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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Enhancing functionalities in carbon fiber composites by titanium dioxide nanoparticles

Carbon fiber composite's high specific strength makes it incredibly useful for structural applications. However, their low strain-to-failure can be problematic in structural applications that can potentially see high strain conditions or high fatigue cycles resulting in sudden, catastrophic failure. This is further complicated by damage manifesting within the composite thus not showing damage indicators on the surface. Therefore, monitoring the structural integrity and strain history of the composite in application by itself—while maintaining positive composite performance—is vital to ensure safe operation. In this study, a homogeneous dispersion of TiO 2 nanoparticles on carbon fiber is demonstrated to generate a piezoresistive carbon fiber polymer matrix composite with enhanced self-sensing capabilities. The nanoparticle-embedded composites also exhibited superior strength and damping potential compared to the nanoparticle-free composites. The apparent interlaminar shear strength increased by up to 15% and the damping loss factor increased by an average of 150% while the piezoresistive sensitivity increased by up to 180% with the addition of a small weight fraction of nanoparticles to the fiber surface. These results demonstrate an approach to simultaneously improve the sensing capabilities, damping behavior and mechanical strength of carbon fiber composites by simply adding nanoparticles in the fiber sizing using a commercially scalable deposition method.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Predicting Microstructural Void Nucleation in Discontinuous Fiber Composites through Coupled in-situ X-ray Tomography Experiments and Simulations

Composite materials have become widely used in engineering applications, in order to reduce the overall weight of structures while retaining their required strength. In this work, a composite material consisting of discontinuous glass fibers in a polypropylene matrix is studied at the microstructural level through coupled experiments and simulations, in order to uncover the mechanisms that cause damage to initiate in the microstructure under macroscopic tension. Specifically, we show how hydrostatic stresses in the matrix can be used as a metric to explain and predict the exact location of microvoid nucleation that occurs during damage initiation within the composite’s microstructure. Furthermore, this work provides evidence that hydrostatic stresses in the matrix can lead to coupled microvoid nucleation and early fiber breakage, and that small fragments of fibers can play an important role in the process of microvoid nucleation. These results significantly improve our understanding of the mechanics that drive the initiation of damage in the complex microstructures of discontinuous fiber reinforced thermoplastics, while also allowing scientists and engineers to predict the microstructural damage behavior of these composites at sub-fiber resolution and with high accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modeling creep behavior in ceramic matrix composites

Here, in this work, a three-dimensional viscoplasticity formulation with progressive damage is developed and used to investigate the complex time-dependent constituent load transfer and progressive damage behavior in ceramic matrix composites (CMCs) subjected to creep. The viscoplasticity formulation is based on Hill's orthotropic plastic potential, an associative flow rule, and the Norton-Bailey creep power law with Arrhenius temperature dependence. A fracture mechanics-informed isotropic matrix damage model is used to account for CMC brittle matrix damage initiation and propagation, in which two scalar damage variables capture the effects of matrix porosity as well as matrix property degradation due to matrix crack initiation and propagation. The Curtin progressive fiber damage model is utilized to simulate progressive fiber failure. The creep-damage formulation is subsequently implemented as a constitutive model in the generalized method of cells (GMC) micromechanics formulation to simulate time-dependent deformation and material damage under creep loading conditions. The developed framework is used to simulate creep of single fiber SiC/SiC microcomposites. Simulation results are in excellent agreement with experimental and numerical data available in the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effect of decades of corrosion on the microstructure of altered glasses and their radiation stability

Understanding the microstructural evolution of glasses during their interaction with water and radiation is of fundamental importance in addressing the corrosion of nuclear waste forms under geological disposal conditions. Here we report the results of more than 21 years of corrosion of two borosilicate glasses showing the formation of mesoporous C–S–H gels in Ca-bearing glasses and a mainly microporous microstructure in Al-bearing glasses. These porous corroded glasses were then irradiated with heavy ions to simulate the effects of recoil nucleus damage and monitored in real time using transmission electron microscopy with in situ ion irradiation. The ballistic collisions remarkably healed the porous corroded glasses to a pore-free homogeneous microstructure. Besides providing new insights and predictions about how doped glasses and actual waste forms may evolve under corrosion and irradiation, the results highlight the non-universal nature of the existing corrosion models and the important role that the glass composition and radiation damage play in the evolution of the microstructure during corrosion.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Transverse impact by RCCs on S-glass and Kevlar® FRC strips

