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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Additively Reinforced Thermoformable PETG Composite Sheets for Improved Structural Efficiency

Thermoforming of short-fiber reinforced thermoplastic sheets offers a viable pathway for producing lightweight composite components; however, inherent anisotropy in fiber-reinforced sheets can limit structural performance under multidirectional loading. In this work, short carbon fiber, glass fiber, and hybrid fiber–reinforced PETG sheets were evaluated as candidate feedstock materials for thermoforming, with flexural and tensile testing performed both along the primary fiber direction and in the off-axis orientation to establish baseline stiffness, strength, and anisotropy. As expected, short carbon fiber PETG exhibited the highest stiffness and strength in the primary fiber direction, while all systems showed reduced performance in the off-axis direction. This off-axis performance reduction provides clear justification for the use of additive reinforcement when such thermoformed sheets are intended for structural applications. The intended manufacturing sequence involves thermoforming the reinforced sheet first, followed by the application of additively manufactured lattice reinforcement; therefore, the reinforcement strategy does not impose limitations on sheet formability during thermoforming. Post-forming lattice reinforcement significantly reduced load-normalized displacement by approximately 95–99% relative to non-reinforced sheets and improved weight-normalized stiffness by ~70%. These findings demonstrate that geometry-driven additive reinforcement can effectively compensate for off-axis property reductions in thermoformed PETG composites, enabling enhanced multidirectional structural performance without compromising manufacturability.

Talabi, Isaac [ORNL] (ORCID:0000000340215594)↗
NASA NTRS2006

Effect of Reinforcement Architecture on Fracture of Selectively Reinforced Metallic Compact Tension Specimens

A computer-based parametric study of the effect of reinforcement architectures on fracture response of aluminum compact-tension (CT) specimens is performed. Eleven different reinforcement architectures consisting of rectangular and triangular cross-section reinforcements were evaluated. Reinforced specimens produced between 13 and 28 percent higher fracture load than achieved with the non-reinforced case. Reinforcements with blunt leading edges (rectangular reinforcements) exhibited superior performance relative to the triangular reinforcements with sharp leading edges. Relative to the rectangular reinforcements, the most important architectural feature was reinforcement thickness. At failure, the reinforcements carried between 58 and 85 percent of the load applied to the specimen, suggesting that there is considerable load transfer between the base material and the reinforcement.

Abada, Christopher H.↗
DOE OSTI2014

Prediction of reinforcement corrosion using corrosion induced cracks width in corroded reinforced concrete beams

This paper studies the evolution of reinforcement corrosion in comparison to corrosion crack width in a highly corroded reinforced concrete beam. Cracking and corrosion maps of the beam were drawn and steel reinforcement was recovered from the beam to observe the corrosion pattern and to measure the loss of mass of steel reinforcement. Maximum steel cross-section loss of the main reinforcement and average steel cross-section loss between stirrups were plotted against the crack width. The experimental results were compared with existing models proposed by Rodriguez et al., Vidal et al. and Zhang et al. Time prediction models for a given opening threshold are also compared to experimental results. Steel cross-section loss for stirrups was also measured and was plotted against the crack width. It was observed that steel cross-section loss in the stirrups had no relationship with the crack width of longitudinal corrosion cracks. -- Highlights: •Relationship between crack and corrosion of reinforcement was investigated. •Corrosion results of natural process and then corresponds to in-situ conditions. •Comparison with time predicting model is provided. •Prediction of load-bearing capacity from crack pattern was studied.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Anisotropic contraction of fiber-reinforced hydrogels

Hydrogels reinforced by fibers can undergo remarkable anisotropic contraction triggered by external stimuli, which has a broad appeal for various applications. However, little is known about how to optimize the contraction anisotropy by tuning the microstructures of fiber-reinforced hydrogels. Here, we investigate the underlying mechanisms controlling the anisotropic contraction of fiber-reinforced hydrogels. Using a simplified model incorporating the directional constraint of the fibers, we show that the contraction anisotropy can be substantially enhanced if the hydrogel is prestretched along the fiber direction. We further explicitly model fibers as periodically distributed cylindrical rods in the finite element simulations, and find that the contraction anisotropy can be maximized by varying the transverse fiber–fiber distance; this maximum anisotropy can be improved by reducing the longitudinal fiber–fiber distance and increasing the fiber length. Our study provides insights into designing novel fiber-reinforced hydrogels, suggesting possible applications in soft robotics, tissue engineering and beyond.

