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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Long Term Degradation of Resin for High Temperature Composites

The durability of polymer matrix composites exposed to harsh environments is a major concern. Surface degradation and damage are observed in polyimide composites used in air at 125 to 300 C. It is believed that diffusion of oxygen into the material and oxidative chemical reactions in the matrix are responsible. Previous work has characterized and modeled diffusion behavior, and thermogravimetric analyses (TGAs) have been carried out in nitrogen, air, and oxygen to provide quantitative information on thermal and oxidative reactions. However, the model developed using these data was not able to capture behavior seen in isothermal tests, especially those of long duration. A test program that focuses on lower temperatures and makes use of isothermal tests was undertaken to achieve a better understanding of the degradation reactions under use conditions. A new low-cost technique was developed to collect chemical degradation data for isothermal tests lasting over 200 hr in the temperature range 125 to 300 C. Results indicate complex behavior not captured by the previous TGA tests, including the presence of weight-adding reactions. Weight gain reactions dominated in the 125 to 225 C temperature range, while weight loss reactions dominated beyond 225 C. The data obtained from isothermal tests was used to develop a new model of the material behavior. This model was able to fully capture the behavior seen in the tests up to 275 C. Correlation of the current model with both isothermal data at 300 C and high rate TGA test data is mediocre. At 300 C and above, the reaction mechanisms appear to change. Attempts (which failed) to measure non-oxidative degradation indicate that oxidative reactions dominate the degradation at low temperatures. Based on this work, long term isothermal testing in an oxidative atmosphere is recommended for studying the degradation behavior of this class of materials.

Patekar, Kaustubh A.↗
NASA NTRS2013

The Effect of Fiber Strength Stochastics and Local Fiber Volume Fraction on Multiscale Progressive Failure of Composites

Continuous fiber unidirectional polymer matrix composites (PMCs) can exhibit significant local variations in fiber volume fraction as a result of processing conditions that can lead to further local differences in material properties and failure behavior. In this work, the coupled effects of both local variations in fiber volume fraction and the empirically-based statistical distribution of fiber strengths on the predicted longitudinal modulus and local tensile strength of a unidirectional AS4 carbon fiber/ Hercules 3502 epoxy composite were investigated using the special purpose NASA Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC); local effective composite properties were obtained by homogenizing the material behavior over repeating units cells (RUCs). The predicted effective longitudinal modulus was relatively insensitive to small (~8%) variations in local fiber volume fraction. The composite tensile strength, however, was highly dependent on the local distribution in fiber strengths. The RUC-averaged constitutive response can be used to characterize lower length scale material behavior within a multiscale analysis framework that couples the NASA code FEAMAC and the ABAQUS finite element solver. Such an approach can be effectively used to analyze the progressive failure of PMC structures whose failure initiates at the RUC level. Consideration of the effect of local variations in constituent properties and morphologies on progressive failure of PMCs is a central aspect of the application of Integrated Computational Materials Engineering (ICME) principles for composite materials.

multiscale analysis↗
NASA NTRSDate not supplied

Stiffness and Fatigue Life Estimator for Polymer Composite Laminates Using Machine Learning

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been established. Results show that both neural net type algorithms can provide an excellent estimate of initial laminate stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminates (PMCs). RNNs are better able to capture the shape of the fatigue curve of a laminate. The resulting tool and GUI can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. Further, the associated surrogate models can also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make micromechanics-based multiscale analyses a viable industrial tool for large scale structural problems.

multiscale analysis↗
NASA NTRS2023

Stiffness and Fatigue Life Estimator for Polymer Composite Laminates Using Machine Learning

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been established. Results show that both neural net type algorithms can provide an excellent estimate of initial laminate stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminates (PMCs). RNNs are better able to capture the shape of the fatigue curve of a laminate. The resulting tool and GUI can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. Further, the associated surrogate models can also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make micromechanics-based multiscale analyses a viable industrial tool for large scale structural problems.

multiscale analysis↗
NASA NTRS2003

High-Frequency Testing of Composite Fan Vanes With Erosion-Resistant Coating Conducted

