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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Tuning the surface energy of fluorinated diamond-like carbon coatings via plasma immersion ion implantation plasma-enhanced chemical vapor deposition with 1,1,1,2-tetrafluoroethane

Here, we demonstrate an environmentally friendly and scalable method to create fluorine-doped diamond-like carbon (F-DLC) coatings using plasma immersion ion implantation plasma-enhanced chemical vapor deposition (PIII-PECVD) with 1,1,1,2-tetrafluoroethane. F-DLC films tend to have low wettability and good mechanical flexibility, which make them suitable for applications in biomedical devices and antibiofouling surfaces. We report on the effects of fluorine incorporation on the surface chemistry, surface energy, and morphology of these coatings, showing that our method is effective in increasing the fluorine content in the F-DLC up to 40%. We show that the addition of fluorine leads to a decrease in surface energy, which is consistent with a reduction in surface wettability.

Contact angle↗
DOE OSTI2020

Reactive chemical transport simulations of geologic carbon sequestration: Methods and applications

Chemical reaction simulations are considerably used to quantitatively assess the long-term geologic carbon sequestration (GCS), such as CO 2 sequestration capacity estimations, leakage pathway analyses, enhanced oil recovery (EOR) efficiency studies, and risk assessments of sealing formations (caprocks), wellbores, and overlying underground water resources. All these require a deep understanding of the CO 2 - associated chemical reactions. To ensure long-term, safe CO 2 sequestration in the intended formations, modeling is the only way to plausibly assess the CO 2 flow, reaction, and transport over thousands of years. This review summarizes the multiple methodologies for describing homogeneous and heterogeneous chemical reaction patterns and multiscale application examples, the recent progress and current status of chemical reaction simulations for GCS, and the impact of such simulations on geological CO2 sequestration performance. Technical gaps and future challenges are also discussed for further study. The trends and challenges of such studies include: (1) the combination of coupled chemical, mechanical, and transport processes with calibrated experiments and associated uncertainty/risk assessments; (2) enhancement of the ability to simulate detailed geophysical and geochemical equations to mimic in situ conditions; and (3) characterization of multiscale subsurface systems with detailed conceptual models and assignment of suitable boundary conditions for field-scale sequestration fields. One major gap remaining is the current lack of accurate (and scale-justified) kinetic and equilibrium chemical reaction parameters under reservoir conditions. Advanced models that couple chemical, mechanical, and transport processes with scale-justified parameters, from lab to field-scale experiments, are required for quantitative assessments of sequestration capacity and the long-term safety of GCS projects.

58 GEOSCIENCES↗
DOE OSTI2026

Innovations in underground hydrogen storage with multiphysics simulations, optimization, and monitoring: A review

Underground Hydrogen Storage (UHS) is a promising solution for large-scale energy storage and a critical component in advancing low-carbon energy system. Ensuring the safety and efficiency of UHS necessitates a comprehensive understanding of multiphysical interactions driven by cyclic pore fluid pressure fluctuations and coupled physicochemical processes. Here, this review examines the key geomechanical responses in UHS, including rock property variations under cyclic loading, fracture evolution and propagation, reservoir stress sensitivity, and fault stability. It also explores the impact of geochemical and microbial reactions on geomechanical characteristics. We provide an in-depth analysis of Thermal-Hydraulic-Mechanical-Chemical (THMC) coupled numerical simulations, highlighting their potential for future multi-scale modeling. Limitations of current machine learning (ML) approaches in addressing UHS challenges are highlighted, emphasizing the need for innovative ML-based methodologies. Operational strategies for hydrogen injection and production are reviewed, focusing on safety, efficiency, and economic viability. The necessity for multi-objective optimization (MOO) to balance storage efficiency, risk mitigation, and cost-effectiveness is also discussed. Current monitoring technologies are evaluated to ensure safe and efficient UHS operations. Finally, this review identifies critical knowledge gaps and underscores the importance of advancing geomechanical understanding under multiphysics-coupling. We highlight the need for ML-driven multiphysics theories, enhanced modeling techniques, and robust optimization strategies to improve UHS performance. This study serves as a comprehensive reference for future research and the large-scale implementation of UHS systems.

