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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Engineering Design of a Linde-BASF Advanced Post-Combustion CO 2 Capture Technology at a Linde Steam Methane Reforming H 2 Plant

Linde carried out an initial engineering design study for a Linde-BASF advanced post combustion CO 2 capture technology to be installed at a commercial-scale steam methane reforming (SMR) hydrogen plant located in the US Gulf Coast. This pre-FEED equivalent study included following: (1) basic design, including specific project scope definition and design basis, (2) basic engineering, including development of process flow diagrams and heat & material balances, (3) inside the battery limit (ISBL) equipment and systems specification, (4) balance of plant outside the battery limit (OSBL) equipment and systems specifications, (5) technology maturation plan, (6) hazard and operability (HAZOP) review, (7) environmental, health and safety (EH&S) assessment and environmental permitting analysis, (8) constructability review, (9) ISBL and OSBL EPC cost estimation, and (10) commercial-scale techno-economic analysis including capital expenditures (CAPEX) and operating expenditures (OPEX) and CO 2 capture cost estimates.

08 HYDROGEN↗
DOE OSTI2024

O'Hare Airport roadway traffic prediction via data fusion and Gaussian process regression

This study proposes an approach of leveraging information gathered from multiple traffic data sources at different resolutions to obtain approximate inference on the traffic distribution of Chicago's O'Hare Airport area. Specifically, it proposes the ingestion of traffic datasets at different resolutions to build spatiotemporal models for predicting the distribution of traffic volume on the road network. Due to its good adaptability and flexibility for spatiotemporal data, the Gaussian process (GP) regression was employed to provide short-term forecasts using data collected by loop detectors (sensors) and supplemented by telematics data. The GP regression is used to make predictions of the distribution of the proportion of sensor data traffic volume represented by the telematics data for each location of the sensors. Consequently, the fitted GP model can be used to determine the approximate traffic distribution for a testing location outside of the training points. Policymakers in the transportation sector can find the results of this work helpful for making informed decisions relating to current and future transportation conditions in the area.

42 ENGINEERING↗
DOE OSTI2025

Probing the pH Effect on Boehmite Particles in Water Using Vacuum Ultraviolet Single-Photon Ionization Mass Spectrometry

Boehmite has been widely used in theoretical research and industry, especially for hazardous material processing. For the liquid-phase treating process, the interfacial properties of boehmite are believed to be affected by pH conditions, which change its physicochemical behavior. However, molecular-level detection on cluster ions is challenging when using bulk approaches. Herein we employ in situ vacuum ultraviolet single-photon ionization mass spectrometry (VUV SPI-MS) coupled with a vacuum-compatible microreactor system for analysis at the liquid–vacuum interface (SALVI) to investigate the solute molecular composition of boehmite under different pH conditions for the first time. The mass spectral results show that more complex clustering of solute molecules exists at the solid–liquid (s–l) interface than conventionally perceived in a “simple” aqueous solution. Besides solute ions, such as boehmite molecules and fragments, the composition and appearance energies of these newly discovered solvated cluster ions are determined by VUV SPI-MS in different pH solutions. We offer new results for the pH-dependent effect of boehmite and provide insights into a more detailed solvation mechanism at the s–l interface. By comparing the key products under different pH conditions, fundamental understanding of boehmite dissolution is revealed to assist the engineering design of waste processing and storage solutions.

SALVI↗
DOE OSTI2020

Electrochemical-assisted leaching of active materials from lithium ion batteries

The development of a circular economy for lithium-ion batteries (LIB) is essential to realize decarbonization and an electrified energy market. However, the current commercial operations that recover valuable constituents from LIBs require significant energy and reagents, which create toxic emissions and additional waste. We report an electrochemical-based method for leaching valuable metals from the active materials of mixed shredded LIBs. In this process, the use electrons, as green reagent, allows the use and regeneration of Fe 2+ in low concentrations as substitute for hydrogen peroxide as a reducing agent. Leaching in a membrane separated two-compartment electrochemical cell contributes to decrease the acid requirements as H+ can be generated electrochemically. With this design, leaching efficiencies over 96% for the active metals (Li, Co, Mn, and Ni) were demonstrated at pulp densities up to 240 g/L. Copper present in the active material is electrowon and recovered separately. Preliminary cost analyses demonstrate ca. 80% reduction in energy and chemical costs as compared to traditional hydrometallurgical routes.

