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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The effect of blood viscosity on shear‐induced hemolysis using a magnetically levitated shearing device

Abstract Introduction Blood contacting medical devices, including rotary blood pumps, can cause shear‐induced blood damage that may lead to adverse effects in patients. Due in part to an inadequate understanding of how cell‐scale fluid mechanics impact red blood cell membrane deformation and damage, there is currently not a uniformly accepted engineering model for predicting blood damage caused by complex flow fields within ventricular assist devices (VADs). Methods We empirically investigated hemolysis in a magnetically levitated axial Couette flow device typical of a rotary VAD. The device is able to accurately control the shear rate and exposure time experienced by blood and to minimize the effects of other uncharacterized stresses. Using this device, we explored the effects of both hematocrit and plasma viscosity on shear‐induced hemolysis to characterize blood damage based on the viscosity‐independent shear rate, rather than on shear stress. Results Over a shear rate range of 20 000 – 80 000 1/s, the Index of Hemolysis (IH) was found to be dependent upon and well‐predicted by the shear rate alone. IH was independent of hematocrit, bulk viscosity, or the suspension media viscosity and less correlated to shear stress (MSE = 0.46–0.75) than to shear rate (MSE = 0.06–0.09). Conclusion This study recommends that future investigations of shear‐induced blood damage report findings with respect to the viscosity‐neutral term of shear rate, in addition to the bulk whole blood viscosity measured at an appropriate shear rate relevant to the flow conditions of the device.

Krisher, James A.↗
DOE OSTI2026

Eddy Covariance Theory: A Review

Eddy covariance (EC), the gold standard for measuring ecosystem scale gas and heat exchanges, has transformed our understanding of the breathing of the biosphere, and thus global change biology. Despite numerous methodological improvements and insights gained from the technique, the community faces persistent challenges that have been present since the first EC measurements. Here, we review the theoretical developments underpinning EC. We present theoretical developments in four important areas that have relevance to EC measurements of the net ecosystem exchanges (NEE) of gases and heat from a single tower: (i) measuring the total vertical flux density, (ii) flux attenuation, (iii) coordinate rotations, and (iv) energy balance closure. Persistent problems with EC measurements, such as the inability to close the energy budget, led us to identify two priorities for revisiting the theory underlying: (i) sensible heat flux calculations, and (ii) constraining the mean vertical wind velocity. We present a framework for improved calculation of sensible heat flux derived from first principles of fluid mechanics and thermodynamics that considers coupled heat and mass transfer so that conservation of both is obeyed. These refinements are motivated by the need for unbiased measurements of energy and mass transfer between the land surface and atmosphere for ecosystem research and to validate satellite observations and land surface models.

ecosystem fluxes↗
DOE OSTI2021

A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations

In this work, we propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional neural networks have proven to be highly advantageous for compressing data arising from systems demonstrating a slow-decaying Kolmogorov n-width. However, these networks are restricted to data on structured meshes. Unstructured meshes are often required for performing analyses of real systems with complex geometry. Our custom graph convolution operators based on the available differential operators for a given spatial discretization effectively extend the application space of deep convolutional autoencoders to systems with arbitrarily complex geometry that are typically discretized using unstructured meshes. We propose sets of convolution operators based on the spatial derivative operators for the underlying spatial discretization, making the method particularly well suited to data arising from the solution of partial differential equations. We demonstrate the method using examples from heat transfer and fluid mechanics and show better than an order of magnitude improvement in accuracy over linear methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Fluid Dynamics of Axial Turbomachinery: Blade- and Stage-Level Simulations and Models

The current generation of axial turbomachines is the culmination of decades of experience, and detailed understanding of the underlying flow physics has been a key factor for achieving high efficiency and reliability. Driven by advances in numerical methods and relentless growth in computing power, computational fluid dynamics has increasingly provided insights into the rich fluid dynamics involved and how it relates to loss generation. This article presents some of the complex flow phenomena occurring in bladed components of gas turbines and illustrates how simulations have contributed to their understanding and the challenges they pose for modeling. The interaction of key aerodynamic features with deterministic unsteadiness, caused by multiple blade rows, and stochastic unsteadiness, i.e., turbulence, is discussed. High-fidelity simulations of increasingly realistic configurations and models improved with help of machine learning promise to further grow turbomachinery performance and reliability and, thus, help fluid mechanics research have a greater industrial impact.

