vehicle (SAEV) fleet. The framework was tested for Austin, Texas, using an agent-based simulator. Fleets can schedule charging to lower daily power costs (averaging 15.5% or $\$0.79$/day/SAEV) while reducing health damages from generation-related pollution (2.8% or $\$0.43$/day/SAEV). Finally, fleet managers can increase profits ($\$8$ per SAEV per day) by adopting a multi-stage charging and discharging strategy that can serve more passengers per day than price-agnostic dispatch strategies.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Machine learning-assisted crystal engineering of a zeolite

Abstract It is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms’ results offers the insight that reduced Na 2 O content is key to formulating FAU materials with high Si/Al ratio. An acid catalyst prepared by partial ion exchange of the high-Si/Al-ratio FAU (Si/Al = 3.5) exhibits improved proton reactivity (as well as specific activity, per unit mass of catalyst) in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al = 2.8).

36 MATERIALS SCIENCE↗

NASA NTRS1978

Floating nut for spacecraft application

Nut overcomes mechanical mismatch from accumulated tolerances and maintains assembly even if mounting screw loosens. Nut and screws can be painted with bonding agent to insure lock. If assemblies are removed frequently, nut and screws can be made of steel to reduce wear and tear on threads and risk of faulty threads.

Ell, L. J.↗

NASA NTRS2003

Deletion of Brca2 exon 27 causes hypersensitivity to DNA crosslinks, chromosomal instability, and reduced life span in mice

The Brca2 tumor-suppressor gene contributes to genomic stability, at least in part by a role in homologous recombinational repair. BRCA2 protein is presumed to function in homologous recombination through interactions with RAD51. Both exons 11 and 27 of Brca2 code for domains that interact with RAD51; exon 11 encodes eight BRC motifs, whereas exon 27 encodes a single, distinct interaction domain. Deletion of all RAD51-interacting domains causes embryonic lethality in mice. A less severe phenotype is seen with BRAC2 truncations that preserve some, but not all, of the BRC motifs. These mice can survive beyond weaning, but are runted and infertile, and die very young from cancer. Cells from such mice show hypersensitivity to some genotoxic agents and chromosomal instability. Here, we have analyzed mice and cells with a deletion of only the RAD51-interacting region encoded by exon 27. Mice homozygous for this mutation (called brca2(lex1)) have a shorter life span than that of control littermates, possibly because of early onsets of cancer and sepsis. No other phenotype was observed in these animals; therefore, the brca2(lex1) mutation is less severe than truncations that delete some BRC motifs. However, at the cellular level, the brca2(lex1) mutation causes reduced viability, hypersensitivity to the DNA interstrand crosslinking agent mitomycin C, and gross chromosomal instability, much like more severe truncations. Thus, the extreme carboxy-terminal region encoded by exon 27 is important for BRCA2 function, probably because it is required for a fully functional interaction between BRCA2 and RAD51. Copyright 2003 Wiley-Liss, Inc.

Non-NASA Center↗

DOE OSTI2024

Employing artificial intelligence to steer exascale workflows with colmena

Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. In conclusion, our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing.

Workflows↗

NASA NTRSDate not supplied

Impact of Traffic-Following on Order of Autonomous Airspace Operations

In this paper, we investigate the dynamic emergence of traffic order in a distributed multi-agent system, aiming to minimize inefficiencies that stem from unnecessary structural impositions. We introduce a methodology for developing a dynamically updating traffic pattern map of the airspace by leveraging information about the consistency and frequency of flow directions used by current as well as preceding traffic. Informed by this map, an agent can discern the degree to which it is advantageous to follow traffic by trading off utilities such as time and order. We show that for the traffic levels studied, for low degrees of traffic-following behavior, there is minimal penalty in terms of aircraft travel times while improving the overall orderliness of the airspace. On the other hand, heightened traffic-following behavior may result in increased aircraft travel times, while marginally reducing the overall entropy of the airspace. Ultimately, the methods and metrics presented in this paper can be used to optimally and dynamically adjust an agent’s traffic-following behavior based on these trade-offs.

Airspace Operations↗

NASA NTRSDate not supplied

Impact of Traffic-Following on Order of Autonomous Airspace Operations

We investigate the dynamic emergence of traffic order in a distributed multi-agent system, aiming to minimize inefficiencies that stem from unnecessary structural impositions. We introduce a methodology for developing a dynamically updating traffic pattern map of the airspace by leveraging information about the consistency and frequency of flow directions used by current as well as preceding traffic. Informed by this map, an agent can discern the degree to which it is advantageous to follow traffic by trading off utilities such as time and order. We show that for the traffic levels studied, for low degrees of traffic-following behavior, there is minimal penalty in terms of aircraft travel times while improving the overall orderliness of the airspace. On the other hand, heightened traffic-following behavior may result in increased aircraft travel times, while marginally reducing the overall entropy of the airspace. Ultimately, the methods and metrics presented in this paper can be used to optimally and dynamically adjust an agent’s traffic-following behavior based on these trade-offs.

