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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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NASA NTRS1989

Maximum likelihood estimation for distributed parameter models of flexible spacecraft

A distributed-parameter model of the NASA Solar Array Flight Experiment spacecraft structure is constructed on the basis of measurement data and analyzed to generate a priori estimates of modal frequencies and mode shapes. A Newton-Raphson maximum-likelihood algorithm is applied to determine the unknown parameters, using a truncated model for the estimation and the full model for the computation of the higher modes. Numerical results are presented in a series of graphs and briefly discussed, and the significant improvement in computation speed obtained by parallel implementation of the method on a supercomputer is noted.

Taylor, L. W., Jr.↗

NASA NTRS2017

Computational Design and Analysis of a Transonic Natural Laminar Flow Wing for a Wind Tunnel Model

A natural laminar flow (NLF) wind tunnel model has been designed and analyzed for a wind tunnel test in the National Transonic Facility (NTF) at the NASA Langley Research Center. The NLF design method is built into the CDISC design module and uses a Navier-Stokes flow solver, a boundary layer profile solver, and stability analysis and transition prediction software. The NLF design method alters the pressure distribution to support laminar flow on the upper surface of wings with high sweep and flight Reynolds numbers. The method addresses transition due to attachment line contamination/transition, Gortler vortices, and crossflow and Tollmien-Schlichting modal instabilities. The design method is applied to the wing of the Common Research Model (CRM) at transonic flight conditions. Computational analysis predicts significant extents of laminar flow on the wing upper surface, which results in drag savings. A 5.2 percent scale semispan model of the CRM NLF wing will be built and tested in the NTF. This test will aim to validate the NLF design method, as well as characterize the laminar flow testing capabilities in the wind tunnel facility.

Lynde, Michelle N.↗

DOE OSTI2024

Microscopy modality transfer of steel microstructures: Inferring scanning electron micrographs from optical microscopy using generative AI

Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.

Computer vision↗

NASA NTRS1996

Finite-Element Vibration Analysis and Modal Testing of Graphite Epoxy Tubes and Correlation Between the Data

Structural materials in the form of graphite epoxy composites with embedded rubber layers are being used to reduce vibrations in rocket motor tubes. Four filament-wound, graphite epoxy tubes were studied to evaluate the effects of the rubber layer on the modal parameters (natural vibration frequencies, damping, and mode shapes). Tube 1 contained six alternating layers of 30-degree helical wraps and 90-degree hoop wraps. Tube 2 was identical to tube 1 with the addition of an embedded 0.030-inch-thick rubber layer. Tubes 3 and 4 were identical to tubes 1 and 2, respectively, with the addition of a Textron Kelpoxy elastomer. This report compares experimental modal parameters obtained by impact testing with analytical modal parameters obtained by NASTRAN finite-element analysis. Four test modes of tube 1 and five test modes of tube 3 correlate highly with corresponding analytical predictions. Unsatisfactory correlation of test and analysis results occurred for tubes 2 and 4 and these comparisons are not shown. Work is underway to improve the analytical models of these tubes. Test results clearly show that the embedded rubber layers significantly increase structural modal damping as well as decrease natural vibration frequencies.

Taleghani, Barmac K.↗

DOE OSTI2026

MOSAIC-CONUS: A Multimodal, Multi-Temporally Paired Dataset for Earth Sciences

Earth embeddings—vector representations of geographic locations indexed in space and time—are emerging as a unifying interface for geospatial AI. However, their quality depends not only on model design, but on how multimodal Earth observation (EO) data are spatially indexed, temporally aligned, and cross-modally associated during pretraining. We introduce MOSAIC-CONUS (Multimodal Observations with Spatially Aligned Imagery, Urban Points of Interest, In-Situ Measurements and Text Captions), a large-scale EO dataset over the contiguous United States, organized around 250,000 stratified point indices that serve as stable spatial keys across seven modalities: active radar, passive optical imagery, lidar-derived elevation, land cover, functional context, hydrometeorological measurements, and textual summaries. Unlike existing EO datasets, MOSAIC-CONUS introduces four contributions not jointly addressed in prior work: 1. an open-source, large-scale multimodal EO corpus structured around point-indexed data designed to support Earth embedding learning; 2. explicit radar-optical pairing tables spanning twelve temporal alignment regimes, formalizing cross-sensor alignment as a controllable variable for analyzing how temporal mismatch across modalities influences learned embeddings quality; 3. a benchmark suite spanning cross-modal retrieval, annual nightlights regression, and basin-held-out streamflow prediction, positioning MOSAIC-CONUS as a benchmark-ready resource for multimodal AI systems; and 4. a language-based embedding layer through co-registered textual summaries, enabling Earth embeddings to function as a queryable interface for agentic AI systems. The dataset and pairing protocols are publicly released.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1998

