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At least 253 records · Page 14

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Exploring 2D X-ray diffraction phase fraction analysis with convolutional neural networks: Insights from kinematic-diffraction simulations

Abstract Deep-learning models are effective for analyzing the complex information in 2D X-ray diffraction (XRD) patterns. Accurately collecting parameters of the material sample is crucial during model training, significantly impacting model performance. In this study, we employ a kinematic-diffraction simulator to generate simulated 2D XRD patterns for Ti–6Al–4V alloy, allowing precise control of sample parameters. These simulated patterns are used to train convolutional neural networks, predicting $$\upbeta$$ β -phase volume fractions. The training data set consists exclusively of 2D XRD patterns with pure $$\upalpha$$ α - or pure $$\upbeta$$ β -phase, while the testing set incorporates patterns with intermediate phase volume fraction. In particular, we investigate how the architectures of the model influence prediction reliability and computational performance. Experimental results reveal that, with appropriate training, the convolutional neural network accurately detects intermediate phase volume fractions even trained with only pure-phase patterns, achieving a mean square error accuracy of $$9.4 \times 10^{-4}$$ 9.4 × 10 - 4 . Graphical abstract

Yue, Weiqi↗

NASA NTRS1994

Modelling of transient two-phase heat transfer for spacecraft thermal management

A computational method for predicting the two-phase transient fluid flow and heat transfer characteristics within a reservoir of the capillary-pumped-loop, intended to be used for spacecraft thermal management, has been developed. The model is based on the enthalpy formulation in an axisymmetric configuration. The reservoir operates under a constant thermodynamic pressure by allowing mass exchange between the reservoir and the outside loop. Both 1 g and 0 g environments have been considered to assess the effects of gravity on the reservoir performance. Depending on the gravity level, the power input and the reservoir orientation, three different convection modes have been identified, namely, the thermocapillary mode, the buoyancy mode, and the rapid-expansion mode (caused by interface movement). The impact of these modes on the performance of the reservoir and the associated physical phenomena have been discussed.

Shyy, W.↗

DOE OSTI2025

Efficiently predicting pressure-composition-temperature diagrams to discover low-stability metal hydrides

Quantitatively accurate computational predictions of metal hydride thermodynamics are challenging but critical for alloy performance optimization across a multitude of technological domains, including hydrogen storage, compression, purification, and getters. Recent machine learning approaches have demonstrated great success in this area, but can potentially suffer from several shortcomings since they rely on imbalanced experimental training data and can have poor out-of-distribution (ood) test performance. Here, in this study, we circumvent such pitfalls by developing a computationally efficient, first principles-based workflow for direct prediction of metal hydride phase equilibrium, i.e., the pressure-composition-temperature (PCT) diagram. We then demonstrate its utility on predicting low stability hydrides derived from compositionally complex C14 Laves phase AB2 alloys. Specifically, we computationally predict and then experimentally validate an AB 2 alloy series (z < 0.6 for Ti 2−z Zr z CrMnFeNi) with ideal hydriding thermodynamics for a two-stage metal hydride-based compressor for pressurizing boil off from liquefied hydrogen. Importantly, this study lays the groundwork for accurate and efficient discovery/optimization of ood, low-stability hydrides for which purely data-driven approaches lack sufficient accuracy.

08 HYDROGEN↗

DOE OSTI2025

CFD modeling of near-wall combustion and unburned methane prediction in natural gas spark ignition engines

Natural gas-powered engines play a critical role in gas drilling, compression, and transmission sectors, but methane (CH 4 ) from engine combustion slip can be significant over their lifespan, contributing to atmospheric pollution and signaling reduced engine efficiency. Here, to address this challenge, computational fluid dynamics (CFD) simulations offer valuable insights into the in-cylinder combustion process, enabling the optimization of combustion strategies and engine designs to minimize unburned CH 4 slip. This study aims to evaluate and improve combustion models for simulating the combustion process and predicting unburned CH 4 concentrations in natural gas spark-ignition (SI) engines, including engines that are part of combined reformer-engine systems. Specifically, the performance of two flamelet-based combustion models—the Extended Coherent Flame Model (ECFM) and the G-equation model—was assessed using experimental engine data collected under varying excess-air ratio (λ) conditions and fuel compositions, including natural gas and syngas blends. In addition, to enhance the predictive capabilities of the G-equation model, a flame-wall interaction (FWI) sub-model was integrated into its framework. The effects of its model parameters, such as quenching and influence distance, on combustion behavior and unburned methane predictions were analyzed in detail. The ECFM tended to predict delayed combustion phasing under diluted mixture conditions, resulting in overprediction of unburned CH 4 concentrations. In contrast, the G-equation model provided reasonable predictions of combustion pressure, while representing higher the CH 4 reduction rate across the operating condition compared to experimental data. Incorporating the FWI sub-model—with the quenching distance calculated based on a pressure-dependent relation (P -0.48 ) and a fixed influence distance of 1.5 mm—further improved the G-equation model’s accuracy in predicting CH 4 reduction rates without compromising its ability to simulate the combustion process.

