Engineering topics
Explore NASA and DOE materials research. Search alloys, composites and material properties, then open the original technical report for test conditions and results.
Health monitoring of polymer matrix composite materials using fiber optic Bragg grating (FBG) sensors is accomplished using a tunable IR (infrared) laser via transmission mode. Results are presented from experiments of composite structures with FBG's embedded at various orientations, and surface measurements of various cryogenic composite vessels.
Advanced, waveform based acoustic emission (AE) techniques have been previously used to evaluate damage progression in laboratory tests of composite coupons. In these tests, broad band, high fidelity acoustic sensors were used to detect signals which were then digitized and stored for analysis. Analysis techniques were based on plate mode wave propagation characteristics. This approach, more recently referred to as Modal AE, provides an enhanced capability to discriminate and eliminate noise signals from those generated by damage mechanisms. This technique also allows much more precise source location than conventional, threshold crossing arrival time determination techniques. To apply Modal AE concepts to the interpretation of AE on larger composite structures, the effects of wave propagation over larger distances and through structural complexities must be well characterized and understood. In this research, measurements were made of the attenuation of the extensional and flexural plate mode components of broad band simulated AE signals in large composite panels. As these materials have applications in a cryogenic environment, the effects of cryogenic insulation on the attenuation of plate mode AE signals were also documented.
A feasibility study on the use of shape memory alloys (SMA) for suppression of the random response of composite panels due to acoustic loads at elevated temperatures is presented. The constitutive relations for a composite lamina with embedded SMA fibers are developed. The finite element governing equations and the solution procedures for a composite plate subjected to combined acoustic and thermal loads are presented. Solutions include: 1) Critical buckling temperature; 2) Flat panel random response; 3) Thermal postbuckling deflection; 4) Random response of a thermally buckled panel. The preliminary results demonstrate that the SMA fibers can completely eliminate the thermal postbuckling deflection and significantly reduce the random response at elevated temperatures.
Stress rupture is not a critical failure mode for most composite structures, but there are a few applications where it can be critical. One application where stress rupture can be a critical design issue is in Composite Overwrapped Pressure Vessels (COPV's), where the composite material is highly and uniformly loaded for long periods of time and where very high reliability is required. COPV's are normally required to be proof loaded before being put into service to insure strength, but it is feared that the proof load may cause damage that reduces the stress rupture reliability. Recently, a fiber breakage model was proposed specifically to estimate a reduced reliability due to proof loading. The fiber breakage model attempts to model physics believed to occur at the microscopic scale, but validation of the model has not occurred. In this paper, the fiber breakage model is re-derived while highlighting assumptions that were made during the derivation. Some of the assumptions are examined to assess their effect on the final predicted reliability.
This slide presentation reviews some of the issues concerning the aging of the materials in a Composite Overwrapped Pressure Vessels (COPV). The basic composition of the COPV is a Boss, a composite overwrap, and a metallic liner. The lifetime of a COPV is affected by the age of the overwrap, the cyclic fatigue of the metallic liner, and stress rupture life, a sudden and catastrophic failure of the overwrap while holding at a stress level below the ultimate strength for an extended time. There is information about the coupon tests that were performed, and a test on a flight COPV.
A framework for the multiscale design and analysis of composite materials and structures is presented. The ImMAC software suite, developed at NASA Glenn Research Center, embeds efficient, nonlinear micromechanics capabilities within higher scale structural analysis methods such as finite element analysis. The result is an integrated, multiscale tool that relates global loading to the constituent scale, captures nonlinearities at this scale, and homogenizes local nonlinearities to predict their effects at the structural scale. Example applications of the multiscale framework are presented for the stochastic progressive failure of a SiC/Ti composite tensile specimen and the effects of microstructural variations on the nonlinear response of woven polymer matrix composites.