Wavelet Filtering to Reduce Conservatism in Aeroservoelastic Robust Stability Margins

Wavelet analysis for filtering and system identification was used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins was reduced with parametric and nonparametric time-frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data was used to reduce the effects of external desirableness and unmodeled dynamics. Parametric estimates of modal stability were also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. F-18 high Alpha Research Vehicle aeroservoelastic flight test data demonstrated improved robust stability prediction by extension of the stability boundary beyond the flight regime.

Brenner, Marty↗

NASA NTRS2016

Model Order Reduction of Aeroservoelastic Model of Flexible Aircraft

This paper presents a holistic model order reduction (MOR) methodology and framework that integrates key technological elements of sequential model reduction, consistent model representation, and model interpolation for constructing high-quality linear parameter-varying (LPV) aeroservoelastic (ASE) reduced order models (ROMs) of flexible aircraft. The sequential MOR encapsulates a suite of reduction techniques, such as truncation and residualization, modal reduction, and balanced realization and truncation to achieve optimal ROMs at grid points across the flight envelope. The consistence in state representation among local ROMs is obtained by the novel method of common subspace reprojection. Model interpolation is then exploited to stitch ROMs at grid points to build a global LPV ASE ROM feasible to arbitrary flight condition. The MOR method is applied to the X-56A MUTT vehicle with flexible wing being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies demonstrated that relative to the fullorder model, our X-56A ROM can accurately and reliably capture vehicles dynamics at various flight conditions in the target frequency regime while the number of states in ROM can be reduced by 10X (from 180 to 19), and hence, holds great promise for robust ASE controller synthesis and novel vehicle design.

aeroelasticity↗

NASA NTRS1988

Stiffnesses of a solid-rocket motor from an ambient vibration survey

Experience with many spacecraft configurations boosted by a variety of launch vehicles indicates that the maximum loads experienced throughout most of the structure are inertial in origin. These loads arise from the dynamic elastic response of the flight vehicle to the transient disturbances of launch and flight, and are highly dependent on the dynamic characteristics of both the spacecraft and the launch vehicle. It has proved to be most advantageous, in the analysis of this critical dependency of loads upon vehicle dynamic properties, to establish a mathematical model in terms of normal mode characteristics. In this way, the vibration behavior of an elastomechanical structure (or substructure) can be described by means of the so-called modal or natural degrees of freedom. The conduct of a mode survey test and the use of a suitably test-verified model in loads analyses is essential to the flight worthiness certification process of space systems. The desirability of such tests is confirmed by the fact that, almost invariably, significant deficiencies in the analytical models are revealed by the results. Therefore, this experimental program was undertaken to determine those properties of a solid-propellant rocket motor (SRM) which are required to characterize a dynamic model. Random ambient-excited accelerations were measured at a series of stations along the motor for the purpose of identifying the motor beam-like stiffnesses in bending, shear, and torsion. From a system identification point of view, it is significant that stiffness properties of a subsystem (the motor) are determined from modes of the full system (motor/stand configuration) using mode shape data of the subsystem only. This contrasts with traditional system identification approaches which rely upon complete system mode shapes.

Rubin, S.↗

NASA NTRS1991

Analytical and experimental study of vibrations in a gear transmission

An analytical simulation of the dynamics of a gear transmission system is presented and compared to experimental results from a gear noise test rig at the NASA Lewis Research Center. The analytical procedure developed couples the dynamic behaviors of the rotor-bearing-gear system with the response of the gearbox structure. The modal synthesis method is used in solving the overall dynamics of the system. Locally each rotor-gear stage is modeled as an individual rotor-bearing system using the matrix transfer technique. The dynamics of each individual rotor are coupled with other rotor stages through the nonlinear gear mesh forces and with the gearbox structure through bearing support systems. The modal characteristics of the gearbox structure are evaluated using the finite element procedure. A variable time steping integration routine is used to calculate the overall time transient behavior of the system in modal coordinates. The global dynamic behavior of the system is expressed in a generalized coordinate system. Transient and steady state vibrations of the gearbox system are presented in the time and frequency domains. The vibration characteristics of a simple single mesh gear noise test rig is modeled. The numerical simulations are compared to experimental data measured under typical operating conditions. The comparison of system natural frequencies, peak vibration amplitudes, and gear mesh frequencies are generally in good agreement.

