Search NASA⌕ Search

SEARCH · Search NASA

Results for “Energy sciences”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Risk assessment of wellbore leakage during underground hydrogen storage

The expansion of renewable energy sources would require large-scale energy storage options to overcome the intermittent nature of these sources. Underground hydrogen storage (UHS) in depleted hydrocarbon reservoirs offers a scalable and practical energy storage solution. These reservoirs are chosen for their availability and large capacity, but the unique properties of hydrogen raise concerns about potential leakage pathways, particularly through wellbores. In this study, we develop and apply, for the first time, reduced-order models (ROMs) specifically designed for efficient leakage risk prediction in UHS systems operating in depleted hydrocarbon reservoirs. Using 3,000 high-fidelity simulation scenarios, we examine the influence of 11 key parameters, including reservoir and aquifer depths, wellbore permeability and porosity, initial saturations of water, oil and gas fractions (hydrogen, light, intermediate, and heavy hydrocarbons), reservoir pressure multiplier, and the aquifer-to-reservoir volume ratio, to simulate leakage behavior over a 1,000-year timescale. We train ROMs using a two-step classification-regression approach, achieving R 2 values exceeding 99 % across all targets. These ROMs effectively capture the leakage evolution and identify critical controls of leakage, guiding the design of mitigation strategies. Results indicate that gas leakage occurs in about 27 % of scenarios as early as five years post-operation, reaching volumes of up to 106 ft3. Oil leakage is less frequent (~17 %) and typically begins decades later. Our findings also show that hydrogen often migrates first, owing to its smaller molecular size and higher buoyancy, followed by heavier hydrocarbons. Over time, these heavier components contribute significantly to the total leaked volume, reinforcing the need for targeted monitoring and remediation strategies. Our analysis highlights that deeper storage reservoirs, shallower aquifers, and low-permeability wellbores significantly reduce leakage risks. In conclusion, this work offers a robust framework for risk-informed UHS deployment, supporting energy security through reliable large-scale hydrogen storage while safeguarding environmental integrity.

08 HYDROGEN↗
DOE OSTI2021

Reactive boride infusion stabilizes Ni-rich cathodes for lithium-ion batteries

Engineered polycrystalline electrodes are critical to the cycling stability and safety of lithium-ion batteries, yet it is challenging to construct high-quality coatings at both the primary- and secondary-particle levels. Here, we present a room-temperature synthesis route to achieve full surface coverage of secondary particles and facile infusion into grain boundaries, thus offering a complete “coating-plus-infusion” strategy. Cobalt boride metallic glass is successfully applied to Ni-rich layered cathode LiNi 0.8 Co 0.1 Mn 0.1 O 2 . Here, it dramatically improves the rate capability and cycling stability, including under high-discharge-rate and elevated-temperature conditions and in pouch full cells. The superior performance originates from simultaneous suppression of microstructural degradation of intergranular cracking and side reactions with electrolyte. Atomistic simulations identified the critical role of strong selective interfacial bonding, which offers not only a large chemical driving force to ensure uniform reactive wetting and facile infusion but also lowered the surface/interface oxygen activity, contributing to the exceptional mechanical and electrochemical stabilities of the infused electrode.

25 ENERGY STORAGE↗
DOE OSTI2025

Dark-field X-ray microscopy with structured illumination for three-dimensional imaging

Dark-field X-ray microscopy is a lens-based technique that enables real-space imaging of heterogeneous micro- and meso-scale ordered materials. However, achieving accurate three-dimensional (3D) reconstruction often requires meticulous sample alignment or rastering, requiring complex rotational setups and extended acquisition times. To address these challenges, we introduce a structured illumination technique optimized for 3D imaging of ordered materials at sub-micrometer length scales. Our approach employs a coded aperture to spatially modulate the incident X-ray beam, enabling 3D structural reconstruction from images captured at various aperture positions. Unlike current 3D imaging approaches, which often rely on rotational or rastering methods, our technique uses scanning X-ray silhouettes of the coded aperture for depth resolution along the diffraction axis. This eliminates the need for sample rotation or rastering, resulting in a highly stable and efficient imaging modality. We validated the efficacy of this approach through experimental imaging of an isolated twin domain within a bulk single crystal of an iron pnictide using a dark-field X-ray microscope. This advancement aligns with the enhanced brightness upgrades of modern synchrotron radiation facilities, unlocking new possibilities for high-resolution imaging of ordered materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Modeling and optimization of steady flow of natural gas and hydrogen mixtures in pipeline networks

