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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Image-based time series analysis to establish differential disease progression for two Fusarium head blight pathogens in oat spikelets with variable resistance

Oat-based value-added products have increased their value as healthy foodstuff. Fusarium head blight (FHB) infections and the mycotoxins accumulated to the oat seeds, however, pose a challenge to oat production. The FHB infections are predicted to become more prevalent in the future changing climates and under more limited use of fungicides. Both these factors increase the pressure for breeding new resistant cultivars. Until now, however, genetic links in oats against FHB infection have been difficult to identify. Therefore, there is a great need for more effective breeding efforts, including improved phenotyping methods allowing time series analysis and the identification of molecular markers during disease progression. To these ends, dissected spikelets of several oat genotypes with different resistance profiles were studied by image-based methods during disease progression by Fusarium culmorum or F. langsethiae species. The chlorophyll fluorescence of each pixel in the spikelets was recorded after inoculation by the two Fusarium spp., and the progression of the infections was analyzed by calculating the mean maximum quantum yield of PSII (F v /F m ) values for each spikelet. The recorded values were (i) the change in the photosynthetically active area of the spikelet as percentage of its initial size, and (ii) the mean of F v /F m values of all fluorescent pixels per spikelet post inoculation, both indicative of the progression of the FHB disease. The disease progression was successfully monitored, and different stages of the infection could be defined along the time series. The data also confirmed the differential rate of disease progression by the two FHB causal agents. In addition, oat varieties with variable responses to the infections were indicated.

oats↗
NASA NTRS1962

Studies in the Fields of Space Flight and Guidance Theory: Progress Report - No. 2

Compiled in this paper are 11 progress papers from 7 of the agencies working under contract to MSFC in the areas of guidance theory and space flight theory. This is the second paper of the "Progress Reports" and covers the period from December 1, 1961 to June 15, 1962. Extensive references are made to "Progress Report No. 1." This second progress report is referred to as "report" and "Progress Report No. 1" will be referred to as the "first report" in this introduction. Information given in the first report is not repeated herein. The reports of the various contractors will be referred to by index number as papers. There are two parallel series of publications covering the over-all activities at MSFC in the areas of guidance theory and space flight theory. One is the series of progress reports of which this paper is the second in the series. The other is the series of "Status Reports on Theory of Space Flight and Adaptive Guidance." These series along with a few other special reports, give a complete picture of the immediate objectives, accomplishments, and final goals of Aeroballistics Division and associated contr

FLIGHT OPTIMIZATION↗
NASA NTRS1994

Characterization of Damage Progression in SCS-6/timetal 21S (0)4 Under Thermomechanical Fatigue Loadings

A detailed experimental investigation was performed at a single maximum cyclic stress (sigma max) level to physically characterize the progression of thermomechanical fatigue (lW) damage in continuously reinforced (0 deg) SCS-6/Timetal 21S, a titanium matrix composite. In-phase (IP) and out of-phase (OP) loadings were investigated at sigma max = 1000 MPa with a temperature cycle from 150 to 6500 C. Damage progression, in terms of macroscopic property degradation, was experimentally quantified through an advanced TMF test methodology which incorporates explicit measurements of the isothermal static moduli at the TMF temperature extremes and the coefficient of thermal expansion (CTE) as functions of the TMF cycles. Detailed characterization of the physical damage progression at the microstructural level was performed by interrupting multiple TMF tests at various stages of mechanical property degradation and analyzing the microstructure through extensive destructive metallography. Further, the extent of damage was also quantified through residual static strength measurements. Results indicated that damage initiation occurred very early in cyclic life (N less than 0.1Nf) for both the IP and OP TMF loadings. IP TMF damage was found to be dominated by fiber breakage with a physical damage progression in the microstructure which was difficult to quantify. OP TMF loadings produced matrix cracking exclusively associated with surface initiations. Here, damage progression was easily distinguished in terms of both the number of cracks and their relative inward progressions toward the outer fiber rows with increased cycling. The point at which the leading cracks reached the outer fiber rows (when localized fiber/matrix de-bonding and matrix crack bridging occurred) appeared to be reflected in the macroscopic property degradation curves.

