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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

High-precision predictions of properties of chemically disordered crystals

Multiple scattering theory (MST) combined with density functional theory (DFT) allows to predict properties of chemically disordered materials from the first principles. However, such predictions often suffer from the systematic errors, which depend on crystal geometry. Each computed property of a particular crystal structure typically has a relatively small random error and a larger systematic error. Cancellation of systematic errors allows more accurate predictions. We propose a computational methodology based on the subtraction of the systematic errors in MST. To exemplify it, we apply it to the precipitated alloys. Considering precipitation strengthening in Ni superalloys, we compute the relative enthalpies of the competing Ni3(Al,Ti)1 crystal structures with a chemical disorder on the Al+Ti sublattice. Such predicted composition-structure-property dependencies are useful for the guided design of the next-generation alloys with improved strength. Our predictions are validated by comparison with the results of other DFT methods (having a higher computational cost) and with experiment. We acknowledge funding of the guided design of stronger superalloys for airspace by NASA Aeronautics Research Mission Directorate (ARMD) via Transformational Tools and Technologies (TTT) Project.

Computational↗
NASA NTRS2022

High-precision predictions of properties of chemically disordered crystals

Multiple scattering theory (MST) combined with density functional theory (DFT) allows to predict properties of chemically disordered materials from the first principles. However, such predictions often suffer from the systematic errors, which depend on crystal geometry. Each computed property of a particular crystal structure typically has a relatively small random error and a larger systematic error. Cancellation of systematic errors allows more accurate predictions. We propose a computational methodology based on the subtraction of the systematic errors in MST. To exemplify it, we apply it to the precipitated alloys. Considering precipitation strengthening in Ni superalloys, we compute the relative enthalpies of the competing Ni_3(Al_{1-x}Ti_x)_1 crystal structures with a chemical disorder on the Al+Ti sublattice. Such predicted composition-structure-property dependencies are useful for the guided design of the next-generation alloys with improved strength. Our predictions are validated by comparison with the results of other DFT methods (having a higher computational cost) and with experiment.

density functional theory↗
NASA NTRS2022

Building a Transdisciplinary, Exascale-Capable Workforce for Geospace Science

Key Points: ●First-principles, self-consistent geospace modeling will require at least exascale-level computing capabilities; however, the technical skills necessary to develop such simulation codes are not taught as part of Heliophysics training/PhD programs. ●Developing exascale Heliophysics codes will require transdisciplinary collaborations between physicists, computer scientists, software engineers, data scientists, and applied mathematics. Such teams must be persistent and formed around specific skills, not specific problems. ●We must have stable, positive long-term career outcomes for Heliophysics scientific developers in order to retain knowledge of and promote future innovation in scientific algorithm development and advanced computing techniques. One such way to do this is by funding long-term scientific programming groups, akin to Heliophysics DRIVE Centers. ●We must have a balance between promoting innovation via funding short-term closed-source development and allowing the open-source community to benefit from and build on the newest scientific modeling techniques.

C Bard↗
NASA NTRSDate not supplied

The Calculation of Diffusion Properties for Open Shell Molecules

- Navier-Stokes requires as input various collision integrals for gases - Diffusion, - Viscosity - Thermal conductivity - Thermal diffusion ... - Levin, Stallcop and Partridge state-of-the-art - Non-Relativistic Born-Oppenheimer Approximation (freeze nuclei first, let them move second) - Solve electron problem: Compute realistic Potential Curves (PC) from first principles, - Solve nuclei problem: Compute scattering properties for each PC using sophisticated semi-classical method - Compute cross sections - Compute appropriate thermal collision integrals

STMD↗
NASA NTRSDate not supplied

Overview of NASA Research Activities in Shock Layer Kinetics and Radiation

Shock layer radiation is an important heating mechanism for entry probes to most planetary destinations and re-entry to Earth from beyond low Earth orbit. Our understanding of shock layer radiation phenomena has improved tremendously over the last decade thanks to NASA investment in fundamental radiation research through Entry Systems Modeling’s (ESM) Shock Layer Kinetics and Radiation (SLKR) task. This talk will overview the recent activities within SLKR, including validation of models through ground testing, flight instrumentation and remote observations; development of first principles ab initio calculations for reaction mechanisms and spectroscopic databases and advancing numerical and computational methods for prediction of radiation in reacting hypersonic flows. 1 Sr. Research Scientist, Aerothermodynamics Branch, and AIAA Associate Fellow.