The mechanical integrity of hard, erosion-resistant coatings were tested using the Structural Dynamics Laboratory at the NASA Glenn Research Center. Under the guidance of Structural Mechanics and Dynamics Branch personnel, fixturing and test procedures were developed at Glenn to simulate engine vibratory conditions on coated polymer-matrix- composite bypass vanes using a slip table in the Structural Dynamics Laboratory. Results from the high-frequency mechanical bench testing, along with concurrent erosion testing of coupons and vanes, provided sufficient confidence to engine-endurance test similarly coated vane segments. The knowledge gained from this program will be applied to the development of oxidation- and erosion-resistant coatings for polymer matrix composite blades and vanes in future advanced turbine engines. Fan bypass vanes from the AE3007 (Rolls Royce America, Indianapolis, IN) gas turbine engine were coated by Engelhard (Windsor, CT) with compliant bond coatings and hard ceramic coatings. The coatings were developed collaboratively by Glenn and Allison Advanced Development Corporation (AADC)/Rolls Royce America through research sponsored by the High-Temperature Engine Materials Technology Project (HITEMP) and the Higher Operating Temperature Propulsion Components (HOTPC) project. High-cycle fatigue was performed through high-frequency vibratory testing on a shaker table. Vane resonant frequency modes were surveyed from 50 to 3000 Hz at input loads from 1g to 55g on both uncoated production vanes and vanes with the erosion-resistant coating. Vanes were instrumented with both lightweight accelerometers and strain gauges to establish resonance, mode shape, and strain amplitudes. Two high-frequency dwell conditions were chosen to excite two strain levels: one approaching the vane's maximum allowable design strain and another near the expected maximum strain during engine operation. Six specimens were tested per dwell condition. Pretest and posttest inspections were performed optically at up to 60 magnification and using a fluorescent-dye penetrant. Accumulation of 10 million cycles at a strain amplitude of two to three times that expected in the engine (approximately 670 Hz and 20g) led to the development of multiple cracks in the coating that were only detectable using fluorescent-dye penetrant inspection. Cracks were prevalent on the trailing edge and on the convex side of the midsection. No cracking or spalling was evident using standard optical inspection at up to 60 magnification. Further inspection may reveal whether these fine cracks penetrated the coating or were strictly on the surface. The dwell condition that simulated actual engine conditions produced no obvious surface flaws even after up to 80 million cycles had been accumulated at strain amplitudes produced at approximately 1500 Hz and 45g.

Bowman, Cheryl L.↗
NASA NTRS1998

Procedure Developed for Ballistic Impact Testing of Composite Fan Containment Concepts

The fan-containment system in a jet engine is designed to prevent a fan blade from penetrating the engine case in the event that the blade or a portion of the blade separates from the rotor during operation. Usually, these systems consist of a thick metal case that is strong enough to survive such an impact. Other systems consist of a dry aramid fabric wrapped around a relatively thin metal case. In large turbofan engines, metal-containment systems can weigh well over 300 kg, and there is a strong impetus to reduce their weight. As a result, the NASA Lewis Research Center is involved in an effort to develop polymer matrix composite (PMC) fan-containment systems to reduce the weight and cost while maintaining the high levels of safety associated with current systems. Under a Space Act Agreement with AlliedSignal Aircraft Engines, a new ballistic impact test procedure has been developed to quantitatively evaluate the performance of polymer matrix composite systems.

Pereira, J. Michael↗
NASA NTRS2003

Microstructure and Properties of Thermally Sprayed Functionally Graded Coatings for Polymeric Substrates

The use of polymer matrix composites (PMCs) in the gas flow path of advanced turbine engines offers significant benefits for aircraft engine performance but their useful lifetime is limited by their poor erosion resistance. High velocity oxy-fuel (HVOF) sprayed polymer/cermet functionally graded (FGM) coatings are being investigated as a method to address this technology gap by providing erosion and oxidation protection to polymer matrix composites. The FGM coating structures are based on a polyimide matrix filled with varying volume fractions of WC-Co. The graded coating architecture was produced using a combination of internal and external feedstock injection, via two computer-controlled powder feeders and controlled substrate preheating. Porosity, coating thickness and volume fraction of the WC-Co filler retained in the coatings were determined using standard metallographic techniques and computer image analysis. The pull-off strength (often referred to as the adhesive strength) of the coatings was evaluated according to the ASTM D 4541 standard test method, which measured the greatest normal tensile force that the coating could withstand. Adhesive/cohesive strengths were determined for three different types of coating structures and compared based on the maximum indicated load and the surface area loaded. The nature and locus of the fractures were characterized according to the percent of adhesive and/or cohesive failure, and the tested interfaces and layers involved were analyzed by Scanning Electron Microscopy.

Ivosevic, M.↗
NASA NTRS2019

Multifunctional Polymers and Composites for Aerospace Applications

System needs and challenges for commercial aviation and space exploration are constantly changing. Multifunctional materials are ideal for aerospace applications because their integration into structural components has the potential to not only improve efficiency, but it can increase payload and mission capabilities without the burden of significant additions to mass and volume. Multiconstituent materials will play an important role in enabling structures with the potential to demonstrate true multifunctionality; however, modifications to traditional polymer processing methods may be necessary. This presentation will cover multifunctional materials development efforts underway at the NASA Glenn Research Center pertaining to fabricating novel electrical insulation to advance electric aircraft propulsion, impact resistant and electrically conductive textiles for space, and hybrid reinforcement for toughened polymer matrix composites.

Textiles↗
NASA NTRS1991

Effects of LDEF flight exposure on selected polymer matrix resin composite materials

The characterization of selected graphite fiber reinforced epoxy and polysulfone matrix resin composites which received exposure to the LEO environment on the LDEF is reported. The changes in mechanical properties of ultimate tensile strength and tensile modulus for exposed flight specimens are compared to the three sets of control specimens. Marked changes in surface appearance are discussed, and resin loss is reported. The chemical characterization including IR, thermal, and selected solution property measurements showed that the molecular structure of the polymeric matrix had not changed significantly in response to this exposure.