This study aims to isolate interactions between plies and tows and reveal fundamental physics involved in transverse impact on fiber-reinforced composite (FRC) structures. Composite strips were sectioned from large panels and characterized by optical photography, three-dimensional synchrotron X-ray computed tomography, and scanning electron microscopy (SEM). Each strip was impacted perpendicularly by a right circular cylinder (RCC) projectile at a velocity ranging from ~ 150 to 600 m/s. The global strip behavior, as well as localized deformation and failure of the strip near the projectile corner, were both captured by high-speed optical imaging. S-Glass FRC strips were observed to fail in tension ahead of the RCC projectiles’ flat surfaces while Kevlar® FRC strips fractured at the projectile corners. The concept of critical velocity region previously used for impact on yarns was introduced to define different failure modes of each composite strip type. The strip damage extent was found to increase with the impact velocity and reach the maximum at the upper limit of the critical velocity region. Above the critical velocity region, the damage extent decreased with impact velocity. Wave propagations and load histories in the composite strips during impact were quantified and compared with Smith’s theory. Finally, critical velocities of single fibers, yarns, and composite strips and ballistic limits of single-ply and multi-ply composite panels were compared to provide insight into the design of impact-resistant fabrics and composites.

42 ENGINEERING↗
DOE OSTI2021

Radiation damage of a two-dimensional carbon fiber composite (CFC)

Carbon fiber composites (CFC) are, among other applications, already in use as low-impedance collimating elements for intercepting TeV-level protons machines, like the CERN Large Hadron Collider. Towards a comprehensive understanding of these materials’ properties and behavior, a series of radiation damage campaigns have been undertaken in order to quantitively investigate the response of AC-150 K CFC to high proton fluences. The CFCs dimensional changes, stability, micro-structural evolution, and structural integrity below and above 5 × 10 20 p/cm 2 fluence threshold were studied. The irradiating particles were protons of kinetic energies in the range of 130–200 MeV. The effects of the irradiation temperature (~90–180°C and 280–500°C) combined with the proton fluence on the dimensional stability and the microstructural evolution of this anisotropic two-dimensional composite structure were studied using precision dilatometry and X-ray diffraction. Our results shed light on the fluence-based limitations for these composite materials. Furthermore, we compare these results with reference unirradiated graphite, and we discuss the similarities and differences in the dimensional changes and post-irradiation annealing properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network

A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Phase-field modeling of radiation-induced composition redistribution: An application to additively manufactured austenitic Fe–Cr–Ni

Multicomponent alloys undergoing irradiation damage develop radiation-induced composition redistribution at point defect sinks such as grain boundaries (GBs) and dislocations. Such redistribution results in undesired changes to their mechanical behavior and corrosion resistance. Additively manufactured alloys proposed for future nuclear applications are expected to demonstrate a distinct response to irradiation owing to their unique microstructure with as-solidified dislocation density and chemical microsegregation. To capture the composition redistribution in such systems, we develop a mesoscale model with coupled evolution of atomic and point defect components in the presence of dislocation density, dislocation heterogeneity, and thermodynamic interactions at the GB. The model is parameterized for an FCC Fe–Cr–Ni alloy as a representative system for austenitic stainless steels, and simulations are performed in 1D and 2D as a function of irradiation temperature, dose, dislocation density, and grain size. Radiation-induced segregation (RIS) characterized by Cr depletion and Ni enrichment is predicted at both the GB and the dislocation cell wall, with RIS being lower in magnitude but wider at the cell wall. Strongly biased absorption of self-interstitials by dislocations is found to suppress Ni enrichment but slightly enhance Cr depletion under certain conditions. Thermodynamic segregation at the GB is predicted to be narrower and opposite in sign to RIS for both Cr and Ni. Importantly, non-monotonic segregation is found to occur when both thermodynamic and RIS mechanisms are considered, providing a novel physical interpretation of experimental observations. The model is expected to serve as a key tool in accelerated qualification of irradiated materials.

