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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Influence of Cryomilling on Crystallite Size of Aluminum Powder and Spark Plasma Sintered Component

The present investigation aims to develop nanocrystalline (NC) pure aluminum powders using cryomilling technique and manufacture bulk components using spark plasma sintering (SPS). The cryomilling was performed on pure Al powders for 2, 6, and 8 h. The cryomilled powders were then consolidated using SPS to produce bulk components. The particle morphology and crystallite size of the powders and the bulk SPS components were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmission electron microscopy (TEM). The results showed that the crystallite size of pure Al powders decreases with increased cryomilling time. The results also showed that the SPS at elevated temperatures resulted in a slight increase in crystallite size, however, the changes were insignificant. The mechanical properties of the bulk components were determined using a Vickers microhardness tester. The hardness of the cryomilled SPS component was determined to be three times higher than that of the unmilled SPS component. The mechanism for the reduction in crystallite size with increasing cryomilling time is discussed. This fundamental study provides an insight into the development of bulk nanomaterials with superior mechanical properties for automotive, aerospace, marine, and nuclear applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Epoxy as Filler or Matrix for Polymer Composites

Epoxy is a widely used polymer because of its ease of processing, high adhesiveness, and high chemical resistance. Epoxy-based composites are commonly used in aerospace, automotive, and marine applications. The epoxy type, function, curing agent, and curing process are discussed in this chapter. Epoxy is used as either a filler or polymer matrix in composite applications. As a filler, the epoxy modification on the fiber is discussed. As a polymer matrix, the epoxy is reinforced by natural and synthetic fibers. The manufacturing process and the fabricated epoxy-based composites’ performance (e.g., mechanical and thermal properties) are investigated. The advantages and disadvantages of epoxy’s function are discussed and summarized. Epoxy modification is an effective approach to improve the composites’ performance.

Zhao, Xianhui↗
DOE OSTI2022

Nitinol Electroslag Remelting: Initial Slag Study

Nitinol’s unique shape memory and super-elastic properties make it attractive for many applications in biomedical, automotive, aerospace, industrial refrigeration, and waste heat reclamation industries. The fatigue performance is limited however, by non-metallic inclusions (NMI) formed during the traditional VIM/VAR or multi-VAR process. ESR is a potential alternative melt process and is commonly used to refine ingot chemistry and enhance the metallurgical structure of many superalloys and specialty steels, but no study on ESR of Nitinol has been reported to date. The key to establishing a successful ESR process is selecting a compatible slag. In this study, several different slag chemistries are evaluated for their compatibility with Nitinol. One slag chemistry is chosen, along with a control, to produce four Nitinol ESR ingots. Pursuit of a novel slag chemistry for adequate Nitinol refinement is discussed.

Fezi, Kyle↗
DOE OSTI2024

High temperature electrical property measurements of ceramics

Emerging applications in energy and aerospace systems require high quality data on the high temperature electrical properties of ceramics, particularly oxides, to guide material selection, design, and modeling. This presentation demonstrates the functionality of a specially designed sample fixture that enables electrical measurements to be conducted up to temperatures as high as 1600°C. Utilizing the mismatch in the coefficient of thermal expansion between two materials, a purely mechanical method for establishing electrical contact in the van der Pauw geometry is used to measure the bulk resistivity of ceramic materials. Measurements conducted on a number of common high temperature materials will be presented, including 20 mol % Gd-doped cerium oxide. Measurements are performed using multiple techniques and compared to literature values finding excellent agreement. The approach described in this work enables the van der Pauw method to be applied to many ceramic materials over a wide range of temperatures and environments.

Cann, David P.↗
DOE OSTI2026

Influence of Numerical Modeling Approaches on Damped Behavior of Flexible Beams: Preprint

Composites structures are widely used in aerospace and wind energy applications for their excellent stiffness and strength-to-weight properties. In these structures, structural damping is critical to predict vibration amplitudes, performance, and reliability. Structural damping is of particular interest for slender wings, rotorcraft blades, and wind turbine blades that can exhibit complex vibration phenomena and are frequently modeled with geometrically exact beam theory (GEBT). Standard approaches of stiffness proportional or modal damping merely assign user defined values and cannot predict damping behavior. This work compares stiffness proportional damping to two more advanced damping approaches: modal strain energy and Prony series. The modal strain energy approach uses a sectional analysis tool to calculate the beam stiffness and postprocess internal stresses from GEBT simulations. The internal stresses are then used to calculate modal damping factors. The Prony series is implemented within GEBT to directly model viscoelastic behavior of the composites. These approaches are compared by modeling the evolution of the damping factors of a realistic flexible wind turbine blade with varying rotational speed. Discrepancies between the approaches suggest areas for future modeling development, but differences in nonlinear damping values are less than current uncertainties about the magnitude of structural damping.

