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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Chloride-Based Volatility for Waste Reduction and/or Reuse of Metallic-, Oxide- and Salt-Based Reactor Fuels

The objective of the chloride based volatility project (CBV) was to demonstrate the ability to separate uranium from used fuel to enable process improvements resulting in 10x reduction of waste volume, while maintaining safeguards standards and global backend costs at $\$$1/MW-hr. Current industrial practices perform separations of used fuel using solvent media in the form of aqueous and molten salt processes, resulting in contaminated process waste. The CBV approach utilized solid state chemistry with no solvent media and was successful in chlorinating uranium and fission product oxides that sublimed into the gas phase and were collected in targeted condensation zones based on temperature gradients of chlorinated products. Recovery of better than 95% of initial uranium in the form of UCl4 was demonstrated when simulated used nuclear fuel was used. Laser induced breakdown spectroscopy, LIBS, and ultraviolet-visible spectroscopy, UV-Vis, were combined into a high temperature flow cell design and utilized as process monitoring techniques to observed chlorinated products leave the reaction vessel in real time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

A Play-Based Exploration of CO 2 Storage in the Illinois Basin: Development and Application of the Play-Based Exploration Workflow

This report documents the workflow developed and applied for a play-based exploration (PBE) of carbon capture and storage (CCS) potential in the Illinois Basin, conducted under U.S. Department of Energy Cooperative Agreement DE-FE0032366. The project was led by the Illinois State Geological Survey (ISGS) at the University of Illinois Urbana-Champaign in collaboration with Visage Energy. The goal of this workflow is to identify locations where subsurface, surface, and societal conditions align to support safe and effective geologic CO 2 storage. To achieve this, the project team developed a systematic, seven-step workflow for conducting play-based exploration: (1) defining play elements, (2) collecting data, (3) constructing a geodatabase, (4) defining suitability criteria, (5) conducting suitability mapping, (6) generating Common Risk Segment (CRS) maps, and (7) creating composite play maps. The workflow is designed to be repeatable and adaptable for CCS assessments in other regions or for alternative subsurface energy applications such as hydrogen or natural gas storage.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction

Snow derived water is a critical component of the US water supply. Measurements of the Snow Water Equivalent (SWE) and associated predictions of peak SWE and snowmelt onset are essential inputs for water management efforts. This paper aims to develop an integrated framework for real-time data ingestion, estimation, prediction and visualization of SWE based on daily snow datasets. In particular, we develop a data-driven approach for estimating and predicting SWE dynamics using the Long Short-Term Memory neural network (LSTM) method. Our approach uses historical datasets (precipitation, air temperature, SWE, and snow thickness) collected at NRCS Snow Telemetry (SNOTEL) stations to train the LSTM network and current year data to predict SWE behavior. The performance of our prediction was compared for different prediction dates and prediction training datasets. Our results suggest that the proposed LSTM network can be an efficient tool for forecasting the SWE timeseries, as well as Peak SWE and snowmelt timing. Results showed that the window size impacts the model performance (where the Nash Sutcliffe efficiency (NSE) ranged from 0.96 to 0.85 and the Rooted Mean Square Error (RMSE) ranged from 0.038 to 0.07) with an optimum number that should be calibrated for different stations and climate conditions. In addition, by implementing the LSTM prediction capability in a cloud based site-monitoring platform, we automate model-data integration. By making the data accessible through a graphical web interface and an underlying API which exposes both training and prediction capabilities. The associated results can be made easily accessible to a broad range of stakeholders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effect of Fatty Acid Polyunsaturation on Synthesis and Properties of Emulsion Polymers Based on Plant Oil-Based Acrylic Monomers

This study demonstrated that polymerization behavior of plant oil-based acrylic monomers (POBMs) synthesized in one-step transesterification reaction from naturally rich in oleic acid olive, canola, and high-oleic soybean oils is associated with a varying mass fraction of polyunsaturated fatty acid fragments (linoleic (C18:2) and linolenic (C18:3) acid esters) in plant oil. Using miniemulsion polymerization, a range of stable copolymer latexes was synthesized from 60 wt.% of each POBM and styrene to determine the impact of POBM chemical composition (polyunsaturation) on thermal and mechanical properties of the resulted polymeric materials. The unique composition of each plant oil serves as an experimental tool to determine the effect of polyunsaturated fatty acid fragments on POBM polymerization behavior and thermomechanical properties of crosslinked films made from POBM-based latexes. The obtained results show that increasing polyunsaturation in the copolymers results in an enhanced crosslink density of the latex polymer network which essentially impacts the mechanical properties of the films (both Young’s modulus and toughness). Maximum toughness was observed for crosslinked latex films made from 50 wt.% of each POBM in the monomer feed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2012