Mechanics↗
DOE OSTI2024

Learning Nonlinear Reduced Models from Data with Operator Inference

This review discusses Operator Inference, a nonintrusive reduced modeling approach that incorporates physical governing equations by defining a structured polynomial form for the reduced model, and then learns the corresponding reduced operators from simulated training data. The polynomial model form of Operator Inference is sufficiently expressive to cover a wide range of nonlinear dynamics found in fluid mechanics and other fields of science and engineering, while still providing efficient reduced model computations. The learning steps of Operator Inference are rooted in classical projection-based model reduction; thus, some of the rich theory of model reduction can be applied to models learned with Operator Inference. This connection to projection-based model reduction theory offers a pathway toward deriving error estimates and gaining insights to improve predictions. Furthermore, through formulations of Operator Inference that preserve Hamiltonian and other structures, important physical properties such as energy conservation can be guaranteed in the predictions of the reduced model beyond the training horizon. This review illustrates key computational steps of Operator Inference through a large-scale combustion example.

Mechanics↗
DOE OSTI2018

High Explosive Detonation–Confiner Interactions

The primary purpose of a detonation in a high explosive (HE) is to provide the energy to drive a surrounding confiner, typically for mining or munitions applications. The details of the interaction between an HE detonation and its confinement are essential to achieving the objectives of the explosive device. For the high pressures induced by detonation loading, both the solid HE and confiner materials will flow. The structure and speed of a propagating detonation, and ultimately the pressures generated in the reaction zone to drive the confiner, depend on the induced flow both within the confiner and along the HE–confiner material interface. The detonation–confiner interactions are heavily influenced by the material properties and, in some cases, the thickness of the confiner. This review discusses the use of oblique shock polar analysis as a means of characterizing the possible range of detonation–confiner interactions. Computations that reveal the fluid mechanics of HE detonation–confiner interactions for finite reaction-zone length detonations are discussed and compared with the polar analysis. Furthermore this includes cases of supersonic confiner flow; subsonic, shock-driven confiner flow; subsonic, but shockless confiner flow; and sonic flow at the intersection of the detonation shock and confiner material interface. We also summarize recent developments, including the effects of geometry and porous material confinement, on detonation–confiner interactions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Fierro Version 2.x

FIERRO is a parallel C++ code designed to simulate fluid mechanics, heat transfer, and solid mechanics in two- and three-dimensional space. FIERRO is written to run on homogeneous (CPU) and heterogeneous (CPU+GPU) high performance computing machines. Fierro can aid a) modeling and design efforts that have historically relied on commercial implicit and explicit finite element codes, b) numerical methods research, c) manufacturing research, and d) computer science research. The code contains diverse numerical methods to solve the governing physics equations for both quasi-static and dynamic problems. Mathematical optimization solvers are coupled to the numerical methods to research topology and shape optimization that has application to additive manufacturing, and to create novel numerical approaches. Phase-field methods with micromechanical solvers are provided to simulate microstructure formation and evolution in manufacturing processes. The micromechanical solvers can also help research efforts create continuum-scale constitutive models for solids, as a function of the microstructure, in situ in a calculation or in a stand-alone manner. No physical data exists within the code.

Morgan, Nathaniel↗
DOE OSTI2023

Fierro

FIERRO is a parallel C++ code designed to simulate fluid mechanics, heat transfer, and solid mechanics in two- and three dimensional space. FIERRO is written to run on homogeneous (CPU) and heterogeneous (CPU+GPU) high performance computing machines. Fierro can aid a) modeling and design efforts that have historically relied on commercial implicit and explicit finite element codes, b) numerical methods research, c) manufacturing research, and d) computer science research. The code contains diverse numerical methods to solve the governing physics equations for both quasi-static and dynamic problems. Mathematical optimization solvers are coupled to the numerical methods to research topology and shape optimization that has application to additive manufacturing, and to create novel numerical approaches. Phase-field methods with micromechanical solvers are provided to simulate microstructure formation and evolution in manufacturing processes. The micromechanical solvers can also help research efforts create continuum-scale constitutive models for solids, as a function of the microstructure, in situ in a calculation or in a stand-alone manner. No physical data exists within the code.