25 ENERGY STORAGE↗
DOE OSTI2020

Understanding the role of water-soluble guar gum binder in reducing capacity fading and voltage decay of Li-rich cathode for Li-ion batteries

The practical application of high-capacity Li-rich cathode materials is hindered by capacity fading and voltage decay. The capacity fading and voltage decay could be effectively overcome by using water-soluble guar gum (GG) binder instead of traditional polyvinylidene fluoride (PVDF). However, the specific role of the GG binder is not clear yet, though the GG binder can significantly improve the electrochemical performance of Li-rich cathode. To understand the effect of GG binder on the morphology, microstructure of electrode and electrode/electrolyte interfaces, ex-situ scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray adsorption near edge spectroscopy (XANES), in-situ electrochemical impedance spectroscopy (EIS) were applied to comparatively study the charge-discharge processes of Li-rich Li 1.2 Ni 0.2 Mn 0.6 O 2 cathode when using GG and PVDF as binders. The results indicate that the GG binder can prevent electrode crack and active material loss, ascribing to the strong mechanical adhesion of GG binder with active material particles and current collector. It has found that the GG binder can also induce the formation of a uniform layer on Li 1.2 Ni 0.2 Mn 0.6 O 2 particles’ surface. As a consequence, both the electrolyte decomposition and the electrode corrosion were significantly inhibited. The strong chelation between Mn 2+ and polar OH group restrain Mn ion dissolution, which contributes to surface structural transformation mitigation. Here, our study reveals the role of water-soluble GG binder in reducing capacity fading and voltage decay of Li-rich material and is of great importance in design functional binders for high-performance Li-rich electrodes.

25 ENERGY STORAGE↗
DOE OSTI2020

Boosting energy efficiency of Li-rich layered oxide cathodes by tuning oxygen redox kinetics and reversibility

In developing electrode materials for next-generation Li-ion batteries, significant efforts have been given to the energy, power density and cycling stability, with much less (if any) attention paid to the energy efficiency – arguably, the most important practical measure for large-scale applications. This is particularly true for the oxygen-redox active electrodes, such as Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 , the notorious energy-inefficient cathode that has an extremely high capacity but comes with large voltage hysteresis and voltage decay. Herein, we report the rational design of an energy-efficient Li-rich layered cathode along with high energy, power density and cycling stability enabled by tuning oxygen redox activity. Specifically, the target material Li 1.12 Ni 0.22 Co 0.13 Mn 0.52 O 2 exhibits an ultrahigh energy efficiency at 1 C (90.6%), high capacity (> 200 mAh g -1 ) with 98.9% retention and less than 150 mV decay at the extended 200 cycles. Overall, through direct comparison between the material and Li 1.2 Ni 0.13 Co 0.13 Mn 0.54 O 2 , we show that the compositional change, although slightly, greatly improves the oxygen redox kinetics and reversibility, thereby boosts energy efficiency. The findings offer a strategy to narrow the gap between scientific interest and practical application of oxygen-redox chemistry.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Climatic controls on erosional efficiency vary with lithology across the Himalaya

Although climate can strongly influence erosional efficiency (i.e., erosion rate for a given topography), demonstrating its impact in tectonically active areas has been challenging due to other confounding controlling factors, such as lithology. Here, we show that 10 Be-derived erosion rates and efficiencies in the Himalayan orogen exhibit distinct relationships with climatic factors depending on lithology. We compile 173 10 Be-derived, basin-averaged erosion rates across the orogen, including 12 newly measured rates from the Dibang and Lohit valleys in the easternmost Himalaya, regions characterized by high precipitation magnitudes and variability. We group basins based on lithologies separated by orogen-scale thrust faults and quantify erosional efficiency coefficients based on the relationships between erosion rates and topographic metrics. Our results show that erosion rates and erosional efficiency from sedimentary and metasedimentary rocks along the Himalayan range front display a positive, nonlinear correlation with climatic factors, such as the number of extreme rainfall events and mean annual precipitation rates. In contrast, erosion rates from crystalline lithologies in the hanging wall of the Main Central thrust show a strong correlation with fluvial topography, whereas erosional efficiency shows no statistically significant correlation with climatic factors. Rapid erosion rates and high erosional efficiencies in the eastern Himalayan range front are likely driven by extreme precipitation on tectonically active, steep slopes composed of mechanically weak metasedimentary rocks. Our findings highlight the importance of the interplay between controlling factors, which include tectonics, lithology, and climate, that drive surface erosion and influence the topographic evolution of orogenic systems.