Here, we extend the canonical problems of simulation and optimization of steady-state gas flows in pipeline networks with compressors to the transport of mixtures of highly heterogeneous gases injected throughout a network. Our study is motivated by proposed projects to blend hydrogen generated using clean energy into existing natural gas pipeline systems as part of efforts to reduce the reliance of energy systems on fossil fuels. Flow in a pipe is related to endpoint pressures by a basic Weymouth equation model, with an ideal gas equation of state, where the wave speed depends on the hydrogen concentration. At vertices, in addition to mass balance, we also consider mixing of incoming flows of varying hydrogen concentrations. The problems of interest are the heterogeneous gas flow simulation (HGFS), which determines system pressures and flows given fixed boundary conditions and compressor settings, as well as the heterogeneous gas flow optimization (HGFO), which extremizes an objective by determining optimal boundary conditions and compressor settings. We examine conditions for uniqueness of solutions to the HGFS, as well as compare and contrast mixed-integer and continuous nonlinear programming formulations for the HGFO. We develop computational methods to solve both problems, and examine their performance using four test networks of increasing complexity.

08 HYDROGEN↗
DOE OSTI2023

Biosourced Antioxidants for Chemical Durability Enhancement of Perfluorosulfonic Acid Membrane

Abstract The chemical durability of perfluorosulfonic acid (PFSA) membranes is a topic of growing interest to meet Department of Energy (DOE) durability targets for heavy‐duty vehicle (HDV) applications. State‐of‐the‐art membranes like Nafion, rely on the use of cerium, heteropolyacids, and other inorganic additives to increase PFSA chemical durability. A less explored avenue for the oxidative stabilization of PFSA and hydrocarbon membranes is the use of organic antioxidants. No reversible organic antioxidant has been demonstrated to date which can enhance membrane lifetime by factors comparable to cerium. Here, ellagic acid (EA) is demonstrated as a promising radical scavenger for PFSA's. It is found that the incorporation of EA enhances the chemical durability of Nafion by 160%. EA, when incorporated with cerium as an electron donorenhances Nafion durability by at least 80% compared to a membrane incorporated with just cerium in DOE‐defined durability tests. EA is found to be reversible in acidic conditions like those of fuel cells and its reversibility could be further enhanced by the use of suitable co‐antioxidants.

36 MATERIALS SCIENCE↗
DOE OSTI2021

On the feasibility of using physics-informed machine learning for underground reservoir pressure management

In this work, we evaluate the feasibility of using physics-informed machine learning (PIML) for underground energy-related pressure management. To this end, we develop a PIML framework to manage underground reservoir pressures by training neural networks to determine fluid extraction rates for dedicated extraction wells during fluid injection operations given a range of reservoir conditions (e.g., transmissivity and storativity). We implement an automatically-differentiable analytical physics model of fluid flow in porous media within the PIML framework as a proxy for more complicated models. This allows us to execute a sufficient number of training scenarios to fully evaluate the feasibility of using PIML to support pressure management activities. We quantify the number of physics-model parameters required for automatic differentiation to become more efficient than finite-difference gradient calculations. We use a simple scenario with a single injector, extractor, and critical location for our feasibility analysis. We evaluate the effect of the size of the training dataset (i.e., the number of reservoir condition samples) on the accuracy and efficiency of the PIML framework. For an equivalent number of model evaluations, the larger training dataset took less time to train and produced a neural network that was able to more accurately manage reservoir pressures. We also evaluate the effect of the training dataset batch size (i.e., number of reservoir condition samples used to update the neural network coefficients during training; i.e., how the training dataset is partitioned). While training ran faster with larger batch sizes, they produced neural networks that managed pressures less accurately. We demonstrate the approach on a more complex scenario involving 10 injectors, 10 extractors, and 4 critical locations (a relatively high well density of 20 wells/km2). We provide the number of forward and adjoint model evaluations required in each case as an indication of the feasibility of using PIML for pressure management when more complicated physics models with longer execution times are used.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Terahertz-field activation of polar skyrons