Morgan, Nathaniel↗
DOE OSTI2023

Utah FORGE: Documentation on Discrete Fracture Network and Fracture Propagation Modelling

This dataset includes reports and a slide presentation on discrete fracture network (DFN) generation and hydraulic fracture modeling at the Utah FORGE site. It details the characterization of natural fractures using well log and core data, as well as stochastic modeling techniques. The reports describe simulations of hydraulic fracture propagation, fluid-mechanical interactions, and induced microseismicity. The dataset also includes history-matching of net pressure and analyses of fracture growth in naturally fractured geothermal reservoirs. The slides summarize key findings and future research directions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Multi-configuration Membrane Distillation Model (MCMD)

Many membrane distillation models have been created to simulate the heat and mass exchange process involved but most of the literature only validates models to a couple of cases with minor configuration changes. Tools are needed that allow tradeoffs between many configurations. The multiconfiguration membrane distillation model handles many configurations. This report introduces membrane distillation, provides theory, and presents the work to verify and validate the model against experimental data from Colorado School of Mines and a lower resolution model created at the National Renewable Energy Laboratory. Though more data analysis and testing are needed, an initial look at the model to experimental comparisons indicates that the model correlates to the data well but that design comparisons are likely to be incorrect across a broad range of configurations. More accurate quantification of heat and mass transfer through computational fluid mechanics is suggested.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Fabrication and Modeling of Laser Additive Manufactured Materials with Multi-Beam Adaptive Beam Shaping

The objective of this research was to fabricate laser additive manufactured samples using the multi-beam fiber laser array system developed by Optonicus, and to test the ability of existing simulation tools to help understand the influence of various beam configurations on the heat transfer and solidification conditions that control microstructure development during processing. Optonicus produced Inconel 718 samples with two different beam configurations that exhibited dramatically different microstructure characteristics. A simplified analytical model was used in conjunction with microstructure selection theory to relate the differences in beam configuration and process conditions to the changes in microstructure. The analytical model was verified against standard quasi-static solutions and a numerical Computational Fluid Dynamics CFD model and the influence of fluid mechanics on the melt pool geometry was evaluated.

36 MATERIALS SCIENCE↗
DOE OSTI2019

The PROPEL Hydro System (Final Report)

This paper summarizes research, development and outcome of a modular system for powering non-powered dams. There are approximately 77,000 non-powered dams in the US, a non-trivial fraction of which could be used to generate power. Development in this area has been slow because building projects costs too much and takes too long. The PROPEL-Hydro system combines modular components for the intake and foundation, an ai driven project design and evaluation tool, and a turbine optimized for performance on small and low head sites. Work included a combination of site evaluation, refinement of power electronics, value analysis supported by computational fluid mechanics and software development to support a project evaluation tool. The result is a reduction in installed cost/kW and resulting LCOE.

13 HYDRO ENERGY↗
DOE OSTI2021

Rapid charging made practical in graphite-based lithium batteries: surface-acoustic wave turbulent electrolyte mixing to overcome diffusion limited charging rates [Final Report]

One of the key limits in rapidly recharging a lithium-ion battery is the depletion of lithium ions within the electrolyte adjacent the anode during charging and long diffusion time to overcome this depletion. It also causes dendrite formation, inefficient use of the lithium, and battery degradation over many charge-discharge cycles. Because the liquid electrolyte remains quiescent and unmixed, this depletion layer’s depth rapidly grows to match the anode-cathode separation distance at even modest charge rates. The solution proposed by PI Prof. James Friend and Co-PI Prof. Ping Liu, both from the University of California, San Diego, is to mix the electrolyte and minimize the Li+ ion concentration gradient during charging. Even in the presence of the separator, the charging rate could then be significantly increased. They propose to accomplish this using surface acoustic wave (SAW)-driven acoustic streaming, a technique employing 10-mW fingernail-sized solid-state devices from the telecommunications industry to drive turbulent mixing to submicron length scales in a manner completely compatible with the typical 20700 and 18650 cells used in electric vehicles. 0.1. Turbulent acoustic streaming mixes the electrolyte during charging—even with separator Our proposed 100-MHz SAW device used to recirculate the electrolyte is compatible with lithium- ion battery electrochemistry, as it is made in our lab of single-crystal lithium niobate. Their solution is straightforward to drive from a DC power source alongside the signal provided during battery charging. The device is only needed during charging and does not consume power dur- ing battery discharge. Uniquely, SAW generates extreme accelerations of over 1 billion meters per second squared in the fluid, driving turbulent mixing from centimeter to submicron length scales, even through the porous separator materials that tend to be used in batteries, all while avoiding interfering with the anode’s solid electrolyte interphase layer that forms during use. By employing novel fluid mechanics, the investigators are proposing a new direction for battery research away from direct use of materials science and electrochemistry. The chemistry agnostic solution may be employed in any battery chemistry that makes use of liquid electrolytes, providing a broader transformative benefit to the battery research community. They are to produce a series of prismatic and 20700-cell sized 2 Ah batteries capable of being charged and discharged at least 500 times without more than 20% loss in battery capacity, and to provide analysis tools useful to the battery research and development community for adopting this approach for other battery chemistries and configurations.