Castelli, Michael G.↗
NASA NTRS2008

Factors Influencing Progressive Failure Analysis Predictions for Laminated Composite Structure

Progressive failure material modeling methods used for structural analysis including failure initiation and material degradation are presented. Different failure initiation criteria and material degradation models are described that define progressive failure formulations. These progressive failure formulations are implemented in a user-defined material model for use with a nonlinear finite element analysis tool. The failure initiation criteria include the maximum stress criteria, maximum strain criteria, the Tsai-Wu failure polynomial, and the Hashin criteria. The material degradation model is based on the ply-discounting approach where the local material constitutive coefficients are degraded. Applications and extensions of the progressive failure analysis material model address two-dimensional plate and shell finite elements and three-dimensional solid finite elements. Implementation details are described in the present paper. Parametric studies for laminated composite structures are discussed to illustrate the features of the progressive failure modeling methods that have been implemented and to demonstrate their influence on progressive failure analysis predictions.

Knight, Norman F., Jr.↗
DOE OSTI2023

Effects of unburnt reaction progress on stretch flame dynamics under elevated temperatures

Flames in practical combustors and engines are inevitably subject to the effects of upstream chemical reaction progress and stretch, induced by elevated thermodynamic conditions and flow non-uniformities, respectively. Recent shock tube experiments and simulation studies on flame propagation have shown that flame speed under engine-relevant conditions can be enhanced with non-negligible upstream chemical reaction progress, especially when low-temperature heat release is involved in the unburnt mixture. On the other hand, depending on the mixture equivalence ratio and diluents, nonequidiffusion (including the non-unity Lewis number effect and the preferential diffusion effect) can couple with the flame stretch to fundamentally affect the flame propagation, which can manifest as either facilitation or suppression. Depending on the transport property of a reacting mixture, there hence can be either inhibition or promotion from unburnt reaction progress and stretch effects on flame propagation. In the current work, through one-dimensional numerical simulations of transient planar and spherical flames of n-heptane/air under elevated thermodynamic conditions, the combined effects of upstream chemical reaction progress and stretch on flame propagation are investigated. Results show that for both lean and rich n-heptane/air mixtures, flame speed can be substantially promoted with reaction progress, while the rich mixtures can exhibit opposite stretch dependence after first-stage ignition. Different definitions of effective Lewis number are adopted to explain the change in Markstein length for spherical flame in lean and rich mixtures reformed by the low temperature chemistry. Further, this work fills an important gap in laminar premixed flame research relevant to practical combustion systems and can provide useful insight into local turbulent flame behaviors and phi-sensitivity in engine combustion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Dynamic Adaptable Asynchronous Progress Model for MPI RMA Multiphase Applications

Casper is a process-based asynchronous progress model for MPI one-sided communication on multi-and many-core architectures. The one-sided communication is not truly one-sided in most MPI implementations: the target process still relies on software progress to complete incoming operations. Casper allows the user to specify an arbitrary number of cores dedicated to background ghost processes and transparently redirects the RMA operations to ghost processes by utilizing the PMPI redirection and MPI-3 shared-memory technologies. Although Casper benefits applications that suffer from lack of asynchronous progress, the operation redirection design might not support complex multiphase applications effectively, which often involve dynamically changing communication density and computing workloads. In this paper, we present an adaptive mechanism in Casper to address the limitation of static asynchronous progress in multiphase applications. For this we exploit two adaptive strategies, a user-guided strategy and a fully transparent and automatic strategy based on self-profiling and prediction, to dynamically reconfigure the asynchronous progress in Casper according to real-time performance characteristics during multiphase execution. We evaluate the adaptive approaches in both microbenchmarks and a real quantum chemistry application suite, NWChem, on the Cray XC30 supercomputer and an Intel Omni-Path cluster.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A General Framework for Progressive Data Compression and Retrieval

