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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Considerations for the temperature stratification in a pre-burn constant-volume combustion chamber

In recent years, the Engine Combustion Network (ECN) has developed as a worldwide reference for understanding and describing engine combustion processes, successfully bringing together experimental and numerical efforts. Since experiments and numerical simulations both target the same boundary conditions, an accurate characterization of the stratified environment that is inevitably present in experimental facilities is required. The difference between the core-, and pressure-derived bulk-temperature of pre-burn combustion vessels has been addressed in various previous publications. Additionally, thermocouple measurements have provided initial data on the boundary layer close to the injector nozzle, showing a transition to reduced ambient temperatures. The conditions at the start of fuel injection influence physicochemical properties of a fuel spray, including near nozzle mixing, heat release computations, and combustion parameters. To address the temperature stratification in more detail, thermocouple measurements at larger distances from the spray axis have been conducted. Both the temperature field prior to the pre-combustion event that preconditions the high-temperature, high-pressure ambient, as well as the stratification at the moment of fuel injection were studied. To reveal the cold boundary layer near the injector with a better spatial resolution, Rayleigh scattering experiments and thermocouple measurements at various distances close to the nozzle have been carried out. The impact of the boundary layers and temperature stratification are illustrated and quantified using numerical simulations at Spray A conditions. Next to a reference simulation with a uniform temperature field, six different stratified temperature distributions have been generated. These distributions were based on the mean experimental temperature superimposed by a randomized variance, again derived from the experiments. The results showed that an asymmetric flame structure arises in the computed results when the temperature stratification input is used. In these predictions, first-stage ignition is advanced by 24 $\mu$s , while second-stage ignition is delayed by 11 $\mu$s. At the same time a lift-off length difference between the top and the bottom of up to 1.1 mm is observed. Furthermore, the lift-off length is less stable over time. Given the shown dependency, the temperature data is made available along with the vessel geometry data as a recommended basis for future numerical simulations.

42 ENGINEERING↗
DOE OSTI2021

Effects of Pore Connectivity on the Sorption of Fluids in Nanoporous Material: Ethane and CO2 Sorption in Silicalite

Adsorption of fluids in nanoporous materials is important for several applications including gas storage and catalysis. The pore network in natural, as well as engineered, materials can exhibit different degrees of connectivity between pores. While this might have important implications for the sorption of fluids, the effects of pore connectivity are seldom addressed in the studies of fluid sorption. We have carried out Monte Carlo simulations of the sorption of ethane and CO2 in silicalite, a nanoporous material characterized by sub-nanometer pores of different geometries (straight and zigzag channel like pores), with varied degrees of pore connectivity. The variation in pore connectivity is achieved by selectively blocking some pores by loading them with methane molecules that are treated as a part of the rigid nanoporous matrix in the simulations. Normalized to the pore space available for adsorption, the magnitude of sorption increases with a decrease in pore connectivity. The increased adsorption in the systems where pore connections are removed by blocking them is because of additional, albeit weaker, adsorption sites provided by the blocker molecules. By selectively blocking all straight or zigzag channels, we find differences in the absorption behavior of guest molecules in these channels.

Gautam, Siddharth (ORCID:0000000314435382)↗
NASA NTRS1998

A Second Law Based Unstructured Finite Volume Procedure for Generalized Flow Simulation

An unstructured finite volume procedure has been developed for steady and transient thermo-fluid dynamic analysis of fluid systems and components. The procedure is applicable for a flow network consisting of pipes and various fittings where flow is assumed to be one dimensional. It can also be used to simulate flow in a component by modeling a multi-dimensional flow using the same numerical scheme. The flow domain is discretized into a number of interconnected control volumes located arbitrarily in space. The conservation equations for each control volume account for the transport of mass, momentum and entropy from the neighboring control volumes. In addition, they also include the sources of each conserved variable and time dependent terms. The source term of entropy equation contains entropy generation due to heat transfer and fluid friction. Thermodynamic properties are computed from the equation of state of a real fluid. The system of equations is solved by a hybrid numerical method which is a combination of simultaneous Newton-Raphson and successive substitution schemes. The paper also describes the application and verification of the procedure by comparing its predictions with the analytical and numerical solution of several benchmark problems.

