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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

High-Performance Simulation of Dynamic Hydrologic Exchange and Implications for Surrogate Flow and Reactive Transport Modeling in a Large River Corridor

Hydrologic exchange flows (HEFs) have environmental significance in riverine ecosystems. Key river channel factors that influence the spatial and temporal variations of HEFs include river stage, riverbed morphology, and riverbed hydraulic conductivity. However, their impacts on HEFs were often evaluated independently or on small scales. In this study, we numerically evaluated the combined interactions of these factors on HEFs using a high-performance simulator, PFLOTRAN, for subsurface flow and transport. The model covers 51 square kilometers of a selected river corridor with large sinuosity along the Hanford Reach of the Columbia River in Washington, US. Three years of spatially distributed hourly river stages were applied to the riverbed. Compared to the simulation when riverbed heterogeneity is not ignored, the simulation using homogeneous riverbed conductivity underestimated HEFs, especially upwelling from lateral features, and overestimated the mean residence times derived from particle tracking. To derive a surrogate model for the river corridor, we amended the widely used transient storage model (TSM) for riverine solute study at reach scale with reactions. By treating the whole river corridor as a batch reactor, the temporal changes in the exchange rate coefficient for the TSM were derived from the dynamic residence time estimated from the hourly PFLOTRAN results. The TSM results were evaluated against the effective concentrations in the hyporheic zone calculated from the PFLOTRAN simulations. Our results show that there is potential to parameterize surrogate models such as TSM amended with biogeochemical reactions while incorporating small-scale process understandings and the signature of time-varying streamflow to advance the mechanistic understanding of river corridor processes at reach to watershed scales. However, the assumption of a well-mixed storage zone for TSM should be revisited when redox-sensitive reactions in the storage zones play important roles in river corridor functioning.

Fang, Yilin↗
DOE OSTI2023

Highly-scalable GPU-accelerated compressible reacting flow solver for modeling high-speed flows

Emerging supercomputing systems utilize a combination of central processing units (CPUs) and graphics processing units (GPUs) in an effort to reach exascale capabilities while minimizing the energy footprint of operating such systems. Such heterogeneous machines introduce new challenges for fluids solvers because the hardware architecture and operation of a GPU are fundamentally different from conventional CPUs. In this work, a general approach for efficient implementation of finite-volume based reacting flow solvers on such heterogeneous systems is presented. Three main challenges, namely, data access pattern, thread divergence, and thread safety, are addressed. Since compressible reacting flows require special methods to deal with chemical reactions, hyperbolic and nonlinear convection terms, and the presence of turbulence, specific algorithms that ensure GPU-based efficiency are developed. The approach is demonstrated on the widely available OpenFOAM open source software by modifying core algorithms for GPU accessibility. The scalability of the resulting solver, is demonstrated using practical test cases, including flow through a scramjet engine and the dynamics of a rotating detonation engine. Here, the solver provides near-ideal scaleup on a large number of GPUs (>3000), and extremely efficient use of the GPUs, with throughput nearly a constant even when processing a large number of control volumes.

42 ENGINEERING↗
DOE OSTI2022

Automated bubble analysis of high-speed subcooled flow boiling images using U-net transfer learning and global optical flow

Capturing and analyzing the bubble dynamics is crucial to improving the understanding of boiling heat transfer mechanisms and predicting boiling heat transfer coefficient and boiling crisis. High speed video (HSV) imaging has been used for decades towards this end. Still, there is no universal approach to quantitatively analyze bubble dynamics from HSV images. In this study, we propose a data-driven post-processing approach to segment, track, and identify wall-attached vapor bubbles from HSV images of the boiling process in subcooled flow conditions. Firstly, we employ a transfer learning framework with a U-Net-based convolution neural network (CNN) architecture to detect and segment bubbles in HSV images of diverse contrast and surface texture using very little data (e.g., 10 images) for training. Then, we evaluate the trained CNN model with 100 ground-truth images, and the validation results show that the model accuracy and precision in detecting the optical footprint of bubbles are higher than 90%. Finally, we suggest a criterion to identify a condensing bubble based on the divergence of the bubble displacement, which is calculated from sequential segmented bubble images using a global optical flow code. Using this combination of machine learning and optical flow, we can identify nucleation sites and track the growth of bubbles nucleating at each site to quantify nucleation site density, nucleation frequency, and other fundamental boiling parameters. The proposed system is validated using results obtained on a special heater, which enables both infrared (IR) thermometry and HSV imaging on a metallic surface. We compare the fundamental boiling parameters obtained by the two different diagnostics. The results show good agreement. In conclusion, the difference between the measurements of nucleation site density, averaged nucleation frequency, and averaged growth time performed with the two techniques is always within ± 20% and mostly ± 10% of the values measured with IR thermometry.