Brett A. Cruden↗
NASA NTRS2023

An Ensemble Neural Network Model for Predicting Rare-Earth Oxide and Silicate Heat Capacities at High Temperature

In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.

environmental barrier coatings↗
NASA NTRSDate not supplied

Hall Magnetohydrodynamic Power Generation and Drag Augmentation Using A Coaxial-Electrode Configuration During Hypersonic Entry

For interplanetary missions, a spacecraft must reduce its relative velocity from orbital speeds of multiple kilometers per second to zero in order to safely land on the surface of a planetary body. During this phase known as planetary reentry, the spacecraft undergoes high speed above Mach 5 up to Mach 30. This hyper-sonic flight regime imposes a strong bow shock in front of the vehicle that imparts extreme aerodynamic and thermal effects. In addition, during portions of this regime, a plasma is formed in the post-shock flow-field around the vehicle due to these high temperatures. Interaction between an applied magnetic field with this electrically conductive fluid, or magnetohydrodynamic (MHD) interaction, can be utilized to convert kinetic energy from the flow into storable electrical energy. Onboard MHD energy generation can benefit the spacecraft by increasing the power of active thermal control components and vehicle control systems during reentry. MHD interaction also exerts a Lorentz body force on the plasma flow that can be manipulated to act as an additional drag force through the vehicle body. MHD drag augmentation can benefit the spacecraft with an additional control mechanism that operates without moving parts. This can be utilized at higher altitudes and subsequently reduce convective heat transfer to the vehicle. MHD interaction with plasma flow suggests that both power generation and drag augmentation can be integrated together in one design. Modern numerical studies have only demonstrated the viability and potential of MHD energy generation and drag modulation separately for implementation into future spacecraft designs [1] [2] [3]. Steeves et. al. presented one possible generator design specifically for reentry vehicles that utilized a modular panel with two embedded electromagnets and two extruding electrodes for air plasma to flow between. Fujino et. al. presented results that indicated increased total drag with a permanent magnet embedded in the blunt body of the Orbital Reentry Experiment (OREX) trajectory. Furthermore, experimental investigations of these two MHD applications have been limited and separate due to the difficult technical nature of experimental de-sign and analysis. For MHD energy generation, there have been two designs which used a cylindrical fore-body with two embedded permanent magnets and two outward facing curved electrodes for tangency to an artificially ionized plasma that flowed along the body length [4] [5]. The first design was studied using an air microwave (MW) ionized supersonic plasma wind tunnel and the second design used radio frequency (RF) ionization of Argon, but both physically demonstrated the feasibility of MHD energy generation in reentry plasma conditions. For MHD drag modulation, one study involved a small-scale cylindrical blunt body embedded with permanent magnets immersed in plasma flow in an arcjet tunnel [6], and another utilized a spherical permanent magnet enclosed with a spherical forebody that was evaluated in a shock expansion tunnel [7]. Both experimental studies provided physical evidence of drag augmentation due to MHD interactions. Therefore, because of the multi-faceted effects of MHD along with both applications having been deemed feasible and beneficial during reentry, this indicates that a coupled power generation and drag modulation design could be achievable. The axi-symmetric nature of blunt bodies for reentry suggests that a spherical set of two ring electrodes near the nose along the body axis. Based on first principles, flow along the walls of the spherical body can then produce MHD interactions between the electrodes. As a result, two currents are generated: a Hall current that produces power and a primary inductive current that augments the drag force. The goal of this work is to provide a simulated design of a coaxial-electrode con-figuration for power generation and drag modulation for experimental testing.

E. Leong↗
NASA NTRSDate not supplied

Machine Learning Approaches for Rare-Earth Silicate Environmental Barrier Coating Thermochemical and Thermomechanical Property Predictions

