Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “aerospace”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

High-Sensitivity Low-Energy Ion Spectroscopy with Sub-Nanometer Depth Resolution Reveals Oxidation Resistance of MoS 2 Increases with Film Density and Shear-Induced Nanostructural Modifications of the Surface

For decades, density has been attributed as a critical aspect of the structure of sputter-deposited nanocrystalline molybdenum disulfide (MoS 2 ) coatings impacting oxidation resistance and wear resistance. Despite its importance, there are few examples in the literature that explicitly investigate the relationship between the density and oxidation behaviors of MoS 2 coatings. Aging and oxidation are primary considerations for the use of MoS 2 coatings in aerospace applications as they inevitably experience prolonged storage in water and oxygen-rich environments prior to use. Oxidation that is either limited to the first few nanometers or through the bulk of the coating can result in seizure due to high initial coefficients of friction or component failure from excessive wear. High-sensitivity low-energy ion spectroscopy (HS-LEIS) and Rutherford backscattering spectrometry (RBS) are both used to understand the extent of oxidation throughout the first ~10 nanometers of the surface of pure sputtered nanocrystalline MoS 2 coatings after high-temperature aging and how it is impacted by the density of coatings as measured by RBS. Results show that low-density coatings (ρ = 3.55 g/cm 3 ) exhibit a more columnar microstructure and voiding, which act as pathways for oxidative species to penetrate and interact with edge sites, causing severe surface and subsurface oxidation. Furthermore, HS-LEIS of surfaces sheared prior to oxidation reveals that the oxidation resistance of low-density MoS 2 coatings can be significantly improved by shear-induced reorientation of the surface microstructure to a basal orientation and elimination of pathways for oxygen into the bulk through compaction of surface and subsurface voids.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effects of a Layered Morphology on Drip Suppression in Burning Polymers

Fire safety in polymers is critically important with products such as textiles and consumer goods, as well as materials used in construction, aerospace, transportation, and furniture. Additives used as anti-dripping and flame-retardant agents impose acute human health issues and have negative environmental impacts. With a focus on developing additive-free solutions, here we used multilayer coextrusion to fabricate layered polyethylene/polypropylene (PE/PP) films and investigated the effect of a layered morphology on dripping and burning rates. The experimental results for multilayered PE/PP samples were compared to control groups of melt blended PE/PP samples. The multilayer configuration provides two significant advantages: 154% delay in the time to the first drip and up to 87% reduction in the number of drips compared to the value for the melt blended samples with no additives. PE/PP blends with up to 2 wt % polytetrafluoroethylene, a commonly used anti-dripping agent in the industry, showed only 85% delay in the time to the first drip and about 60% reduction in the number of drips. We discovered a linear relationship between film thickness and burning rate, which allows for the use of the volumetric burning rate to normalize the effect of film thickness. No benefit in the burning rate was observed when comparing multilayered structures and conventional melt blended architectures or blends with PTFE. Annealing studies and transmission electron microscopy at areas close to the combustion zones confirmed that higher extensional viscosities induced by the layered structures and morphological changes due to the layer breakup play an important role in reducing dripping. By combining the first drip time and overall drip numbers, a parameter, “dripping index”, was defined to establish a fire safety map to simplify material comparisons. This study demonstrates that a multilayer film geometry can provide an additive-free solution to yield control over fire safety parameters in polymers.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Topology Optimization of Curved Electrodes with Suggested Simplified Corrugated Designs

The demand for space-efficient energy storage, from grid to device scale, has soared with the rapid electrification of society. To increase energy density while maintaining high rate performance, shaping electrodes has been shown to be an effective strategy. Computational optimization can suggest designs that improve energy storage performance; however, generated designs are often complex, leading to manufacturing challenges. Additionally, most optimization is typically conducted in standard rectangular domains, whereas some applications, such as in the aerospace industry or in consumer electronics, may benefit from optimized electrodes that fit in a nonstandard form factor. Here, in this work, we use topology optimization to design capacitive full-cell electrodes in a curved domain. We propose a simplified corrugated electrode design based on the optimization results and show that the energy stored in the simplified design is only reduced by about 5–27% when compared to that in the fully optimized design, with more deviation occurring when ionic transport in the porous electrode is particularly impeded. Overall, we find that these simplified designs are promising, particularly given the more conventional manufacturing techniques required, and have a 42–369% increase in energy stored compared to monolithic electrodes.

