Search NASA⌕ Search

SEARCH · Search NASA

Results for “effective volume”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Irradiation Impact on Uranium Recovery Under Direct Extraction Conditions

Reducing the quantity of high-level radioactive waste is essential for minimizing environmental impact and improving efficiency of using natural resources for nuclear power. The current standard, Plutonium Uranium Solvent EXtraction (PUREX), uses tributyl phosphate (TBP) ligands to extract complexes of uranium and plutonium from a nitric acid (HNO3) phase. Although this method is effective, large volumes of HNO3 and the non-incinerable phosphate ligands increase the amount of hazardous waste produced. Alternative extractants and flowsheets have been proposed that allow for more selective extraction of radioactive metals, reduced nitric acid use, and easier incineration by only containing carbon, hydrogen, oxygen, and nitrogen (CHON). One candidate, N,N-di(2-ethylhexyl)-isobutyramide (DEHiBA) exhibits promising properties for direct extraction. A HNO3 pre-equilibrated DEHiBA phase selectively extracts U(VI), leaving plutonium, transuranics, and fission products behind as precipitate and reducing the volume of radioactive HNO3 produced.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗
DOE OSTI2022

Rotational flexural strength of cylindrical brittle specimens

Conventional static flexural strength testing of brittle cylindrical rods only subjects a small fraction of the entire specimen's area or volume to the maximum tensile stress. Thus, a nonconservative measured strength likely results since most flaws on the surface or in the bulk are not subjected to a sufficiently high tensile stress that can cause fracture. Here, to mitigate this, a rotational flexural tester and corresponding test method were developed whereby rotation and monotonically increasing three-point flexure were superimposed to investigate fracture response of solid glass cylinders. This combination of rotation and flexure subjects more area and volume of a cylindrical test specimen to tensile stress than a standard static (nonrotating) flexural test. As anticipated, failure stresses were lower for the rotational flexural test. Expressions for effective area and volume are provided for rotating solid rods and tubes subjected to three-point, four-point, uniform, and uniformly distributed load bending configurations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Review of the Effects of Polymer Binder Properties on Microstructure and Irreversible Volume Growth of Plastic Bonded Explosives Formulations

The rational design of effective polymeric binders for the formulation of plastic-bonded explosives (PBX) is challenging due to their inherent compositional complexity. The composites comprise irregularly shaped energetic material (EM) powders coated with low weight fractions of polymer via non-equilibrium processes such as slurry coating. Defects can deleteriously affect PBX stability and performance: nano- to micrometer-scale voids can act as loci for hot spots, lowering deflagration and detonation temperatures in unpredictable ways. Furthermore, some nominally desirable polymer properties are at odds with each other: e. g. good flow characteristics are desirable for coating and adhesion, but mechanical stiffness is needed to prevent deformation and cracking of PBX under mechanical stress. Good binder adhesion is critical, but the best means to predict and measure adhesion in PBX is not obvious. Experimental methods of determining binder adhesion on model surfaces may not capture polymer structural configurations relevant to deposition during coating. Molecular dynamics-based computational models have predicted key observables in PBX formulation, suggesting that they may be powerful tools for binder selection. In this review, primarily recent (~2006 and later) literature on polymeric binders for insensitive HE (IHE) is surveyed. We focus on how binder properties influence observable PBX properties as resistance to irreversible volume growth and void formation in PBX formulations mainly (but not exclusively) featuring 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) as the EM (e. g. PBX 9502, LX-17 and others). Conclusions from these studies yield useful guidelines for choosing HE binder candidates, as well as for the general design of highly-filled polymer composite materials. Finally, studies describing challenges in PBX formulation with the newer and more energetically dense high explosive, LLM-105, will be discussed.

