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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

A mechanical property and stress corrosion evaluation of 431 stainless steel alloy

The mechanical properties of type 431 stainless steel in two conditions: annealed bar and hardened and tempered bar are presented. Test specimens, manufactured from approximately 1.0 inch (2.54 cm) diameter bar stock, were tested at temperatures of 80 F (+26.7 C), 0 F (-17.8 C), -100 F (-73 C), and -200 F (-129 C). The test data indicated excellent tensile strength, notched/unnotched tensile ratio, ductility, shear, and impact properties at all testing temperatures. Results of the alternate immersion stress corrosion tests on stressed and unstressed longitudinal tensile specimens 0.1250 inch (0.3175 cm) diameter and transverse C-ring specimens, machined from 1.0 inch (2.54 cm) diameter bar stock, indicated that the material is not susceptible to stress corrosion cracking when tested in a 3.5 percent NaCl solution for 180 days.

Montano, J. W.↗
NASA NTRS1990

One- and two-dimensional Stirling machine simulation using experimentally generated flow turbulence models

Investigations of one- and two-dimensional (1- or 2-D) simulations of Stirling machines centered around experimental data generated by the U. of Minnesota Mechanical Engineering Test Rig (METR) are covered. This rig was used to investigate oscillating flows about a zero mean with emphasis on laminar/turbulent flow transitions in tubes. The Space Power Demonstrator Engine (SPDE) and in particular, its heater, were the subjects of the simulations. The heater was treated as a 1- or 2-D entity in an otherwise 1-D system. The 2-D flow effects impacted the transient flow predictions in the heater itself but did not have a major impact on overall system performance. Information propagation effects may be a significant issue in the simulation (if not the performance) of high-frequency, high-pressure Stirling machines. This was investigated further by comparing a simulation against an experimentally validated analytic solution for the fluid dynamics of a transmission line. The applicability of the pressure-linking algorithm for compressible flows may be limited by characteristic number (defined as flow path information traverses per cycle); this warrants further study. Lastly the METR was simulated in 1- and 2-D. A two-parameter k-w foldback function turbulence model was developed and tested against a limited set of METR experimental data.

Goldberg, Louis F.↗
NASA NTRS2011

Man-Machine Integration Design and Analysis System (MIDAS) v5: Augmentations, Motivations, and Directions for Aeronautics Applications

As automation and advanced technologies are introduced into transport systems ranging from the Next Generation Air Transportation System termed NextGen, to the advanced surface transportation systems as exemplified by the Intelligent Transportations Systems, to future systems designed for space exploration, there is an increased need to validly predict how the future systems will be vulnerable to error given the demands imposed by the assistive technologies. One formalized approach to study the impact of assistive technologies on the human operator in a safe and non-obtrusive manner is through the use of human performance models (HPMs). HPMs play an integral role when complex human-system designs are proposed, developed, and tested. One HPM tool termed the Man-machine Integration Design and Analysis System (MIDAS) is a NASA Ames Research Center HPM software tool that has been applied to predict human-system performance in various domains since 1986. MIDAS is a dynamic, integrated HPM and simulation environment that facilitates the design, visualization, and computational evaluation of complex man-machine system concepts in simulated operational environments. The paper will discuss a range of aviation specific applications including an approach used to model human error for NASA s Aviation Safety Program, and what-if analyses to evaluate flight deck technologies for NextGen operations. This chapter will culminate by raising two challenges for the field of predictive HPMs for complex human-system designs that evaluate assistive technologies: that of (1) model transparency and (2) model validation.

