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Various types of internal defects occur during manufacturing and handling of composite materials. It is practically impossible to manufacture composite structures without defects, making it crucial to understand the effect of defects on their failure behavior to maintain structural safety. In this work, the effect of pre-defects on the failure behavior of plain weave textile composites was studied. Unit cell configurations with symmetric, in-phase, and shifted fiber tow arrangements were considered. Inter-laced warp and fill tows and matrix pockets of plain weave unit cells were modeled in three-dimensional finite elements, and cohesive elements were inserted between all bulk elements to account for the fracture modes of the fiber and matrix direction failure of warp and fill tows, matrix pocket failure, and interface failure. Unit cell models containing pre-defects of voids, tow-matrix pocket separation, warp-fill tow separation, and cracks in the warp and fill tows were analyzed, and their effects on progressive failure behavior were investigated in terms of the interaction between fiber tow arrangements and defects. Results indicated that initial failure occurred in matrix-direction failure mode in fill tows, whereas fiber tow-matrix pocket separation was the major failure mode under uniaxial tensile load. Furthermore, failure behavior was found to be highly dependent on the fiber tow arrangement pattern and the location of pre-defects.

In this work, the effective electrical conductivity (EEC) and percolation threshold (PT) of a nanocomposite containing spherical particle fillers are predicted by computational homogenization schemes (CHS) with three-dimensional random representative volume elements (RVEs) by random filler removal (RFR). For this prediction, we prepare the RVE having the maximum filler volume fraction (Vf) of 52% with random particle fillers, also termed the master model, by the discrete element method (DEM), and the corresponding finite-element (FE) model is created. Then, 100 RVE samples for each Vf are derived by randomly replacing the material properties of several fillers by those of the matrix from the master model with diverse Vf from 5% to 50%. In addition, the interphase layer is employed by replacing some matrix elements with interphase elements according to the neighboring distance of the fillers. To demonstrate the performance of the proposed scheme, its randomness of RVEs is verified by spatial and physical metrics in terms of autocorrelation analysis, near-neighbor analysis, and directional conductivity ratio. The EEC prediction results with diverse Vf values are compared with those of an analytical model and test results. As a result, the PT at which EEC of the nanocomposites suddenly increases is successfully evaluated, and the effect of the void, interphase thickness, and conductivity, as well as the size of fillers on the EEC and PT is investigated through a sensitivity analysis.

In this paper, we propose a simple and efficient generator of random fiber distribution with diverse fiber volume fractions ( V f ) for unidirectional composites by a random fiber removal technique. From the representative volume element (RVE) consisting of 100 fibers that have a maximum V f of about 65% in this work, also termed the master model, we randomly eliminate fibers to match the predefined V f ranging from 60%, 55%, 45%, 35%, 25%, 15%, and 5%, which are lower than that of the master model. Accordingly, 100 RVE samples for each V f can be constructed in a straightforward manner. To demonstrate the performance of the proposed algorithm, its fiber locations are verified in terms of statistical spatial metrics, such as the nearest neighbor orientation, Ripley's K function, and pair distribution function. Furthermore, the elastic properties and the anisotropic ratios of the generated RVEs are investigated and compared to those of other random fiber generation algorithms.

For transient analysis of structural dynamics with reduced order model (ROM), data recovery procedures that use modal methods such as the classical mode-acceleration (MA) and modal-displacement (MD) methods are an important step in order to increase the convergence and accuracy of the solution. In this work, we propose novel MA and MD methods for highly accurate transient analysis with a reduced order model based on enhanced Craig–Bampton (ECB) formulation, which is an extension of the classical Craig–Bampton (CB) method that is a way to reduce the size of a finite element (FE) model. The performance of the proposed data recovery approach is demonstrated with two numerical examples. We also investigate the relation between the proposed and classical MA and MD methods.

Computational modeling and simulation are essential to solid and structural mechanics [...]