Improved Wide Operating Temperature Range of LiNiCoAiO2-based Li-ion Cells with Methyl Propionate-based Electrolytes

Demonstration of wide operating temperature range Li-ion electrolytes Methyl propionate-based wide operating temperature range electrolytes were demonstrated to provide dramatic improvement of the low temperature capability of Quallion prototype Li-ion cells (MCMB-LiNiCoAlO2). Some formulations were observed to deliver over 60% of the room temperature capacity using a 5C rate at - 40oC !! Represents over a 4-fold improvement over the baseline electrolyte system. Demonstrated operational capability of a number of systems over a wide temperature range (-40 to +70 C) Demonstrated reasonably good long term cycle life performance at high temperature (i.e., at +40deg and +50 C) A number of formulations containing electrolytes additives (i.e., FEC, VC, LiBOB, and lithium oxalate) have been shown to have enhanced lithium kinetics at low temperature and promising high temperature resilience. Demonstrated good performance in larger capacity (12 Ah) Quallion Li-ion cells with methyl propionate-based electrolytes. Current efforts focused upon performing life studies and the impact upon low temperature capability.

Li-ion electrolytes↗
NASA NTRS2024

How the INCOSE Model-Based Capability Matrix Has Steered Model-Based Systems Engineering Transformation at NASA

The National Aeronautics and Space Administration (NASA) is embarking on new, complex, and diverse missions to accomplish its scientific and exploration objectives, and it views digital transformation as a key enabler for those missions. The NASA Model-Based Systems Engineering (MBSE) Lead-ership Team (MLT) is leading the charge in the digital transformation of the systems engineering domain at NASA, and it is using the INCOSE Model-Based Capability Matrix (MBCM) as a roadmap. This paper discusses the modifications and tailoring of the INCOSE MBCM (Hale & Hoheb, 2020) for use at NASA, the process the team has taken on multiple rounds of assessment, findings to date, and work products that have been generated as a result of the assessment. The paper will also discuss findings and potential changes that should be made to the original product.

MBSE↗
DOE OSTI2024

Probing Signal-Based Inertia and Frequency Response Estimation for Power Systems with High Penetration of Inverter-Based Resources: Preprint

Power system inertia is the inherent capability of a power system to resist changes in its frequency during disturbances. Real-time inertia estimation technology has become more important due to the low-inertia issues caused by the increasing integration levels of inverter-based resources (IBRs) from renewable energy; however, existing inertia estimation methods hardly consider multiple frequency response controls that act within the same time frame as conventional inertial response, thus making measured inertia values vary under different testing conditions. To resolve this issue, this paper proposes a novel real-time estimation method to simultaneously estimate a power system's inertia constant and frequency response droop constant using a well-designed probing signal. First, we formulate the inertia and frequency response model of a power system with IBRs. Second, through the integration and manipulation of the developed model, we propose a multivariate linear regression- based estimation method that is resilient to measurement noise. Third, we design a probing signal that can be injected by IBRs to incite the required transients for estimation. Finally, we validate the proposed estimation method through comprehensive power- hardware-in-the-loop experiments using inverter hardware and a realistic island power system model. The results demonstrate that the proposed method can accurately estimate the inertia and droop value of the power system with grid-following IBRs and grid-forming IBRs with virtual synchronous machine control.

frequency response↗
DOE OSTI2024

Covalent Triazine-Based Frameworks with Ru-tda Based Catalyst Anchored via Coordination Bond for Photoinduced Water Oxidation

Light-induced water splitting (hν-WS) for the production of hydrogen as a solar fuel is considered a promising sustainable strategy for the replacement of fossil fuels. An efficient system for hν-WS involves a photoactive material that, upon shining light, is capable of separating and transferring charges to catalysts for the hydrogen and oxygen evolution processes. Covalent triazine-based frameworks (CTFs) represent an interesting class of 2D organic light-absorbing materials that have recently emerged thanks to their tunable structural, optical and morphological properties. Typically, catalysts (Cat) are metallic nanoparticles generated in situ after photoelectroreduction of metal precursors or directly drop-casted on top of the CTF material to generate Cat-CTF assemblies. Here, in this work, the synthesis, characterization and photocatalytic performance of a novel hybrid material, Ru-CTF, is reported, based on a CTF structure featuring dangling pyridyl groups that allow the Ru-tda (tda is [2,2':6',2'“-terpyridine]-6,6'”-dicarboxylic acid) water oxidation catalyst (WOC) unit to coordinate via covalent bond. The Ru-CTF molecular hybrid material can carry out the light-induced water oxidation reaction efficiently at neutral pH, reaching values of maximum TOF of 17 h -1 and TONs in the range of 220 using sodium persulfate as a sacrificial electron acceptor.