Gore, Brian F.↗
DOE OSTI2023

Polarized fusion and potential in situ tests of fuel polarization survival in a tokamak plasma

Abstract The use of spin-polarized fusion fuels would provide a significant boost towards the ignition of a burning plasma. The cross section for D + T → α + n, would be increased by 1.5 if the fuels were injected with parallel polarization. Furthermore, our simulations demonstrate additional non-linear power gains in large-scale machines such as ITER, due to increased alpha heating. Such benefits require the survival of spin polarizations for periods comparable to the particle confinement time. During the 1980s, calculations predicted that polarizations could survive a plasma environment, although concerns persisted regarding the cumulative impacts of wall recycling. In that era, technical challenges prevented direct tests and left the large scale fueling of a power reactor beyond reach. Over the last decades, this situation has changed dramatically. Detailed simulations of ITER have predicted negligible wall recycling in a high-power reactor, and recent advances in laser-driven sources project the capability of producing large quantities of ∼100% polarized D and T. The remaining crucial step is an in-situ demonstration of polarization survival in a plasma. For this, we outline a measurement strategy using the isospin-mirror reaction, D + 3 He → α + p. Polarized 3 He avoids the complexities of handling tritium, while encompassing the same spin-physics. We evaluate two methods of delivering deuterium, using dynamically polarized Lithium-Deuteride (with vector polarization P V D of 70%) or frozen-spin Hydrogen-Deuteride (with P V D of 40%), together with a method of injecting optically-pumped 3 He (with 65% polarization). Pellets of these materials all have long polarization decay times (∼6 min for LiD at 2 K, ∼2 months for HD at 2 K, and ∼3 d for 3 He at 77 K), all far greater than a plasma shot in a research tokamak such as DIII-D (∼20 s). Both species can be propelled from a single cryogenic injection gun. We review plasma requirements and strategies for detecting polarization survival. Polarization alters both fusion yields and the angular distribution of fusion products, and each of these provides a potential signal. In this paper we simulate a selection of shots with similar characteristics in a future high-T ion H plasma, and find ratios of yields from shots with fuel spins parallel and antiparallel reaching 1.3 (HD + 3 He) to 1.6 (LiD + 3 He) over a wide range of poloidal angles. (A companion paper finds sensitivity to fusion product angular distributions as reflected in the pitch angles of protons and alphas reaching the plasma facing wall.)

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Dynamic Interaction of Grid-Forming and Grid-Following Inverters with Synchronous Generators in Hybrid Power Plants

As the integration of renewable energy resources such as wind and solar generation continues to grow, the use of hybrid power plants with both synchronous machines and power electronic inverters operating in parallel becomes more common. This paper studies the impact on small-signal stability for these pairs of devices, first as an isolated two-device system and then as part of a 9-bus test system. Each operational scenario was studied with both a grid-following inverter and a grid-forming inverter. In each scenario, the state variables associated with the synchronous generator's excitation system were found to be responsible for the inception of instability. These results underscore the importance of accurate modeling of excitation systems when modeling power systems with hybrid power plants.

hybrid power plant↗
DOE OSTI2025

Surrogate-driven Variance-based Sensitivity Analysis of Thermal Storage Tanks in Integrated Energy Systems

Sensitivity analysis and uncertainty quantification are essential steps for enhancing the accuracy of computational models by identifying and mitigating uncertainties. This study focuses on these steps for the Thermal Energy Delivery System at Idaho National Laboratory, specifically targeting the thermocline tank. Using a Modelica/Dymola simulation model, the study perturbed various design parameters and boundary conditions, including shape factor, porosity, outlet temperature, inlet mass flow rate, and system pressure, to predict and quantify uncertainty in the tank’s ax- ial temperature. A dataset of over 1,000 simulations was generated, and surrogate models were developed using the pyMAISE (Michigan Artificial Intelligence Standard Environment) library, which is an Automatic Machine Learning library for nuclear engineering applications. The optimal model, a feedforward neural network with two hidden layers, achieved an R2 score above 0.99 and a mean absolute error below 1 Kelvin. Sensitivity analyses using Sobol indices and Fourier amplitude sensitivity testing methods on this surrogate model revealed that the inlet mass flow rate at initial timestamps and porosity significantly impacts predicted temperatures across all sensors and time steps.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRSDate not supplied

Recent NASA Research into the Psychoacoustics of Urban Air Mobility Vehicles

NASA maintains multiple psychoacoustic laboratories across the United States. One focus of research in these facilities is to investigate the human reaction to the noise of notional UAM vehicles. This effort will look for answers to several questions: What does a “good” UAM sound like? What might be the impact on existing urban soundscapes? What will people think when there are an [economically viable] number of these machines operating? Although this effort is ongoing, a number of advances have already been made: A psychoacoustic test to guide UAM sound design has been recently completed. Several algorithms to assess the audibility of a UAM vehicle in an existing background soundscape have been put forward. A technical memorandum detailing best practices for recording and reproduction of background sounds for UAM psychoacoustic research has been published (TM 20210017504). Flexible data analysis methods that could be agile to changes in perception that occur when large numbers of vehicles are flying have been developed by extending existing metrics. This presentation gives an overview of this completed and continuing research.