In scientific simulations, observations, and experiments, the transfer of data to and from disk and across networks has become a major bottleneck for data analysis and visualization. Compression techniques have been employed to tackle this challenge, but traditional lossy methods often demand conservative error tolerances to meet the numerical accuracy requirements of both anticipated and unknown data analysis tasks. Progressive data compression and retrieval has emerged as a promising solution, where each analysis task dictates its own accuracy needs. However, few analysis algorithms inherently support progressive data processing, and adapting compression techniques, file formats, client/server frameworks, and APIs to support progressivity can be challenging. Here, this paper presents a framework that enables progressive-precision data queries for any data compressor or numerical representation. Our strategy hinges on a multi-component representation that successively reduces the error between the original and compressed field, allowing each field in the progressive sequence to be expressed as a partial sum of components. We have implemented this approach with four established scientific data compressors and assessed its effectiveness using real-world data sets from the SDRBench collection. The results show that our framework competes in accuracy with the standalone compressors it is based upon. Additionally, (de)compression time is proportional to the number of components requested by the user. Finally, our framework allows for fully lossless compression using lossy compressors when a sufficient number of components are employed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Progressive hearing loss and degeneration of hair cell stereocilia in taperin gene knockout mice

The TPRN gene encodes taperin, which is prominently present at the taper region of hair cell stereocilia. Mutations in TPRN have been reported to cause autosomal recessive nonsyndromic deafness 79(DFNB 79). To investigate the role of taperin in pathogenesis of hearing loss, we generated TPRN knockout mice using TALEN technique. Sanger sequencing confirmed an 11 bp deletion at nucleotide 177–187 in exon 1 of TPRN, which results in a truncated form of taperin protein. Heterozygous TPRN{sup +/−} mice showed apparently normal auditory phenotypes to their wide-type (WT) littermates. Homozygous TPRN{sup −/−} mice exhibited progressive sensorineural hearing loss as reflected by auditory brainstem response to both click and tone burst stimuli at postnatal days 15 (P15), 30 (P30), and 60 (P60). Alex Fluor-594 phalloidin labeling showed no obvious difference in hair cell numbers in the cochlea between TPRN{sup −/−} mice and WT mice under light microscope. However, scanning electronic microscopy revealed progressive degeneration of inner hair cell stereocilia, from apparently normal at postnatal days 3 (P3) to scattered absence at P15 and further to substantial loss at P30. The outer hair cell stereocilia also showed progressive degeneration, though much less severe, Collectively, we conclude that taperin plays an important role in maintenance of hair cell stereocilia. Establishment of TPRN knockout mice enables further investigation into the function of this gene. - Highlights: • TPRN{sup −/−} mice were generated using TALEN technique. • TPRN{sup −/−} mice presented progressive hearing loss. • WT and TPRN{sup −/−} mice showed no difference in hair cell numbers. • TPRN{sup −/−} mice showed progressive degeneration of hair cell stereocilia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Quantification of glioblastoma progression in zebrafish xenografts: Adhesion to laminin alpha 5 promotes glioblastoma microtumor formation and inhibits cell invasion