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

Oxidation Behavior of Welded Fe-Based and Ni-Based Alloys in Supercritical CO 2

Next-generation supercritical CO 2 (sCO 2 ) power cycles will require different classes of alloy throughout the operational temperatures to optimize tradeoff of creep strength, oxidation performance and cost. This will necessitate joining methods such as welding, which might pose compatibility concerns at the joined interfaces. In this study, similar and dissimilar metal welds were generated from a variety of candidate alloys for sCO 2 systems including ferritic/martensitic steels, austenitic steels, and Ni-based superalloys. Samples were extracted from different regions of the welds and exposed to sCO 2 at 550 °C and 20 MPa for 2500 h, then characterized to understand their behavior in this environment. Unsurprisingly, the local oxidation behavior was largely dictated by the Cr content in the underlying metal. High-Cr austenitic steels and Ni alloys formed slow-growing Cr-rich oxide scales with minimal carburization of the underlying metal, while low-Cr ferritic/martensitic steels formed fast-growing Fe-rich oxide scales with significant carburization. Most welds did not show any unusual oxidation behavior at the interfaces, considering the local Cr content. The one exception was the 347H similar metal weld, where a larger grain size and complex grain structure in the fusion zone led to a significantly higher rate of Fe-rich oxide nodule formation compared to the base metal. This suggests that microstructural changes at joined interfaces can play an important role on the oxidation-limited lifetimes in future sCO 2 systems. The composition changes across the interfaces enabled study of the effect of Fe on the growth rate of Cr-rich oxides and of the origins of the sub-surface recrystallization zone that forms beneath them.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model

We report two generative deep-learning models that predict amino acid sequences and 3D protein structures on the basis of secondary-structure design objectives via either the overall content or the per-residue structure. Both models are robust regarding imperfect inputs and offer de novo design capacity because they can discover new protein sequences not yet discovered from natural mechanisms or systems. The residue-level secondary-structure design model generally yields higher accuracy and more diverse sequences. These findings suggest unexplored opportunities for protein designs and functional outcomes within the vast amino acid sequences beyond known proteins. Our models, based on an attention-based diffusion model and trained on a dataset extracted from experimentally known 3D protein structures, offer numerous downstream applications in the conditional generative design of various biological or engineering systems. Future work could include additional conditioning and an exploration of other functional properties of the generated proteins for various properties beyond structural objectives.

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

A microstructure-based modeling approach to predict the mechanical properties of Zr alloy with hydride precipitates

In nuclear reactors, hydrides can form in fuel cladding due to hydrogen absorption in ZIrcaloy and cause embrittlement. This work presents a microstructure-based finite element model to predict the stress-strain response of Zircaloy containing hydrides. Quantitative microstructural details extracted from scanning electron microscopy (SEM) images were used to generate heterogeneous microstructures including the morphology and spatial distribution of hydrides. The constitutive material model for zircaloy in this study is based on crystal plasticity theory which considers the hexagonal close-packed (HCP) atomic structure of Zircaloy material. The hydrides were modeled as brittle material along with a damage model. Hydride formation inside the zircoloy matrix results in residual stress. This phenomenon is also captured in this model. A parametric study has been conducted to understand the effect of volume fraction, orientation, and lamellae thickness of the hydride phase on the mechanical properties of the overall material.

Kulkarni, Shank S.↗
DOE OSTI2020

Microstructural characterization and assessment of mechanical properties of concrete based on combined elemental analysis techniques and Fast-Fourier transform-based simulations

This paper presents a pixel-based modeling approach of concrete which combines an experimental characterization of concrete and the Fast-Fourier transform simulations. High-resolution phase maps created from experimental characterization by micro X-ray fluorescence, energy dispersive X-ray analysis, and X-ray diffraction contain 9 different phases, including 22.17 vol.% of hydrated cement paste, 54.21 vol.% of minerals, and 23.62 vol.% of interfaces. These phases provide the input for determining the effective elastic properties, coupled with Fast-Fourier transform-based simulation. The simulation results show that the effective range of Young’s modulus of concrete is comparable with the range of experimental values ~ 37 ± 4 GPa with the assumption of realistic properties of interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2023

How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