catalysis (homogeneous), solar (photovoltaic), bio↗
DOE OSTI2026

Additively Manufactured Carbon Fiber-Reinforced Siliconized Silicon Carbide Composites Using Carbon Fiber-Reinforced Poly-Ether-Ether-Ketone (PEEK) as a Precursor

Herein, we report a method to additively manufacture carbon fiber-reinforced siliconized silicon carbide composites. The process involves the pyrolysis of a 3D-printed carbon fiber-reinforced poly-ether-ether-ketone (PEEK) composite to produce a porous carbon fiber-reinforced carbon matrix composite preform, which is subsequently infiltrated with molten silicon to obtain a carbon fiber-reinforced siliconized silicon carbide composite. A key aspect of the method is limiting polymer melt flow during pyrolysis of PEEK, which is achieved by thermally annealing the 3D-printed carbon fiber-reinforced PEEK preform in air at a temperature below PEEK’s melting temperature. Rheological and differential scanning calorimetry (DSC) measurements demonstrate that the thermal annealing treatment altered the melting behavior of PEEK, while NMR and FTIR measurements provided a mechanistic explanation for the structural changes responsible for the behavior. It was also found that dimensional changes during pyrolysis were anisotropic with greater shrinkage in the stacking direction of the material.

Yoon, Bola [ORNL] (ORCID:0000000260875373)↗
DOE OSTI2021

Hydro-chemo-mechanical phase field formulation for corrosion induced cracking in reinforced concrete

Steel corrosion induced cracking in reinforced concrete structures is often caused by volumetric straining at the steel-concrete interface. This volumetric strain is mainly controlled by the rate of generation of corrosion product, which depends on the diffusion of Cl{sup −} and dissolved O{sub 2} as well as the chemical reaction at the steel-concrete interface. While many numerical modeling techniques have been proposed to tackle this complex phenomenon, most have been limited to using staggered solution schemes, where the diffusion, chemical reaction, mechanical strain, and crack fields are solved for separately. Unfortunately, such approaches introduce numerical errors that can lead to unrealistic predictions. Therefore, a coupled solution, is needed to remove the formulation induced errors and understand this complex phenomenon. To provide a unifying solution framework for corrosion induced cracking in reinforced concrete, a phase field formulation is presented and implemented with FEM algorithms. The governing equations are derived from the system's free energy and Fick's law of diffusion. The proposed formulation was first validated in 1D by comparing the modeling results with the semi-analytical solution approximated via Laurent series to avoid singularities in the complex domain. 2D plane strain solutions to the embedded rebar undergoing corrosion induced cracking with pre-cracks are also presented and compared with the approximated solutions from linear elastic fracture mechanics theory (LEFM). The modeling of crack propagation enabled by the proposed formulation is also presented along with parametric studies to reveal the roles of the fracture toughness and permeability of the concrete on the corrosion induced cracking. A case study was also conducted on H-pile steel with concrete jackets to demonstrate the feasibility of modeling corrosion induced fracture in reinforced concrete structures with complex steel-concrete interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins

Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.

Multi-Agent Reinforcement Learning, Digital Twin, ↗
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↗
DOE OSTI2021

Influence of alkali-silica reaction on the shear capacity of reinforced concrete beams with minimum transverse reinforcement

A growing number of critical concrete infrastructure are affected by alkali-silica reaction (ASR) damage such as the Seabrook Nuclear Power Plant in New Hampshire, Parker arch-gravity dam in Arizona, and several highway bridges in California and Texas. ASR causes expansion and cracking and degrades the concrete mechanical properties. Despite a wealth of material level studies there is still limited large-scale experimental data regarding the effects of ASR on reinforced concrete (RC) members. Due to the brittle nature of the shear failure in RC structures, this study focuses on the shear response of full-scale ASR damaged RC beams with minimum shear reinforcement. Six RC beams were built with different levels of ASR susceptibility and conditioned in different environments during which continuous expansion monitoring was performed. The beams all contained reactive fine aggregate (sand) and two of them had additional alkali, 1.25% by weight of cement, to accelerate ASR. The highest expansion rate of the beams happened during the first 150 days and the expansions stayed constant after 240 days and 330 days for beams conditioned in outdoor and laboratory conditions, respectively, until the last measurement at 575 days. The highest expansion, 0.4%, was seen in the beams with additional alkali and conditioned outside with regular water spray. Out of the six beams, three were selected at different levels of ASR damage and two shear tests were performed on the minimally reinforced spans close to the ends of each beam. Results indicated that beams gain shear strength from ongoing cement hydration in the presence of moisture for ASR expansions less than 0.2%. Compared to one of the beams with 0.2% ASR expansion, one of the other samples with 0.4% expansion lost 6% of its shear strength, 25% of its shear stiffness, and showed about two times larger shear cracks and shear deformations at peak load. Finally, the shear reinforcement yielded at 20% less load in the beam with 0.4% expansion compared to the beam with 0.2% expansion.