Highlights: • Developed an embryo-larval zebrafish model to study GBM progression. • Quantitatively measured GBM BV association, invasion and microtumor formation. • Laminin alpha 5 decreases U251MG dispersal and increases microtumors. • Microenvironmental laminin alpha 5 slows U251MG cell invasion in vivo. Glioblastoma (GBM) is a deadly disease due to its ability to quickly invade and destroy brain tissue. Slowing or stopping GBM cell progression is crucial to help those inflicted with the disease. Our lab created an embryo-larval zebrafish xenograft model as a tool to study human GBM progression in an observable brain environment. The zebrafish brain is a dynamic and complex environment providing an optimal setting for studying GBM cell progression. Here we demonstrate the ability of our model to quantitate GBM proliferation, dispersal, blood vessel association, microtumor formation, and individual cell invasion by evaluating the importance of an extracellular matrix protein, laminin alpha 5 (lama5), on U251MG cell progression. Lama5 has been implicated in cancer cell survival, proliferation and invasion and is a known adhesion site for GBM cells. While lama5 is highly expressed in endothelial cells in the brain, it is unknown how lama5 affects GBM behavior. Using a lama5 morpholino, we discovered that lama5 decreased U251MG dispersal by 23% and doubles the formation of blood vessel dependent microtumors. Despite lama5 being a known attachment site for GBM, lama5 expression had no effect on U251MG association with blood vessels. Analysis of individual U251MG cells revealed lama5 significantly lowered invasion as mobile U251MG cells traveled 32.5 μm less, invaded 5.0 μm/hr slower and initiated invasion 60% few times per cell.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Long noncoding RNA SOX2OT contributes to gastric cancer progression by sponging miR-194-5p from AKT2

Highlights: • The role of SOX2/miR-194-5p/AKT2 axis in gastric cancer progression has been investigated. • SOX2OT acted as a sponge LncRNA of miR-194-5p in gastric cancer. • Suppressing SOXO2 expression can inhibit gastric cancer progression. Gastric cancer (GC) is a highly malignant cancer with poor prognosis. Long non-coding RNA (LncRNA) may play an important role in tumor progression. Our present study aimed to explore the effect of LncRNA SOX2OT on GC progression. We observed that SOX2OT was overexpressed in GC tissues and cell lines. Overexpressed SOX2OT promoted cell proliferation and metastasis of GC cells (SGC-7901, TMK-1) and the phosphorylation of AKT2 as well, while knockdown of SOX2OT reversed these effects. Besides that, miR-194-5p was predicted to be a target of SOX2OT and decreased expression of miR-194-5p was observed in GC tissues and cell lines. Overexpressed miR-194-5p counteracted the promoting role of SOX2OT on cell proliferation and invasion of GC cells. Moreover, AKT2 was predicted to be a target of miR-194-5p. The expression of AKT2 was negatively regulated by miR-194-5p while positively regulated by SOX2OT. Overexpressed AKT2 also promoted GC cell proliferation and invasion. Our in vitro experiments suggested that SOX2OT promoted cell proliferation and metastasis of GC cells via sponging miR-194-5p from AKT2. Finally, our in vivo experiments indicated that overexpressed SOX2OT promoted GC tumor growth and metastasis in nude mice. Taken together, our present study suggested that SOX2OT contributed to GC progression via sponging miR-194-5p from AKT2 both in vitro and in vivo. The SOX2OT-miR-194-5p-AKT2 axis may provide a new perspective for treatment of GC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

G protein nucleolar 3 promotes Non-Hodgkin lymphoma progression by activating the Wnt/β-catenin signaling pathway

G protein nucleolar 3 (GNL3), which acts as an oncoprotein in various carcinomas, is associated with tumor progression; however, little is known regarding GNL3 function in non-Hodgkin lymphoma (NHL). In this study, we first used in silico analysis to determine associations between GNL3 and diffuse large B-cell lymphoma (DLBCL). We then examined the effect of GNL3 on NHL progression, including cell proliferation, apoptosis, and cell cycle progression, and determined its underlying molecular mechanism using in vitro lymphoma cell lines and in vivo mouse xenograft models. We found that GNL3 mRNA levels were markedly higher in DLBCL tissues than in normal tissues, with these higher levels associated with poor prognosis. Additionally, GNL3 overexpression promoted NHL cell proliferation and cell cycle progression and reduced apoptosis in vitro, and enhanced tumorigenesis in an in vivo xenograft model. Moreover, we found that GNL3 upregulated the levels of Wnt/β-catenin signaling pathway-related factors and downstream target genes, whereas the opposite result was observed in GNL3-silenced cells. Furthermore, a rescue experiment using a Wnt/β-catenin inhibitor (XAV939) confirmed that GNL3 promotes NHL progression by activating the Wnt/β-catenin signaling pathway. These findings demonstrated that GNL3 functions as an oncogenic driver in NHL via the Wnt/β-catenin pathway.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