Slemp, Wayne S.↗
NASA NTRS1992

Effects of LDEF flight exposure on selected polymer matrix resin composite materials

The characterization of selected graphite fiber reinforced epoxy (934 and 5208) and polysulfone (P1700) matrix resin composites materials which received over five years and nine months of exposure to the low earth orbit (LEO) environment in experiment AO134 on the Long Duration Exposure Facility is reported. The changes in mechanical properties of ultimate tensile strength and tensile modulus for exposed flight specimens are compared to the three sets of control specimens. Marked changes in surface appearance are discussed, and resin loss is reported. The chemical characterization including infrared, thermal, and selected solution property measurements showed that the molecular structure of the polymetric matrix had not changed significantly in response to this exposure.

Slemp, Wayne S.↗
NASA NTRS1991

Characterization of selected LDEF polymer matrix resin composite materials

The characterization of selected graphite fiber reinforced epoxy (934 and 5208) and polysulfone (P1700) matrix resin composite materials which received 5 years and 10 months of exposure to the LEO environment on the Long Duration Exposure Facility is reported. Resin loss and a decrease in mechanical performance as well as dramatic visual effects were observed. However, chemical characterization including infrared, thermal, and selected solution property measurements showed that the molecular structure of the polymeric matrix had not changed significantly in response to this exposure. The potential effect of a silicon-containing molecular contamination of these specimens is addressed.

Young, Philip R.↗
NASA NTRS1992

Overview of NASA's advanced high temperature engine materials technology program

NASA's 'HITEMP' program has been charged with development of propulsion systems technologies for next-generation civil and military aircraft, stressing high-temperature/low-density composites. These encompass polymer-matrix composites for fans, ducts, and compressor cases, and intermetallic and metallic alloy matrix composites for applications in turbine disks, blades, and vanes, and ceramic matrix composites for combustors and turbines. An overview is presented of program concerns and achievements to date.

Ginty, Carol A.↗
NASA NTRS2004

Plate Deformation Behavior of Polymer Matrix Composite-Ti Honeycomb-Metal Sandwiches for Pressurized Propulsion Component Applications

THe objectives are: 1. To experimentally validate bi-axial plate flexural performance of PMC-Ti H/C-A286 sandwich panels for the internally pressurized RBCC combustion chamber support structure. 2. To explore ASTM 2-D plate flexure test (D 6416) to simulate the internal pressure loading and to correlate the results with analytical and FE modeling based on 2-D flexure properties.

Bertelsen, William D.↗
NASA NTRS2010

Micromechanics Modeling of Composites Subjected to Multiaxial Progressive Damage in the Constituents

The high-fidelity generalized method of cells composite micromechanics model is extended to include constituent-scale progressive damage via a proposed damage model. The damage model assumes that all material nonlinearity is due to damage in the form of reduced stiffness, and it uses six scalar damage variables (three for tension and three for compression) to track the damage. Damage strains are introduced that account for interaction among the strain components and that also allow the development of the damage evolution equations based on the constituent material uniaxial stress strain response. Local final-failure criteria are also proposed based on mode-specific strain energy release rates and total dissipated strain energy. The coupled micromechanics-damage model described herein is applied to a unidirectional E-glass/epoxy composite and a proprietary polymer matrix composite. Results illustrate the capability of the coupled model to capture the vastly different character of the monolithic (neat) resin matrix and the composite in response to far-field tension, compression, and shear loading.

Bednarcyk, Brett A.↗
NASA NTRS2021

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the-art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts. The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners. However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen Michael Middleton↗
NASA NTRSDate not supplied

Machine Learning to Predict Joint Performance in Epoxy Composites Based on Process Parameters

Polymer matrix composites are gaining popularity in the aerospace industry due to their high specific strength, fatigue properties, and processability. However, based on current FAA certification guidelines, manufacturers utilizing current state-of-the art composites made with adhesive bonds commonly install redundant fasteners to guarantee the strength of these adhesively bonded composite parts.1,2 The number of fasteners in a single-aisle commercial transport aircraft is typically on the order of 105, which reduces manufacturing rate, increases cost tremendously, and reduces the advantage of the specific strength composites provide. Due to this, the Adhesive Free Bonding of Composites (AERoBOND) project at NASA Langley Research Center has developed a novel assembly process to manufacture complex composite parts without the use of adhesives and fasteners.1 However, optimization of the process is currently challenging due to the complex and interdependent process parameters. To assist with the optimization, four machine learning algorithms utilizing gradient boosting decision trees were created to provide predictions for the mechanical and characterization properties of the composite parts. Approximately 200 random states from each algorithm were tested, and the models from each state were isolated and analyzed based on their accuracy, a validation process, and their feature importance. This analysis concluded that the models created from the machine learning algorithms could accelerate a parametric study for the AERoBOND process by rapidly optimizing process parameters to achieve desired performance characteristics.

Brennen M Middleton↗