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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Volumetric efficiency and pressure pulsations of a triplex reciprocating supercritical CO 2 pump

A reciprocating triplex pump has been integrated into a closed-loop supercritical carbon dioxide test facility. The pump is designed to compress liquid or supercritical carbon dioxide and has a maximum volumetric flow rate of 51 L/min. A sweep across the pump’s speed range of 100–1000 rev/min was performed with inlet conditions between 60 and 160 bar. The volumetric efficiency was found to increase as pump speed increased from 10 to 40 % before it plateaued at higher pump speeds. The mechanical efficiency increased linearly with pump speed and was not affected by system pressure. Pressure pulsations caused by the plungers and valves were also measured at each speed. Additionally, a 35-liter buffer volume, connected near the pump’s discharge was isolated at each speed to determine its effectiveness as a pulsation dampener. Overall the reciprocating pump was shown to perform exceptionally with liquid or supercritical inlet conditions applicable to many supercritical fluid applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Dark Energy Survey Year 3 results: Simulation-based 𝑤CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. Here, this work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the CosmoGridV1 suite of N-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological 𝑤CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent Buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving 2−3× higher figures of merit in the 𝛺 𝑚 − 𝑆 8 plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming Stage-IV wide-field imaging surveys.

Thomsen, A. [Zurich, ETH] (ORCID:0000000203099021)↗
DOE OSTI2021

Strongly Anisotropic Thermomechanical Response to Shock Wave Loading in Oriented Samples of the Triclinic Molecular Crystal 1,3,5-Triamino-2,4,6-trinitrobenzene

All-atom molecular dynamics (MD) simulations were used to study shock wave loading in oriented single crystals of the highly anisotropic triclinic molecular crystal 1,3,5-triamino-2,4,6-trinitrobenzene (TATB). The crystal structure consists of planar hydrogen-bonded sheets of individually planar TATB molecules that stack into graphitic-like layers. Shocks were studied for seven systematically prepared crystal orientations with limiting cases that correspond to shock propagation exactly perpendicular and exactly parallel to the graphitic-like layers. The simulations were performed for initially defect-free crystals using a reverse-ballistic configuration that generates explicit, supported shocks. Final longitudinal stress components are between ≈8.5 and ≈10.5 GPa for the 1.0 km s –1 impact speed studied. Orientation-dependent properties are reported including shock speeds, stresses, temperatures, compression ratios, and local material strain rates. Spatiotemporal maps of the temperature, stress tensor, material flow, and molecular orientations reveal complicated processes that arise for specific shock directions. Furthermore, the results indicate that TATB shock response is highly sensitive to crystal orientation, with significant qualitative differences for the time evolution of the stress tensor and temperature, elastic/inelastic compression response, defect formation and growth, critical von Mises stress, and strain rates during shock rise that span nearly an order of magnitude. A variety of inelastic deformation mechanisms are identified, ranging from crumpling of graphitic-like layers to dislocation-mediated plasticity to intense shear strain localization. To our knowledge, these are the first systematic MD simulations and analysis of explicit shock wave propagation along nontrivial crystal directions in a triclinic molecular crystal.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Probing turbulence with lasers and coherent light sources

Fluid and plasma turbulence consists of multi-scale interactions where the energy of the large scales is dissipated through a cascade to the smallest scales set by the microphysics. Turbulence and mixing can be detrimental to system performance, such as in inertial confinement fusion (ICF). Our current understanding of turbulence is mostly derived from studies of incompressible flows, where the intermediate-scaled eddies in the “inertial range” bridge the transfer of energy from the large eddies to the dissipative range, and that there is no communication between eddies of disparate scales. Whether a similar cascade process exists for compressible turbulence, however, remains to be seen; experiments have been limited by diagnostics and supersonic flow facilities. This project has focused on the development of techniques to measure the evolution of flows and microphysical transport properties using particle tracking velocimetry. This project has investigated three main areas: (1) particle-tracking experiments with x-ray radiography, (2) shock-particle interactions, (3) quantifying viscous effects on particles in shocked flows.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