adhesion↗
DOE OSTI2021

Impact of humidity on gas transport in polybenzimidazole membranes

Polybenzimidazoles (PBIs) are promising materials for high temperature H 2 /CO 2 separation in applications such as steam reforming and pre-combustion carbon capture where significant amounts of water are often present. However, PBIs are hydrophilic, and the impact of humidity on PBI gas separation properties is relatively unexplored. Furthermore, opportunity exists to elucidate the interplay between plasticization, free volume, and gas transport in glassy polymer membranes such as PBIs. Here, this study investigates the effect of humidity on H 2 , O 2 , and CO 2 permeabilities at 35 °C in a commercial PBI and two sulfone-containing PBIs. Water uptake significantly reduces PBI gas permeabilities at low humidities due to competitive sorption and antiplasticization. At high humidities, plasticization increases the permeabilities of larger gases in more hydrophilic PBIs. Effective fractional free volumes evaluated from gas permeation data and previously reported water sorption and dilation data suggest water plasticizes PBIs by increasing accessible free volume via enhanced molecular dynamics rather than by creating new free volume cavities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Irradiation and corrosion testing of laser powder bed fusion-manufactured materials in the AMMT program

The Advanced Materials and Manufacturing Technologies (AMMT) program operating under the Department of Energy Office of Nuclear Energy is accelerating the qualification and deployment of additively manufactured materials in advanced reactor environments. Laser powder bed fusion (LPBF) has emerged as a promising additive manufacturing technique to fabricate complex components with the potential of tailored material properties. In the nuclear industry, metal additive manufacturing can offer numerous advantages, such as reduced lead times, streamlined quality assurance, and cost-effective low-volume production of new and replacement components with conventional or novel materials and geometries. The rapid and effective qualification of the effect of processing variability on the performance and degradation of additively manufactured materials is essential for the deployment of these components into advanced reactor environments. As part of this effort, AMMT is performing irradiation and corrosion testing of several materials built by LPBF, including 316L and 316H stainless steels. Our efforts encompass neutron irradiation testing, ion irradiation testing, and advanced modeling to provide a robust technical basis for understanding the effect of process variability on materials degradation and developing a sound methodology for accelerated ion irradiation testing. We develop focused irradiation test plans that align well with risk-informed and technologically inclusive approaches to licensing. We will also discuss our plans and specific concerns for corrosion testing additively manufactured material, with a primary focus on molten salt and liquid sodium environments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Environmental Degradation in Advanced Reactor Environments

The Advanced Materials and Manufacturing Technologies (AMMT) program operating under the Department of Energy Office of Nuclear Energy is accelerating the qualification and deployment of additively manufactured materials in advanced reactor environments. Laser powder bed fusion (LPBF) has emerged as a promising additive manufacturing technique to fabricate complex components with the potential of tailored material properties. In the nuclear industry, metal additive manufacturing can offer numerous advantages, such as reduced lead times, streamlined quality assurance, and cost-effective low-volume production of new and replacement components with conventional or novel materials and geometries. The rapid and effective qualification of the effect of processing variability on the performance and degradation of additively manufactured materials is essential for the deployment of these components into advanced reactor environments. This talk presents an overview of the Environmental Effects area activities within AMMT. AMMT is performing irradiation and corrosion testing of several materials built by LPBF, including 316L and 316H stainless steels. Our irradiation efforts encompass neutron irradiation testing and ion irradiation testing to provide a robust technical basis for understanding the effect of process variability on materials degradation. We develop focused irradiation test plans that align well with risk-informed and technologically inclusive approaches to licensing. We will also discuss our plans and specific concerns for corrosion testing additively manufactured material, with a primary focus on molten salt and liquid sodium environments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Effects of processing temperature, pressure, and fiber volume fraction on mechanical and morphological behaviors of fully-recyclable uni-directional thermoplastic polymer-fiber-reinforced polymers