42 ENGINEERING↗
DOE OSTI2022

PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems: Preprint

We present the PowerGridworld software package to provide users with a light-weight, modular, and customizable framework for creating power systems-focused, multi-agent gym environments that readily integrate with existing training frameworks for reinforcement learning (RL). While many frameworks exist for training multi-agent (MA) RL policies, none exist to rapidly prototype and develop the environments themselves, especially in the context of heterogeneous (composite, multi-device) power systems where power flow solutions are required to define grid-level variables and costs. PowerGridworld is an open-source software package that helps to fill this gap. To highlight PowerGridworld's key features, we present two case studies and demonstrate learning multi-agent RL policies using both OpenAI's MADDPG and RLLib's PPO algorithms where, in both cases, at least some subset of agents incorporate elements of the power flow solution at each time step as part of their reward (negative cost) structures.

MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quantifying Uncertainty of Deep Reinforcement Learning Based Decision Making for Operations and Maintenance of Nuclear Power Plant

This paper summarizes research that integrates condition monitoring and prognostics with decision making for nuclear power plant operations and maintenance. As part of this research, we have developed an online asset management tool to help reduce life-cycle maintenance and repair costs. Using the latest advancements in condition monitoring, supply chain analytics, and deep reinforcement learning, we have created a predictive maintenance tool that can optimize the maintenance and spare-part management of a repairable nuclear system. To demonstrate these methods, preliminary studies were conducted on a simple, representative maintenance system undergoing a stochastic degradation process that requires repairs or replacement to continue operation. Through Monte Carlo simulations, we were able to reduce maintenance spending by approximately 50% compared to optimized, time-based maintenance strategies. Not only does the decision maker reduce the average life-cycle costs, it also minimizes the chance of high cost scenarios, lowering the variance of the expected cost distributions, and reducing overall financial risk. Furthermore, this work also studies the ability of the decision maker to handle various levels of noise from observation uncertainty. By introducing uncertainty into the decision-making process, we have quantified the robustness and resiliency of the decision maker, as well as identified necessary levels of observability to demonstrate cost effectiveness.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Deep Reinforcement Learning Based HVAC Control for Reducing Carbon Footprint of Buildings

In this paper, we present our work on deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) with the goal of reducing carbon emission. We performed this task using 1) Marginal Operating Emission Rates (MOER), where the objective was to shift the demand to the low emission period of the day and 2) Time-Of-Use (TOU) demand-response price where the objective was to shift the demand to low price period of the day. This was achieved by learning an optimal pre-cooing strategy. We found the carbon emission reduction in the range of 6%-16% depending on the opportunity presented by the MOER signal. Similarly, we observed the carbon emission reduction in the range of 23%-29% during the peak price period when TOU price was used. The results clearly demonstrated the applicability of our approach in reducing the carbon footprint of the building.

carbon emission↗
DOE OSTI2023

Reinforcement-Learning-Based Smart Water Heater Control: An Actual Deployment

Utilizing smart control algorithms for electric water heaters (EWHs) is essential for fully harnessing the demand response (DR) potential of EWHs. For this reason, the use of reinforcement learning (RL) algorithms for EWHs has received increasing attention in recent years. However, existing RL approaches are either simulation-based or use pretrained RL agents. To this end, this paper presents the real-world deployment of a set of model-free RL approaches that aim to minimize the electricity cost of a EWH under a time-of-use electricity pricing policy using standard DR commands (e.g., shed, load up). The experiment results showed that the RL agents can help save electricity cost in the range of 11% to 14% compared to the baseline operation. This study demonstrated that RL-based EWH controllers can be deployed in real world without any prior training and can still save electricity cost.

deep learning↗
DOE OSTI2020

Towards Off-policy Evaluation as a Prerequisite for Real-world Reinforcement Learning in Building Control

We present an initial study of off-policy evaluation (OPE), a problem prerequisite to real-world reinforcement learning (RL), in the context of building control. OPE is the problem of estimating a policy's performance without running it on the actual system, using historical data from the existing controller. It enables the control engineers to ensure a new, pretrained policy satisfies the performance requirements and safety constraints of a real-world system, prior to interacting with it. While many methods have been developed for OPE, no study has evaluated which ones are suitable for building operational data, which are generated by deterministic policies and have limited coverage of the state-action space. After reviewing existing works and their assumptions, we adopted the approximate model (AM) method. Furthermore, we used bootstrapping to quantify uncertainty and correct for bias. In a simulation study, we evaluated the proposed approach on 10 policies pretrained with imitation learning. On average, the AM method estimated the energy and comfort costs with 1.84% and 14.1% error, respectively.