42 ENGINEERING↗
DOE OSTI2024

Electrochemical Conversion of CO 2 to Methyl Formate in a Flow Electrolyzer with Mixed Propylene Carbonate/Methanol Catholyte

Despite the promise of electrochemical carbon dioxide reduction as a technology for the production of clean fuels and decarbonization of the chemical industry, research has mostly focused on aqueous systems with a relatively limited set of products that have been achieved via electrosynthesis. Increasingly, CO 2 electroreduction in nonaqueous solvents is being pursued to develop new avenues for expanding the suite of products that can be made with high selectivity. CO 2 reduction in alcohols coupled with in situ esterification to produce esters is one such route that utilizes nonaqueous electrolyte. To be practical, such electrochemical syntheses need to be translated to a high-performance reactor such as a flow electrolyzer. However, many organic solvents, such as alcohols, wet and flood porous electrodes, thus impeding reactor performance. In this work, methanol was mixed with propylene carbonate as a catholyte for a gas-fed CO 2 flow electrolyzer that avoided cathode flooding. Simultaneously, a dual aqueous anolyte was used for water oxidation as a scalable and sustainable anodic half-reaction. The performance effect of methanol concentration, catholyte acidity, CO 2 flow rate, and dilute water in the catholyte were investigated. With 10 vol % methanol in 90 vol % propylene carbonate, 63% faradaic efficiency for methyl formate ester product was sustained without cathode flooding. However, improvements are still needed to lower the cell resistance and further increase the operating current density.

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

Counter-Current Flow Limitation Studies in Complex Geometries Utilizing Interface Capturing Simulations Coupled with PID Flow Rate Controller

In nuclear thermal-hydraulic studies, counter-current flow limitation (CCFL) typically refers to steam rising at a fast rate such that it prevents coolant from draining down within a confined channel. CCFL is a crucial issue in nuclear reactor safety analysis. This study investigates CCFL in debris bed channels using high-resolution interface-capturing simulations. A novel proportional-integral-derivative flow rate controller is developed to efficiently achieve the CCFL conditions. Verification studies confirm that CCFL occurs under the same conditions with or without the controller, demonstrating that PID control ensures accurate prediction. Three debris bed channel geometries were examined: a cylindrical channel, a channel with small obstacles, and a channel with large obstacles. Results show that obstacles significantly impact flow behavior, interfacial shear, wall shear, and pressure gradients required for CCFL. Furthermore, the comparison with experimental data confirmed that simulations incorporating geometric complexities align more closely with experimental CCFL conditions. A pressure gradient correlation was also developed for CCFL prediction.

Counter-current flow limitation↗
DOE OSTI2020

Collaborative investigation of the internal flow and near-nozzle flow of an eight-hole gasoline injector (Engine Combustion Network Spray G)

The internal details of fuel injectors have a profound impact on the emissions from gasoline direct injection engines. However, the impact of injector design features is not currently understood, due to the difficulty in observing and modeling internal injector flows. Gasoline direct injection flows involve moving geometry, flash boiling, and high levels of turbulent two-phase mixing. In order to better simulate these injectors, five different modeling approaches have been employed to study the engine combustion network Spray G injector. Here these simulation results have been compared to experimental measurements obtained, among other techniques, with X-ray diagnostics, allowing the predictions to be evaluated and critiqued. The ability of the models to predict mass flow rate through the injector is confirmed, but other features of the predictions vary in their accuracy. The prediction of plume width and fuel mass distribution varies widely, with volume-of-fluid tending to overly concentrate the fuel. All the simulations, however, seem to struggle with predicting fuel dispersion and by inference, jet velocity. This shortcoming of the predictions suggests a need to improve Eulerian modeling of dense fuel jets.