SPH Modeling of Biomass Granular Flow: Engineering Application in Hoppers and Augers

Numerical modeling of granular biomass material flow in handling operations is indispensable to decipher flow upsets, commonly manifested as clogging and jamming in hoppers and augers. With a computational tool developed based on smoothed particle hydrodynamics (SPH), we simulated the hopper flow and auger feeding of six granular biomass materials. The good agreement between the experimental and numerical flow rates demonstrated the capability of the developed SPH solver in modeling the complicated flow of granular biomass materials. Further, the impact of physical and numerical parameters is investigated, and the major results show that the hopper flow pattern is controlled by shear band evolution; hopper clogging is collectively influenced by the opening size, wall friction, and material packing, and the impact of material compressibility on the auger feed rate is minimal. These parametric studies validate the solver’s robustness in simulating biomass flow in handling equipment and demonstrate that the SPH computational tool can provide insights about granular flow mechanics to facilitate handling equipment design and optimize handling operations.

09 BIOMASS FUELS↗
DOE OSTI2022

Modeling atomically mixed graded density impactors

Graded density impactors (GDIs) are multi-material composite impactors used in gas gun experiments to tailor the drive conditions imparted to a sample test material. Previous graded density impactors generally rely on thin, but discrete, layers of different materials. The thinner and the greater number of layers will result in smoother compression. Taken to the limit of very thin layers would be pure material 1 at one surface, such as the front surface of an impactor, smoothly transitioning at the atomic scale to pure material 2 on the back surface. Such an impactor can initially shock, then smoothly compresses a material during a dynamic experiment. This type of experiment can serve to explore a larger region of thermodynamic space than a single or even multi-shock experiments. An overview of how graded density impactors are made is reviewed and sample results are given. Additionally, a strategy for modeling these kinds of impactors is presented. The length scales of constituent mixing are given from the experimental build through electrochemical-deposition. Equation of state models for pure constituents and their subsequent mixtures are presented. It is demonstrated that the time scales for pressure and temperature equilibration, for atomically mixed GDIs, are short enough to be a justifiable closure for the resulting multiphase flow. Furthermore, we present simulation results of dynamic shock followed by a ramp compression, utilizing a silver/gold graded density impactor, onto a tantalum sample.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Elastic Flow Modeling for Hydropower Digital Twins

This report details the elastic, unsteady, one-dimensional flow equations that are used to model flow through a penstock in a hydropower facility. The elastic flow model is accurate even in cases of fast transients, long penstock length, and high gravitation head. The compressibility of the water and elasticity of the pipe walls are explicitly accounted for in the developed models so that the water hammer phenomena can be accurately captured. The elastic flow model is coupled to a mechanistic turbine model to resolve the dynamic feedbacks between the elastic water column and the turbine rotation rate. A finite volume method is employed to solve the governing equations, and the method is shown to have low numerical diffusion in sharp gradient phenomena, such as those encountered when simulating water hammer. The method is applied to single dimensional linear advection benchmark problem and numerical results are compared with analytic results. This is followed on by an application to a full hydropower system with a coupled turbine. The elastic flow model results are compared against an inelastic model.