additively manufactured microstructure↗
DOE OSTI2020

An integrated computational materials engineering framework to analyze the failure behaviors of carbon fiber reinforced polymer composites for lightweight vehicle applications

A bottom-up multi-scale modeling approach is used to develop an Integrated Computational Materials Engineering (ICME) framework for carbon fiber reinforced polymer (CFRP) composites, which has the potential to reduce development to deployment lead time for structural applications in lightweight vehicles. In this work, we develop and integrate computational models comprising of four size scales to fully describe and characterize three types of CFRP composites. In detail, the properties of the interphase region are determined by an analytical gradient model and molecular dynamics analysis at the nano-scale, which is then incorporated into micro-scale unidirectional (UD) representative volume element (RVE) models to characterize the failure strengths and envelopes of UD CFRP composites. Then, the results are leveraged to propose an elasto-plastic-damage constitutive law for UD composites to study the fiber tows of woven composites as well as the chips of sheet molding compound (SMC) composites. Subsequently, the failure mechanisms and failure strengths of woven and SMC composites are predicted by the meso-scale RVE models. Finally, building upon the models and results from lower scales, we show that a homogenized macro-scale model can capture the mechanical performance of a hat-section-shaped part under four-point bending. Along with the model integration, we will also demonstrate that the computational results are in good agreement with experiments conducted at different scales. The present work illustrates the potential and significance of integrated multi-scale computational modeling tools that can virtually evaluate the performance of CFRP composites and provide design guidance for CFRP composites used in structural applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

MOSAIC: An Effective FFT-based Numerical Method to Assess Aging Properties of Concrete

As the nuclear fleet in the United States ages and subsequent license renewal applications grow, the prediction of concrete durability at extended operation becomes more important. To address this issue, a Fast-Fourier Transform (FFT) method is utilized to simulate aging-related degradation of concrete within the Microstructure Oriented Scientific Analysis of Irradiated Concrete (MOSAIC) software. MOSAIC utilizes compositional phase maps to simulate damage from radiation-induced volumetric expansion (RIVE), applied force, creep, and thermal expansion. This compositional detail allows each mineral in the microstructure to be assigned specific material properties, allowing the simulation to be as accurate and representative as possible. The principal goal of MOSAIC is to simulate the effects of nonlinear aging mechanisms occurring in nuclear concrete on the macroscopic mechanical properties, using only the aggregate microstructure compositional information as a starting point. Here, several realistic example simulations are shown to demonstrate the utility and uniqueness of the MOSAIC software.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2022

Influence of weave architecture on mechanical response of SiC f -SiC m tubular composites

Due to their high resistance to radiation damage and elevated temperature, silicon carbide fiber-reinforced, silicon carbide matrix composites (SiC f -SiC m ) are identified as potential cladding structures for use in nuclear reactors. In this study, four composite architectures with varying ply numbers along the thickness direction and different biaxial or triaxial plain weave orientations at either 45° or 60°, were systematically evaluated under various stress states to assess the influence of weave architecture on mechanical performance. Experiments were conducted on SiC f -SiC m composite tubes under tensile hoop, axial compression, and rotating flexural loading to evaluate the mechanical response and investigate the failure modes using high-speed imaging and digital image correlation (DIC) techniques. It was found that for tensile hoop burst and flexural loading, the braiding angle had the most significant influence on the strength of the composite, whereas the effect of fiber angle was more limited for compression testing. Under axial compression a unique failure mode where a microcrack nucleates and grows only to a length equal to the thickness of a single yarn was identified. This crack growth behavior is reflected as periodic oscillations in the load-displacement response. For both axial and hoop loading, regardless of weave angle and number of plies, failure always initiated parallel to the tube axis in a single yarn and the cumulative interaction of these microcracks lead to either axial burst or fracture at an angle to the tube axis along a yarn. Furthermore, these results point to the importance of customizing the design of tube architecture for enhanced performance in specified nuclear applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗
DOE OSTI2022

A review of the fabrication methods and mechanical behavior of continuous thermoplastic polymer fiber–thermoplastic polymer matrix composites