25 ENERGY STORAGE↗
DOE OSTI2021

The mechanics of brittle granular materials with coevolving grain size and shape

The influence of particle shape on the mechanics of sand is widely recognized, especially in mineral processing and geomechanics. However, most existing continuum theories for engineering applications do not encompass the morphology of the grains and its evolution during comminution. Similarly, the relatively few engineering models accounting for grain-scale processes tend to idealize particles as spheres, with their diameters considered as the primary and sole geometric descriptor. This paper inspires a new generation of constitutive laws for crushable granular continua with arbitrary, yet evolving, particle morphology. We explore the idea of introducing multiple grain shape descriptors into Continuum Breakage Mechanics (CBM), a theory originally designed to track changes in particle size distributions during confined comminution. We incorporate the influence of these descriptors on the elastic strain energy potential and treat them as dissipative state variables. In analogy with the original CBM, and in light of evidence from extreme fragmentation in nature, the evolution of the additional shape descriptors is postulated to converge towards an attractor. Comparisons with laboratory experiments, discrete element analyses and particle-scale fracture models illustrate the encouraging performance of the theory. The theory provides insights into the feedback among particle shape, compressive yielding and inelastic deformation in crushable granular continua. These results inspire new questions that should guide future research into crushable granular systems using particle-scale imaging and computations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Creep Behavior of a High-$\gamma^{\prime}$ Ni-Based Superalloy Fabricated via Electron Beam Melting

Additive manufacturing enables the fabrication of complex engineering components previously inaccessible through traditional processes. Nickel-base superalloys with large γ' volume fraction are typically considered non-weldable and therefore exhibit a propensity for cracking during the fusion process. These crack-prone materials, however, are of great importance in gas turbine engines due to their excellent high temperature creep resistance. In this study we investigate the creep behavior of IN738LC produced by the electron beam melting process. Here, we find that with appropriate post-build heat treatment the creep response of material oriented in the build direction exhibits deformation and rupture behavior comparable to that of conventionally cast IN738 & IN738LC. In the transverse direction properties fall below the expected cast behavior, however, we argue this is likely due to differences in grain scale and crystallographic texture. It may be possible to coarsen the grain morphology with appropriate process-parameter optimization in order to reduce the severity of intergranular fracture in the transverse direction. These results illustrate that high temperature properties exhibited by additively manufactured IN738LC are suitable for the hot section of gas turbine engines.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

This paper introduces a surrogate modeling scheme based on Grassmannian manifold learning to be used for cost-efficient predictions of high-dimensional stochastic systems. The method exploits subspace-structured features of each solution by projecting it onto a Grassmann manifold. This point-wise linear dimensionality reduction harnesses the structural information to assess the similarity between solutions at different points in the input parameter space. The method utilizes a solution clustering approach in order to identify regions of the parameter space over which solutions are sufficiently similarly such that they can be interpolated on the Grassmannian. In this clustering, the reduced-order solutions are partitioned into disjoint clusters on the Grassmann manifold using the eigen-structure of properly defined Grassmannian kernels and, the Karcher mean of each cluster is estimated. Then, the points in each cluster are projected onto the tangent space with origin at the corresponding Karcher mean using the exponential mapping. For each cluster, a Gaussian process regression model is trained that maps the input parameters of the system to the reduced solution points of the corresponding cluster projected onto the tangent space. Using this Gaussian process model, the full-field solution can be efficiently predicted at any new point in the parameter space. In certain cases, the solution clusters will span disjoint regions of the parameter space. In such cases, for each of the solution clusters we utilize a second, density-based spatial clustering to group their corresponding input parameter points in the Euclidean space. The proposed method is applied to two numerical examples. Here, the first is a nonlinear stochastic ordinary differential equation with uncertain initial conditions where the surrogate is used to predict the time history solution. The second involves modeling of plastic deformation in a model amorphous solid using the Shear Transformation Zone theory of plasticity, where the proposed surrogate is used to predict the full strain field of a material specimen under large shear strains.

