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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Computational design of high entropy alloy coating for hydrogen turbine applications

This project aims to develop novel high entropy alloy (HEA)-based coatings to protect critical components in hydrogen-fueled turbine power system. The HEA-coatings will demonstrate superior performance in hydrogen combustion environment to commercial NiCoCrAlY coating in current natural gas turbine system. The HEA coating facilitates the formation of a protective scale of alpha-alumina to slow down the inward diffusion of oxidizing species and the outward diffusion of metal elements, and possesses ultrahigh corrosion and spallation resistance to prolong the service lifetime of critical components in hydrogen turbine power system. Aimed to accelerate the discovery of novel HEA coating compositions, high throughput computational modeling including CALPHAD and density functional theory and machine learning are performed to predict phase stability, oxygen permeability, oxidation rate constant, coefficient of thermal expansion, and mechanical properties. Based on the modeling and machine learning prediction, experimental validation is performed. Preliminary results will be presented and approaches to minimize oxidation will be discussed.

alloy design↗
NASA NTRS1980

Investigation of direct integrated optics modulators

Direct modulation techniques applicable to integrated optics data preprocessors were investigated. Several methods of modulating a coherent optical beam by interaction with an incoherent beam were studied. It was decided to investigate photon induced conductivity changes in thin semiconductor cladding layers on optical waveguides. Preliminary calculations indicate significant changes can be produced in the phase shift in a propagating wave when the conductivity is changed by ten percent or more. Experimental devices to verify these predicted phase changes and experiments designed to prove the concept are described.

Batchman, T. E.↗
NASA NTRS2012

Space Environments and Effects Concept: Transitioning Research to Operations and Applications

The National Aeronautics and Space Administration (NASA) is embarking on a course to expand human presence beyond Low Earth Orbit (LEO) while expanding its mission to explore the solar system. Destinations such as Near Earth Asteroids (NEA), Mars and its moons, and the outer planets are but a few of the mission targets. NASA has established numerous offices specializing in specific space environments disciplines that will serve to enable these missions. To complement these existing discipline offices, a concept focusing on the development of space environment and effects application is presented. This includes space climate, space weather, and natural and induced space environments. This space environment and effects application is composed of 4 topic areas; characterization and modeling, engineering effects, prediction and operation, and mitigation and avoidance. These topic areas are briefly described below. Characterization and modeling of space environments will primarily focus on utilization during Program mission concept, planning, and design phases. Engineering effects includes materials testing and flight experiments producing data to be used in mission planning and design phases. Prediction and operation pulls data from existing sources into decision-making tools and empirical data sets to be used during the operational phase of a mission. Mitigation and avoidance will develop techniques and strategies used in the design and operations phases of the mission. The goal of this space environment and effects application is to develop decision-making tools and engineering products to support the mission phases of mission concept through operations by focusing on transitioning research to operations. Products generated by this space environments and effects application are suitable for use in anomaly investigations. This paper will outline the four topic areas, describe the need, and discuss an organizational structure for this space environments and effects application.

Edwards, David L.↗
DOE OSTI2025

Electrodeposition of near-equiatomic CoCuFeNi multi-principal element alloys from an acidic glycine-citrate-triton X100 aqueous electrolyte

Understanding the composition and morphology control of electrodeposited CoCuFeNi is the first step to finding a general strategy for developing electrodeposition processes of unconventional alloys with large redox potential differences and complicated deposition mechanisms. Here, in this work, we have successfully synthesized the near-equiatomic (<5 at% error) CoCuFeNi films with ~200nm thickness by electrodeposition from glycine-citrate-Triton X-100 acidic electrolytes. This system generally follows the Principle II in Brenner’s paradigm on alloy composition control in electrodeposition. X-ray diffraction (XRD) profiles show that the films only consist of one crystalline phase, different from the deposit from ammonia-citrate-boric acid electrolytes and the equilibrium phases predicted by CALPHAD. The near-equiatomic deposits at Ru substrates were successfully annealed at 400 ° C without significant intermixing between the substrate and the deposit, in which no phase separation of the crystalline phase was observed in its XRD profile. Xray photoelectron spectroscopy (XPS) reveals that some depositing elements (mainly Fe) exist as metal oxides. Cu is dissolved in the crystalline phases, stabilized by small crystalline domain size and the amorphous metal oxides inside the CoCuFeNi deposits.