UAM↗
NASA NTRS2004

Durability Assessment of Gamma Tial

Gamma TiAl was evaluated as a candidate alloy for low-pressure turbine blades in aeroengines. The durability of g-TiAl was studied by examining the effects of impact or fretting on its fatigue strength. Cast-to-size Ti-48Al-2Cr-2Nb was studied in impact testing with different size projectiles at various impact energies as the reference alloy and subsequently fatigue tested. Impacting degraded the residual fatigue life. However, under the ballistic impact conditions studied, it was concluded that the impacts expected in an aeroengine would not result in catastrophic damage, nor would the damage be severe enough to result in a fatigue failure under the anticipated design loads. In addition, other gamma alloys were investigated including another cast-to-size alloy, several cast and machined specimens, and a forged alloy. Within this Ti-48-2-2 family of alloys aluminum content was also varied. The cracking patterns as a result of impacting were documented and correlated with impact variables. The cracking type and severity was reasonably predicted using finite element models. Mean stress affects were also studied on impact-damaged fatigue samples. The fatigue strength was accurately predicted based on the flaw size using a threshold-based, fracture mechanics approach. To study the effects of wear due to potential applications in a blade-disk dovetail arrangement, the machined Ti-47-2-2 alloy was fretted against In-718 using pin-on-disk experiments. Wear mechanisms were documented and compared to those of Ti-6Al-4V. A few fatigue samples were also fretted and subsequently fatigue tested. It was found that under the conditions studied, the fretting was not severe enough to affect the fatigue strength of g-TiAl.

Draper, Susan L.↗
NASA NTRS1985

Grain refinement of high strength steels to improve cryogenic toughness

Grain-refining techniques using multistep heat treatments to reduce the grain size of five commercial high-strength steels were investigated. The goal of this investigation was to improve the low-temperature toughness as measured by Charpy V-notch impact test without a significant loss in tensile strength. The grain size of four of five alloys investigated was successfully reduced up to 1/10 of original size or smaller with increases in Charpy impact energy of 50 to 180 percent at -320 F. Tensile properties were reduced from 0 to 25 percent for the various alloys tested. An unexpected but highly beneficial side effect from grain refining was improved machinability.

Rush, H. F.↗
DOE OSTI2021

A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems

This article proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models

The environmental impacts of global warming driven by methane (CH 4 ) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH 4 . Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH 4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH 4 as a classification problem and (ii) predict the intensity of CH 4 as a regression problem. The classification model performance for CH 4 detection was evaluated using accuracy, F1 score, Matthew’s Correlation Coefficient (MCC), and the area under the receiver operating characteristic curve (AUC ROC), with the top-performing model being 97.2%, 0.972, 0.945 and 0.995, respectively. The R 2 score was used to evaluate the regression model performance for CH 4 intensity prediction, with the R 2 score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH 4 detection and intensity prediction can be used with fixed environmental sensors deployed on the ground or with sensors mounted on unmanned aerial vehicles (UAVs) for mobile detection.