42 ENGINEERING↗
DOE OSTI2024

A crystal-plasticity-informed Gaussian Process Regression model to capture anisotropy in single crystal shape memory alloys

This work presents a machine learning (ML) framework that model the anisotropic actuation responses in a shape memory alloy. A Gaussian Process Regression (GPR) based ML model is trained on a set of different crystal orientations subjected to different actuation conditions. The training employed thermo-mechanical responses from a crystal-plasticity model that captures phase-transformation, stress-induced plasticity, and transformation-induced plasticity. Further, on training the GPR-ML model at fixed stress level for different orientations, it captured the thermo-mechanical responses accounting for the anisotropy, and predicted responses for new orientations with good accuracy. The GPR-ML model is able to capture the transformation temperature variations even when trained using multiple stress levels, and the transformation strain showed significant deviations. The developed GPR-ML model gave reasonable predictions for an unexplored sample set of orientations and loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Green solvent-processable poly(3-hexylthiophene) derivative maintains high hole mobility in diodes

The development of environmentally sustainable fabrication methods for organic solar cells (OSCs) is critical to enable their large-scale adoption. Conventional solution-processed OSCs often rely on halogenated, toxic solvents that pose health and environmental risks, limiting their scalability. This is because π-conjugated polymers tend to have a low solubility in non-halogenated solvents. A common strategy to enhance solubility in alternative solvents is through the incorporation of polar solubilizing groups, often on every repeat unit of at least one monomer. However, too many polar side chains tend to disrupt favorable morphology and decrease charge transport properties. In this study, we demonstrate that a minimal degree of side chain functionalization can improve green solvent processability while preserving electrical performance. We tested this hypothesis using our previously reported random copolymer derivative of poly(3-hexylthiophene) (P3HT) where ∼10 mol% of the side chains are 6-pentanoatehexyl side chains. We find that this low-level functionalization significantly broadens the solvents that can be used to process P3HT to less toxic solvents such as o-xylene and anisole. Furthermore, the copolymer processed from greener solvents showed high hole mobilities (∼10⁻ 3 cm 2 /Vs by the Space-Charge Limiting Current method in diodes), comparable to P3HT films cast from toxic chloroform. These findings suggest that minimal side-chain modification is a viable strategy for expanding green solvent compatibility while preserving electrical performance, paving the way toward more sustainable organic semiconductor designs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Low Threshold Voltages Electrochemically Drive Gold Migration in Halide Perovskite Devices

The constituent ions of halide perovskite materials are mobile in the solid state and known to participate in reduction/oxidation reactions. Yet few parameters related to electrochemical processes occurring within devices have been carefully determined. Here, we characterize such reactions in model MAPbI3 perovskite devices, and we quantify threshold voltages for key reactions. Gold is oxidized and mobilized under nitrogen, in the dark, at the MAPbI3/Au interface at 0.8 V. When this interface is buffered with the organic hole transport material (HTM), 2,2',7,7'-tetrakis(N,N-di-p-methoxyphenylamino)-9,9'-spirobifluorene, MAPbI3 begins to degrade at 1.2 V; degradation liberates ions that subsequently enable Au migration. Thresholds are insensitive to MAPbI3 thickness or choice of organic HTM or oxide cathode, which suggests that these reactions are driven electrochemically; the electric field across the device is less important. These results have profound implications for understanding in operando degradation pathways of optoelectronic perovskite devices that are varied in terms of interface structures, active material compositions, and ranges of external stressors.