25 ENERGY STORAGE↗
DOE OSTI2021

On Two-Phase Pressure and Temperature Equilibration with Mie-Grüneisen Equations of State

In the course of reactive flow modeling, the phases of high explosive (HE) reactants and products introduce additional degrees of freedom to the underlying hydrodynamic equations of compressible reactive fluid mechanics. Hydrodynamic simulations track the bulk (mixture) conservation properties of mass, momentum and energy. When multiple phases are introduced, one must decide on appropriate closure rules to determine the relative phase volumes and energy distribution between the phases. One typical closure is to assume that the phases are in pressure and temperature equilibrium. Here, the requisite equations of p-T equilibration are explored for Mie-Grüneisen Equations of State (EOS). Examples with Davis reactants and Davis products EOSs will be given. The Davis forms are used in both the WSD and AWSD reactive flow models.

42 ENGINEERING↗
DOE OSTI2022

High-Speed Diagnostic and Simulation Capabilities for Reacting Hypersonic Reentry Flows (LDRD Final Report)

High-enthalpy hypersonic flight represents an application space of significant concern within the current national-security landscape. The hypersonic environment is characterized by high-speed compressible fluid mechanics and complex reacting flow physics, which may present both thermal and chemical nonequilibrium effects. We report on the results of a three-year LDRD effort, funded by the Engineering Sciences Research Foundation (ESRF) investment area, which has been focused on the development and deployment of new high-speed thermochemical diagnostics capabilities for measurements in the high-enthalpy hypersonic environment posed by Sandia's free-piston shock tunnel. The project has additionally sponsored model development efforts, which have added thermal nonequilibrium modeling capabilities to Sandia codes for subsequent design of many of our shock-tunnel experiments. We have cultivated high-speed, chemically specific, laser-diagnostic approaches that are uniquely co-located with Sandia's high-enthalpy hypersonic test facilities. These tools include picosecond and nanosecond coherent anti-Stokes Raman scattering at 100-kHz rates for time-resolved thermometry, including thermal nonequilibrium conditions, and 100-kHz planar laser-induced fluorescence of nitric oxide for chemically specific imaging and velocimetry. Key results from this LDRD project have been documented in a number of journal submissions and conference proceedings, which are cited here. The body of this report is, therefore, concise and summarizes the key results of the project. The reader is directed toward these reference materials and appendices for more detailed discussions of the project results and findings.

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

Modeling of Chemical Slurry Rheology in DWPF Sludge Batch (SB) 10 Simulants

The Defense Waste Processing Facility (DWPF) treats high-activity radionuclides from sludge through a process called vitrification. This process converts radioactive liquid waste currently stored in tank farms into a solid glass form that is suitable for long-term storage and disposal. Due to the complexities involved in vitrifying this waste within each operation of the Chemical Processing Cell (CPC), waste rheology is studied to characterize the fluid-mechanical properties as it passes through the CPC and into the Melter. To better understand the waste and validate flow behavior, slurry rheology of simulants that represents the waste was studied at various acid stoichiometry percentages and solids concentrations to determine the simulant’s yield stress and viscosity. This research work has been supported by the DOE-FIU Science & Technology Workforce Development Initiative, an innovative program developed by the U.S. Department of Energy’s Office of Environmental Management (DOE-EM) and Florida International University’s Applied Research Center (FIU-ARC). During the spring of 2022, a DOE Fellow intern, Brendon Cintas, spent 10 weeks doing a summer internship at Savannah River National Laboratory (SRS) under the supervision and guidance of Dan Lambert, Chemical Flowsheet Development. The intern’s project was initiated on June 6, 2022, and continued through August 11, 2022 with the objective of assisting scientists at SRNL’s Rheology and Grout Laboratory at Aiken Country Technology Lab (ACTL) better understand the sludge composition on the rheology of a simulant slurry using a HAAKE RheoStress 6000 rheometer and extrapolate the results to the real-waste data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