13 HYDRO ENERGY↗
DOE OSTI2021

Improvement of Numerical Methods in Pronghorn

During the fiscal year of 2021, the finite-volume method (FVM) was deployed in Pronghorn to provide improved efficiency, stability, and accuracy for coarse-mesh, thermal-hydraulics problems. While the main goals of fiscal year 2021 were met, several issues emerged from the early deployment of the finite-volume method in Pronghorn. These issues were: The compressible and incompressible formulations that were implemented are inadequate for many nuclear reactor flow problems; Omission of terms accounting for the porosity and Darcy-Forchheimer body force discontinuities in the Rhie-Chow interpolation lead to oscillations in pressure and velocity at these discontinuities; The FVM lacks a correction for non-orthogonal grids for computing accurate pressure gradient leading to loss of accuracy in regions with skewed elements; The FVM currently uses a monolithic solver. Monolithic solvers have issues dealing with the saddle-point nature of the discretized fluid equations. The result are bad convergence if direct factorization is not used and large memory consumption when direct factorization is used. These four issues are addressed in this report. In particular, we report the completion of the following task: Implementation of a weakly compressible formulation in the MOOSE Navier-Stokes module; Implementation of Moukalled’s method for including body forces in the RCI. Additionally, we identified the need to smooth the porosity using Moukalled’s face-cell smoothing operator; Implementation of a non-orthogonal correction for the Green-Gauss gradient computation; Preliminary implementation of a SIMPLE segregated solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Effect of PBX prill size on the compressive properties of PBX 9502

Plastic-Bonded Explosive (PBX) prills are agglomerates a few millimeters in diameter composed of High Explosive crystals non-uniformly distributed in and surrounded by a polymer binder. When pressed at elevated temperatures at or above the binder melt temperature the polymer flows to uniformly coat the HE crystals to create a well-consolidated compaction. In reality it does so imperfectly, such that x-ray tomographic scans often look like a collection of prills mashed together. It is speculated that the larger the prill, the more non-uniform the binder distribution, but this has never been proven. Even if that were not so, the larger the prill the farther binder must flow toward the middle to homogenize during pressing. Thus, the degree of homogenization depends in part on prill size. The degree to which binder flows during pressing in turn affects the void distribution within pressed charges, which one suspects will affect shock sensitivity and material strength. In this paper we explore, for ~30 PBX 9502 formulation batches using the same TATB powder lot, how prill size affects the compressive mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Understanding latent timescales in neural ordinary differential equation models of advection-dominated dynamical systems

The neural ordinary differential equation (ODE) framework has shown considerable promise in recent years in developing highly accelerated surrogate models for complex physical systems characterized by partial differential equations (PDEs). For PDE-based systems, state-of-the-art neural ODE strategies leverage a two-step procedure to achieve this acceleration: a nonlinear dimensionality reduction step provided by an autoencoder, and a time integration step provided by a neural-network based model for the resultant latent space dynamics (the neural ODE). This work explores the applicability of such autoencoder-based neural ODE strategies for PDEs in which advection terms play a critical role. More specifically, alongside predictive demonstrations, physical insight into the sources of model acceleration (i.e., how the neural ODE achieves its acceleration) is the scope of the current study. Such investigations are performed by quantifying the effects of both autoencoder and neural ODE components on latent system time-scales using eigenvalue analysis of dynamical system Jacobians. To this end, the sensitivity of various critical training parameters – de-coupled versus end-to-end training, latent space dimensionality, and the role of training trajectory length, for example – to both model accuracy and the discovered latent system timescales is quantified. Furthermore, this work specifically uncovers the key role played by the training trajectory length (the number of rollout steps in the loss function during training) on the latent system timescales: larger trajectory lengths correlate with an increase in limiting neural ODE time-scales, and optimal neural ODEs are found to recover the largest time-scales of the full-order (ground-truth) system. Demonstrations are performed across fundamentally different unsteady fluid dynamics configurations influenced by advection: (1) the Kuramoto–Sivashinsky equations (2) Hydrogen-Air channel detonations (the compressible reacting Navier–Stokes equations with detailed chemistry), and (3) 2D Atmospheric flow.