Environmental barrier coatings (EBCs) are a necessary enabling technology for the transition from superalloys to silicon carbide (SiC) ceramic matrix composites (CMCs) in gas turbine engines for increased efficiency and decreased fuel costs. SiC-based CMCs are prone to oxidation-based degradation in the engine hot section, and rare-earth (RE) silicates are promising candidates for EBCs due to their close thermal expansion match to the composite substrate and oxidation resistance. However, the design of EBCs is hindered by the large chemical space of candidate materials and the difficulty in obtaining material properties for engineering optimization. This is especially difficult as research continues into mixed-cation or “high-entropy” RE silicates. First-principles computational methods such as density functional theory (DFT) are highly effective at calculating material properties to guide coating design but are limited by their computational cost. Atomistic simulations have the potential to both accelerate property calculations and expand the properties able to be calculated due to their lower computational compared to DFT. However, they require interatomic potentials (IAPs) specific to the material system of interest, and, to our knowledge, there are no suitable IAPs for RE silicates. Machine learning (ML) is a promising technique to accelerate material property predictions indirectly by generating IAPs for atomistic simulations or via direct prediction. In this work, we present two ML approaches to accelerate the calculation of RE silicate properties relevant to EBC design: 1) a ML-derived interatomic potential (IAP) for atomistic simulations of yttrium disilicate (Y2Si2O7) from DFT training data, and 2) a neural network (NN) model to directly predict thermochemical properties of RE silicates and oxides directly from easily obtainable unit cell parameters. Classical MD simulations using the IAP yield lattice properties and bond lengths in good agreement with both DFT and experimental results from x-ray diffraction. Thermodynamic properties calculated using the finite-displacement phonon method and quasi-harmonic approximation were orders of magnitude faster than DFT with good agreement to the DFT results. The IAP was also used to calculate properties such as coefficient of thermal expansion (CTE) that require large simulation supercells and are therefore difficult with DFT. The IAP correctly predicted the anisotropic nature of the CTE in three different phases of Y2Si2O7. The NN model predicts constant pressure heat capacity, Cp, orders of magnitude faster than DFT calculations, which can enable its use as a surrogate model for multiscale simulations. The two methods presented in this work demonstrate the utility of ML for accelerating the prediction of RE silicate properties, which can in turn accelerate EBC design and optimization.

machine learning↗
NASA NTRS2020

Simulation-Based Analysis and Prediction of Thrust Vector Servoelastic Coupling

A method of analysis and prediction of servoelastic coupling in launch vehicles is presented, surveying the discovery and subsequent resolution of a predicted servoelastic resonance phenomenon affecting the NASA Space Launch System launch vehicle at specific flight conditions. A physics-based linearized multibody mechanization of the governing equations is combined with first principles analysis to demonstrate that antisymmetric bending of the solid rocket motors leads to a reduction of equivalent viscous modal damping through coupling with the thrust vector control actuators. The sensitivity to parameters and the effects of the resonance phenomenon on flight control performance and stability are confirmed through extensive simulation verification in the time and frequency domain. A novel enhancement in model fidelity that accounts for Coriolis effects of fluid flow on bending within the solid rocket motor case and nozzle is shown to add sufficient damping to reduce the risk of adverse control-structure interaction.

Jeb S Orr↗
NASA NTRSDate not supplied

Parametric Analysis of the Charge-Hold-Vent Method for Cryogenic Propellant Tank Chill Down

In the absence of external heat exchangers, the on-orbit transfer of cryogenic propellants requires the receiver tank to first be quenched to a sufficiently low energy state to allow for a continuous no-vent fill to avoid unnecessary venting of liquid. One proposed method for tank chilldown that minimizes the potential for venting liquid is the charge hold vent (CHV) method. CHV follows a cyclic process that gradually removes thermal energy from the receiver tank by injecting liquid with the vent valve closed and allowing the fluid and wall to reach near-thermal equilibrium before venting the superheated vapor. However, the CHV method must be optimized to minimize complexity, mass, and time. This paper presents a modular CHV analytical model used to quantify the number of cycles and propellant mass consumed based on first principles. The model is used to examine the effect of eight parameters: receiver tank material, volume, mass, maximum expected operating pressure, and initial pressure, liquid injection pressure and temperature, and the target temperature. The model is validated against the only two available CHV datasets. Based on results, the tank mass-to-volume ratio is the most important factor in determining the number of CHV cycles and thus degree of difficulty in tank chilldown. The model can easily be used for early-stage design, sizing, and analysis of cryogenic propellant transfer systems.