In this work, we generated a set of random representative volume elements (RVEs) of unidirectional composites considering actual noncircular cross-sections and positions of fibers with the aid of a shape-library approach. The cross-section of the noncircular carbon fiber was extracted from the M55J/M18 composite using image processing and a signed-distance-based mesh trimming scheme, and they were stored in a particle-shape library. The obtained noncircular fibers randomly chosen from the particle-shape library were applied to random fiber array generation algorithms to generate RVEs of various fiber volume fractions. To check the randomness of the proposed RVEs, we calculated spatial and physical metrics, and concluded that the proposed method is sufficiently random. Furthermore, to compare the effective elastic properties and the maximum von Mises stress in the matrix, it was applied to composite materials with different relative ratios of elastic moduli of M55J/M18 and T300/PR319. In the case of T300/PR319 having a high RRT (relative ratio of the transverse elastic moduli), simulation results were deviated up to about 5% in the effective elastic properties and 13% in the maximum von Mises stress in the matrix according to the fiber shapes.

We present the development of Linear Astigmatism Free - Three Mirror System (LAF-TMS). This is a prototype of an off-axis telescope that enables very wide field of view (FoV) infrared satellites that can observe Paschen-$\alpha$ emission, zodiacal light, integrated star light, and other infrared sources. It has the entrance pupil diameter of 150 mm, the focal length of 500 mm, and the FoV of 5.5$^\circ$ $\times$ 4.1$^\circ$. LAF-TMS is an obscuration-free off-axis system with minimal out-of-field baffling and no optical support structure diffraction. This optical design is analytically optimized to remove linear astigmatism and to reduce high-order aberrations. Sensitivity analysis and Monte-Carlo simulation reveal that tilt errors are the most sensitive alignment parameters that allow $\sim$1$^\prime$. Optomechanical structure accurately mounts aluminum mirrors, and withstands satellite-level vibration environments. LAF-TMS shows optical performance with 37 $\mu$m FWHM of the point source image satisfying Nyquist sampling requirements for typical 18 $\mu$m pixel Infrared array detectors. The surface figure errors of mirrors and scattered light from the tertiary mirror with 4.9 nm surface micro roughness may affect the measured point spread function (PSF). Optical tests successfully demonstrate constant optical performance over wide FoV, indicating that LAF-TMS suppresses linear astigmatism and high-order aberrations., Comment: 26 pages, 13 figures, will be published in Publications of the Astronomical Society of the Pacific

In this work, we propose a prediction model of the transverse mechanical behavior of unidirectional (UD) composites containing complex microstructure with the help of a convolutional neural network (CNN). For this prediction, a total of 900 representative volume elements (RVE) samples were generated by constructing 300 RVEs for each V f of 40%, 50%, and 60% with the random sequential expansion (RSE) algorithm. The stress-strain (S–S) curves in terms of transverse elastic modulus, transverse tensile strength, and toughness considering interphase debonding were obtained by a finite element (FE) simulation with the RVE samples. After converting FE models with 900 RVE samples to corresponding microstructural binary images, CNN modeling was employed to construct a prediction model on the microstructural images. To demonstrate the performance of the proposed CNN model, we predicted the transverse mechanical behavior in terms of the S–S curves on various test datasets. Prediction accuracy was verified in terms of the loss functions and the error of the S–S curve. The prediction results were in excellent agreement with the test datasets, and the transverse mechanical behavior was quickly predicted for other microstructures. This confirmed that the proposed CNN model is simple and powerful and can efficiently clarify the relationship between the microstructure and transverse mechanical behavior of UD composites.

An improved finite-element (FE) modeling scheme of misaligned noncircular fibers of a unidirectional composite is proposed with the aid of micro-computed tomography (μCT). The proposed scheme selects several key images among many raw CT images containing fiber misalignment and noncircular fibers. Then, it constructs two-dimensional (2D) drawings based on the signed distance obtained by solving the level-set equation with the trimmed mesh technique for phase segmentation meshing. Subsequently, these 2D drawings are bridged to construct a complex three-dimensional (3D) solid model for 3D FE model generation. To demonstrate the performance of the proposed scheme, we made an intensive comparison with other microstructure FE models in terms of the fiber volume fraction and equivalent elastic properties. Furthermore, the transverse strength was predicted through simulation and compared with the test results, showing excellent agreement with the test result that cannot be achieved with other simple modeling methods.