This work explores a type of composite called thermoplastic polymer-fiber-reinforced polymers (PFRPs), often referred to as self-reinforced composites (SRCs). A representative PFRP was exemplified using unidirectional (UD) ultra-high-molecular-weight polyethylene (UHMWPE) fibers embedded in a high-density polyethylene (HDPE) matrix. The effects of compression molding temperature and pressure on the mechanical and morphological behaviors of the filament-wound PFRPs with various fiber volume fractions (V f ) were experimentally investigated. The results elucidate the evolution of morphologies and tensile properties of the PFRPs due to thermal melting, fiber misalignment from pressure, and (V f )-induced structural variance, which has not been comprehensively reported yet. The highest specific tensile strength and modulus of the PFRP laminae reach 600 MPa/(g/cm 3 ) and 31 GPa/(g/cm 3 ), respectively. These properties are comparable to glass-/aramid-fiber-reinforced polymers (GFRPs, GFRTPs, AFRPs, and AFRTPs), with PFRPs exhibiting better ductility (specific strain at peak load ≈ 4%/(g/cm 3 )) than other common polymer composites. The motivation for this work was the high recyclability of PFRPs, which can be recycled by melting both the fibers and the matrix, and then reshaped them for re-manufacturing composites to maximize the efficiency in material reuse. This process simplifies the implementation of closed-loop recycling, re-manufacturing, and reuse to support sustainability in composites. This work aims to contribute to advancing thermoplastic PFRPs for their potential applications in various industries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Suppressing the sample variance of DESI-like galaxy clustering with fast simulations

Ongoing and upcoming galaxy redshift surveys, such as the Dark Energy Spectroscopic Instrument (DESI) survey, will observe vast regions of sky and a wide range of redshifts. In order to model the observations and address various systematic uncertainties, N-body simulations are routinely adopted, however, the number of large simulations with sufficiently high mass resolution is usually limited by available computing time. Therefore, achieving a simulation volume with the effective statistical errors significantly smaller than those of the observations becomes prohibitively expensive. In this study, we apply the Convergence Acceleration by Regression and Pooling (CARPool) method to mitigate the sample variance of the DESI-like galaxy clustering in the AbacusSummit simulations, with the assistance of the quasi-N-body simulations FastPM. Based on the halo occupation distribution (HOD) models, we construct different FastPM galaxy catalogs, including the luminous red galaxies (LRGs), emission line galaxies (ELGs), and quasars, with their number densities and two-point clustering statistics well matched to those of AbacusSummit. We also employ the same initial conditions between AbacusSummit and FastPM to achieve high cross-correlation, as it is useful in effectively suppressing the variance. Our method of reducing noise in clustering is equivalent to performing a simulation with volume larger by a factor of 5 and 4 for LRGs and ELGs, respectively. We also mitigate the standard deviation of the LRG bispectrum with the triangular configurations k 2 = 2k 1 = 0.2 h Mpc -1 by a factor of 1.6. With smaller sample variance on galaxy clustering, we are able to constrain the baryon acoustic oscillations (BAO) scale parameters to higher precision. The CARPool method will be beneficial to better constrain the theoretical systematics of BAO, redshift space distortions (RSD) and primordial non-Gaussianity (NG).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Arm and shoulder muscle segmentation in axial MRI with UNet deep learning model

Quantifying individual upper-limb muscle volumes from MRI provides key insight into muscle-specific strength, deficits, and adaptations. Manual delineation is the gold standard but time‑intensive, and the performance of current deep learning approaches, particularly for small or anatomically complex muscles, remains incompletely characterized. We evaluated a state‑of‑the‑art deep learning framework across the entire upper limb and analyzed factors governing segmentation performance, with attention to the forearm. Three previously published MRI datasets (1.5 T, 3D GRE T1‑weighted; total n = 39) spanning young, middle‑aged, and older adults were curated and quality‑checked, including expert manual segmentations for 31 muscles. Following multiclass mask reconstruction, we trained three 3D nnU‑Net multiclass models matched to the muscle subsets present across datasets, using five‑fold cross‑validation and a composite Dice Similarity Coefficient (DSC) + cross entropy loss. Segmentation accuracy was assessed with DSC. Performance varied across muscles (mean DSC = 0.806 ± 0.098), ranging from 0.920 (Deltoid) to 0.461 (Extensor pollicis brevis). In uncertainty‑weighted regressions, muscle volume was positively associated with DSC (R2 = 0.36, p < 0.001), whereas training segmentation count and muscle orientation showed negligible associations (R2 ≤ 0.06). A weighted mixed‑effects model identified volume as the strongest evaluated predictor, explaining 23.9% of variance in DSC; orientation and training count each contributed <1%, leaving 61.5% unexplained. These results indicate that deep learning–based segmentation can accurately quantify muscle volume for many upper‑limb muscles but remains constrained for small, low‑contrast forearm muscles.