Tank Chilldown↗
NASA NTRSDate not supplied

Parametric Analysis of the Charge-Hold-Vent Method for Cryogenic Propellant Tank Chill Down

Abstract. In the absence of external heat exchangers, the on-orbit transfer of cryogenic propellants requires the receiver tank to first be quenched to a sufficiently low energy state to allow for a continuous no-vent fill to avoid unnecessary venting of liquid. One proposed method for tank chilldown that minimizes the potential for venting liquid is the charge hold vent (CHV) method. CHV follows a cyclic process that gradually removes thermal energy from the receiver tank by injecting liquid with the vent valve closed and allowing the fluid and wall to reach near-thermal equilibrium before venting the superheated vapor. However, the CHV method must be optimized to minimize complexity, mass, and time. This paper presents a modular CHV analytical model used to quantify the number of cycles and propellant mass consumed based on first principles. The model is used to examine the effect of eight parameters: receiver tank material, volume, mass, maximum expected operating pressure, and initial pressure, liquid injection pressure and temperature, and the target temperature. The model is validated against the only two available CHV datasets. Based on results, the tank mass-to-volume ratio is the most important factor in determining the number of CHV cycles and thus degree of difficulty in tank chilldown. The model can easily be used for early-stage design, sizing, and analysis of cryogenic propellant transfer systems. Keywords: tank chilldown, charge-hold-vent, no-vent fill

Tank Chilldown↗
NASA NTRSDate not supplied

Predicting Melt Properties Using Atomistic Simulations With A Highly Accurate Physically Informed Neural Network Interatomic Potential

The use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties will be presented. Such properties are critical for process modeling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.

molecular dynamics↗
NASA NTRSDate not supplied

ICME for NASA Aerospace Applications: Batteries for Electric Aviation

NASA’s approach to computational materials modeling is detailed in the NASA Vision 2040 Roadmap for Multiscale Modeling and Simulation of Materials and Systems. This report is in the spirit of national initiatives such as the Material Genome Initiative (MGI), Integrated Computational Materials Engineering (ICME), and others. We utilize a combination of fundamental modeling, computational high-throughput screening, and data science methods, e.g., machine learning, are used to find innovative solutions to NASA or national technology challenges. Applications of interest are wide ranging from advanced alloys to batteries to coatings, among others. In this talk, we present three examples for recent work related to NASA applications. First, doping advanced sulfur battery cathodes with selenium boosts electrical conductivity important for electric aircraft applications. First principles calculations will be discussed that result in compositional design maps for these materials. Second, development of icephobic coatings is important to mitigate safety hazards associated with icing for aircraft. Molecular dynamics simulations are reported for ice-surface interfaces to understand adhesion mechanisms and help screen optimal ice-phobic coatings. Third, shape memory alloys have numerous applications as actuators, superelastic materials, etc. for aerospace. We report machine learning models that predict martensitic transition temperatures across a broad swath of compositional space.

John Lawson↗
NASA NTRSDate not supplied

A General Model for the Electrochemical Double Layer in Solids

It has long been clear that electrochemical double layer / space charge zone in the vicinity of interfaces and other extended defects greatly influences transport and reactivity. Over the past several years, multiple microscopic measurements of defect concentrations in the vicinity of solid-solid interfaces made clear that dilute-case thermodynamic theories to describe the behavior of charged species in space charge zones are untenable. Inhomogeneous thermodynamics – i.e., phase field models – have delivered the most success in describing both microscopic and macroscopic experiments. We still need a generalized approach that quantitatively reproduces the microscopic and macroscopic evidence, while remaining accessible to a wide range of researchers and engineers. In my talk I will present such a framework, which builds on the previously introduced Poisson-Cahn theory but utilizes data science methods which come into the thermodynamic framework in a well-defined way. I will review recent applications to both microscopic and macroscopic datasets while previewing our plans for connecting the theory to atomistic / first principles calculations and experiment simultaneously. I will also review plans for releasing open source code that runs on desktop-level computational resources.

David Mebane↗
NASA NTRS2022

Thermospheric Neutral Density Variation During the “SpaceX” Storm: Implications From Physics-Based Whole Geospace Modeling