Energy - Storage↗
DOE OSTI2026

Brittle-to-Ductile Transition Temperature Decreases at the Microscale in Muscovite Mica

Characterizing the structural and chemical transitions of muscovite mica across various scales and thermal profiles is essential for ensuring the reliability of high-voltage power insulation, aerospace thermal barriers, and nanoscale semiconductor substrates under extreme operational stress. Here, we investigate notch fracture toughness $K^{n}_{Ic}$ of muscovite mica as a function of feature size w and temperature T with microscale shear testing. $K^{n}_{Ic}$ decreased and then stayed constant as w increased, likely from fabrication-induced changes to the microstructure and roughness. Moreover, $K^{n}_{Ic}$ initially decreased and then increased as temperature T increased, with the reversal in trend indicative of a brittle-to-ductile transition. Complementary Raman spectroscopy and molecular dynamics showed that the observed trends were due to an increase in bond lengths and a decrease in critical stresses/strains. In comparing the microscale results to macroscale counterparts, it was surmised that the decrease in brittle-to-ductile transition temperature was due to an increase in the dehydroxylation rate.

DelRio, Frank W. [Sandia National Laboratories (SN↗
DOE OSTI2019

Programmable Ultralight Magnets via Orientational Arrangement of Ferromagnetic Nanoparticles within Aerogel Hosts

The actuation and levitation of air-suspended objects by a magnetic field, due to its noncontact and holonomic manipulation modes, are important technological capabilities for device applications. However, owing to a higher density of conventional ferromagnets or nanoparticle-containing polymers and strong magnetic fields required for actuation, fabricating lightweight materials with a sensitive magnetic response for weight critical applications is challenging. Here, we report ultralight aerogel-based magnets (aero-magnets) comprising assembled ferromagnetic nanomaterials with highly magnetic anisotropy where the magnetic domains can be programmed by external predesigned fields. To demonstrate the breadth of manufacturing methods for this breed of aero-magnet composites, both silica/nanocellulose aerogel hosts and ferromagnetic nanorod/nanoplatelet guests have been explored. Single and double domains with out-of-plane magnetization are programmed into the aero-magnets and characterized by magnetic force microscopy. The levitation and actuation of the aero-magnets are realized while exposed to a small external magnetic field of 11 mT and introduced to a switching circuit. Furthermore, the elastic moduli of the aero-magnets are estimated by dynamic magnetic responses of the ferromagnetic nanoparticles tightly tethered in the aerogel hosts under rapid cyclic fields. These programmable aero-magnets could serve as monolithic magnetic actuator units in the fields of tiny robots and aerospace components.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Recycling Epoxy by Vitrimerization: Influence of an Initial Thermoset Chemical Structure

Epoxy/anhydride cured thermosets are widely used in aerospace and wind energy structures. Recycling the enormous amounts of epoxy thermoset waste remains a global challenge. Vitrimerization, a practical, low-cost, eco-friendly and scalable method was developed for closed-loop recycling of the crosslinked epoxy by converting the permanent network into a vitrimer type dynamic network. Here we study the effect of virgin epoxy network properties on network reforming efficiency and the activation energy of the dynamic network by varying the epoxy/anhydride ratio. The results point out that the permanent network crosslink density and glass transition temperature have a more pronounced effect on vitrimerization efficiency by comparison with the concentration of free hydroxyl groups in the system. Furthermore, the result can be potentially used to better control vitrimerization efficiency in recycling epoxy thermoset polymers and tailor the vitrimerized products properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Mixed Enthalpy–Entropy Descriptor for the Rational Design of Synthesizable High-Entropy Materials Over Vast Chemical Spaces