MPACT Verification With Magnox Reactor Neutronics Progression Problems

MPACT is a state-of-the-art core simulator designed to perform high-fidelity analysis using whole-core, three-dimensional, pin-resolved neutron transport calculations on modern parallel computing hardware. MPACT was originally developed to model light water reactors, and its capabilities are being extended to simulate gas-cooled, graphite-moderated cores such as Magnox reactors. To verify MPACT’s performance in this new application, the code is being formally benchmarked using representative problems. Progression problems are a series of example models that increase in complexity designed to test a code’s performance. The progression problems include both beginning-of-cycle and depletion calculations. Reference solutions for each progression problem have been generated using Serpent 2, a continuous-energy Monte Carlo reactor physics burnup calculation code.Using the neutron multiplication eigenvalue ke as a metric, MPACT’s performance is assessed on each of the progression problems. Initial results showed that MPACT’s multi-group cross section libraries, originally developed for pressurized water reactor problems, were not sufficient to accurately solve Magnox problems. MPACT’s improved performance on the progression problems is demonstrated using this new optimized cross section library.

Luciano, Nicholas↗
DOE OSTI2024

Error-controlled Progressive Retrieval of Scientific Data under Derivable Quantities of Interest

The unprecedented amount of scientific data has introduced heavy pressure on the current data storage and transmission systems. Progressive compression has been proposed to mitigate this problem, which offers data access with on-demand precision. However, existing approaches only consider precision control on primary data, leaving uncertainties on the quantities of interest (QoIs) derived from it. In this work, we present a progressive data retrieval framework with guaranteed error control on derivable QoIs. Our contributions are three-fold. (1) We carefully derive the theories to strictly control QoI errors during progressive retrieval. Our theory is generic and can be applied to any QoIs that can be composited by the basis of derivable QoIs proved in the paper. (2) We design and develop a generic progressive retrieval framework based on the proposed theories, and optimize it by exploring feasible progressive representations. (3) We evaluate our framework using five real-world datasets with a diverse set of QoIs. Experiments demonstrate that our framework can faithfully respect any user-specified QoI error bounds in the evaluated applications. This leads to over 2.02× performance gain in data transfer tasks compared to transferring the primary data while guaranteeing a QoI error that is less than 1E-5.

Wu, Xuan↗
DOE OSTI2021

Progressive transfer learning for low-frequency data prediction in full waveform inversion

To effectively overcome the cycle-skipping issue in full waveform inversion (FWI), we developed a deep neural network (DNN) approach to predict the absent low-frequency components by exploiting the hidden physical relation connecting the low- and the high-frequency data. To efficiently solve this challenging nonlinear regression problem, two novel strategies were proposed to design the DNN architecture and to optimize the learning process: (1) dual data feed structure; (2) progressive transfer learning. With the dual data feed structure, not only the high-frequency data, but also the corresponding beat tone data are fed into the DNN to relieve the burden of feature extraction. The second strategy, progressive transfer learning, enables us to train the DNN using a single evolving training dataset. Within the framework of the progressive transfer learning, the training dataset continuously evolves in an iterative manner by gradually retrieving the subsurface information through the physics-based inversion module, progressively enhancing the prediction accuracy of the DNN and propelling the inversion process out of the local minima. Here, the synthetic numerical experiments suggest that, without any a priori geological information, the low-frequency data predicted by the progressive transfer learning are sufficiently accurate for an FWI engine to produce reliable subsurface velocity models free of cycle-skipping artifacts.