The properties of fiber reinforced composites are mainly determined by the fraction of reinforcement and matrix. Thus, to design a system based on composite materials, it is vital to carefully measure the volume fractions of the composites with a proper method. Digestion by strong acid or ignition at high temperature in an oxidizing environment are conventional for measuring content fractions. In essence, these methods assume that the reinforcement does not lose weight by digestion or ignition. However, by neglecting reported vulnerable oxidization characteristics of carbon fiber, these conventional methods result in inaccurate fiber volume fractions of carbon fiber/epoxy composites. In this study, an effective and accurate method, having only 2 steps in measuring process and 1.5%p maximum error, for determining the fiber volume fractions of two different PAN (Polyacylonitrile)-based carbon fiber reinforced composites via thermo-gravimetric analysis was developed and subsequently verified using the results from 80 microscopic images.

In this work, we conducted a coupled dynamic loads analysis (CDLA) of satellites with an enhanced Craig–Bampton (ECB) approach to predict maximum response (acceleration, displacement, and stress). The satellite was subjected to a relatively high frequency launch vehicle (LV) interface load ( 20 – 50 Hz) when it was launched by multiple satellite launcher or experienced the combustion instability caused by LV instead of a typical low frequency LV interface load ( 0 – 10 Hz). To minimize the error caused by mode truncation, ECB-like formulation, which considers the effect of residual modes, is employed and computes the maximum response of the given dynamic system. By using this method, we found that the response by the ECB model is more accurate and efficient over the classical Craig–Bampton (CB) model due to the enhanced transformation matrix from being subjected to an unexpected high frequency LV load. To demonstrate this performance, we solved several benchmark problems associated with CDLA.

A simple and accurate scheme for modeling microstructures is proposed with the help of element trimming combined with signed distance function based boundary smoothing. To accommodate randomly distributed fibers in unidirectional composites, digital image processing is used. The interfaces of multi-materials are identified by introducing a signed distance function, and then, square background elements crossing the interfaces are simply trimmed and divided to represent a single material behavior by a single element. After element trimming, the elements that are polygon-shaped in the two-dimensional domain are split into conventional three-node triangle elements (six-node prism elements in the three-dimensional domain) available in many commercial software packages. The present modeling scheme was verified through benchmark examples in terms of the accuracy and efficiency and then applied to the modeling of unidirectional composites based on real microscopic images to evaluate the equivalent elastic properties.

We propose an enhanced Brinson model with modified kinetics for martensite transformation. Two additional material constants are considered to follow the stress-temperature diagram above austenite start temperature (As) along with treatment to keep the continuity of the martensite volume fraction and the path dependency of the phase transformation. To demonstrate the performance of the proposed model, we implement this algorithm into ABAQUS user subroutine, then conduct several numerical simulations and compare their results with SMA wire experiments as well as those of three-dimensional SMA constitutive models. From the results, it turns out that the proposed model is as accurate as the three-dimensional models and shows better accuracy over original Brinson model in terms of recovery stress.

This paper presents the development of artificial neural network (ANN) modeling for predicting the transverse elastic modulus of a unidirectional composite (E-glass/MY750) with fiber positions and volume fraction. For this prediction, random representative volume elements were generated according to the fiber volume fraction ( V f ). A training dataset consisting of input data ( V f and fiber locations) and output data (the effective elastic modulus), which were computed by the computational homogenization scheme (CHS), was used to train the ANN model to have proper weight and bias by back propagation. To demonstrate the performance of the proposed ANN model, prediction of the transverse elastic modulus of various test datasets whose transverse elastic moduli are known by CHS was conducted. The prediction accuracy was verified in terms of the mean squared error, correlation coefficient (R), and relative error. The prediction results showed excellent agreement with the test dataset and quickly predicted the transverse elastic modulus having random microstructures.