17 WIND ENERGY↗
DOE OSTI2025

Compression Response of Silicone-Based Composites with Integrated Multifunctional Fillers

Polydimethylsiloxane (PDMS) is known for its exceptional mechanical properties, chemical stability, and flexibility. Recent advancements have focused on developing functional PDMS composites by integrating various functional fillers, including polymers, ceramics, and metals, for advanced applications such as electronics, medical devices, and aerospace. Consequently, there is a growing need to investigate PDMS composites to achieve higher filler loadings offering enhanced mechanical performance. This study addresses this need by utilizing the high molecular weight (MW) PDMS resin we have developed, offering its high elongation capacity of up to >6500%. We incorporated boron (B), hollow glass microballoons (HGMs), and tungsten-coated hollow glass microballoons (WHGMs) into the developed high MW PDMS. The resulting composites demonstrated excellent elastic properties and significant compression resilience (35–80%) and elastic modulus (1.28–10.15 MPa) at high filler loadings (~60 vol.%). Specifically, B/PDMS composites achieved up to 67.6 vol.% of B, HGM/PDMS composites held up to 68.6 vol.% of HGM, and WHGM/PDMS composites incorporated up to 54.0 vol.% of WHGM. These findings highlight the potential of high MW PDMS for developing high-performance PDMS composites suitable for advanced applications such as aerospace, automotive, and medical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enabling Industrial Re-Use of Large-Format Additive Manufacturing Molding and Tooling

Large-format additive manufacturing (LFAM) is an enabling manufacturing technology capable of producing large parts with highly complex geometries for a wide variety of applications, including automotive, infrastructure/construction, and aerospace mold and tooling. In the past decade, the LFAM industry has seen widespread use of bio-based, glass, and/or carbon fiber reinforced thermoplastic composites which, when printed, serve as a lower-cost alternative to metallic parts. One of the highest-volume materials utilized by the industry is carbon fiber (CF)-filled polycarbonate (PC), which in out-of-autoclave applications can achieve comparable mechanical performance to metal at a significantly lower cost. Previous work has shown that if this material is recovered at various points throughout the manufacturing process for both the lab and pilot scale, it can be mechanically recycled with minimal impacts on the functional performance and printability of the material while significantly reducing the feedstock costs. End-of-life (EOL) CF-PC components were processed through industrial shredding, melt compounding, and LFAM equipment, followed by evaluation of the second-life material properties. Experimental assessments included quantitative analysis of fiber length attrition, polymer molecular weight degradation using gel permeation chromatography (GPC), density changes via pycnometry, thermal performance using dynamic mechanical analysis (DMA), and mechanical performance (tensile properties) in both the X- and Z-directions. Results demonstrated a 24.6% reduction in average fiber length compared to virgin prints, accompanied by a 21% decrease in X-direction tensile strength and a 39% reduction in tensile modulus. Despite these reductions, Z-direction tensile modulus improved by 4%, density increased by 6.8%, and heat deflection temperature (HDT) under high stress retained over 97% of its original value. These findings underscore the potential for integrating mechanically recycled CF-PC into industrial LFAM applications while highlighting the need for technological innovations to mitigate fiber degradation and enhance material performance for broader adoption. This critical step toward circular material practices in LFAM offers a pathway to reducing feedstock costs and environmental impact while maintaining functional performance in industrial applications.

additive manufacturing↗
DOE OSTI2021

Thermal Barrier Coatings Overview: Design, Manufacturing, and Applications in High-Temperature Industries