The practically unlimited high-dimensional composition space of high-entropy materials (HEMs) has emerged as an exciting platform for functional material design and discovery. However, the identification of stable and synthesizable HEMs and robust design rules remains a daunting challenge. Here, we propose a mixed enthalpy–entropy descriptor (MEED) that enables highly efficient, robust, high-throughput prediction of synthesizable HEMs across vast chemical spaces from first-principles. The MEED is based on two parameters: the relative formation enthalpy with respect to the most stable competing compound and the spread of the point-defect formation energy spectrum. The former measures the relative synthesizability of an HEM to its most stable competing phase, going beyond the conventional thermodynamic understanding. Further, the latter gauges the relative entropy forming ability of an HEM, entailing no sampling over numerous alloy configurations. By applying the MEED to two structurally distinct representative material systems (i.e., 3D rocksalt carbides and 2D layered sulfides), we not only successfully identify all experimentally reported HEMs within these systems but also reveal a cutoff criterion for assessing their relative synthesizability within each system. By the MEED, tens of new high-entropy carbides and 2D high-entropy sulfides are also predicted, which have the potential for a wide variety of applications such as coating in aerospace devices, energy conversion and storage, and flexible electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Snapshot multispectral imaging using a diffractive optical network

Abstract Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image field-of-view. This diffractive multispectral imager performs spatially-coherent imaging over a large spectrum, and at the same time, routes a pre-determined set of spectral channels onto an array of pixels at the output plane, converting a monochrome focal-plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery algorithms. Furthermore, the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states. Through numerical simulations, we present different diffractive network designs that achieve snapshot multispectral imaging with 4, 9 and 16 unique spectral bands within the visible spectrum, based on passive spatially-structured diffractive surfaces, with a compact design that axially spans ~72 λ m , where λ m is the mean wavelength of the spectral band of interest. Moreover, we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially repeating virtual spectral filter array with 2 × 2 = 4 unique bands at terahertz spectrum. Due to their compact form factor and computation-free, power-efficient and polarization-insensitive forward operation, diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-specific-power flexible transition metal dichalcogenide solar cells

Abstract Semiconducting transition metal dichalcogenides (TMDs) are promising for flexible high-specific-power photovoltaics due to their ultrahigh optical absorption coefficients, desirable band gaps and self-passivated surfaces. However, challenges such as Fermi-level pinning at the metal contact–TMD interface and the inapplicability of traditional doping schemes have prevented most TMD solar cells from exceeding 2% power conversion efficiency (PCE). In addition, fabrication on flexible substrates tends to contaminate or damage TMD interfaces, further reducing performance. Here, we address these fundamental issues by employing: (1) transparent graphene contacts to mitigate Fermi-level pinning, (2) MoO x capping for doping, passivation and anti-reflection, and (3) a clean, non-damaging direct transfer method to realize devices on lightweight flexible polyimide substrates. These lead to record PCE of 5.1% and record specific power of 4.4 W g −1 for flexible TMD (WSe 2 ) solar cells, the latter on par with prevailing thin-film solar technologies cadmium telluride, copper indium gallium selenide, amorphous silicon and III-Vs. We further project that TMD solar cells could achieve specific power up to 46 W g −1 , creating unprecedented opportunities in a broad range of industries from aerospace to wearable and implantable electronics.

42 ENGINEERING↗
DOE OSTI2024

Reversible two-way tuning of thermal conductivity in an end-linked star-shaped thermoset

Polymeric thermal switches that can reversibly tune and significantly enhance their thermal conductivities are desirable for diverse applications in electronics, aerospace, automotives, and medicine; however, they are rarely achieved. Here, we report a polymer-based thermal switch consisting of an end-linked star-shaped thermoset with two independent thermal conductivity tuning mechanisms—strain and temperature modulation—that rapidly, reversibly, and cyclically modulate thermal conductivity. The end-linked star-shaped thermoset exhibits a strain-modulated thermal conductivity enhancement up to 11.5 at a fixed temperature of 60 °C (increasing from 0.15 to 2.1 W m –1 K –1 ). Additionally, it demonstrates a temperature-modulated thermal conductivity tuning ratio up to 2.3 at a fixed stretch of 2.5 (increasing from 0.17 to 0.39 W m –1 K –1 ). When combined, these two effects collectively enable the end-linked star-shaped thermoset to achieve a thermal conductivity tuning ratio up to 14.2. Moreover, the end-linked star-shaped thermoset demonstrates reversible tuning for over 1000 cycles. The reversible two-way tuning of thermal conductivity is attributed to the synergy of aligned amorphous chains, oriented crystalline domains, and increased crystallinity by elastically deforming the end-linked star-shaped thermoset.