Airspace Operations↗

DOE OSTI2023

Reinforcement learning building control approach harnessing imitation learning

Reinforcement learning (RL) has shown significant success in sequential decision making in fields like autonomous vehicles, robotics, marketing and gaming industries. This success has attracted the attention to the RL control approach for building energy systems which are becoming complicated due to the need to optimize for multiple, potentially conflicting, goals like occupant comfort, energy use and grid interactivity. However, for real world applications, RL has several drawbacks like requiring large training data and time, and unstable control behavior during the early exploration process making it infeasible for an application directly to building control tasks. To address these issues, an imitation learning approach is utilized herein where the RL agents starts with a policy transferred from accepted rule based policies and heuristic policies. This approach is successful in reducing the training time, preventing the unstable early exploration behavior and improving upon an accepted rule-based policy - all of these make RL a more practical control approach for real world applications in the domain of building controls.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Amine Structure Governs Corrosion Rates of Copper Catalysts in Electrochemical Reactive Capture of CO 2

Reactive capture of CO 2 (RCC) offers an integrated approach that combines CO 2 capture with its direct electrochemical conversion, eliminating the need for CO 2 release from the capture agent. By avoiding the pH, pressure, and temperature swings required for the release step, RCC has the potential to reduce both energy consumption and capital costs compared to the conventional sequential process of CO 2 capture, release, concentration, and conversion. Amines, widely used in industrial CO 2 capture, face challenges in RCC systems due to their incompatibility with transition metal catalysts as well as their tendency to promote electrode corrosion and parasitic hydrogen evolution. Identifying suitable combinations of amines and catalysts is therefore critical to enabling integrated CO 2 capture and conversion. Here, this work systematically investigates the performance of four primary and four secondary amines for RCC on polycrystalline Cu catalysts. Among the eight tested amines, only dimethylamine showed no measurable Cu corrosion near the open circuit potential. In contrast, ammonia, methylamine, ethylamine, monoethanolamine, diethylamine, diethanolamine, and piperazine all induced Cu corrosion. Corrosion rates correlate with the pK a and steric hindrance of the amines, highlighting key parameters for catalyst–amine codesign. Grand canonical DFT calculations indicate a correlation between the adsorption strength of protonated amines, their pK a , and the extent of Cu corrosion, suggesting that both the surface binding of protonated amines and the lability of their protons play critical roles in corrosion acceleration near open circuit potentials. These finding suggest that amines with high pK a values and weak binding of their protonated forms to Cu surfaces are preferred, as they offer better corrosion resistance.

Choi, Jounghwan [Univ. of California, Los Angeles,↗

DOE OSTI2025

Integrated Routing and Traffic Signal Control for CAVs via Reinforcement Learning Approach

Incorporating Connected and Automated Vehicles (CAVs) into urban traffic networks presents opportunities and challenges for traffic management systems. This paper aims to develop an integrated routing and traffic signal control system designed explicitly for CAVs, utilizing a Reinforcement Learning (RL) approach. The objective is to enhance traffic flow and improve overall transportation efficiency in the controlled areas. We propose an innovative framework that employs the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Q-network (DQN), to dynamically adjust the number of vehicles in the routes and the duration of traffic signals. Our simulation results demonstrate that a DQN agent successfully optimizes the number of vehicles in the routes and traffic signal timings of traffic signal controllers, eventually reducing total travel time. The study illustrates the potential usage of RL-based systems in managing routing and traffic signals for CAVs, offering a promising opportunity for future urban traffic management strategies.

Park, Jiho [New York University]↗

DOE OSTI2020

An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning

Buildings, as major energy consumers, can provide great untapped demand response (DR) resources for grid services. However, their participation remains low in real-life. One major impediment for popularizing DR in buildings is the lack of cost-effective automation systems that can be widely adopted. Existing optimization-based smart building control algorithms suffer from high costs on both building-specific modeling and on-demand computing resources. To tackle these issues, this paper proposes a cost-effective edge-cloud integrated solution using reinforcement learning (RL). Beside RL’s ability to solve sequential optimal decision-making problems, its adaptability to easy-to-obtain building models and the off-line learning feature are likely to reduce the controller’s implementation cost. Using a surrogate building model learned automatically from building operation data, an RL agent learns an optimal control policy on cloud infrastructure, and the policy is then distributed to edge devices for execution. Simulation results demonstrate the control efficacy and the learning efficiency in buildings of different sizes. A preliminary cost analysis on a 4-zone commercial building shows the annual cost for optimal policy training is only 2.25% of the DR incentive received. Results of this study show a possible approach with higher return on investment for buildings to participate in DR programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