Today’s competitive world economy is creating an indispensable demand for increased efficiency of engineering components that operate in harsh environments (i.e., very high-temperature, corrosive, or neutron irradiation environments), for applications in the energy, automotive, aerospace, electronics, and power industries. Increased research is being done on thermal barrier coatings (TBCs) for protecting such components, since the versatility of manufacturing techniques and the scale of deployment result in increased life, economics, performance, and durability. This review focuses on the advances that led to using TBCs for component life extension and, more recently, as an integral part of advanced component design for high-temperature and other types of harsh environments, such as those found in nuclear-related applications. Furthermore, factors that led to state-of-the-art advanced coating-fabrication techniques [e.g., electron-beam physical vapor deposition (EB-PVD), plasma spray deposition, and electrophoretically deposited TBCs, as well as functionally graded material (FGM) manufacturing] have also been emphasized in current coating R&D. This review explores the current state of TBCs, i.e., the latest advances regarding their fabrication and performance, associated challenges, and recommendations for their future use in aerospace, nuclear, high-temperature, or otherwise harsh environments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Additive manufacturing of LiCoO 2 electrodes via vat photopolymerization for lithium ion batteries

Additive manufacturing has the potential to revolutionize the fabrication of lithium-ion batteries for a diversity of applications including in portable, biomedical, aerospace, and the transportation fields. Standard commercial batteries consist of stacked layers of various components (current collectors, cathode, anode, separator and electrolyte) in a two-dimensional manner. By leveraging the latest advances in additive manufacturing and computer-aided design, an improved geometric and electrochemical configuration of these batteries can maximize energy efficiency while allowing design optimization to reduce dead space for a given application. In this work, a composite UV photosensitive resin was prepared and used as feedstock in a vat photopolymerization system. The resin was loaded with LiCoO 2 acting as electrochemically active material for the cathode of a lithium-ion battery, and was further improved with the addition of conductivity-enhancing carbonaceous additives. Challenges to additive manufacturing arise from the opacity and high viscosity of the composite nature of these electrochemically-active resins, which cause light refraction during selective UV curing. Subsequently, items were printed and subjected to a thermal post-processing step to obtain an adequate compromise between electrochemical performance and mechanical integrity. Both sintered and green state 3D printed cathodes were assembled into half-cell lithium-ion batteries using lithium metal as a reference and counter electrode. Electrochemical cycling of these batteries yielded satisfactory results approaching commercial LiCoO 2 cathodes’ performance, with the potential advantages of additive manufacturing – high surface area anode–cathode configurations for power performance as well as shape conformability.

25 ENERGY STORAGE↗
DOE OSTI2025

Operation at Reduced Atmospheric Pressure and Concept of Reliability Redundancy for Optimized Design of Insulation Systems

Electrified transportation is calling for insulation design criteria that is adequate to provide elevated levels of power density, power dynamics and reliability. Increasing voltage levels are expected to cause accelerated intrinsic and extrinsic aging effects which will not be easily predictable at the design stage due to a lack of suitable modeling. Designing reliable insulation systems would require finding solutions able to control accelerated aging due to an unpredictable increase of intrinsic stresses and the onset of extrinsic stresses as partial discharges. This paper proposes the concept of reliability redundancy for the insulation design of aerospace electrical asset components, which is also validated at lower-than-standard atmospheric pressure. The principle is that extrinsic-aging-free design might be achieved upon determining the aging stress or abnormal service stresses distribution and being sure that aging will not generate conditions that can incept extrinsic aging (partial discharges) during operation life. However, such information is never, in practice, fully available to insulation system designers. Hence, especially in critical applications such as electrified aircraft, aerospace, and combat ships a further level of reliability should be added to a partial-discharge-free design, which can consist of the use of corona-resistant materials and/or of life models able to consider the accelerated aging effect of partial discharges (or any other type of extrinsic-accelerated aging factor). Innovative life modeling considering both extrinsic and intrinsic aging stresses, insulating material testing to estimate model parameters, and a metric for quantifying the extent of corona (or partial discharge) resistance can lead to establishing feasibility and limit conditions for optimized or fully reliability-redundant design. It is shown in the paper that if an extrinsic-aging-free design is not feasible, and it is therefore replaced by a redundant design, a further level of reliability redundancy can be provided by effective condition monitoring plans.

Montanari, Gian Carlo↗
DOE OSTI2021

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗
DOE OSTI2021

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.