Majumdar, Alok↗
DOE OSTI2021

Synergistic Coupling of CO2 and H2O during Expansion of Clays in Supercritical CO2-CH4 Fluid Mixtures

A combination of in operando IR and XRD methods were used to investigate the interaction of variably hydrated supercritical CO2-CH4 fluids with Na/NH4/Cs substituted montmorillonites. Comparing the behavior of Na-clay exposed to CH4 versus CO2 dominant fluid phases demonstrated that CO2 disrupts the H-bond network of intercalated H2O. At relatively low potentials of H2O, CO2 facilitates H2O intercalation and expansion of the interlayer region of the Na-clay. In contrast, CO2 inhibited H2O intercalation but promoted expansion of the Cs-clay. The NH4-clay displayed intermediated behavior. Methane likely intercalated the clays but opportunistically, filling unoccupied space after CO2 and H2O actively expanded the interlayer region. By comparing and contrasting the behavior of the Cs and NH4 clays to the Na Clay we conclude that, whereas H2O is required for initial Na-clay expansion, CO2 synergistically facilitated expansion and hydration of Na-clay by mixing with H2O (the entropic contribution) and participating in the outer solvation sphere of Na (the enthalpic contribution). In contrast, CO2 can directly solvate interlayer Cs and NH4 and cause expansion in the absence of H2O; nonetheless, H2O and CO2 also act synergistically to facilitate expansion of the Cs and NH4 clays at low H2O concentrations.

Loring, John S.↗
NASA NTRS1993

NASA/ASEE Summer Faculty Fellowship Program

This document is a collection of technical reports on research conducted by the participants in the 1993 NASA/ASEE Summer Faculty Fellowship Program at KSC. The basic common objectives of the Program are: to further the professional knowledge of qualified engineering and science faculty members; to stimulate an exchange of ideas between participants and NASA; to enrich and refresh the research and teaching activities of participants' institutions; and to contribute to the research objectives of the NASA centers. 1993 topics include wide band fiber optic communications, a prototype expert/information system for examining environmental risks of KSC activities, alternatives to premise wiring using ATM and microcellular technologies, rack insertion end effector (RIEE) automation, FTIR quantification of industrial hydraulic fluids in perchloroethylene, switch configuration for migration to optical fiber network, and more.

Hosler, E. Ramon↗
NASA NTRS2017

Liquefaction Study of Gaseous Oxygen Inside Mars Ascent Vehicle Propellant Tank

The in-situ production of propellants for Mars missions will utilize carbon dioxide (CO2) in the Mars atmosphere to produce oxygen. The oxygen then needs to be cooled, liquefied, and stored to be available for Mars ascent propulsion, which could be up to 2 years after liquefaction starts. Recent investigations have demonstrated the feasibility of both achieving zero boiloff and controlling the pressure of oxygen within a tank using high-efficiency reverse turbo-Brayton-cycle cryocoolers. A tube-on-tank configuration is being studied in this work. The cooling fluid circulating in the cryocooler system is routed through a network of cooling tubes on the oxygen tank. The oxygen gas produced from the in-situ production process is introduced into the chilled tank. A series of analysis of this configuration has been performed to investigate the liquefaction rate inside the tank, the thermal gradient near the top of the tank where the oxygen gas feeding tubing is located. The analyses include 2D axisymmetric CFD analysis using ANSYS Fluent, 1D thermal analysis using Matlab, and 3D thermal analysis using MSC Patran/pthermal. These three models correlate and validate each other.