Advection-dominated dynamical systems↗
DOE OSTI2025

Durability Optimization of CO 2 Electrolyzers for Syngas Evolution

Recently, there has been an increased interest in mitigating anthropogenic CO 2 emissions through the electrochemical conversion of CO 2 into fuels and fuel feedstocks, including hydrogen gas (H 2 ), carbon monoxide (CO), and mixtures of the two to yield syngas. Commercial applications of these systems require high catalytic selectivity for the desired products, while exhibiting operational lifetimes exceeding thousands of hours. Advancements in this field have produced systems that display high selectivity of the desired products at faradaic efficiencies exceeding 95%. Despite the advancements made in CO 2 electrolysis, system durability remains a standing challenge in the field. CO 2 electrolyzer lifetimes are often limited by carbonate fouling, catalyst degradation, detrimental flooding of electrode microporous layers and anion exchange membrane (AEM) failures. In this report, a 5 cm 2 membrane electrode assembly (MEA) device is used to investigate potential failure modes and to optimize AEM CO 2 electrolyzer operation. Key findings of this study include the importance of CO 2 flow rate, use of a thin PiperION PTFE-reinforced membrane, optimizing compression to enhance contact under 40 in-lb compression, and the effect of more compressible, commercial iridium oxide anodes on system durability.

Abouremeleh, Mohammed H. [Lawrence Berkeley Nation↗
DOE OSTI2022

Metal Hydride Compressor for High-Pressure (875 bar) Hydrogen Delivery

Metal hydride hydrogen compression utilizes a reversible heat-driven interaction of a hydride-forming metal alloy with hydrogen gas. This paper reports on the development of a laboratory scale two-stage Metal Hydride Compressor (MHC) system with a feed pressure of 150 bar delivering high purity H2 gas at outlet pressures up to 875 bar. Stage 1 and stage 2 AB 2 metal hydrides are identified based on experimental characterization of the pressure-composition-temperature (PCT) behavior of candidate materials. The selected metal hydrides are each combined with expanded natural graphite, increasing the thermal conductivity of the composites by an order of magnitude. These composites are integrated in two compressor beds with internal heat exchangers that alternate between hydrogenation and dehydrogenation cycles by thermally cycling between 20 °C and 150 °C. The prototype compressor achieved compression of hydrogen from 150 bar to 700 bar with an average flow rate of 33.6 g/hr.

08 HYDROGEN↗
DOE OSTI2019

Collapse of Reacted Fracture Surface Decreases Permeability and Frictional Strength

Abstract Geochemical and geomechanical perturbations of the subsurface caused by the injection of fluids present risks of leakage and seismicity. This study investigated how acidic fluid flow affects hydraulic and frictional properties of fractures using experiments with 3.8‐cm‐long specimens of Eagle Ford shale, a laminated shale with carbonate‐rich strata. In low‐pressure flow cells, one set of samples was exposed to acidic brine and another set was exposed to neutral brine. X‐ray computed tomography and energy‐dispersive X‐ray spectroscopy revealed that samples exposed to acidic brine were calcite‐depleted and had developed a porous altered layer, while the other set showed no evidence of alteration. After reaction, samples were compressed and sheared in a triaxial cell that supplied normal stress and differential pore pressure at prescribed sliding velocities, independently measuring friction and permeability. During the initial compression, the porous altered layer collapsed into fine particles that filled the fracture. This effectively impeded flow and sealed the fracture, resulting in fracture permeability to decrease 1 to 2 orders of magnitude relative to the unaltered fractures. This is a favorable outcome in subsurface applications where the goal is to reduce leakage risks. However, during shear the reacted fracture had lower frictional strength because the fine‐grained particles in the collapsed layer prevented the formation of interlocking microasperities. Therefore, coupled geochemical and geomechanical processes that could favorably seal fractures could also increase the likelihood of induced seismicity. These findings have important implications for geological carbon sequestration, pressurized fluid energy storage, geothermal energy, and other subsurface technologies.