97 MATHEMATICS AND COMPUTING↗
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 OSTI2023

Aerosol-Jet Printed Sensors for Environmental, Safety, and Health Monitoring: A Review

An emergent direct-write approach, aerosol-jet printing (AJP), is gaining attention for the deployment of rapid and affordable microadditively manufactured energy-efficient sensors and printed electronics. AJP enables a broad range of ink viscosities (0.001–1 Pa s) for printing diverse materials ranging from ceramics and metals to polymers and biological matter. Reproducible, high-spatial-resolution features (≈10 µm), and wide standoff distances (1–11 mm) between the nozzle and the substrate facilitate conformal printing of complex geometrical designs on nonplanar—e.g., stepped or curved—surfaces. Here this paper aims to provide a comprehensive overview of state-of-the-art AJP-based sensors (e.g., strain and temperature gauges, biosensors, photosensors, humidity and surface acoustic wave sensors, dielectric elastomer actuators, and motion, smoke, and hazardous gas detectors) and to discuss prospective applications. The drive toward cost-effective devices that are smaller, lighter, and better-performing remains a frontier challenge in the field of printed electronics. Consequently, as AJP becomes increasingly utilized in the high-volume manufacturing of miniaturized active and passive sensors, it opens a pathway for facile large-scale fabrication of devices for a wide range of consumer and industrial applications, including transportation, agriculture, infrastructure, aerospace, national defense, and healthcare.

3-dimensional↗
DOE OSTI2023

Interlocking metasurfaces

Abstract Interlocking metasurfaces (ILMs) are architected arrays of mating features that enable joining of two bodies. Complementary to traditional joining technologies such as bolts, adhesives, and welds, ILMs combine ease of assembly, removal, and reassembly with robust mechanical properties. Structural in nature, they act in a quasi-continuous manner across a surface and enable joining of complex surfaces, e.g., lattices. In this perspective, we define an ILM, begin exploring the design domain and illustrate its breath, and pragmatically evaluate mechanical performance and manufacturability. ILMs will find applications in various fields from aerospace to micro-robotics, civil engineering, and prosthetics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A renewable lignin-based thermoplastic adhesive for steel joining

Adhesive bonding of metals has become increasingly relevant in recent years due to the demand for reducing weight and improving performance in structural applications such as automobiles and aerospace. We developed renewable thermoplastic adhesives from technical organosolv lignin isolated from hardwood biomass and acrylonitrile butadiene co-polymer rubber (NBR) for joining steel substrates. NBR33, NBR41 and NBR51 with acrylonitrile molar ratios of 33, 41 and 51%, respectively, were blended with lignin to form two-phase thermoplastic adhesives, and their adhesion, viscoelastic and surface properties were measured. Lignin content in the compositions were varied, ranging from 40% to 80% (w/w), to alter toughness, stiffness, and surface energy characteristics of the material. Better interaction or reactivity between the lignin and NBR phases was observed with greater nitrile content in NBR, leading to greater modulus and stiffness of the adhesive. Simultaneously, increasing the proportion of lignin reduced toughness and improved stiffness, with the highest adhesive strength of 13.1 MPa measured in a 60% lignin loading ratio with NBR51. Surface energy measurements revealed that total surface energy (sum of polar and dispersive surface energy) raised with lignin loading, suggesting that both surface energy and matrix strength play a critical role in the adhesive properties of the synthesized materials. A finite element-based cohesive zone model (CZM) was developed and implemented to study the failure strength of the adhesively bonded joint. Here, this study demonstrates the viability of lignin as a valuable building block for adhesives, not only due to its inherent chemical structure and rigidity, but also for its surface energy characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

State-of-Charge Investigation of Lithium Manganese Dioxide Primary Batteries Utilizing X-ray Computed Tomography

Lithium primary batteries (LPBs) represent a class of energy storage devices, uniquely suited for mission-critical applications including emergency backup power, aerospace and defense electronics, implantable medical devices, and remote sensing. Despite their technological maturity and distinct advantages over rechargeable lithium-ion batteries (i.e., superior shelf life and operational simplicity), state-of-charge (SoC) estimation in these batteries remains a persistent challenge due to the lack of a reliable, quantifiable diagnostic technique. In this work, we investigate X-ray computed tomography (X-CT) as a transformative and accurate SoC estimation technique for LPBs. Results gained through X-CT measurements reveal systematic, quantifiable structural changes in cathode morphology and geometry during discharge, establishing a direct structural basis for SoC estimation, with a maximum relative standard deviation of 2.3%. This work establishes a pathway toward operando, imaging-driven SoC diagnostics that can significantly enhance the reliability of SoC estimation in LPBs.

25 ENERGY STORAGE↗
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↗