Gillespie, Samuel↗
DOE OSTI2020

Seasonal and Long‐Term Groundwater Unloading in the Central Valley Modifies Crustal Stress

Abstract Changes in terrestrial water content cause elastic deformation of the Earth's crust. This deformation is thought to play a role in modulating crustal stress and seismicity in regions where large water storage fluctuations occur. Groundwater is an important component of total water storage change in California, helping to drive annual water storage fluctuations and loss during periods of drought. Here we use direct estimates of groundwater volume loss during the 2007–2010 drought in California's Central Valley obtained from high resolution Interferometric Synthetic Aperture Radar‐based vertical land motion data to investigate the effect of groundwater volume change on the evolution of the stress field. We show that GPS‐derived elastic load models may not capture the contribution of groundwater to terrestrial water loading, resulting in an underestimation of nontectonic crustal stress change. We find that groundwater unloading during the drought causes Coulomb stress change of up to 5.5 kPa and seasonal fluctuations of up to 2.6 kPa at seismogenic depth. We find that faults near the Valley show the largest stress change and the San Andreas fault experiences only ~40 Pa of Coulomb stress change over the course of a year from groundwater storage change. Annual Coulomb stress change peaks dominantly in the fall, when the groundwater level is low; however, some faults experience peak stress in the spring when groundwater levels are higher. Additionally, we find that periods of increased stress correlate with higher than average seismic moment release but are not correlated with an increase in the number of earthquakes. This indicates groundwater loading likely contributes to nontectonic loading of faults, especially near the Valley edge, but is not a dominant factor in modulation of seismicity in California because the amplitude of stress change declines rapidly with distance from the Valley. By carefully quantifying and spatially locating groundwater fluctuations, we will improve our understanding of what drives nontectonic stress and forces that modulate seismicity in California.

58 GEOSCIENCES↗
DOE OSTI2023

Rethinking the Role of Financial Transmission Rights in Wind-Rich Electricity Markets in the Central U.S.

Transmission congestion can cause a divergence between wholesale power prices at the individual pricing nodes where power is generated and the more-liquid trading hubs where that power is often delivered and sold. This nodal price difference is commonly referred to as the “locational basis” (or just “basis”). Because the basis varies over time, it can—if not hedged—unpredictably affect a wind plant’s revenue and/or value, which increases investor risk and potentially slows deployment. We find wind plants typically face a larger and more-negative basis than do thermal generators, and hence are more-negatively impacted by congestion. Moreover, while most thermal generators can effectively hedge basis risk by purchasing conventional fixed-volume financial transmission rights (FTRs), these fixed-volume FTRs do not effectively hedge basis risk for variable wind generation. More-effective hedging mechanisms may be required to support those generators most-impacted by congestion, and to promote continued investment in variable generation resources in congested markets.