Majumder, Reek↗
DOE OSTI2024

Sub-pilot-scale Production of High-Value Products from U.S. Coals

Investigators from the University of Utah, University of Wyoming and Marshall University pursued a program to study the conversion of raw coal to high-value products of carbon fiber and silicon carbide. Team members also developed an initial framework for a data portal that can incorporate laboratory data on coal processing and product quality, and also work with tools for machine learning for data analysis, data visualization and economic assessment. Experimental R&D efforts focused on the conversion of raw coal to coal tar and other byproducts, and the resulting tar intermediates were upgraded to form anisotropic and isotropic pitch materials. These pitch materials were produced from coal using both thermal (pyrolysis) and chemical (mild solvolysis liquefaction) decomposition of raw coal. Four different coals were studied: Utah bituminous coal (Sufco), Wyoming PRB coal (Black Thunder), Illinois bituminous coal (Illinois #6), and West Virginia bituminous coal (Flying Eagle). Both metallurgical-grade coking coals and lower-grade steam coals were investigated, and controlled secondary gas-phase reactions were used during a two-stage pyrolysis process to induce cracking and condensation reactions among the pyrolytic tar species. This approach successfully improved the performance of the lower grade coals for yielding pitch materials, with properties more consistent with a commercial-grade pitch that had previously demonstrated success for quality carbon fiber production. The use of waste plastic materials was also studied, to help improve physical and chemical characteristics of the intermediate tars and final pitch product; in particular, for lowering the pitch softening point to an acceptable level for melt spinning carbon fiber. Mild solvolysis liquefaction was also used as a method for producing pitch for carbon fiber production. As expected, significantly higher pitch yields were obtained using this approach, and waste plastic materials were also successfully used to reduce pitch softening point to an acceptable level. The plastic materials were also utilized to create a solvent for the mild solvolysis process, and this plastic-derived solvent was shown to provide results consistent with more expensive commercial chemical solvents, and could thus avoid the need for costly recovery and recycle of a liquefaction solvent. Additional experimental R&D focused on the production of silicon carbide (β-SiC) from the residual char byproduct from pitch production, and also on the production of carbon fiber from the anisotropic pitch. SiC was successfully synthesized using a mixture of residual char and sandstone at a ratio of 1:1. Reaction temperature and residence time were optimized and yielded a product purity of 81%. For carbon fiber production, the most successful pitch samples were obtained from the mild solvolysis liquefaction approach, combined with the use of a plastic (HDPE)-derived solvent. Fiber properties improved over time as laboratory fiber production methodologies improved, and final yields of carbon fiber were obtained with a diameter of 12.14 ± 1.10 um, Modulus of 173.73 ± 15.25 GPa, and Tensile Strength of 1.04 ± 0.10 GPa. A proof-of-concept Modern Community Research Data Portal (MCRDP) was developed and deployed for coal and coal-derived pitch characterization, with the full support of (i) remote web-based access, (ii) distributed analysis, (iii) interactive visualization and exploration, (iv) shared and long-term data access, (v) advanced query capabilities and (vi) real-time collaboration. The Coal to Products Data Portal “coaltoproducts.org” provides researchers with space to store and share data within a project, tools for analyzing and understanding data for scientific investigation, and the ability to publish data to the broader community for reproducibility. The portal leverages the Material Commons 2.0 (MC) platform developed by the Center for PRedictive Integrated Structural Materials Science (PRISMS) of the University of Michigan, to achieve long-term longevity of data collections and, more importantly, collaborative science. A number of data visualization tools were also assessed and implemented for interrogating the experimental and modeling data. The machine learning portion of this project analyzed datasets from two different coal conversion processes performed on a diverse set of coal samples from both the coal pyrolysis experiments and the solvent liquefaction experiments. The work was initiated by exploring standard regression models on the pyrolysis data, aiming to understand the impact of sample characteristics and processing conditions on key product metrics. Over the course of the project, the focus expanded to include a variety of machine learning tools, delving into both supervised and unsupervised learning methods. Models tested on the pyrolysis data included linear, ridge, lasso, elastic-net, Gaussian process, random forest regression, and AutoSklearn, and the approach was continually refined to enhance predictive accuracy and model interpretability. Similar techniques were applied to the liquefaction data with an additional focus on feature engineering. Along with mesophase content, additional outputs of interest were the pitch yield, softening point, and QI content. Insights derived from these analyses are crucial in determining the factors influencing the quality and yield of coal-derived products. As the work progressed, the research evolved from foundational model comparisons to analyses of random forests, decision paths, and feature importance scores. A thorough market analysis was performed to examine the prospects of coal-based carbon fibers. The best opportunities for coal come from its lower and more stable price relative to petroleum, particularly for subbituminous coals, which is the primary advantage that a coal refinery may have over a petroleum refinery. Before a commercial CTP production facility can be modeled, however, several things need to be understood regarding the nature of the would-be coal refinery. These include the technology to be deployed, the size of facility, the volume(s) of co-product(s), and the waste and emissions profile of the plant. The volume of co-products and waste may be substantial and will require separate market analysis to ensure viability. In the near-term, the importance of coal tar pitch, in the form of carbon pitch, to the aluminum and steel industries is likely to overshadow the alternative use of this material as an input for carbon fiber. The importance of steel and aluminum in building materials, and the need for carbon materials in their manufacturing, will ensure that demand for these products remains for the long run. In addition, carbon fiber may also be the best substitute for steel and aluminum well into the future. While society will eventually be able to shift production of much of its electricity needs to renewables, it will not be able to shift away from fossil fuels for production of high-strength construction and vehicular materials. Demand for carbon fiber is expected to increase quickly, but the volume of carbon fiber and the amount of coal that would be needed to produce even a sizeable share of this market may still be relatively small compared to current coal production. Thus, other coal-based products like graphene, graphite, carbon foams, resins, and carbon-based building products will play important roles in sustaining coal production as coal-fired power generation continues to decline.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2024