CoCuFeNi↗
NASA NTRSDate not supplied

An Initial Vibro-Acoustic Model for Predicting Electric Motor Noise

A system-level tool to predict electric motor noise, suitable for trade studies of eVTOL and UAM aircraft, does not exist. This work describes the ongoing effort to develop such a tool. It utilizes a 3 phase prediction routine that models the electro-magnetic interactions of the electric motor, the resulting structural vibrations of the motor rotor, and finally the acoustic propagation generated by those structural vibrations.

Electric Motor Noise↗
NASA NTRS1995

Late-time spectra and type Ia supernova models: New clues from the Hubble Space Telescope

Calculated late-time spectra of two classical hydrodynamical models for Type Ia supernovae (deflagration model W7 of Nomoto, Thielemann, & Yokoi, and delayed detonation model DD4 of Woosley & Weaver) are compared with observations of SN 1992A and other spectroscopically normal SNe Ia. An important new piece of information is provided by observations done with the Hubble Space Telescope (HST) which cover the ultraviolet range at the nebular phase of a SN Ia: SN 1992A in NGC 1380. For the first time a picture of SN Ia emission from the ultraviolet through the optical is obtained at these phases. Predictions of the classical model (W7 and DD4) are compared with the observed spectrum of SN 1992A and with the optical spectra of SN 1989M in NGC 4579 and SN 1990N in NGC 4639 at similar epochs. The absolute B and V magnitudes of the models are also estimated at these late phases. Taken at face value the nebular spectra of these 'classical' models are more consistent with the long extragalactic distance scale, pointing to distances to NGC 4579 around 21 +/- 3 Mpc and a slightly larger distance, 22 +/- 3 Mpc, to NGC 4639, on the back side of the Virgo Cluster. However, the calculated Fe(+3) luminosity as predicted from the models exceeds the observed limit from the HST data of SN 1992A. Other differences in the ratios of the line intensities between calculated and observed spectra, show some disagreement with the observed spectra at the nebular phases. They may not be the best choice for spectroscopically normal SNe Ia, and their use as an independent calibration of the extragalactic distance scale should be viewed with caution.

Ruiz-Lapuente, P.↗
DOE OSTI2026

Computational Design of Interlayers for Thermally Stable Compositionally Graded Coatings on Nickel Alloys

To extend the service life of Ni-based superalloys, refractory metal coatings are often used. However, direct bonding between metals with dissimilar crystal structure promotes brittle intermetallic phase formation. This work presents a computational thermodynamic framework for high throughput design of functionally graded interlayers to suppress deleterious phases that may form at the interlayer. The Thermo-Calc software package was used to screen candidate metallic interlayer elements based on stability of solid-solution phases. Vanadium was identified as a promising interlayer due to its consistent suppression of intermetallic phases. Temperature-dependent phase diagram mapping between 600 and 1000 °C guided selection of a compositional pathway that significantly reduced intermetallic formation compared to directly joining the Ni-based and Nb refractory alloys. Time–temperature–transformation analysis was performed to assess whether equilibrium-predicted phases are kinetically accessible along regions of the graded path where non-solid-solution phases are not fully suppressed. The methodology was further applied to additional Ni-based alloy and coating systems, illustrating its transferability as an approach for rapid computational design of graded interlayers in dissimilar high-temperature materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Neural Networks for Prediction of Complex Chemistry in Water Treatment Process Optimization

Water chemistry plays a critical role in the design and operation of water treatment processes. Detailed chemistry modeling tools use a combination of advanced thermodynamic models and extensive databases to predict phase equilibria and reaction phenomena. The complexity and formulation of these models preclude their direct integration in equation-oriented modeling platforms, making it difficult to use their capabilities for rigorous water treatment process optimization. Neural networks (NN) can provide a pathway for integrating the predictive capability of chemistry software into equation-oriented models and enable optimization of complex water treatment processes across a broad range of conditions and process designs. Herein, we assess how NN architecture and training data impact their accuracy and use in equation-oriented water treatment models. We generate training data using PhreeqC software and determine how data generation and sample size impact the accuracy of trained NNs. The effect of NN architecture on optimization is evaluated by optimizing hypothetical black-box desalination processes using a range of feed compositions from USGS brackish water data set, tracking the number of successful optimizations, and testing the impact of initial guess on the final solution. Our results clearly demonstrate that data generation and architecture impact NN accuracy and viability for use in equation-oriented optimization problems.