Choy, F. K.↗

DOE OSTI2021

Phonon gas model for thermal conductivity of dense, strongly interacting liquids

Developing predictive thermal property models for liquids based on microscopic principles has been elusive. The difficulty is that liquids have gas-like and solid-like attributes that are at odds when considering the frameworks of microscopic models: Models for gases are simple due to randomness and low density, whereas models for crystalline solids rely on symmetry and long-range order for easier calculation. The short-range order in liquids does, however, provide structure to neighboring molecules similar to amorphous solids, and there have been recent advances indicating that collective vibrational modes store heat in liquids. Models combining Debye approximations from solid-state physics and Frenkel’s theory of liquids can accurately predict the heat capacity of liquids. Phonon-like dispersions in liquids have also been widely observed in neutron scattering experiments. These developments motivate us to propose a model where high-frequency vibrational modes, which travel at the speed of sound and have a mean free path on the order of the average intermolecular distance, conduct heat in liquids. We use this liquid phonon gas model to calculate the thermal conductivity of liquids with varying intermolecular interaction energies from strongest to weakest—Coulomb, hydrogen-bonding, Keesom, and London dispersion energy. Generally, the model is more accurate as the intermolecular interaction energy and density of liquids increase. The calculated thermal conductivity of Coulombic-bound molten sodium nitrate and hydrogen-bonded water is within 1.46% and 2.98% of the experimentally measured values, respectively, across their entire temperature ranges. Further modal analysis of the velocity and the mean free path of collective vibrations could establish the liquid phonon gas model as an accurate model for weakly interacting liquids as well.

36 MATERIALS SCIENCE↗

NASA NTRS1987

Modeling of controlled flexible structures with impulsive loads

The characteristic wave approach is developed as an alternative to modal methods which may lead to significant errors in the presence of impulsive or concentrated loads. The method is applied to periodic structures. Some special phenomena like cumulation effects and transitions to ergodicity are analyzed.

Zak, M.↗

NASA NTRS1989

Issues in CSI analysis for large scale systems

Future spacecraft such as the International Space Station result in flexible models with hundreds, or perhaps thousands of modes in a frequency range where the potential for control/structure interaction exists. This provides the analyst with a formidable model reduction problem at both the component and the system level. Approaches to normal modes as a structural representation, applicability of alternate structural representations and algorithms for selecting important modal degrees of freedom at both the component and the system level are discussed. Practical implementation of these techniques on a large scale model of the Space Station are presented.

Blelloch, Paul↗

DOE OSTI2022

Multi-modal Dataset of a Polycrystalline Metallic Material: 3D Microstructure and Deformation Fields

The development of high-fidelity mechanical property prediction models for the design of polycrystalline materials relies on large volumes of microstructural feature data. Concurrently, at these same scales, the deformation fields that develop during mechanical loading can be highly heterogeneous. Spatially correlated measurements of 3D microstructure and the ensuing deformation fields at the micro-scale would provide highly valuable insight into the relationship between microstructure and macroscopic mechanical response. They would also provide direct validation for numerical simulations that can guide and speed up the design of new materials and microstructures. However, to date, such data have been rare. Here, a one-of-a-kind, multi-modal dataset is presented that combines recent state-of-the-art experimental developments in 3D tomography and high-resolution deformation field measurements.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Machine Learning for Dynamic Test Sensor Placement

There are multiple different algorithms to perform modal test sensor placement optimization: effective independence, residual kinetic energy, iterative Guyan reduction, genetic algorithms, or a brute-force methodology. However, any of these methods may be computationally expensive, especially for structural models with a large number of degrees of freedom. Given the high-cost and the need to optimize the solution, modal sensor placement is a great application for machine learning (ML) algorithms. In this paper, we will apply ML algorithms to determine the optimal sensor locations for simple and complex structures. We will also discuss the benefits and drawbacks of using machine learning over other sensor placement algorithms.