Additively Manufactured Plastic Plasma Spectrometer

We report results in development and testing of a low resource top hat electrostatic analyzer (ESA) for space plasma measurements. This device has been additively manufactured (3D-printed) using fused deposition modeling. The classic top hat design is composed of four plastic pieces, without any surface coatings. The three conducting electrodes are printed from carbon nanotube infused polyether ether ketone (CNT-PEEK). The fourth piece, an insulating electrode support, uses pure PEEK. This ESA is designed to detect electrons in space from 10eV up to 30 keV. We demonstrate that the printed CNT-PEEK is sufficiently electrically conductive to support the fast high voltage slewing often required for high time resolution measurements. The plastic ESA has been successfully vibrated beyond standard pre-flight levels, tested under keV electron beam illumination over a wide range of temperatures, and tested under UV illumination, simulating the solar Ly-α flux. In comparison with an identical machined aluminum ESA, our CNT-PEEK ESA provides nominal energy/angle band passes, closely matching simulation. These band passes imply minimal impact from surface charging at beam energies of 2-3 keV, although more investigation is needed. We also find that the CNT-PEEK ESA provides far superior out-of-band electron rejection and UV photon rejection compared to the machined aluminum ESA. We do not detect any problems with trapped gases or outgassing. This development offers potential for significant mass savings, implementation of otherwise unattainable geometric configurations, and dramatic simplification in manufacturing and assembly processes required for development of space plasma instruments.

Quetzal Larrick↗
NASA NTRS2004

Characterization of Composite Fan Case Resins

The majority of commercial turbine engines that power today s aircraft use a large fan driven by the engine core to generate thrust which dramatically increases the engine s efficiency. However, if one of these fan blades fails during flight, it becomes high energy shrapnel, potentially impacting the engine or puncturing the aircraft itself and thus risking the lives of passengers. To solve this problem, the fan case must be capable of containing a fan blade should it break off during flight. Currently, all commercial fan cases are made of either just a thick metal barrier or a thinner metal wall surrounded by Kevlar-an ultra strong fiber that elastically catches the blade. My summer 2004 project was to characterize the resins for a composite fan case that will be lighter and more efficient than the current metal. The composite fan case is created by braiding carbon fibers and injecting a polymer resin into the braid. The resin holds the fibers together, so at first using the strongest polymer appears to logically lead to the strongest fan case. Unfortunately, the stronger polymers are too viscous when melted. This makes the manufacturing process more difficult because the polymer does not flow as freely through the braid, and the final product is less dense. With all of this in mind, it is important to remember that the strength of the polymer is still imperative; the case must still contain blades with high impact energy. The research identified which polymer had the right balance of properties, including ease of fabrication, toughness, and ability to transfer the load to the carbon fibers. Resin deformation was studied to better understand the composite response during high speed impact. My role in this research was the testing of polymers using dynamic mechanical analysis and tensile, compression, and torsion testing. Dynamic mechanical analysis examines the response of materials under cyclic loading. Two techniques were used for dynamic mechanical analysis. The ARES Instrument analyzed the material through torsion. The second machine, TA Instruments apparatus, applied a bending force to the specimen. These experiments were used to explore the effects of temperature and strain rate on the stiffness and strength of the resins. The two different types of loading allowed us to verify our results. An axial-torsional load frame, manufactured by MTS Systems, Inc., was used to conduct the tensile, compression, and torsional testing. These tests were used to determine the stress-strain curves for the resins. The elastic and plastic deformation data was provided to another team member for characterization of high fidelity material property predictions. This information was useful in having a better understanding of the polymers so that the fan cases could be as sturdy as possible. Deformation studies are the foundation for the computational modeling that provides the structural design of a composite engine case as well as detailed analysis of the blade impact event.