Dudchenko, Alexander V↗
DOE OSTI2025

Observation of a mixed close-packed structure in superionic water

The study of superionic (SI) water has been a highly active research area since its theoretical prediction. Despite significant experimental and computational efforts, its melting curve and the stability of different oxygen lattices remain debated, impacting our understanding of SI ice’s peculiar transport properties. Experimental results at lower pressures show disagreement, whereas data at higher pressures are scarce due to the extreme challenges of such experiments. In this work, we present ultrafast X-ray diffraction results of water compressed by multiple shocks to pressures up to ~ 180 GPa. At pressures exceeding 150 GPa and temperatures around 2500 K, our diffraction patterns challenge the pure FCC-SI phase model, providing experimental evidence of the mixed close-packed superionic phase predicted by advanced ab initio calculations. At lower pressures, we observe simultaneous signatures of BCC and FCC structures within a pressure-temperature range consistent with some static-compression experiments, helping to resolve contradictory results in literature. These insights offer new constraints on the stability domains of SI phases and reveal detailed structural features, such as stacking faults. Our results advance the structural understanding of high-pressure SI ice to a level approaching that of ice I polymorphs, with potential implications for water-rich interiors of giant planets.

Andriambariarijaona, Leon [Centre National de la R↗
NASA NTRSDate not supplied

Off-Nominal Event Analysis in Autonomous Flights Based on Explainable Artificial Intelligence

A key objective in the Urban Air Mobility program at NASA is to intelligently perform an autonomous flight in a complex urban environment under all weather conditions with guaranteed levels of safety. To accomplish this, the mission manager (central decision-making module) of the vehicle needs to make informed decisions between various Courses of Action (CoA) based on its' interpretation of the inputs it receives. If an off-nominal event is detected either based on the amalgamation of sensor data or the use of machine learning models, the mission manager may greatly benefit from identification of the input features that most likely contributed to that specific event. Such an understanding is usually not possible to obtain from the classical machine learning models (deep learning) due to the inherent black box like structure. However, this understanding is achieved using eXplainable Artificial Intelligence (XAI) models that provide a human interpretable rationale for the predictions made. This work presents a game theory inspired XAI model for the off-nominal assessment of autonomous flights. The proposed approach based on Shapley values is model agnostic, provides local as well as global explanation and satisfies the four axioms (efficiency, symmetry, dummy, additivity) to achieve fair contribution. The versatility of the approach is first demonstrated on a simulated dataset in which the significance of each input to flight phase prediction is clearly identified. Subsequently, data from simulated flight trajectories are fed into the model which reveal the input features that most likely contributed to a rotor failure event thereby empowering the mission manager to take the appropriate CoA.

autonomy↗
NASA NTRSDate not supplied

Off-Nominal Event Analysis in Autonomous Flights Based on Explainable Artificial Intelligence

A key objective in the Urban Air Mobility program at NASA is to intelligently perform an autonomous flight in a complex urban environment under all weather conditions with guaranteed levels of safety. To accomplish this, the mission manager (central decision-making module) of the vehicle needs to make informed decisions between various Courses of Action (CoA) based on its' interpretation of the inputs it receives. If an off-nominal event is detected either based on the amalgamation of sensor data or the use of machine learning models, the mission manager may greatly benefit from identification of the input features that most likely contributed to that specific event. Such an understanding is usually not possible to obtain from the classical machine learning models (deep learning) due to the inherent black box like structure. However, this understanding is achieved using eXplainable Artificial Intelligence (XAI) models that provide a human interpretable rationale for the predictions made. This work presents a game theory inspired XAI model for the off-nominal assessment of autonomous flights. The proposed approach based on Shapley values is model agnostic, provides local as well as global explanation and satisfies the four axioms (efficiency, symmetry, dummy, additivity) to achieve fair contribution. The versatility of the approach is first demonstrated on a simulated dataset in which the significance of each input to flight phase prediction is clearly identified. Subsequently, data from simulated flight trajectories are fed into the model which reveal the input features that most likely contributed to a rotor failure event thereby empowering the mission manager to take the appropriate CoA.

autonomy↗
DOE OSTI2026

From molecular to macroscopic: predicting liquid–liquid phase equilibria and small-angle scattering of mixtures of organic liquids from atomistic simulation using Kirkwood–Buff theory