Abstract Thermoplastic polymer fiber–thermoplastic polymer matrix composites (PPCs or PRFPs), often recognized as self‐reinforced or single polymer composites, are potential candidates for future advanced polymer composites because of various advantages ( e.g., recyclability, formability, low‐cost, ultra‐lightweight, environmental friendliness, etc.). The manufacturability and mechanical behavior of these composites compared to conventional carbon‐/glass‐/aramid‐fiber‐reinforced polymers is of great interest to the composites community, but there are a limited number of studies in this area. To this end, this paper reviewed fabrication methods with different processing parameters and mechanical behavior of uni‐/multi‐directional thermoplastic PPCs featuring continuous thermoplastic polymer fibers from limited data in the literature. It was shown that most specific behaviors (normalized by density) of these materials in various loading conditions (e.g., quasi‐static tension/shear/flexure, tension‐tension fatigue, and out‐of‐plane impacting, etc.) are comparable to or better than glass‐/aramid‐fiber‐reinforced polymers. Particularly, the specific ductility in the foregoing conditions outperforms all the carbon‐/glass‐/aramid‐fiber‐reinforced polymers. Thermoplastic PPCs with remarkable performance can be achieved through several uncomplicated methods (e.g., film stacking, hot compaction, powder and solution impregnations, matrix infusion and injection molding, additive manufacturing, etc.), which have some similarities to the methods used for carbon‐/glass‐/aramid‐fiber‐reinforced polymers. Moreover, several opportunities and challenging problems of thermoplastic PPCs were summarized at the end of this review paper. Efficient solutions may require countless efforts in the composites community to further strengthen the performance and understanding of thermoplastic PPCs for wide applications in various engineering fields in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Photochemically induced acousto-optics in gases

Acousto-optics consists of launching acoustic waves in a medium (usually a crystal) in order to modulate its refractive index and create a tunable optical grating. Here, in this article, we present the theoretical basis of an alternative scheme to generate acousto-optics in a gas, where the acoustic waves are initiated by the localized absorption (and thus gas heating) of spatially modulated UV light, as was demonstrated by Michine and Yoneda [Commun. Phys. 3, 24 (2020)]. We identify the chemical reactions initiated by the absorption of UV light via the photodissociation of ozone molecules present in the gas, and calculate the resulting temperature increase in the gas as a function of space and time. Solving the Euler fluid equations shows that the modulated, isochoric heating initiates a mixed acoustic-entropy wave in the gas, whose high-amplitude density (and thus refractive index) modulation can be used to manipulate a high-power laser. We calculate that diffraction efficiencies near 100% can be obtained using only a few millimeters of gas containing a few percent ozone fraction at room temperature, with UV fluences of less than 100 mJ/cm 2 —consistent with the experimental measurements. Our analysis suggests possible ways to optimize the diffraction efficiency by changing the buffer gas composition. Gases have optics damage thresholds 2–3 orders of magnitude beyond those of solids; these optical elements should therefore be able to manipulate kilojoule-class lasers.

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

The effect of composition on helium bubble nucleation in complex concentrated alloys

Molecular dynamics simulations of helium implantation have been performed in the MoNbTaTi complex concentrated alloy (CCA) to determine the impact of composition on helium clustering and bubble nucleation. It was found that compositions with lower interstitial atomic volume resulted in smaller cluster sizes, higher helium-to-vacancy ratios, and larger migration barriers. As the atomic volume decreases, it is harder to accommodate additional helium in the lattice and for helium to diffuse between interstitial sites, resulting in smaller cluster sizes. In particular, increasing molybdenum directly correlated with a decreased interstitial atomic volume and increased migration barrier. Niobium exhibited the opposite trend, where more niobium in the material resulted in larger interstitial atomic volumes and cluster sizes but lower migration barriers. The atomic volume was determined to be an indicator of the susceptibility of the material to helium bubble growth and could potentially be used as a metric to screen compositions for resistance to helium damage. In this way, properties such as available interstitial volumes could be used to screen materials more rapidly given the vast compositional space of CCAs.

McCarthy, Megan Jeanne [Sandia National Laboratori↗