Liquefaction↗
NASA NTRSDate not supplied

Axisymmetric Two-Dimensional Modeling of No Vent Filling of a Cryogenic Tank using Generalized Fluid System Simulation Program

Filling a tank with cryogenic fluid is more challenging than filling a tank with water or any other fluid that is in liquid state at atmospheric condition. Filling a tank with cryogenic fluid is a two-step process. First the tank and the transfer line mustbe chilled. Liquid cryogens start flowing into the tank only after the tank and transfer lines are chilled to the fluid saturation temperature. In normal gravity, cryogenic tanks are usually filled from the bottom at nearly atmospheric pressure. The vapor, caused by heat transfer from the warm tank walls, is allowed to vent from the top of the tank while the tank is being filled. Filling a cryogenic tank in the absence of gravity is more challenging because in a non-stratified environment liquid propellantmay not settle at the tank bottom as it does on earth. There is a strong possibility that liquid propellant may exit through the vent valve, which is typically located at the top of the tank to vent propellant vapor. There are several methodsof filling atank in space: Charge-Hold-Vent method [1], Vented-Chill / No-Vent-Fill (VCNVF)[2],and No Vent Fill with TVS assisted injector [3].This paper addressesnumerical modelingofthe No Vent Fill (NVF) processwhere a TVS (Thermodynamic Vent System) augmented injector was used with the vent valve closed during the entire filling process. NVF tests were conducted in the CRYOgenic Orbital Testbed (CRYOTE) tankat NASA/Marshall Space Flight Centerin 2018.The Generalized Fluid System Simulation Program (GFSSP), a general-purpose flow network code [4] was used to simulate the chilland fill process.

Cryogenics↗
NASA NTRSDate not supplied

Nodal Numerical Modeling of Submerged Helium Injection in a Cryogenic Propellant Tank

Subcooling of cryogenic propellant by helium injection is one of the most effective methods for suppressing bulk boiling and keeping subcooled propellant conditions for pre-launch, launch, and post-launch pressurization applications. For tank pressurization, submerged helium injection can substantially reduce helium consumption by infusing gaseous propellant into the tank ullage. This paper presents a mathematical model of the helium bubbling process in liquid oxygen to estimate the amount of oxygen vapor absorbed by the rising helium bubbles and the amount of subcooling of liquid oxygen due to evaporative heat and mass transfer. This mathematical model was incorporated in a simulation model of tank pressurization built with Generalized Fluid System Simulation Program (GFSSP), a general-purpose flow network code developed at NASA/Marshall Space Flight Center. The numerical predictions of subcooling have been compared with the experimental data of Cho et al. which investigated the propellant subcooling effect as a function of system pressure, helium injection temperature, and flowrate for a non-drained submerged injection system. The numerical predictions of helium consumption have been compared with the test data from a NASA Centaur test vehicle which included both direct and submerged injection with draining of propellants. The numerical model developed with GFSSP has been validated against two sets of experimental data and has been shown to predict both propellant subcooling and helium consumption to within 30% in most cases. The test data used for the model validation were taken in 1-g, but the mass diffusion model was developed to be applied in both 1-g and micro-g environments.

Cryogenics↗
NASA NTRSDate not supplied

Nodal Modeling of Submerged Helium Injection Pressurization of a Cryogenic Propellant Tank

Subcooling of cryogenic propellant by helium injection is one of the most effective methods for suppressing bulk boiling and keeping subcooled propellant conditions for pre-launch, launch, and post-launch pressurization applications. For tank pressurization, submerged helium injection can substantially reduce helium consumption by infusing gaseous propellant into the tank ullage. This paper describes a thermodynamic model of the helium bubbling process in liquid oxygen to estimate the amount of oxygen vapor absorbed by the rising helium bubbles and the amount of subcooling of liquid oxygen due to evaporative heat and mass transfer. The physics of helium dissolution during the pressurization process is also modeled, primarily for liquid hydrogen propellant where the dissolution is more significant. The analyses were performed in a simulation model of tank pressurization built with Generalized Fluid System Simulation Program (GFSSP), a general-purpose flow network code developed at NASA/Marshall Space Flight Center. The numerical predictions of subcooling have been compared with the experimental data of Cho et al. which investigated the propellant subcooling effect as a function of system pressure, helium injection temperature, and flowrate for a non-drained submerged injection system. The numerical predictions of helium consumption have been compared with the test data from a NASA Centaur test vehicle which included both direct and submerged injection with draining of propellants. Hydrogen propellant testing of the Cryogenic Propellant Storage and Transfer Engineering Developmental Unit (CPST EDU) conducted at NASA/Glenn Research Facility was also analyzed. The data used for the model validation were taken in 1-g, but the model was developed to be applicable in both multi-g and micro-g environments.