Macroscopic phase equilibria between solutions define the functionality of many biological and industrial processes, yet they are challenging to predict due to the inherent complexity of liquids containing large molecules. This work introduces an approach for the purely predictive calculation of such phase equilibria in temperature-composition space from molecular dynamics (MD) simulations at one temperature in the single-phase region. We use an approach developed previously to obtain the entropic and enthalpic contributions to the free energy of mixing from the atomic-scale information given by MD simulations via Kirkwood–Buff theory. This allows us to accurately estimate the free energy of mixing as a function of temperature, and thus obtain liquid–liquid phase equilibria, including liquid–liquid critical points, associated binodal and spinodal lines, and composition fluctuations across a region of temperature and composition. Results for binary malonamide–alkane systems are validated by comparison to a direct experimental probe of the fluctuations: the small angle X-ray scattering intensity near zero wavenumber. The MDKB → Phase method demonstrated here provides a significant improvement in predicting liquid–liquid equilibria and free energy as a function of temperature for our systems of interest compared to conventional thermodynamic models. The accurate performance of this purely predictive approach lies in its preservation of atomistic details when determining thermodynamic properties. Furthermore, its inherent extensibility to multi-component systems will likely make the MDKB → Phase approach a valuable general tool for connecting molecular interactions to macroscopic phase equilibria and for the computational screening of materials for targeted thermodynamic behavior.

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

Enhancing stability, magnetic anisotropy, and coercivity of manganese aluminum: Machine learning, ab initio , and micromagnetic modeling

The binary manganese aluminum (MnAl) alloy with L⁢1 0 crystal structure is a promising rare earth (RE) element-free permanent magnetic material because of its exceptional magnetic properties. However, experimentally synthesizing it in a stable bulk form is extremely challenging. Here, in this study, an alternative method of stabilizing the material, a pathway for experimental synthesis and validation, is proposed and theoretically verified. This is done by partially substituting Mn and Al sites with Fe and Ni and identifying its enhanced phase stability, saturation magnetization density, magnetic anisotropy, and coercivity from density functional theory (DFT), machine learning (ML) crystal graph convolution neural network (CGCNN), and micro-magnetic modeling. When considering a fixed 50% Ni, the magnetic anisotropy increases with the increasing Fe content but decreases the formation energy. The calculated formation energies, elastic constants, and phonon frequencies demonstrate that the binary and quaternary compositions are stable. Most importantly, in 50% Fe and Ni-substituted-equiatomic phase, magnetic anisotropy constants and saturation magnetization density increase by 56% and 23% as compared to the MnAl. Further, the coercivity of the equiatomic phase predicted with micro-magnetic modeling is higher by 17% than the parent compound.

Bhandari, Churna [Ames National Laboratory, and Io↗
DOE OSTI2025

Summary of Multiphysics Modeling for Sublimation Mass Transfer

This report summarizes the effort to develop a multiphysics modeling framework for sublimation mass transfer processes. The aim of the project was to develop a multiphysics code capable of predicting phase change between solid and gas in a closed container, and the movement of material within such a container when exposed to various exterior environmental conditions. Two modeling frameworks were developed towards this aim: one with a high-fidelity computational fluid dynamics (CFD) structure, and the other as a fast reduced-order model. Ultimately, both attempts were unsuccessful due to instabilities in the code and physical processes for which there is no adequate numerical representation. Both modeling attempts are briefly detailed before providing a brief survey of recent updates in the literature, and a recommendation for future work on this subject.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Planar Systems for Quantum Information

This project aims to develop two‐dimensional (2D) moiré materials as a quantum simulator to implement model Hamiltonians and their phase diagrams. Progress in quantum information science (QIS) requires the development of advanced quantum materials systems. The rich family of layered van der Waals materials and their heterostructures present opportunities to create previously unrealized types of applications for QIS. Specifically, when two layers of van der Waals materials are overlaid with a small twist angle or/and lattice mismatch, a moiré superlattice with a period of about ten nanometers is formed. This provides a periodic trapping potential for electrons. Electrons can tunnel between the traps and repel each other by their mutual Coulomb interactions. The platform of 2D moiré materials provides many attractive features, including tunability of length and energy scales, charge density, and even lattice symmetry. It presents new possibilities for realizing quantum simulation of the many-body physics in a solid-state platform. This integrated team of six investigators seeks to develop relevant theoretical treatments to link ab-initio studies of 2D moiré materials to model Hamiltonians and to evaluate correlated phases predicted by these model Hamiltonians in the relevant regimes. On the experimental side, the team aims to develop methods to realize a homogeneous and highly controlled potential landscape for the electrons and to initiate, protect, and measure their quantum many-body states.

36 MATERIALS SCIENCE↗