Unraveling collective modes arising from coupled degrees of freedom is crucial for understanding complex interactions in solids and developing new functionalities. Unique collective behaviors emerge when two degrees of freedom, ordered on distinct length scales, interact. Polar skyrmions, three-dimensional electric polarization textures in ferroelectric superlattices, disrupt the lattice continuity at the nanometer scale with nontrivial topology, leading to previously unexplored collective modes. Here, using terahertz-field excitation and femtosecond x-ray diffraction, we discover subterahertz collective modes, dubbed “skyrons”, which appear as swirling patterns of atomic displacements functioning as atomic-scale gearsets. The key to activating skyrons is the use of the THz field that couples primarily to skyrmion domain walls. Momentum-resolved time-domain measurements of diffuse scattering reveal an avoided crossing in the dispersion relation of skyrons. Atomistic simulations and dynamical phase-field modeling provide microscopic insights into the three-dimensional crystallographic and polarization dynamics. The amplitude and dispersion of skyrons are demonstrated to be controlled by sample temperature and electric-field bias. The discovery of skyrons and their coupling with terahertz fields opens avenues for ultrafast control of topological polar structures.

ferroelectrics↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2025

Interactive Gas Chemistry for Enhanced Science Capabilities of the Energy Exascale Earth System Model Version 3

Atmospheric chemistry plays a crucial role in Earth system models (ESMs), controlling atmospheric composition and radiative balance; it is highly interactive with the physical climate, biogeochemical cycles, and human systems. However, it often imposes computational challenges in an ESM. Here we develop a full troposphere‐stratosphere interactive chemistry module for the US Department of Energy's Energy Exascale Earth System Model (E3SM). We intentionally build a streamlined module based on E3SM version 2 that interacts with other components and maintains all of major chemical and chemistry‐climate feedbacks. The module incorporates a new, highly efficient tracer advection scheme; linearization of stratospheric chemistry; and abridged tropospheric chemical mechanism with 28 reactive tracers. This new model, E3SM‐chem, can readily perform century‐long climate simulations of ozone, methane, and nitrous oxide based on emission scenarios as well as provide hourly budgets for the gas‐phase radicals that drive aerosol chemistry. We evaluate E3SM‐chem with an atmosphere‐only simulation as in the recent climate model intercomparison project (CMIP6) finding results similar to the other CMIP6 models. For the present‐day, E3SM‐chem matches the standard measurement metrics for stratospheric and tropospheric ozone, surface air quality, other key reactive gases like carbon monoxide, and the methane lifetime. Overall, E3SM‐chem maintains the climate fidelity of the baseline model while adding at most 20% to the computational cost of the atmosphere model. Hence, interactive chemistry can be a default configuration for long climate simulations at resolutions of 1° or finer, which is crucial for producing self‐consistent chemistry‐climate feedbacks that alter the climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Cryogenic tracer irradiation facility at the university of Texas at Austin

We report on a cryogenic gas irradiation facility in the 1.1 MW TRIGA reactor at The University of Texas at Austin. The system was designed to produce radioactive xenon and argon for environmental studies, and it can be applied to produce other gaseous radiotracers. The system design includes modeling accident scenarios to ensure operation does not risk damage to the reactor and minimizes risk of release of radioactive material. In conclusion, the SCALE code was used to model a 30-day irradiation of one liter of Xe-126 and the predicted activity is 0.9 Ci of Xe-127 at irradiation end.

Ar-37↗
DOE OSTI2023

Large-scale metal strip for power storage and energy conversion applications by machining-based deformation processing

Machining-based deformation processing is used to produce metal foil and flat wire (strip) with suitable properties and quality for electrical power and renewable energy applications. In contrast to conventional multistage rolling, the strip is produced in a single-step and with much less process energy. Examples are presented from metal systems of varied workability, and strip product scale in terms of size and production rate. By utilizing the large-strain deformation intrinsic to cutting, bulk strip with ultrafine-grained microstructure, and crystallographic shear-texture favourable for formability, are achieved. In conclusion, implications for production of commercial strip for electric motor applications and battery electrodes are discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Multi-scale stabilization of high-voltage LiCoO 2 enabled by nanoscale solid electrolyte coating