02 PETROLEUM↗
DOE OSTI2018

Progress Update on Segmentation of Neutron Tomographic Images, 2018

The present report documents the techniques developed to segment fast-neutron tomographic images of objects consisting of assemblies of machined parts with distinct boundaries. This report satisfies the fiscal year 2018 technical deliverable for project OR16-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons,” to report on the extension of geometric shape-finding algorithms to three dimensions. The project has two overall goals. The first of these goals is to extend associated-particle fast-neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. This aspect of the project is beyond the scope of this report. The second goal is to automatically segment the resultant tomographic images into constituent parts and then extract information about the parts, such as the class of shape and potentially the shape parameters. This report describes progress for the image segmentation part of the project. Imaging techniques have been developed as high-confidence methods for confirming the presence and configuration of special nuclear materials (SNM). This high confidence is achieved at the cost of revealing considerable information that may be undesirable to share with the operator of the equipment. One potential way to make use of the high-confidence of imaging methods without revealing imaging data is to employ automated analysis that can extract meaningful attributes of the SNM without showing imaging data to the operator. An essential step of this automated analysis is the segmentation of the image into its constituent parts. For example, a three-dimensional (3D) fast-neutron tomographic image of an assembly containing uranium could be reconstructed and then segmented into discrete parts. The properties of a constituent part identified as uranium would be of interest and could be extracted. For instance, the volume and density of the part would together determine its mass. Moreover, its shape might be expected to fall into a particular geometric class, such as a cube, cylinder, or sphere, and the parameters of the shape (such as side length, height, or diameter) would be expected to fall in a given range. In such an analysis, automated identification of the boundaries and properties of the uranium part is an essential step. A key goal of the present work is to infer the boundaries of objects in fast-neutron tomographic images via the general method of image segmentation. Development of this capability can be broken into more manageable steps, including 1. operator-guided segmentation of two-dimensional (2D) fast-neutron tomographic images, 2. operator-guided segmentation of 3D fast-neutron tomographic images, 3. development of automated segmentation algorithms, and 4. extraction of shape parameters from constituent volumes in 3D. In the first year, the project team reported progress on the first step. Last year, progress on the second step, the extension into 3D, was reported with application to simulated data. The present report describes additional progress on the second step, and progress toward the third step, automated segmentation algorithms. In particular, a multiphase level set approach for image segmentation in three dimensions has been applied to measured 3D data for the first time. Activities related to the fourth step will also be reported, but it will focus on estimation of 3D volumes rather than shape parameters. In this report, the steps before image segmentation will be described, and the multiphase level sets approach to image segmentation will be briefly reviewed. This will be followed by a discussion of the issues encountered when applying the level set technique to measured 3D transmission images, and then a description of the inclusion of induced-reaction data will be provided. Finally, segmentation results for various measured target assemblies will be presented and the image segmentation portion of this project will be summarized and future directions will be discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1971

Economic impact of stimulated technological activity. Part 1: Overall economic impact of technological progress: Its measurement

Investigations were performed at the national economic level to explore the aggregate effects of technological progress on economic growth. Inadequacies in existing marco-economic yardsticks forced the study to focus on the cost savings effects achieved through technological progress. The central questions discussed in this report cover: (1) role of technological progress in economic growth, (2) factors determining the rate of economic growth due to technological progress; (3) quantitative measurements of relationships between technological progress, its determinants, and subsequent economic growth; and (4) effects of research and development activities of the space program. For Part 2, see N72-32174.

Source record↗
NASA NTRS2002

Analysis for the Progressive Failure Response of Textile Composite Fuselage Frames

A part of aviation accident mitigation is a crash worthy airframe structure, and an important measure of merit for a crash worthy structure is the amount of kinetic energy that can be absorbed in the crush of the structure. Prediction of the energy absorbed from finite element analyses requires modeling the progressive failure sequence. Progressive failure modes may include material degradation, fracture and crack growth, and buckling and collapse. The design of crash worthy airframe components will benefit from progressive failure analyses that have been validated by tests. The subject of this research is the development of a progressive failure analysis for textile composite. circumferential fuselage frames subjected to a quasi-static, crash-type load. The test data for these frames are reported, and these data, along with stub column test data, are to be used to develop and to validate methods for the progressive failure response.

Johnson, Eric R.↗