The Starlink satellites launched on 3 February 2022 were lost before they fully arrived in their designated orbits. The loss was attributed to two moderate geomagnetic storms that occurred consecutively on February 3-4. We investigate the thermospheric neutral mass density variation during these storms with the Multiscale Atmosphere-Geospace Environment (MAGE) model, a first-principles, fully coupled geospace model. Simulated neutral density enhancements are validated by Swarm satellite measurements at the altitude of 400-500 km. Comparison with standalone TIEGCM and empirical NRLMSIS 2.0 and DTM-2012 models suggests better performance by MAGE in predicting the maximum density enhancement and resolving the gradual recovery process. Along the Starlink satellite orbit in the middle thermosphere (∼ 200 km altitude), MAGE predicts up to 150% density enhancement near the second storm peak while standalone TIEGCM, NRLMSIS 2.0 and DTM-2012 suggest only ∼ 50% increase. MAGE also suggests altitudinal, longitudinal, and latitudinal variability of storm-time percentage density enhancement due to height dependent Joule heating deposition per unit mass, thermospheric circulation changes, and travelling atmospheric disturbances. This study demonstrates that a moderate storm can cause substantial density enhancement in the middle thermosphere. Thermospheric mass density strongly depends on the strength, timing, and location of high-latitude energy input, which cannot be fully reproduced with empirical models. A physics-based, fully coupled geospace model that can accurately resolve the high-latitude energy input and its variability is critical to modeling the dynamic response of thermospheric neutral density during storm time.

Starlink↗
NASA NTRS2023

Simulated Inherent Optical Properties of Aquatic Particles Using the Equivalent Algal Populations (EAP) Model

Paired measurements of phytoplankton absorption and backscatter, the inherent optical properties central to the interpretation of ocean colour remote sensing data, are notoriously rare. We present a dataset of Chlorophyll a (Chl a) -specific phytoplankton absorption, scatter and backscatter for 17 different phytoplankton groups, derived from first principles using measured in vivo pigment absorption and a well-validated semi-analytical coated sphere model which simulates the full suite of biophysically consistent phytoplankton optical properties. The optical properties of each simulated phytoplankton cell are integrated over an entire size distribution and are provided at high spectral resolution. The model code is additionally included to enable user access to the complete set of wavelength-dependent, angularly resolved volume scattering functions. This optically coherent dataset of hyperspectral optical properties for a set of globally significant phytoplankton groups has potential for use in algorithm development towards the optimal exploitation of the new age of hyperspectral satellite radiometry.

Equivalent Algal Populations↗
NASA NTRSDate not supplied

Downfolding Complex Materials Problems Onto Model Hamiltonians for Quantum Computers

Simulating the properties of quantum materials is expected to be one of the exciting applications for quantum computers and where we hope to see advantages over classical hardware. The complexity of ab initio Hamiltonians describing the physics of application-relevant materials places them beyond the realm of possibility for solution on near-term hardware with a limited number of qubits. Various Hamiltonian approximations, including Hamiltonian downfolding, offers a possibility towards simulating complex materials on near-term hardware. This is accomplished by approximating the relevant physics of a given material through their representation by simpler model Hamiltonians, such as the Hubbard Hamiltonian or extensions of it. Here we employ a well-defined first-principles methodology for deriving downfolded multi-band extended Hubbard Hamiltonians of materials, capturing strong electronic correlation and electron-phonon coupling, based on the formalism of Wannier functions and the calculation of the screened Coulomb interaction. We demonstrate for a variety of systems that quantum simulation of these downfolded Hamiltonians reproduces key properties, thus establishing downfolding as a promising route to achieve near-term simulation of application-relevant systems on quantum hardware.

Antonios Markos Alvertis↗
NASA NTRS2022

Electroactive ZnO: Mechanisms, Conductivity, and Advances in Zn Alkaline Battery Cycling

Zinc oxide is of great interest for advanced energy devices because of its low cost, wide direct bandgap, non-toxicity, and facile electrochemistry. In zinc alkaline batteries, ZnO plays a critical role in electrode passivation, a process that hinders commercialization and remains poorly understood. Here, novel observations of an electroactive type of ZnO formed in Zn-metal alkaline electrodes are disclosed. The electrical conductivity of battery-formed ZnO is measured and found to vary by factors of up to 104, which provides a first-principles-based understanding of Zn passivation in industrial alkaline batteries. Simultaneous with this conductivity change, protons are inserted into the crystal structure and electrons are inserted into the conduction band in quantities up to ≈1020 cm−3 and ≈1 mAh gZnO−1. Electron insertion causes blue electrochromic coloration with efficiencies and rates competitive with leading electrochromic materials. The electroactivity of ZnO is evidently enabled by rapid crystal growth, which forms defects that complex with inserted cations, charge-balanced by the increase of conduction band electrons. This property distinguishes electroactive ZnO from inactive classical ZnO. Knowledge of this phenomenon is applied to improve cycling performance of industrial-design electrodes at 50% zinc utilization and the authors propose other uses for ZnO such as electrochromic devices.

Brendan E. Hawkins↗