LiCoO 2 (LCO) possess a high theoretical specific capacity of 274 mAh g -1 , and currently LCO charged to 4.48 V with a capacity of ~190–195 mAh g -1 is penetrating the commercial markets. Scalable strategies to further enhance the performance of LCO are highly attractive. Here, we develop a scalable ball-milling and sintering method to tackle this long-standing challenge by modifying LCO surface with only 1.5–3.5% ceramic solid electrolyte nanoparticles, specifically Li 1.5 Al 0.5 Ge 1.5 (PO 4 ) 3 (LAGP) as an example. Consequently, the atomic-to-meso multiscale structural stabilities have been significantly improved, even with a high cut-off voltage of 4.5 V vs. Li/Li + , leading to excellent electrochemical stabilities. The nano-LAGP modified Li|LCO cell exhibits high discharge capacity of 196 mAh g -1 at 0.1 C, capacity retention of 88% over 400 cycles, and remarkably enhanced rate capability (163 mAh g -1 at 6 C). These results show significant improvement compared to the Li|LCO cells. The as-prepared graphite|LAGP-LCO full cells also show steady cycling with 80.4% capacity retention after 200 cycles with a voltage cut-off of 4.45 V. This work provides a simple and scalable approach to achieve stable cycling of LCO at high voltage with high energy density.

25 ENERGY STORAGE↗
DOE OSTI2022

Rheological effects of moisture content on the anatomical fractions of loblolly pine (Pinus taeda)

Here, this study focused on assessing the influence of moisture content on the rheological properties of different anatomical fractions of pine residue using the FT4 powder rheometer. Moisture content exhibited some influence on the 2 mm bark and needle fractions, increasing their cohesion, angle of internal friction, unconfined yield strength, and major principal stress. Stem and whole, on the other hand, presented minimal changes upon moisture introduction. All 4 mm fractions did not show many changes in their rheological properties with moisture introduction, which can be due to less particle-particle interactions and consequently reducing any liquid bridging the particles might undergo; however, they were shown to have a more unpredictable behavior compared to the 2 mm analogues (i.e., higher standard deviation) which can be due to a higher degree of particle-interlocking. Out of the three fractions, needle presented the poorest rheological properties for flowability in addition to being susceptible by moisture, unlike other fractions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Calculating Reactivity and Fusion Energy Yield

During my time at Lawrence Livermore National Lab, I had the opportunity to intern at the HEDS (High Energy Density Science) Center and become an IFE (Inertial Fusion Energy) summer student for three weeks with Dr. Veronika Kruse as my mentor. Within this time frame, my research project’s goal was to develop an understanding of nuclear fusion while building technical skills in calculating and analyzing the required data. The following report outlines the process of calculating reactivity curves and energy yields of specific fusion reactions. For context, nuclear fusion is when two atomic nuclei combine, and energy is released. NIF (the National Ignition Facility), located at LLNL, uses laser-based inertial confinement fusion to achieve ignition. To simplify the process, NIF uses lasers to heat a small sphere or capsule, made up of primarily deuterium and tritium, to extremely high temperatures and densities to achieve fusion ignition, where more energy is released than the lasers introduced.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

SYNTHESIS AND MODIFICATION OF HIGH-NICKEL CATHODE MATERIALS FOR NEXT GENERATION LITHIUM-ION BATTERIES

The limited worldwide Cobalt resource has spurred demand for new cathode materials with reduced Cobalt reliance. Substituting Cobalt with Nickel giving high-Nickel cathode material is a promising solution due to its high energy density and cheaper price. However, the poor cycling stability of high-nickel cathode materials is the major challenge that hinders their widespread adoption in electric vehicles and other energy storage applications. This work reports a significant advance in the development of high-nickel cathode materials with improved cycling stability. Firstly, a scalable synthesis route is developed to produce high-nickel cathodes with favorable morphology and high nickel content of 92%. Various low-cost doping elements including Mg, Al and Ti are screened to improve the structure of high-nickel cathodes during cycling. A Dual Protective strategy with AlPO4 nanoparticles as a representative is introduced as a low cost and effective approach to prolong the cycle stability of high-nickel cathodes. Finally, single-crystalline high-nickel cathodes are synthesized, and their electrochemical performance is compared with polycrystalline cathodes. The synthesized high-nickel cathode materials exhibit excellent electrochemical performance and significantly improved cycling stability. This work demonstrates promising pathways towards the commercialization of high-nickel cathode materials for Lithium Ion Batteries.

25 ENERGY STORAGE↗