42 ENGINEERING↗
DOE OSTI2023

Combining synchrotron X-ray diffraction, mechanistic modeling and machine learning for in situ subsurface temperature quantification during laser melting

Laser melting, such as that encountered during additive manufacturing, produces extreme gradients of temperature in both space and time, which in turn influence microstructural development in the material. Qualification and model validation of the process itself and the resulting material necessitate the ability to characterize these temperature fields. However, well established means to directly probe the material temperature below the surface of an alloy while it is being processed are limited. To address this gap in characterization capabilities, a novel means is presented to extract subsurface temperature-distribution metrics, with uncertainty, from in situ synchrotron X-ray diffraction measurements to provide quantitative temperature evolution data during laser melting. Temperature-distribution metrics are determined using Gaussian process regression supervised machine-learning surrogate models trained with a combination of mechanistic modeling (heat transfer and fluid flow) and X-ray diffraction simulation. The trained surrogate model uncertainties are found to range from 5 to 15% depending on the metric and current temperature. The surrogate models are then applied to experimental data to extract temperature metrics from an Inconel 625 nickel superalloy wall specimen during laser melting. The maximum temperatures of the solid phase in the diffraction volume through melting and cooling are found to reach the solidus temperature as expected, with the mean and minimum temperatures found to be several hundred degrees less. The extracted temperature metrics near melting are determined to be more accurate because of the lower relative levels of mechanical elastic strains. However, uncertainties for temperature metrics during cooling are increased due to the effects of thermomechanical stress.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Biogas Biocatalysis

Biogas derived from anaerobic digestion of waste streams such as biorefinery wastewater, animal, agricultural, and municipal solid waste, offers a versatile renewable energy source. Total domestic methane potential from landfill material, animal manure, wastewater, and organic waste, combined with biogas generated from AD of lignocellulosic biomass, is estimated to offer >4 quadrillion Btu potential energy. This energy could displace nearly half of current domestic natural gas consumption in the electric power sector and all consumption in the transportation sector. However, despite this promise of this feedstock, its gaseous state prevents facile integration with extant transportation and industrial infrastructure. Microbial conversion of biogas to liquid fuel and chemical intermediates offers valorization potential. However, biogas biocatalysis is currently limited by poor substrate gas-to-liquid mass transfer, low conversion efficiencies, and incomplete biogas utilization. To this end, the Biogas Biocatalysis AOP aims to develop a carbon- and energy-efficient biogas bioconversion process via techno-economic-informed strain and fermentation engineering strategies. Efforts here will improve both process economics and sustainability via process-intensified, carbon-efficient biogas bioconversion to value-added platform molecules, enabling bolt-on deployment for valorization of biogas derived from standalone AD infrastructure.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗
DOE OSTI2023

Time‐Lapse Image Classification Using a Diffractive Neural Network

Diffractive deep neural networks (D 2 NNs), comprised of spatially engineered passive surfaces, collectively process optical input information at the speed of light propagation through a thin diffractive volume, without any external computing power. Diffractive networks were demonstrated to achieve all‐optical object classification and perform universal linear transformations. Herein, a “time‐lapse” image classification scheme using a diffractive network is demonstrated for the first time, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other. In a different context, such relative movements of the objects and/or the camera are routinely being used for image super‐resolution applications; inspired by their success, a time‐lapse diffractive network is designed to benefit from the complementary information content created by controlled or random lateral shifts. The design space and performance limits of time‐lapse diffractive networks are numerically explored, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR‐10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network on the CIFAR‐10 dataset. Time‐lapse diffractive networks will be broadly useful for the spatiotemporal analysis of input signals using all‐optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computational Fermi Level Engineering and Doping-Type Conversion of Ga2O3 Via Three-Step Processing