ECN↗
DOE OSTI2021

The Area Localized Coupled Model for Analytical Mean Flow Prediction in Arbitrary Wind Farm Geometries

This work introduces the area localized coupled (ALC) model, which extends the applicability of approaches that couple classical wake superposition models and atmospheric boundary layer models to wind farms with arbitrary layouts. Coupling wake and top–down boundary layer models is particularly challenging since the latter requires averaging over planform areas associated with turbine-specific regions of the flow that need to be specified. The ALC model uses Voronoi tessellation to define this local area around each turbine. A top–down description of a developing internal boundary layer is then applied over Voronoi cells upstream of each turbine to estimate the local mean velocity profile. Coupling between the velocity at hub-height based on this localized top–down model and a wake model is achieved by enforcing a minimum least-square-error in mean velocity in each cell. The wake model in the present implementation takes into account variations in wind farm inflow velocity and represents the wake profile behind each turbine as a super-Gaussian function that smoothly transitions between a top-hat shape in the region immediately following the turbine to a Gaussian profile downstream. Detailed comparisons to large-eddy simulation (LES) data from two different wind farms demonstrate the efficacy of the model in accurately predicting both wind farm power output and local turbine hub-height velocity for different wind farm geometries. These validations using data generated from two different LES codes demonstrate the model's versatility with respect to capturing results from different simulation setups and wind farm configurations.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2021

Higher Ion Selectivity with Lower Energy Usage Promoted by Electro-osmotic Flow in the Transport through Conical Nanopores

The tradeoff between selectivity and throughput presents fundamental challenges to improve desalination and charge storage, salinity gradient-based energy harvesting, memory device/circuit development, and so forth. The well-known ion-current rectification and the recently resolved time-dependent transport hysteresis in conical nanopores or asymmetric nanointerfaces offer new opportunities for the selective transport of matter. This report shows that electro-osmotic flow (EOF) is an overlooked factor that increases ion selectivity while maintaining enhanced transport throughput in rectified nanoscale electrokinetic transport. The increased selectivity originates primarily from the suppression of anion flux by the fluid flow in the opposite direction under the applied electrical field. By solving the Poisson and Nernst–Planck (PNP) equations without and with coupled Navier–Stokes (PNP–NS) equations, the EOF effects on cation and anion transport are unequivocally revealed in asymmetric nanopipettes. The flux of cations and anions as well as the transference number and flow velocity are elucidated using the models and boundary conditions validated by previous experiments. A dimensionless parameter, radius over the Debye length, reveals optimal ion selectivity and energy cost at intermediate ion concentrations and nanopore sizes, up to hundreds of millimolars and tens of nanometers under time-dependent potential stimulus. Furthermore, the fundamental insights into EOF at nanointerfaces suggest new routes/strategies for better separation, analysis, and energy applications.

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

Learning turbulent flows with generative models for super resolution and sparse flow reconstruction

Neural operators are promising surrogates for dynamical systems but when trained with standard L 2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that combining operator learning with generative modeling overcomes this limitation. We consider three practical turbulent-flow challenges where conventional neural operators fail: spatio-temporal super-resolution, forecasting, and sparse flow reconstruction. For Schlieren jet super-resolution, an adversarially trained neural operator (adv-NO) reduces the energy-spectrum error by 15 × while preserving sharp gradients at neural operator-like inference cost. For 3D homogeneous isotropic turbulence, adv-NO trained on only 160 timesteps from a single trajectory forecasts accurately for five eddy-turnover times and offers 114 × wall-clock speed-up at inference than the baseline diffusion-based forecasters, enabling near-real-time rollouts. For reconstructing cylinder wake flows from highly sparse Particle Tracking Velocimetry-like inputs, a conditional generative model infers full 3D velocity and pressure fields with correct phase alignment and statistics. These advances enable accurate reconstruction and forecasting at low compute cost, bringing near-real-time analysis and control within reach in experimental and computational fluid mechanics.

Fluid dynamics↗
DOE OSTI2022

Observations on the deformation of metal microspheres in shock-driven polymer flows

We report that solid particles can be fragmented by a fast-moving fluid if their velocity difference is great enough, such as during the atmospheric entry of meteoroids or the shock compression of engineered particulate composites. The extent of particle deformation and breakup in such systems is poorly understood because the necessary extreme conditions make observation difficult and data scarce. To meet this need, experiments combining ultrafast synchrotron-based radiography with plate impact loading were performed at the dynamic compression sector at the advanced photon source. Metal microspheres of several densities and strengths (Au, Ta, and W) were placed inside a polymer matrix. A planar shock wave was then produced in the polymer by the impact of a gun-launched flyer plate. X-ray images of the resulting flow were collected at ~150ns intervals. These images document the progression of particle deformation across a range of flow conditions and particle materials. They show that the extent of deformation is sensitive to the ratio of drag stress to particle strength. The deforming particle's shape is determined by the initial shock–particle interaction, fluid stagnation pressure, and vorticity, each acting on its own timescale. A set of scaling relationships is presented to capture these observations and enable comparison with prior hydrodynamic data. The result is a framework for predicting the conditions under which strong particles are severely deformed by a shock-driven flow.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Dean vortex-enhanced blood plasma separation in self-driven spiral microchannel flow with cross-flow microfilters