Combustion model↗

DOE OSTI2026

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning

Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure−morphology models. Here, we introduce a data-driven highthroughput workflow that integrates modular polymer synthesis, robotic formulation, automated morphology characterization, and machine learning (ML) for accelerated SPB discovery. Using a plug-and-play synthetic strategy, 33 hydrogen-bonding endfunctional homopolymers were prepared and orthogonally combined to generate 260 SPBs in 1 day. A fully automated atomic force microscopy (AFM) pipeline enabled systematic imaging, producing 2340 morphology data sets with minimal human intervention. Domain spacings were extracted through complementary imageprocessing methods and used to train ML models. A support vector regression (SVR) model accurately predicted target phase-separation sizes (50, 100, and 150 nm), which were experimentally validated. This work demonstrates the power of coupling high-throughput experimentation with ML to accelerate morphology discovery and provides one of the first large-scale experimental data sets for supramolecular polymer systems.

ML-guided polymer design↗

NASA NTRS1994

H2-rich interstellar grain mantles: An equilibrium description

Experiments simulating the codeposition of molecular hydrogen and water ice on interstellar grains demonstrate that amorphous water ice at 12 K can incorporate a substantial amount of H2, up to a mole ratio of H2/H2O = 0.53. We find that the physical behavior of approximately 80% of the hydrogen can be explained satisfactorily in terms of an equilibrium population, thermodynamically governed by a wide distribution of binding site energies. Such a description predicts that gas phase accretion could lead to mole fractions of H2 in interstellar grain mantles of nearly 0.3; for the probable conditions of WL5 in the rho Ophiuchi cloud, an H2 mole fraction of between 0.05 and 0.3 is predicted, in possible agreement with the observed abundance reported by Sandford, Allamandola, & Geballe. Accretion of gas phase H2 onto grain mantles, rather than photochemical production of H2 within the ice, could be a general explanation for frozen H2 in interstellar ices. We speculate on the implications of such a composition for grain mantle chemistry and physics.

Dissly, Richard W.↗

DOE OSTI2025

COCOMO2: A Coarse-Grained Model for Interacting Folded and Disordered Proteins

Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parametrized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multiscale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Correction to “COCOMO2: A Coarse-Grained Model for Interacting Folded and Disordered Proteins”

Molecular interactions↗

DOE OSTI2024

New Constraints on the Melting Temperature and Phase Stability of Shocked Iron up to 270 GPa Probed by Ultrafast X-Ray Absorption Spectroscopy

Studying the properties and phase diagram of iron at high-pressure and high-temperature conditions has relevant implications for Earth’s inner structure and dynamics and the temperature of the inner core boundary (ICB) at 330 GPa. Also, a hexagonal-closed packed to body-centered cubic (bcc) phase transition has been predicted by many theoretical works but observed only in a few experiments. The recent coupling of high-power laser with advanced x-ray sources from synchrotrons allows for novel approaches to address these issues. Here, we present a study on shock compressed iron up to 270 GPa and 5800 K probed by single-pulse (100 ps FWHM) x-ray absorption spectroscopy (XAS). Based on the analysis of the XAS spectra, we provide structural identification and bulk temperature measurements along the Hugoniot up to the melting. These results rule out the predicted transition to a high-temperature bcc phase and allow one to discriminate among existing equations of state models and melting curves. In particular, we report the first bulk temperature measurement in shock compressed iron on the melting plateau at 240(20) GPa and 5345(600) K. The melting curve resulting from our work extrapolates to a temperature of 6202(514) K at 330 GPa and represents a refined upper bound for the ICB temperature. Published by the American Physical Society 2024