Channel steepness↗
DOE OSTI2021

Data source authentication of synchrophasor measurement devices based on 1D-CNN and GRU

Synchrophasor measurement devices (SMDs) have been widely deployed to support real-time monitoring and control of power systems. In the meantime, data spoofing has emerged in recent years. Therefore, it is of great importance to study data authentication algorithms for detecting and defending the data spoofing effectively. Here, a one-dimensional convolutional neural network (1D-CNN) is utilized to extract temporal signatures hidden in frequency, voltage angle and amplitude data; then the gated recurrent unit (GRU) employs these temporal signatures for data source authentication. In case studies, the performances of different algorithms are tested in large-scale power systems with numerous SMDs for the first time, and comparisons among different algorithms show that the proposed algorithm can achieve a higher accuracy of data source authentication with a shorter time window.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

New data-driven approach to bridging power system protection gaps with deep learning

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this paper, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A combined convolutional neural network and long short-term memory (CNN-LSTM) network is implemented to achieve data translation invariance and capture the temporal correlation of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN-LSTM--based method avoids the complicated, manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on two kinds of protection gaps: high-impedance faults and transformer inter-turn faults. Lastly, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

42 ENGINEERING↗
DOE OSTI2022

Parameterization of generic positive sequence models to represent behavior of inverter based resources in low short circuit scenarios

The connection of large inverter based resources (IBR) in transmission systems is often located geographically and electrically far away from load centers. This, coupled with the displacement of synchronous machine plants, results in a reduction of the network short circuit strength at the point of connection. Under these conditions, state-of-the-art positive sequence simulation platforms and models can have difficulties maintaining numerical stability and/or providing an accurate representation of IBR plant dynamic behavior. As a result, computationally heavy time domain electromagnetic transient (EMT) simulations may be required to evaluate these systems. In this paper, a recently developed improved generic positive sequence model is parameterized to provide a representation of IBR behavior in low short circuit networks. Further, comparisons against generic and detailed EMT models demonstrate the suitability of the improved positive sequence model to study practical stability issues experienced with presently in-service plants. Such a model can provide some of the accuracy of an EMT representation with a much lower computational burden. The performance of the positive sequence model is validated against the behavior shown by both open white box and closed black box EMT domain models from around the world.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

Here, this paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56 000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Modeling strategy for progressive failure prediction in lithium-ion batteries under mechanical abuse

The prediction for the internal failure of lithium-ion batteries (LIBs) under external mechanical abuse loading remains a challenge for safe design. This paper systematically studies the modeling approach for progressive failure simulation and short-circuit prediction. Hemispherical indentation experiments are conducted on a battery specimen containing 5 representative sandwich (RS) layers to examine the deformation and progressive failure behavior of battery components. Three different models, viz, a high-fidelity detailed model, an intermediate homogenized model and a fully homogenized model, are developed and employed to simulate the progressive failure in a multi-layer battery specimen. The simulation results obtained using the three models all correlate well with experimental phenomena, capturing the overall stress-strain response and ultimate failure of the battery structure. The numerical results can help us understand the failure behavior and the mechanism for mechanical abuse-induced short circuit in LIBs. The feasibility of the three models for use in progressive failure prediction is compared by analyzing the capability of the models in predicting the critical failure events, the computational efficiency of the models, and the feasibility to implement coupled mechanical-electrochemical-thermal simulations. The results of this work provide useful insights on practical choices for the modeling strategy and safety design of LIBs under mechanical abuse conditions.

25 ENERGY STORAGE↗
DOE OSTI2023

Microscale dynamics in thermoreversible hydrogels: Impact of probe size and concentration

Passive microrheology techniques using dynamic light scattering (DLS) and X-ray photon correlation spectroscopy (XPCS) have emerged as important techniques for characterizing the dynamics and viscoelastic properties of soft polymeric biomaterials. However, the impact of probe particle type, size, and concentration are important considerations in interpretation of results and comparison to properties obtained from bulk rheology measurements. In this work, we investigate a model thermoreversible polymeric hydrogel and compare results from DLS-microrheology with different size and concentrations of polystyrene probe particles and XPCS-microrheology with inorganic nanoparticles. We obtained trends that aligned most closely with the macroscopic rheology with probe particles that are slightly larger than the characteristic length scale of the gel structure, but within the same order of magnitude. By contrast, larger probe particles yielded microrheology data that are more dominated by elastic behavior than what we might expect from bulk rheology experiments, while use of small inorganic nanoparticles in XPCS-microrheology resulted in a more a more viscous response than would be expected based on the bulk rheology. Finally, this study demonstrates the utility of passive DLS- and XPCS-based microrheology in characterizing the rheological properties of complex polymeric biomaterials, while also highlighting important considerations in experimental design and choice of type, size, and concentration of probe particles used.

77 NANOSCIENCE AND NANOTECHNOLOGY↗