42 ENGINEERING↗
DOE OSTI2024

AI-assisted discovery of high-temperature dielectrics for energy storage

Abstract Electrostatic capacitors play a crucial role as energy storage devices in modern electrical systems. Energy density, the figure of merit for electrostatic capacitors, is primarily determined by the choice of dielectric material. Most industry-grade polymer dielectrics are flexible polyolefins or rigid aromatics, possessing high energy density or high thermal stability, but not both. Here, we employ artificial intelligence (AI), established polymer chemistry, and molecular engineering to discover a suite of dielectrics in the polynorbornene and polyimide families. Many of the discovered dielectrics exhibit high thermal stability and high energy density over a broad temperature range. One such dielectric displays an energy density of 8.3 J cc −1 at 200 °C, a value 11 × that of any commercially available polymer dielectric at this temperature. We also evaluate pathways to further enhance the polynorbornene and polyimide families, enabling these capacitors to perform well in demanding applications (e.g., aerospace) while being environmentally sustainable. These findings expand the potential applications of electrostatic capacitors within the 85–200 °C temperature range, at which there is presently no good commercial solution. More broadly, this research demonstrates the impact of AI on chemical structure generation and property prediction, highlighting the potential for materials design advancement beyond electrostatic capacitors.

25 ENERGY STORAGE↗
DOE OSTI2025

Ultra-light antennas via charge programmed deposition additive manufacturing

Abstract The demand for lightweight antennas in 5 G/6 G communication, wearables, and aerospace applications is rapidly growing. However, standard manufacturing techniques are limited in structural complexity and easy integration of multiple material classes. Here we introduce charge programmed multi-material additive manufacturing platform, offering unparalleled flexibility in antenna design and the capability for rapid printing of intricate antenna structures that are unprecedented or necessitate a series of fabrication routes. Demonstrating its potential, we present a transmitarray antenna composed of an interconnected, multi-layered array of dielectric/conductive S-ring unit cells, reducing 94% mass of conventional antenna configurations. A fully printed circular polarized transmitarray system fed by a source and a Risley prism antenna system operating at 19 GHz both show close alignment between testing results and numerical simulations. This printing method establishes a universal platform, propelling discovery of new antenna designs and enabling data-driven design and optimizations where rapid production of antenna designs is crucial.

Science & Technology - Other Topics↗
DOE OSTI2023

An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework for elastic properties of materials. In this work, the microstructure is represented by the Orientation Distribution Function (ODF) that determines the volume densities of crystallographic orientations. The framework was evaluated on two crystal systems, cubic and hexagonal, for Titanium (Ti) in Joint Automated Repository for Various Integrated Simulations (JARVIS) database and is expected to be widely applicable for materials with multiple crystal systems. The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A high-throughput framework for lattice dynamics

We develop an automated high-throughput workflow for calculating lattice dynamical properties from first principles including those dictated by anharmonicity. The pipeline automatically computes interatomic force constants (IFCs) up to 4th order from perturbed training supercells, and uses the IFCs to calculate lattice thermal conductivity, coefficient of thermal expansion, and vibrational free energy and entropy. It performs phonon renormalization for dynamically unstable compounds to obtain real effective phonon spectra at finite temperatures and calculates the associated free energy corrections. The methods and parameters are chosen to balance computational efficiency and result accuracy, assessed through convergence testing and comparisons with experimental measurements. Deployment of this workflow at a large scale would facilitate materials discovery efforts toward functionalities including thermoelectrics, contact materials, ferroelectrics, aerospace components, as well as general phase diagram construction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Insight into multi-step geological evolution of C-type asteroids from Ryugu particles