Ga2O3 is being actively explored for power electronics, and other applications due to its ultra- wide bandgap and low projected fabrication cost of high-quality crystals. N-type doping of Ga2O3 can be achieved and tuned, but p-type doping faces fundamental obstacles. Successful engineering of Ga2O3 based devices requires critical control of doping density, Fermi level position, providing opportunities for predictive process simulation. We use first-principles defect equilibrium calculations to simulate a 3-step growth- annealing-quench protocol for hydrogen assisted Mg doping in Ga2O3, taking into account the hydrogen- oxygen-water gas phase equilibrium. We predict type conversion to a net p-type regime following O-rich annealing after growth under reducing conditions in the presence of H2. We show that there is an optimal temperature that maximizes the net acceptor density during the equilibrium annealing step for a given Mg doping level. Quenching of non-equilibrium annealed samples then results in a Fermi level EF below mid-gap down to about EV +1.5 eV, creating a significant number of uncompensated neutral MgGa0 acceptors. This type converted Ga2O3 can create significant built-in field in a p-n junction with an adjoining n-type material and could enable vertical MOSFETs.

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS,M↗
DOE OSTI2022

Delaunay walk for fast nearest neighbor: accelerating correspondence matching for ICP

Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of ICP and exploits the iterative aspect of ICP by caching previous correspondences to expedite each iteration. An algorithmic analysis and comparison is conducted showing an order of magnitude speedup for both serial and vector processor implementation.

3d point cloud processing↗
DOE OSTI2025

Mechanical and Thermal Characterization of Additively Manufactured Carbon/Nylon 12 and Carbon/PEEK Composites

This study explores additive manufacturing of carbon fiber-reinforced thermoplastic composites using the Composite-Based Additive Manufacturing (CBAM) process. Carbon/Nylon 12 and Carbon/PEEK composites were fabricated and evaluated through mechanical (compression, tensile, flexural, and impact) and thermal (DSC and TGA) tests. Carbon/PEEK exhibited superior mechanical performance, with 97.5% higher tensile strength, 79.8% higher elastic modulus, and 59.6% higher flexural strength compared to Carbon/Nylon 12. Thermal testing showed that Carbon/PEEK had higher thermal stability, beginning degradation at 350 °C versus 298 °C for Carbon/Nylon. These results indicate that CBAM-fabricated Carbon/PEEK composites are suitable for applications requiring high strength and temperature resistance.

Additive manufacturing↗
DOE OSTI2020

Self-organized criticality in multi-pulse gamma-ray bursts

The variability in multi-pulse gamma-ray bursts (GRBs) may help to reveal the mechanism of underlying processes from the central engine. To investigate whether the self-organized criticality (SOC) phenomena exist in the prompt phase of GRBs, we statistically study the properties of GRBs with more than 3 pulses in each burst by fitting the distributions of several observed physical variables with a Markov Chain Monte Carlo approach, including the isotropic energy E iso , the duration time T, and the peak count rate P of each pulse. Our sample consists of 454 pulses in 93 GRBs observed by the CGRO/BATSE satellite. The best-fitting values and uncertainties for these power-law indices of the differential frequency distributions are: α$^{d}_{E}$ = 1.54±0.09, α$^{d}_{T}$ = 1.82±$^{+0.14}_{0.15}$ and α$^{d}_{P}$ = 2.09$^{+0.18}_{–0.19}$, while the power-law indices in the cumulative frequency distributions are: α$^{c}_{E}$ = 1.44$^{+0.08}_{–0.10}$, α$^{c}_{T}$ = 1.75$^{+0.141}_{–0.13}$ and α$^{c}_{P}$ = 1.99$^{+0.16}_{–0.19}$. We find that these distributions are roughly consistent with the physical framework of a Fractal-Diffusive, Self-Organized Criticality (FD-SOC) system with the spatial dimension S = 3 and the classical diffusion β=1. The results of this study support that the jet responsible for the GRBs should be magnetically dominated and magnetic instabilities (e.g., kink model, or tearing-model instability) lead the GRB emission region into the SOC state.

79 ASTRONOMY AND ASTROPHYSICS↗