Balugani, S.↗

DOE OSTI2024

Equilipy: a python package for calculating phase equilibria

The CALPHAD (CALculation of PHAse Diagram) approach (Nigel Saunders & Miodownik, 1998) provides predictions for thermodynamically stable phases in multicomponent-multiphase materials across a wide range of temperatures. Consequently, the CALPHAD calculations became an essential tool in materials and process design (Luo, 2015). Such design tasks frequently require navigating a high-dimensional space due to multiple components involved in the system. This increasing complexity demands high-throughput CALPHAD calculations, especially in the rapidly evolving field of alloy design. In response to the need, we developed Equilipy an open-source Python package designed for calculating phase equilibria of multicomponent-multiphase systems. Equilipy is specifically tailored for high-throughput CALPHAD calculations, offering parallel computations across multiple processors and nodes with the given NPT input conditions namely elemental compositions (N), pressure (P), and temperature (T). Equilipy utilizes the program structure and Gibbs energy functions from the Fortran-based program, Thermochimica (Piro et al., 2013), with incorporating a new Gibbs energy minimization algorithm. This algorithm, originally developed by Capitani and Brown in 1987 (Capitani & Brown, 1987), has been revised and implemented to enhance the stability and performance of calculations. The Fortran codes are precompiled and interfaced with Python via F2PY, ensuring high computation speed. Benchmark tests shown in Figure 1 demonstrate that Equilipy’s computation speed is comparable to those of established commercial software, TC-Python and PanPython. This result highlights its efficiency and potential applications in various scientific and industrial fields.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2005

Analysis of Wake VAS Benefits Using ACES Build 3.2.1: VAMS Type 1 Assessment

The FAA and NASA are currently engaged in a Wake Turbulence Research Program to revise wake turbulence separation standards, procedures, and criteria to increase airport capacity while maintaining or increasing safety. The research program is divided into three phases: Phase I near term procedural enhancements; Phase II wind dependent Wake Vortex Advisory System (WakeVAS) Concepts of Operations (ConOps); and Phase III farther term ConOps based on wake prediction and sensing. The Phase III Wake VAS ConOps is one element of the Virtual Airspace Modelling and Simulation (VAMS) program blended concepts for enhancing the total system wide capacity of the National Airspace System (NAS). This report contains a VAMS Program Type 1 (stand-alone) assessment of the expected capacity benefits of Wake VAS at the 35 FAA Benchmark Airports and determines the consequent reduction in delay using the Airspace Concepts Evaluation System (ACES) Build 3.2.1 simulator.

Smith, Jeremy C.↗

NASA NTRS2012

Jet-Surface Interaction Test: Far-Field Noise Results

Many configurations proposed for the next generation of aircraft rely on the wing or other aircraft surfaces to shield the engine noise from the observers on the ground. However, the ability to predict the shielding effect and any new noise sources that arise from the high-speed jet flow interacting with a hard surface is currently limited. Furthermore, quality experimental data from jets with surfaces nearby suitable for developing and validating noise prediction methods are usually tied to a particular vehicle concept and, therefore, very complicated. The Jet/Surface Interaction Test was intended to supply a high quality set of data covering a wide range of surface geometries and positions and jet flows to researchers developing aircraft noise prediction tools. During phase one, the goal was to measure the noise of a jet near a simple planar surface while varying the surface length and location in order to: (1) validate noise prediction schemes when the surface is acting only as a jet noise shield and when the jet/surface interaction is creating additional noise, and (2) determine regions of interest for more detailed tests in phase two. To meet these phase one objectives, a flat plate was mounted on a two-axis traverse in two distinct configurations: (1) as a shield between the jet and the observer (microphone array) and (2) as a reflecting surface on the opposite side of the jet from the observer.

Brown, Clifford A.↗

NASA NTRS1973

Life prediction of turbine components: On-going studies at the NASA Lewis Research Center

An overview is presented of the many studies at NASA-Lewis that form the turbine component life prediction program. This program has three phases: (1) development of life prediction methods for major failure modes through materials studies, (2) evaluation and improvement of these methods through a variety of burner rig studies on simulated components in research engines and advanced rigs. These three phases form a cooperative, interdisciplinary program. A bibliography of Lewis publications on fatigue, oxidation and coatings, and turbine engine alloys is included.

Spera, D. A.↗

DOE OSTI2025

Mg-Ion Conduction in Antiperovskite Solid Electrolytes Revealed by 25 Mg Ultrahigh Field NMR and First-Principles Calculations