Chen, B↗
DOE OSTI2017

Proposed Methodology for Design of Carbon Fiber Reinforced Polymer Spike Anchors into Reinforced Concrete

The included methodology, calculations, and drawings support design of Carbon Fiber Reinforced Polymer (CFRP) spike anchors for securing U-wrap CFRP onto reinforced concrete Tbeams. This content pertains to an installation in one of Los Alamos National Laboratory’s facilities. The anchors are part of a seismic rehabilitation to the subject facility. The information contained here is for information purposes only. The reader is encouraged to verify all equations, details, and methodology prior to usage in future projects. However, development of the content contained here complied with Los Alamos National Laboratory’s NQA-1 quality assurance program for nuclear structures. Furthermore, the formulations and details came from the referenced published literature. This literature represents the current state of the art for FRP anchor design. Construction personnel tested the subject anchor design to the required demand level demonstrated in the calculation. The testing demonstrated the ability of the anchors noted to carry loads in excess of 15 kips in direct tension. The anchors were not tested to failure in part because of the hazards associated with testing large-capacity tensile systems to failure. The calculation, methodology, and drawing originator was Eric MacFarlane of Los Alamos National Laboratory’s (LANL) Office of Seismic Hazards and Risk Mitigation (OSHRM). The checker for all components was Mike Salmon of the LANL OSHRM. The independent reviewers of all components were Insung Kim and Loring Wyllie of Degenkolb Engineers. Note that Insung Kim contributed to the initial formulations in the calculations that pertained directly to his Doctoral research.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Carbon Fiber Reinforced Epoxy Vitrimer: Robust Mechanical Performance and Facile Hydrothermal Decomposition in Pure Water

Conventional carbon fiber reinforced thermosetting polymers (CFRPs) are neither recyclable nor repairable due to their crosslinked network. The rapid growing CFRP market raises a serious concern of the waste management. In this work, a viable method to develop a readily recyclable CFRP based on epoxy vitrimer is introduced. First, a self-catalytic epoxy prepolymer with built-in hydroxy and tertiary amine groups is designed, which upon reaction with an anhydride formed a catalyst-free epoxy vitrimer. The epoxy prepolymer is synthesized from a diamine and an excess of bisphenol A epoxy resin. The hydroxyls and tertiary amines of the epoxy prepolymer efficiently catalyze both curing and the dynamic transesterification of the crosslinked polymer without the need of a catalyst. Then, the epoxy vitrimer is used as the matrix resin to prepare CFRP. The resulting CFRP exhibited a tensile strength as high as 356 MPa. More interestingly, the matrix of the CFRP is efficiently degraded in pure water at above 160 °C. This is because the built-in tertiary amines catalyze the hydrolysis of the ester bonds of the crosslinked network. The simple method developed in this work provides a framework for the development of recyclable CFRP.

Liu, Tuan↗
DOE OSTI2015

High voltage LIB cathodes enabled by salt-reinforced liquid electrolytes

We report on electrochemical properties of Li/Li 1.2 Ni 0.15 Co 0.1 Mn 0.55 O 2 secondary batteries in electrolytes designed to stabilize electrodeposition of lithium. Ethylene carbonate (EC): dimethyl carbonate (DMC) containing a LiPF6/LiF salt blend stabilizes lithium electrodeposition and enables Li/Li 1.2 Ni 0.15 Co 0.1 Mn 0.55 O 2 batteries with a high discharge capacity of 270 mAh·g - 1 at 0.05 mA cm - 2 . Cells containing the LiF-reinforced electrolytes also exhibit excellent capacity retention over 500 cycles with Columbic efficiencies approaching 100%. Post-mortem SEM analysis of the lithium anode shows more compact deposition in the presence of the LiF salt additive, while XPS depth profile analysis of cathodes show a more uniform distribution of Mn over the first 180 nm from the electrode/electrolyte interface. The results imply that LiF reinforced electrolytes simultaneously facilitate stable lithium electrodeposition and reduce Mn dissolution.

25 ENERGY STORAGE↗