17 WIND ENERGY↗
DOE OSTI2022

Initial ASME code rule analysis on Simple and Full assessment

ASME & ASTM codes present simple and full assessment methodologies to qualify nuclear grade graphite components. This report explains the theory behind the statistical portion of the codes and describes the issues discussed in a 2020 workshop in the simple assessment, as well as the full assessment. The methodology for the full assessment was developed by Hindley []. He used a validation methodology to match the experimental average failure load of test specimens from several geometries to the calculated 50% POF load to tune the grouping criteria parameters based on an RMSE penalty function. The grouping criteria consists of a minimum volume to satisfy the weakest link theory of the Weibull distribution and a minimum stress range parameter. Hindley’s work found a minimum link volume of 10 times the grain size and a stress range parameter of 7% [satisfy the validation requirements. Several studies found that the link volume of 10 times the grain size was not satisfactory for the volume grouping criteria for graphite grades with fine grains. ASME 2021 code adopted a new volume grouping criteria based on fracture toughness for calculation of the process zone volume. However, errors were found in the ASME 2021 code process zone volume equation and the question is now open as to what volume grouping criteria is satisfactory. This report presents results from sensitivity studies that were done to evaluate the volume grouping criteria effect on the full assessment POF. This report also presents results from sensitivity studies on other aspects of the code that affect the POF, the mesh size and the choice of the Weibull threshold parameter. All three sensitivity studies: the volume grouping criteria, the mesh size, and the threshold parameter choice are found to affect the component qualification decision for components of structural reliability class 1 (SRC-1). The sensitivity analysis results are presented in the body of the report for NBG-18, which is the same grade of graphite as Hindley used in his thesis. Results from NBG-17, IG-110, 2114, and PCEA are presented in the Appendix. For NBG-18, it was found that increasing the threshold increases the POF, finer meshes result in higher POFs, and that smaller link volume requirements lead to higher POFs (which is inconsistent with Hindley’s findings). This report proposes a new validation study similar to Hindley’s []. The purpose of the new validation study is to tune the threshold, mesh size and grouping criteria based on results from multiple grades of graphite, including finer grain graphites T220 (1-3 microns) and NG-CT-50 (5 microns). The test specimens will be broken at 3 labs, such that we can measure between lab measurement variability, with at least 5 test specimens per lab such that the within lab uncertainty can also be measured. Hindley’s validation method tried matching the average experimental load with the median. The future study will attempt to match failures at lower percentiles, which would be closer to the SRC POF limit. We are currently looking into what geometries make sense and the best method to measure multi-axial stress states. These design decisions are still being set.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Longitudinal coherence and short-term wind speed prediction based on a nacelle-mounted Doppler lidar

The spatial structure of turbulence in atmospheric boundary layer flows is highly relevant to wind energy. In particular, wind turbine control strategies based on inflow preview measurements require knowledge of the longitudinal evolution of turbulent flow as it approaches the rotor. These upstream measurements are usually obtained with nacelle-mounted wind lidars. In contrast to traditional in situ anemometry, lidars collect measurements within a probe volume which varies in size depending on the technology of the commercial system being used. Here, we address two issues related to the use of wind lidar to measure the incoming flow to a wind turbine: (i) whether existing longitudinal coherence models can be used to predict flow at the rotor, based on measurements performed at a distance away from the rotor; and (ii) what effect probe-volume averaging has on the inflow predictions. These two questions are critical to the design and implementation of robust wind turbine control strategies. To address these questions, we perform field measurements and large-eddy simulations to determine which incoming flow structures can be readily predicted with existing coherence models, and which require additional corrections to account for lidar volumetric averaging effects. Results reveal that the wind turbine induction zone has a negligible impact on the longitudinal coherence and first-order turbulence quantities, such as the standard deviation of velocity fluctuations. However, the phase of the signal, from which advection time periods of the turbulent structures are derived, is affected by the rotor blockage effect.