tank pressurization↗
DOE OSTI2014

Modeling the Sedimentation of Red Blood Cells in Flow under Strong External Magnetic Body Force using a Lattice Boltzmann Fictitious Domain Method

To model the sedimentation of the red blood cell (RBC) in a square duct and a circular pipe, the recently developed technique derived from the lattice Boltzmann method and the distributed Lagrange multiplier/fictitious domain method (LBM-DLM/FD) is extended to employ the mesoscopic network model for simulations of the sedimentation of the RBC in flow. The flow is simulated by the lattice Boltzmann method with a strong magnetic body force, while the network model is used for modeling RBC deformation. The fluid-RBC interactions are enforced by the Lagrange multiplier. The sedimentation of the RBC in a square duct and a circular pipe is simulated, revealing the capacity of the current method for modeling the sedimentation of RBC in various flows. Numerical results illustrate that that the terminal setting velocity increases with the increment of the exerted body force. The deformation of the RBC has significant effect on the terminal setting velocity due to the change of the frontal area. The larger the exerted force is, the smaller the frontal area and the larger deformation of the RBC are.

Shi, Xing↗
DOE OSTI2017

Metal sulfide electrodes and energy storage devices thereof

The present invention generally relates to energy storage devices, and to metal sulfide energy storage devices in particular. Some aspects of the invention relate to energy storage devices comprising at least one flowable electrode, wherein the flowable electrode comprises an electroactive metal sulfide material suspended and/or dissolved in a carrier fluid. In some embodiments, the flowable electrode further comprises a plurality of electronically conductive particles suspended and/or dissolved in the carrier fluid, wherein the electronically conductive particles form a percolating conductive network. An energy storage device comprising a flowable electrode comprising a metal sulfide electroactive material and a percolating conductive network may advantageously exhibit, upon reversible cycling, higher energy densities and specific capacities than conventional energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2017

Post-Fracturing Experiment Simulation of Hydraulic Fracture Propagation in a Deep Mine Using a Fully Coupled 3D Network-Flow and Quasi-static Discrete Element Model

Aimed at supporting the hydraulic fracturing experiments in a vertical borehole in the Poorman phyllite at approximately 1500 m depth at the Sanford Underground Research Facility (SURF), we perform post-hydraulic fracturing experiment simulations and compare the simulated borehole pressure changes and flow rates with actual measurements using a newly developed fully coupled three-dimensional (3D) network flow and quasi-static discrete element model (DEM). The quasi-static DEM model, which is constructed by Delaunay tessellation of the rock volume, considers rock fabric heterogeneities by using the “disordered” DEM mesh and adding random perturbations to the stiffness and tensile/shear strengths of individual DEM elements and the elastic beam between them. A conjugate 3D flow network based on DEM lattice is constructed to calculate the fluid flow in both the fracture and porous matrix. One distinctive advantage of the DEM model is that fracturing is naturally described by the breakage of elastic beams between DEM elements. It is also extremely convenient to introduce mechanical anisotropy into the model by simply assigning orientation-dependent tensile/shear strengths to the elastic beams.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Evolution of Induced Fracture Networks Using Machine Learning Correlation Image Analysis

Enhanced geothermal systems (EGS) can enrich the Nation’s energy portfolio by increasing the amount of renewable baseload power available to the electrical grid. Microseismic activity, due to fluid resource injection and production, provides critical insight into the processes that occur within EGS subsurface fracture networks. To more precisely characterize these fracture networks, we leverage recent advances in deep neural network (DNN) image analysis techniques. Using seismic spectrogram data collected during a meso scale EGS field experiment as image inputs, we developed a new approach to correlate microseismic events. Our results indicate that our chosen DNN is able to estimate the correlation value between highly correlated events (i.e., cross-correlation coefficients greater than approximately 0.7), but has difficulty estimating correlation values between poorly correlated events. In physical systems however, the highly correlated microseismic events are the events of interest. Therefore, this data-driven method has shown potential for improving domestic Energy and Resource Security, advancing the current methods available to image subsurface EGS thermal reservoirs to enhance the availability of clean and reliable energy to the national electric grid.