Kelsey Buckles↗

NASA NTRS2010

Ares I-X Launch Vehicle Modal Test Measurements and Data Quality Assessments

The Ares I-X modal test program consisted of three modal tests conducted at the Vehicle Assembly Building at NASA s Kennedy Space Center. The first test was performed on the 71-foot 53,000-pound top segment of the Ares I-X launch vehicle known as Super Stack 5 and the second test was performed on the 66-foot 146,000- pound middle segment known as Super Stack 1. For these tests, two 250 lb-peak electro-dynamic shakers were used to excite bending and shell modes with the test articles resting on the floor. The third modal test was performed on the 327-foot 1,800,000-pound Ares I-X launch vehicle mounted to the Mobile Launcher Platform. The excitation for this test consisted of four 1000+ lb-peak hydraulic shakers arranged to excite the vehicle s cantilevered bending modes. Because the frequencies of interest for these modal tests ranged from 0.02 to 30 Hz, high sensitivity capacitive accelerometers were used. Excitation techniques included impact, burst random, pure random, and force controlled sine sweep. This paper provides the test details for the companion papers covering the Ares I-X finite element model calibration process. Topics to be discussed include test setups, procedures, measurements, data quality assessments, and consistency of modal parameter estimates.

Templeton, Justin D.↗

DOE OSTI2024

A data-driven framework for predicting machining stability: employing simulated data, operational modal analysis, and enhanced transfer learning

Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. Addressing this challenge requires a reliable predictive model that can accommodate the complex dynamics of various machining scenarios. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets and employing advanced techniques such as operational modal analysis (OMA), enhanced transfer learning (TL), and receptance coupling substructure analysis (RCSA). By integrating these methodologies, the framework effectively classifies and predicts chatter across diverse operational modes, achieving robust and accurate outcomes. Our model utilizes a Random Forest (RF) classifier trained with the comprehensive dataset, which demonstrates substantial improvements in both predictive accuracy and robustness. Specifically, the RF model achieved an accuracy rate of 85%, an area under the curve (AUC) of 0.90, and an F1 score of 0.88, underscoring its capability to adapt to varying machining configurations. These results highlight the framework’s potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining parameters. In conclusion, this research thus offers a significant advancement in predictive maintenance for machining processes, enabling more stable and efficient manufacturing operations.

42 ENGINEERING↗

DOE OSTI2024

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Molecular tail chemistry controls thermal transport in fullerene films

Herein we report on the thermal conductivities of alkyl- and indene-group functionalized fullerene derivative thin films as measured via time domain thermoreflectance and steady state thermoreflectance. The thermal conductivities vary from 0.064 ± 0.007 W m – 1 K – 1 for [6,6]-phenyl C 61 -butyric acid methyl ester (PCBM) to 0.15 ± 0.017 W m – 1 K – 1 for indene- C 60 bisadduct at room temperature and do not exhibit significant temperature dependence from 300 to 375 K. In comparison to the thermal conductivity of PCBM, increasing the length of the alkyl chain, as in the case of [6,6]-phenyl C 61 -butyric acid butyl ester, and [6,6]-phenyl C 61 -butyric acid octyl ester leads to higher thermal conductivities. Likewise, increasing the number of alkyl chains attached to the fullerenes as in the case of bisadduct PCBM leads to a higher thermal conductivity compared to that of PCBM. We present atomistic insights into the role of chemical functionalization on the overall heat transfer in these fullerene derivatives by conducting molecular dynamics simulations and lattice dynamics calculations. The thermal conductivities predicted via our atomistic simulations qualitatively agree with the experimental trends for our fullerene derivatives. Lattice dynamics calculations reveal that one of the main factors dictating the ultralow thermal conductivities in fullerene derivatives is the large reduction in modal diffusivities in the molecular crystals as calculated from the Allen-Feldman model, thus providing an explanation for their largely reduced thermal conductivities as compared to that of C 60 crystals. The low diffusivities result from high degrees of localization of Einstein-like vibrations in fullerene derivatives due to the molecular side chains, providing the ability to dial-in the properties of these low thermal conductivity solids via molecular engineering.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