17 WIND ENERGY↗
DOE OSTI2022

Clustering of inertial particles in turbulent flow through a porous unit cell

We report direct numerical simulation is used to investigate effects of turbulent flow in the confined geometry of a face-centred cubic porous unit cell on the transport, clustering and deposition of fine particles at different Stokes numbers (St = 0.01, 0.1, 0.5, 1, 2) and at a pore Reynolds number of 500. Particles are advanced using one-way coupling and the collision of particles with pore walls is modelled as perfectly elastic with specular reflection. Tools for studying inertial particle dynamics and clustering developed for homogeneous flows are adapted to take into account the embedded, curved geometry of the pore walls. The pattern and dynamics of clustering are investigated using the volume change of Voronoi tesselation in time to analyse the divergence and convergence of the particles. Similar to the case of homogeneous, isotropic turbulence, the cluster formation is present at large volumes, while cluster destruction is prominent at small volumes and these effects are amplified with the Stokes number. However, unlike homogeneous, isotropic turbulence, the formation of a large number of very small volumes was observed at all Stokes numbers and attributed to the collision of particles with the pore wall. Multiscale wavelet analysis of the particle number density indicates that the peak of the energy density spectrum, representative of enhanced particle clustering, shifts towards larger scales with an increase in the Stokes number. Scale-dependent skewness and flatness quantify the intermittent void and cluster distribution, with cluster formation observed at small scales for all Stokes numbers, and void regions at large scales for large Stokes numbers.

42 ENGINEERING↗
DOE OSTI2021

Insights from Computational Studies on the Anisotropic Volume Change of Li x NiO 2 at High States of Charge (x < 0.25)

The need for high-capacity Li-ion battery cathodes has favored the increase of Ni content in commercial battery cells. However, at high states of charge (SOCs), Ni-rich materials undergo a phase transition and volume collapse with deleterious effects on battery performance. It is uncertain whether this drastic volume change is caused by the phase transition or not. To provide more insight into the volume-phase transition relationship in the high Ni cathode LixNiO 2 , we performed density functional theory calculations, along with molecular dynamics simulations using machine learning potentials to calculate the temperature- and composition-dependent free energy differences between the suspected phases at high SOCs (x < 0.25). Herein, we find that the calculated free energy difference between the suspected phases containing different oxygen stacking sequences is small at room temperature. Furthermore, we find that the collapse of the layered LiNiO 2 c-lattice parameter at high SOCs is mainly due to the electronic depletion of the oxygen sublattice and the lack of screening from positive Li ions. The interactions between adjacent oxygen ions across an empty Li layer (NiO 2 ) are largely controlled by van der Waals interactions and are in fact similar regardless of the oxygen stacking, which explains the negligible free energy differences between O1 and O3 stacking in NiO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Toward more-robust, AI-enabled subsurface seismic imaging for geotechnical applications

Non-invasive seismic imaging has the potential to cost-effectively evaluate large volumes of subsurface material to inform geotechnical site investigation. However, seismic imaging using full waveform inversion (FWI) requires significant computational time and is dependent on an initial starting model. As a result, FWI has not yet been widely adopted into geotechnical practice. Previous efforts, on relatively simple two-layered models, indicate that data-driven artificial intelligence (AI) models may be as effective as FWI at predicting 2D images of shear wave velocity (V s ). Furthermore, the AI model predictions can be made almost instantaneously after data acquisition and do not require an initial starting model. We examine the generality of these findings by developing a new AI model for subsurface seismic imaging, whereby we make several notable contributions. First, we architect a multimodal AI model that combines time- and frequency-domain representations of the seismic wavefield to predict a 50 m by 20 m subsurface image of V s . Second, we developed a new diverse dataset of 100,000 images with their corresponding seismic wavefields to train the AI model. Third, we propose four physics-informed data augmentations for data-driven seismic imaging. Fourth, we develop two prediction consistency tests to evaluate the model’s performance when the true subsurface is unknown. Our final model, which has been made publicly available, is capable of predicting a subsurface V s image from a single seismic wavefield with an average, mean absolute percent error (MAPE) of 24 %. The predictive model is applied to a field dataset and shown to be consistent with local geology and shear-wave refraction measurements from the same location.

Artificial intelligence↗