A framework for the multiscale design and analysis of composite materials and structures is presented. The ImMAC software suite, developed at NASA Glenn Research Center, embeds efficient, nonlinear micromechanics capabilities within higher scale structural analysis methods such as finite element analysis. The result is an integrated, multiscale tool that relates global loading to the constituent scale, captures nonlinearities at this scale, and homogenizes local nonlinearities to predict their effects at the structural scale. Example applications of the multiscale framework are presented for the stochastic progressive failure of a SiC/Ti composite tensile specimen and the effects of microstructural variations on the nonlinear response of woven polymer matrix composites.
Models that predict the failure probability of brittle materials under multiaxial loading have been developed by authors such as Batdorf, Evans, and Matsuo. These "unit-sphere" models assume that the strength-controlling flaws are randomly oriented, noninteracting planar microcracks of specified geometry but of variable size. This methodology has been extended to predict the multiaxial strength response of transversely isotropic brittle materials, including polymer matrix composites (PMCs), by considering (1) flaw-orientation anisotropy, whereby a preexisting microcrack has a higher likelihood of being oriented in one direction over another direction, and (2) critical strength, or K (sub Ic) orientation anisotropy, whereby the level of critical strength or fracture toughness for mode I crack propagation, K (sub Ic), changes with regard to the orientation of the microstructure. In this report, results from finite element analysis of a fiber-reinforced-matrix unit cell were used with the unit-sphere model to predict the biaxial strength response of a unidirectional PMC previously reported from the World-Wide Failure Exercise. Results for nuclear-grade graphite materials under biaxial loading are also shown for comparison. This effort was successful in predicting the multiaxial strength response for the chosen problems. Findings regarding stress-state interactions and failure modes also are provided.
Fire safety in the indoor spacecraft environment is concerned with a unique set of fuels which are designed to not combust. Unlike terrestrial flaming fires, which often can consume an abundance of wood, paper and cloth, spacecraft fires are expected to be generated from overheating electronics consisting of flame resistant materials. Therefore, NASA prioritizes fire characterization research for these fuels undergoing oxidative pyrolysis in order to improve spacecraft fire detector design. A thermal precipitator designed and built for spacecraft fire safety test campaigns at the NASA White Sands Test Facility (WSTF) successfully collected an abundance of smoke particles from oxidative pyrolysis. A thorough microscopic characterization has been performed for ten types of smoke from common spacecraft materials or mixed materials heated at multiple temperatures using the following techniques: SEM, TEM, high resolution TEM, high resolution STEM and EDS. Resulting smoke particle morphologies and elemental compositions have been observed which are consistent with known thermal decomposition mechanisms in the literature and chemical make-up of the spacecraft fuels. Some conclusions about particle formation mechanisms are explored based on images of the microstructure of Teflon smoke particles and tar ball-like particles from Nomex fabric smoke.
Magnetically aligned carbon nanoparticle composites have enhanced electrical properties. The composites comprise carbon nanoparticles, a host material, magnetically sensitive nanoparticles and a surfactant. In addition to enhanced electrical properties, the composites can have enhanced mechanical and thermal properties.
The radiation resistance of polymeric and composite materials to space radiation is currently based on irradiating materials with Co-60 gamma-radiation to the equivalent total ionizing dose (TID) expected during mission. This is an approximation since gamma-radiation is not truly representative of the particle species; namely, Solar Particle Event (SPE) protons and Galactic Cosmic Ray (GCR) nucleons, encountered in space. In general, the SPE and GCR particle energies are much higher than Co-60 gamma-ray photons, and since the particles have mass, there is a displacement effect due to nuclear collisions between the particle species and the target material. This effort specifically bridges the gap between estimated service lifetimes based on decades old Co-60 gamma-radiation data, and newer assessments of what the service lifetimes actually are based on irradiation with particle species that are more representative of the space radiation environment.