Magnesium-ion batteries hold the potential to outperform the energy density of lithium-ion batteries, given the divalent charge carried by each Mg 2+ cation, but remain in an early stage of development. Here, in this study, 25 Mg solid-state nuclear magnetic resonance (ssNMR) is used to gain insight into the local structure and Mg-ion dynamics of candidate Mg-ion solid electrolytes, the antiperovskites Mg 3 SbN and Mg 3 AsN. Using the highest available magnetic field (35.2 T) for high-resolution solid-state NMR, the largest 25 Mg quadrupole coupling constants (C Q ) yet measured of up to 22 MHz are reported and corroborated by first-principles calculations. Predicted C Q values are shown to correlate with the antiperovskite’s tolerance factor; thus, 25 Mg NMR linewidths can report on lattice distortions and phase stability of these antiperovskites. Variable-temperature 25 Mg NMR spectra demonstrate changes at elevated temperatures, ascribed to Mg-ion motional effects. 25 Mg T 1 relaxometry measurements at ultrahigh field reveal a lower activation energy for the more distorted Mg 3 AsN phase, matching computational predictions of a lower energy barrier for Mg 2+ ion migration and suggesting that additional scrutiny of antiperovskites as Mg-ion conductors is warranted. Given the inherent challenges of 25 Mg NMR, this work demonstrates the benefits of combining ultrahigh field NMR spectroscopy, advanced pulse sequences, modern signal processing, and first-principles calculations to facilitate NMR of quadrupolar nuclei as a tool to probe the local structure and ion dynamics in beyond-Li battery materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1991

Steady-state phase error for a phase-locked loop subjected to periodic Doppler inputs

The performance of a carrier phase locked loop (PLL) driven by a periodic Doppler input is studied. By expanding the Doppler input into a Fourier series and applying the linearized PLL approximations, it is easy to show that, for periodic frequency disturbances, the resulting steady state phase error is also periodic. Compared to the method of expanding frequency excursion into a power series, the Fourier expansion method can be used to predict the maximum phase error excursion for a periodic Doppler input. For systems with a large Doppler rate fluctuation, such as an optical transponder aboard an Earth orbiting spacecraft, the method can be applied to test whether a lower order tracking loop can provide satisfactory tracking and thereby save the effect of a higher order loop design.

Chen, C.-C.↗

DOE OSTI2025

A structured framework for predicting sustainable aviation fuel properties using liquid-phase FTIR and machine learning

Sustainable aviation fuels have the potential to improve efficiency, reduce emissions, and enhance energy security. To help identify viable sustainable aviation fuels and accelerate research, machine learning models have been developed to predict relevant physicochemical properties. However, many models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that offer limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid FTIR spectra can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 ml of sample. The method first decomposes FTIR spectra into fundamental building blocks using non-negative matrix factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15°C, and kinematic viscosity at -20°C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict key properties across a broad range of neat molecules and representative fuels and blends, while enabling interpretation of relationships between compositional elements, such as functional groups or chemical classes, and their resulting properties. This demonstrates strong potential to support sustainable aviation fuel research and development. The models and data are available on an interactive web tool.

Fourier transform infrared spectroscopy↗

NASA NTRS1991

Phase selection during crystallization of undercooled liquid eutectic lead-tin alloys

During rapid solidification substantial amounts of undercooling are in general required for formation of metastable phases. Crystallization at varying levels of undercooling and melting of metastable phases were studied during slow cooling and heating of emulsified PB-Sn alloys. Besides the experimental demonstration of the reversibility of metastable phase equilibra, two different principal solidification paths have been identified and compared with the established metastable phase diagram and predictions from classical nucleation theory. The results suggest that the most probable solidification path is described by the 'step rule' resulting in the formation of metastable phases at low undercooling, whereas the stable eutectic phase mixture crystallizes without metastable phase formation at high undercooling.

Fecht, H. J.↗

NASA NTRSDate not supplied

Mastering the Cryogenic Frontier: Predicting Sloshing in Cryogenic Propellant Tanks

In recent years, NASA has undertaken the development and validation of computation fluid dynamic (CFD) models for predicting cryogenic two-phase sloshing within propellant tanks. These models have been rigorously anchored against cryogenic ground tests that induce sloshing, as well as limited microgravity tests. The primary focus of these models is to accurately predict the two-phase condensation and, at times, evaporation within the cryogenic propellant. This approach is important for estimating the required autogenous and/or helium pressurant for completing propulsion system burns or ensuring adequate pressure for in-space propellant transfers. Unlike in hypergolic propulsion systems, where the propellant is stored at room temperature, cryogenic propellant is highly volatile. Therefore, ensuring the accurate estimation of pressurant necessitates the precise modeling of two-phase interfacial heat and mass transfer processes. This paper aims to provide an overview of NASA’s continuing efforts in developing and validating these CFD models, with particular emphasis on benchmark cases with sloshing that enhances phase change, tracing their historical development, and demonstrating their predictive accuracy when compared to test results.

low-gravity fluid modeling↗