58 GEOSCIENCES↗
DOE OSTI2021

Understanding the evolution of liquid and solid microjets from grooved Sn and Cu samples using radiography

Experiments were performed on grooved Sn and Cu samples to study the temporal evolution of microjets. Jets were generated by the impact of gun-launched flyer plates against the back of grooved targets made from either Cu or Sn (groove depth of ~250 μm). The Hugoniot states in the various Sn targets encompassed conditions where solid phases are maintained throughout (7 and 16 GPa) and also conditions where melting occurs upon the release of compression (25 and 34 GPa); the transition occurs near a Hugoniot pressure of 23 GPa. Cu targets at 27 and 56 GPa provide comparisons in which the jets move at similar speeds but remain solid. In all cases, the spatial distribution of mass within the microjets was measured using high-speed synchrotron radiography. The result is a time history of the jet thickness profile from which quantities like total jet mass and jet velocity can be derived. In both the solid and liquid states, we generally observe that an increase in the shock strength leads to an increase in jet mass. However, this trend breaks down for Hugoniot states near the transition from continuously solid to melted-on-release. This is evidenced by the observation that there was no difference in the rate of mass flow in Sn jets at 16 and 25 GPa, while similar pressure jumps on either side of this range caused substantial changes in the jet mass. This contrasts with the behavior of smaller polishing defects that were present on the same samples (~1 μm deep). From these, no ejecta mass was detected below the melt boundary, but obvious microjets were generated once melting occurred. This indicates that crossing the bulk melt-on-release threshold can alternately promote or inhibit the flow of mass into microjets based on the amplitude of the initial perturbation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Hierarchical microstructures and deformation behavior of laser direct-metal-deposited Cu–Fe alloys

Cu25Fe75 and Cu50Fe50 (nominal composition, at. %) alloys were fabricated using laser direct metal deposition (DMD) based additive manufacturing technique. These alloys exhibit hierarchical microstructures with bi-phasic Cu and Fe dendrites that contain nanoscale precipitates of varying sizes and structures. In the Cu25Fe75 alloy, Fe dendrites contained nanoscale, coherent, metastable BCC Cu and semi-coherent FCC Cu precipitates while the Cu matrix had nanoscale, coherent, metastable FCC Fe precipitates. In the Cu50Fe50 alloy, Fe dendrites only contained nanoscale semi-coherent FCC Cu precipitates while the Cu matrix had nanoscale coherent metastable FCC Fe precipitates. Both alloys exhibited enhanced flow strengths in the range of 750 – 980 MPa and significant plasticity, in compression. Here, the Cu25Fe75 alloy had lower yield strength than Cu50Fe50 alloy but higher maximum compressive strength due to higher strain hardening resulting from slightly coarser dendrites with hierarchy of nanoscale precipitation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Additive manufacturing of an ultrastrong, deformable Al alloy with nanoscale intermetallics

Abstract Light-weight, high-strength, aluminum (Al) alloys have widespread industrial applications. However, most commercially available high-strength Al alloys, like AA 7075, are not suitable for additive manufacturing due to their high susceptibility to solidification cracking. In this work, a custom Al alloy Al 92 Ti 2 Fe 2 Co 2 Ni 2 is fabricated by selective laser melting. Heterogeneous nanoscale medium-entropy intermetallic lamella form in the as-printed Al alloy. Macroscale compression tests reveal a combination of high strength, over 700 MPa, and prominent plastic deformability. Micropillar compression tests display significant back stress in all regions, and certain regions have flow stresses exceeding 900 MPa. Post-deformation analyses reveal that, in addition to abundant dislocation activities in Al matrix, complex dislocation structures and stacking faults form in monoclinic Al 9 Co 2 type brittle intermetallics. This study shows that proper introduction of heterogeneous microstructures and nanoscale medium entropy intermetallics offer an alternative solution to the design of ultrastrong, deformable Al alloys via additive manufacturing.

Science & Technology - Other Topics↗
DOE OSTI2021

AN AUTOMATED MACHINE LEARNING-GENETIC ALGORITHM FRAMEWORK WITH ACTIVE LEARNING FOR DESIGN OPTIMIZATION

The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Owoyele, Opeoluwa↗