With the resurgence of human curiosity to explore planetary bodies beyond our own, comes the possibility of health risks associated with the materials covering the surface of these planetary bodies. In order to mitigate these health risks and prepare ourselves for the eventuality of sending humans to other planetary bodies, toxicological evaluations of extraterrestrial materials is imperative (Harrington et al. 2017). Given our close proximity, as well as our increased datasets from various missions (e.g., Apollo, Mars Exploration Rovers, Dawn, etc…), the three most likely candidates for initial human surface exploration are the Moon, Mars, and asteroid 4Vesta. Seven samples, including lunar mare basalt NWA 4734, lunar regolith breccia NWA 7611, martian basalt Tissint, martian regolith breccia NWA 7034, a vestian basalt Berthoud, a vestian regolith breccia NWA 2060, and a terrestrial mid-ocean ridge basalt, were examined for bulk chemistry, mineralogy, geochemical reactivity, and inflammatory potential. In this study, we have taken alliquots from these samples, both the fresh samples and those that underwent iron leaching (Tissint, NWA 7034, NWA 4734, MORB), and performed low pressure, high temperature melting experiments to determine the bulk composition of the materials that were previously examined.
The present invention relates to magnetically aligned carbon nanoparticle composites and methods of preparing the same. The composites comprise carbon nanoparticles, host material, magnetically sensitive nanoparticles and surfactant. The composites may have enhanced mechanical, thermal, and/or electrical properties.
A method for depositing III-V alloys on substrates and compositions therefrom. A first layer comprises a Group III element. A second layer comprises a silica. A substrate has a surface. The second layer is deposited onto a first layer. The depositing is performed by a sol-gel method. The second layer is exposed to a precursor that comprises a Group V element. At least one of the precursor or the Group V element diffuse through the silica. The first layer is transformed into a solid layer comprising a III-V alloy, wherein at least a portion of the first layer to a liquid. The silica retains the liquified first layer, enabling at least one of the precursor or the Group V element to diffuse into the liquid, resulting in the forming of the III-V alloy.
The present disclosure is directed to variable composition ceramics. Zr (2−x) Hf (x) WP 2 O 12 and Hf 2 WP 2 O 12 exhibit large negative thermal expansion that is linear over a large temperature range up to at least 900° C., These new ceramic material particles may be mixed with polymers to make a composite suitable for use in backsheets for photovoltaic modules or in other applications. The thermal expansion coefficient of the composite can be tailored to match that of the solar cell in order to reduce stress resulting from daily thermal cycling.
Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.
This project utilized a laser ultrasonic technique to systematically study temperature-induced evolution of material properties in a set of interrelated binary alloys and a high entropy alloy (HEA) fabricated using arc-melting and spark plasma sintering processes. This technique involved the use of a nanosecond duration, high-intensity pulsed laser to thermo-elastically generate ultrasonic waves that propagate in the bulk of the metal alloy. Sub-nanometer-scale displacements associated with the propagating bulk ultrasonic waves were detected along the epicenter on the opposite surface of the sample using a 1 GHz bandwidth photorefractive interferometer. Phase transformations and microstructural changes were inferred from the temperature-dependent trends in the bulk acoustic velocities and features in the ultrasonic epicentral waveforms measured in the binary alloys. These inferences were then correlated with electron/optical microscopy observations and predictions using calculations of phase diagrams (CALPHAD). The results showed that the laser-generated ultrasonic pulses were strongly influenced by changes in material microstructure and could accurately track thermally driven phase transformations and detect the presence of microscale heterogeneities (grain boundaries, dendritic structures, etc.) in the set of binary alloy samples. The measurement approach was then applied to a quinary HEA sample for estimating phase transition temperature and determining microstructural heterogeneity. The rapid, non-contact and non-destructive ultrasonic testing approach demonstrated here is amenable to high throughput combinatorial investigations that can be applied to graded composition HEAs produced using advanced manufacturing methods. When paired with atomistic simulations and CALPHAD modeling, this approach can overcome the bottlenecks faced by current material characterization methods in efficiently screening the vast discovery space of HEAs that spans over a hundred million unique quinary alloy compositions.
Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.