C-type asteroids are the source of the carbonaceous chondrite meteorites and represent remnants of primitive planetesimals that formed at the outer margins of the early Solar System and may have delivered volatiles to the inner Solar System, in particular the early Earth. However, the nature of carbonaceous chondrites is not well understood owing to terrestrial alteration. Here, in this work, we present the petrology and mineral chemistry of surface materials collected by the Japan Aerospace Exploration Agency (JAXA) Hayabusa2 spacecraft from the C-type asteroid Ryugu. The Ryugu particles we studied are similar to CI (Ivuna-type) chondrites but with some important differences, such as the presence of Na–Mg phosphates and Na-rich phases and the lack of ferrihydrite and gypsum. Ryugu particles experienced several steps of aqueous alteration, metasomatism and brecciation under variable conditions. These materials represent mixed lithologies and formed at different locations within their parent asteroid. The evidence presented here demonstrates that the C-type asteroid Ryugu experienced a complex geologic evolution shortly after its formation.

58 GEOSCIENCES↗
DOE OSTI2026

Mechanical behaviour of additively manufactured metals

Additive manufacturing is reshaping the production of engineering components in diverse industries, such as the automotive, aerospace, defense, and biomedical sectors, by offering unprecedented design flexibility. The non-equilibrium processing conditions of additive manufacturing generate materials with unique microstructures and tailored mechanical properties that are often unattainable through conventional routes. This review focuses on recent advances in additively manufactured metals that demonstrate distinctive mechanical behaviors, including strength-ductility synergy, microstresses and gradient plasticity, fracture and fatigue resistance, and high-temperature creep performance. Here, we examine the mechanisms and micromechanical effects arising from the heterogeneous microstructures fabricated by additive manufacturing, to guide the design of a wide range of high-performance structural materials. Furthermore, we discuss critical research needs and emerging opportunities in process control, alloy design, advanced characterization, high-fidelity computational modeling, and machine learning aimed at achieving exceptional mechanical properties in additively manufactured metals.

Additive Manufacturing↗
DOE OSTI2022

A reusable neural network pipeline for unidirectional fiber segmentation

Abstract Fiber-reinforced ceramic-matrix composites are advanced, temperature resistant materials with applications in aerospace engineering. Their analysis involves the detection and separation of fibers, embedded in a fiber bed, from an imaged sample. Currently, this is mostly done using semi-supervised techniques. Here, we present an open, automated computational pipeline to detect fibers from a tomographically reconstructed X-ray volume. We apply our pipeline to a non-trivial dataset by Larson et al . To separate the fibers in these samples, we tested four different architectures of convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients reaching up to 98%, showing that these automated approaches can match human-supervised methods, in some cases separating fibers that human-curated algorithms could not find. The software written for this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Material Fracturing and Failure Simulation Datasets

Fracturing is a fundamental physics phenomena with broad relevance across multiple domains, ranging from infrastructure integrity, aerospace durability, reservoir production, and seismic events. We present a diverse dataset of simulated fracture evolution and material failure generated from two numerical solvers: the phase-field method and the combined finite-discrete element method (FDEM). These solvers differ in formulation, physical fidelity, and computational efficiency. The dataset includes five materials: PBX, anisotropic shale, tungsten, aluminum, and steel. For each, phase-field simulations span 400,000 cases: 200,000 under uniaxial tension and 200,000 under biaxial tension. The computationally expensive FDEM simulations include 90,000 split evenly among PBX, shale, and tungsten under uniaxial loading. All simulations begin with randomized initial fracture patterns. Each entry includes temporal data capturing fracture propagation dynamics. This comprehensive dataset is designed to support the development of foundational or surrogate machine learning approaches for predicting material failure. While no such models are introduced here, the dataset lays a robust foundation for advancing future research and innovation in these areas.

36 MATERIALS SCIENCE↗