Dvoracek, Charlene M.↗
NASA NTRS1992

Manufacture and performance of carbon/epoxy 3-D woven composites

This paper evaluates 3-D orthogonal woven carbon/epoxy composites. Preforms were manufactured on an automatic 3-D weaving machine developed at N.C. State University. Matrix infiltration was conducted at MBB Central Laboratories. Testing was carried out at both locations and the joint results will be reported. The properties investigated include: interlaminar shear strength, compression, compression after impact, bending, tensile and penetration resistance. The 3-D orthogonal woven composites were compared with laminated and other 3-D composites made with preforms having interlock structure. C-scans were used to examine the quality of infiltration and the damage area after impact. The performance of the composites made from the 3-D orthogonal preforms showed superior properties compared to the other composites. The penetration resistance test showed unexpectedly very good performance.

Brandt, J.↗
DOE OSTI2026

Diagnostics, Prognostics, and Optimization for Lithium-Ion Battery Systems

Health management of lithium-ion battery systems presents a host of challenges due to their complex physics, large numbers of components, and a wide variety of degradation behaviors across different battery types. Dr. Paul Gasper will present on research from the Electrochemical Energy Storage Group on Lithium-ion battery diagnostics, prognostics, and optimization. Diagnostics research, including state-estimation via machine-learning from electrochemical impedance spectroscopy and DC pulses as well as continuous state-estimation via Kalman filters, will highlight the ongoing challenges for accurately measuring the state of batteries without performing time-consuming characterization tests. NLR's industry-recognized battery prognostics work, which predicts real-world battery degradation by identifying degradation rate models from accelerated aging data using statistical modeling and machine-learning, will be used to demonstrate the critical impact of battery controls, thermal management, and operating strategy on durability and lifetime. Finally, the use of prognostic models for financial or lifetime optimization will be discussed.

25 ENERGY STORAGE↗
NASA NTRS1997

Compression Response of a Sandwich Fuselage Keel Panel With and Without Damage

Results are presented from an experimental and analytical study of a sandwich fuselage keel panel with and without damage. The fuselage keel panel is constructed of graphite-epoxy skins bonded to a honeycomb core, and is representative of a highly loaded fuselage keel structure. The face sheets of the panel contain several terminated or dropped plies along the length of the panel. The results presented provide a better understanding of the load distribution in damaged and undamaged thick-face-sheet composite sandwich structure with dropped plies and of the failure mechanisms of such structure in the presence of low-speed impact damage and discrete-source damage. The impact-damage condition studied corresponds to barely visible impact damage (BVID), and the discrete-source damage condition studied is a notch machined through both face sheets. Results are presented from an impact-damage screening study conducted on another panel of the same design to determine the impact energy necessary to inflict BVID on the panel. Results are presented from compression tests of the panel in three conditions: undamaged; BVID in two locations; and BVID in two locations and a notch through both face sheets. Surface strains in the face sheets of the undamaged panel and the notched panel obtained experimentally are compared with finite element analysis results. The experimental and analytical results suggest that for the damage conditions studied, discrete-source damage influences the structural performance more than BVID.

McGowan, David M.↗