Point-of-care (POC) diagnostic devices have been developing rapidly in recent years, but they are mainly using saliva instead of blood as a test sample. A highly efficient self-separation during the self-driven flow without power systems is desired for expanding the point-of-care diagnostic devices. Microfiltration stands out as a promising technique for blood plasma separation but faces limitations due to blood cell clogging, resulting in reduced separation speed and efficiency. These limitations are mainly caused by the high viscosity and hematocrit in the blood flow. A small increment in the hematocrit of the blood significantly increases the pressure needed for the blood plasma separation in the micro-filters and decreases the separation speed and efficiency. Addressing this challenge, this study explores the feasibility of diluting whole blood within a microfluidic device without external power systems. This study implemented a spiral microchannel utilizing the inertial focusing and Dean vortex effects to focus the red blood cells and extract the blood with lower hematocrit. The inertial migration of the particles during the capillary flow was first investigated experimentally; a maximum of 88% of the particles migrated to the bottom and top equilibrium positions in the optimized 350 × 60 μm (cross-sectional area, 5.8 aspect ratio) microchannel. With the optimized dimension of the microchannel, the whole blood samples within the physiological hematocrit range were tested in the experiments, and more than 10% of the hematocrit reduction was compared between the outer branch outlet and inner branch outlet in the 350 × 60 μm microchannel.

Biochemistry & Molecular Biology↗
DOE OSTI2021

Scale setting the Möbius domain wall fermion on gradient-flowed HISQ action using the omega baryon mass and the gradient-flow scales 𝑡 0 and 𝑤 0

We report on a subpercent scale determination using the omega baryon mass and gradient-flow methods. The calculations are performed on 22 ensembles of 𝑁 𝑓 =2 +1 +1 highly improved, rooted staggered sea-quark configurations generated by the MILC and CalLat Collaborations. The valence quark action used is Möbius domain wall fermions solved on these configurations after a gradient-flow smearing is applied with a flowtime of 𝑡 gf = 1 in lattice units. The ensembles span four lattice spacings in the range 0.06 ≲ 𝑎 ≲0.15 fm, six pion masses in the range 130 ≲ 𝑚 𝜋 ≲ 400 MeV and multiple lattice volumes. On each ensemble, the gradient-flow scales 𝑡 0 /𝑎 2 and 𝑤 0 /𝑎 and the omega baryon mass 𝑎⁢𝑚 Ω are computed. The dimensionless product of these quantities is then extrapolated to the continuum and infinite volume limits and interpolated to the physical light, strange and charm quark mass point in the isospin limit, resulting in the determination of $\sqrt{t}_0$ = 0.1422⁢(14) fm and 𝑤 0 = 0.1709⁢(11) fm with all sources of statistical and systematic uncertainty accounted for. The dominant uncertainty in both results is the stochastic uncertainty, though for $\sqrt{t}_0$ there are comparable continuum extrapolation uncertainties. For 𝑤 0 , there is a clear path for a few-per-mille uncertainty just through improved stochastic precision, as recently obtained by the Budapest-Marseille-Wuppertal Collaboration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Using onset times from frequent geophysical surveys to understand reservoir fluid flow and to estimate flow properties

Seismic characterization of flow properties is a difficult prospect, due to the indirect relationship between permeability and seismic velocity and attenuation. At best, seismic time-lapse changes can detect the effects of saturation and pressure changes in a reservoir due to fluid flow. Even in this case, the interpretation of the seismic observation in terms of the state of the reservoir depends intimately on the properties of the rock physics model, which are usually poorly known. The onset time, the calendar time of geophysical changes, provides an alternative datum for characterizing properties such as reservoir permeability. The main advantage of an onset time is that it is sensitive to the flow properties of the reservoir yet insensitive to the details of the rock physics model. Two examples of the utility of onset times are discussed: The use of travel time shifts induced by the injection of carbon dioxide between two wells, and the time-lapse time shifts for elastic waves propagating through a reservoir undergoing enhanced oil recovery. In both examples, the onset times are mapped into permeability estimates using a trajectory-based approach akin to seismic tomography.

58 GEOSCIENCES↗