58 GEOSCIENCES↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2024

Physics-informed machine learning of the correlation functions in bulk fluids

The Ornstein–Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids. In this work, machine learning models, notably physics-informed neural networks and physics-informed neural operator networks, are explored to solve the OZ equation. The physics-informed machine learning models demonstrate great accuracy and high efficiency in solving the forward and inverse OZ problems of various bulk fluids. The results highlight the significant potential of physics-informed machine learning for applications in thermodynamic state theory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Chemical Reactor Network Modeling of Ammonia Rich-Quench-Lean Combustion Using a Partially Stirred Reactor Approach

Ammonia is a promising alternative to hydrogen with high energy density and favorable storage and transport characteristics. However, low flammability and a propensity for high nitrogen oxide (NO x ) emissions make direct utilization challenging. Recently, two-stage rich-quench-lean (RQL) combustion strategies have shown promise in achieving low NO x emissions with ammonia. In this approach, the rich stage serves to oxidize a portion of the fuel while thermally decomposing as much of the remaining ammonia as possible, generating hydrogen. In the second (lean) stage, air is rapidly introduced, burning out the hydrogen and residual ammonia. Two-stage RQL combustion of ammonia has been investigated in the open literature both experimentally and numerically. In general, idealized chemical reactor network (CRN) models predict NO x concentrations below those of 2D/3D computational fluid dynamics models and experiments. The primary drivers of these discrepancies may be largely attributed to finite rate mixing nonadiabatic operation. The typical CRN model is comprised of a perfectly-stirred-reactor (PSR), followed by a plug-flow-reactor (PFR), meant to represent the flame, and postflame zones, respectively. In the two-stage RQL approach two PSR-PFR networks are arranged sequentially, corresponding to the rich and lean stages, with secondary air injection in between. In the authors' past work, this arrangement has demonstrated the significant sensitivity of exit NO x to the rich stage equivalence ratio, while the amount of secondary air injection was shown to be less critical. In this paper, the CRN model is extended to (1) include the impacts of heat loss and (2) utilize a partially-stirred-reactor (PaSR) approach to study the impacts of mixing on emissions performance. Varying amounts of heat loss are applied to the rich relaxation zone to understand emissions performance and changes to optimization of equivalence ratio and residence time. Premixed and nonpremixed configurations are considered in the rich stage PaSR, with varying degrees of mixing intensity to study the interaction between mixing, transport, and kinetic timescales. Critically, the impact of mixing between hot products and secondary air injection is studied to understand practical injector needs. Results show unburnt ammonia leaving the rich stage as a primary contributor to NO x emissions – driven both by increased heat loss and reduced mixing rates. Furthermore, heat losses have been shown to create conditions that are conducive to increased N 2 O formation in the lean stage. In conclusion, the results of this study will be considered in the context of developing optimized two-stage RQL combustors for ammonia.

Combustion↗
DOE OSTI2021

Development of a Filtered CFD-DEM Drag Model with Multiscale Markers Using an Artificial Neural Network and Nonlinear Regression

Here, the accuracy of coarse-grained Euler-Lagrangian simulations of fluidized beds heavily depends on the mesoscale drag models to account for the influences of the unresolved sub-grid structures. Traditional filtered drag models are regressed with mesoscale markers such as voidage and slip velocities. In this research, a filtered drag was regressed with both mesoscale and macro-scale markers using fine grid Computational Fluid Dynamics - Discrete Element Method (CFD-DEM) simulations. The traditional non-linear regression method was compared with machine learning regression using an Artificial Neural Network (ANN) implemented in PyTorch and coupled with MFiX. The new drag showed higher accuracy than the Wen-Yu drag and another filtered drag derived from the two-fluid model. The nonlinear regression shows slightly better results than ANN regression in cases with similar R 2 values. The utilization of the gas inlet velocity as an additional macro-scale marker reduced the errors by up to 55.3% in the tested cases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