Intelligent methods for optimization design of lightweight fiber-reinforced composite structures: A review and the-state-of-the-art

As the application of lightweight fiber-reinforced composite structures reaches an unprecedented scale in industry, design technology for composite structures becomes crucial for enhancing performance, improving productivity, and reducing cost. In recent years, the rapid development of intelligent technology, such as big data, deep learning, and machine learning, has promoted the development of design technology. However, the current situation and intellectualization of the design technology is not well summarized. This paper reviews the advance in design technologies for fiber-reinforced composite structures, including prediction and optimization methods for composite properties. Then, their intellectualization development is overviewed. Finally, the development trend of intelligent design technologies and intelligent composite structures are discussed. This work can provide a reference for researchers in the related field

The 2nd International Conference on Advances in Mechanical Engineering

The Second International Conference on Advances in Mechanical Engineering, ICAME-22, was held on 25th August, 2022 at the Mechanical Engineering Department of Capital University of Science and Technology. All articles underwent a rigorous single-blind peer review process. ICAME-22 accepted papers in the disciplines of experimental and computational fluid dynamics, thermodynamics, heat Ttransfer, machine and mechanisms, design, solid mechanics, manufacturing, production and industrial engineering, engineering management, technology management, renewable energy, environmental engineering, bioengineering, materials, failure analysis, and related fields

Large space structures and systems in the space station era: A bibliography with indexes

Bibliographies and abstracts are listed for 1219 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1, 1990 and December 31, 1990. The purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system, interactive analysis and design, structural and thermal analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems

Bibliography of Lewis Research Center technical publications announced in 1993

This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1993. All the publications were announced in the 1993 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses

Aeronautical enginnering: A cumulative index to a continuing bibliography (supplement 312)

This is a cumulative index to the abstracts contained in NASA SP-7037 (301) through NASA SP-7073 (311) of Aeronautical Engineering: A Continuing Bibliography. NASA SP-7037 and its supplements have been compiled by the Center for AeroSpace Information of the National Aeronautics and Space Administration (NASA). This cumulative index includes subject, personal author, corporate source, foreign technology, contract number, report number, and accession number indexes

Technology for large space systems: A bibliography with indexes (supplement 13)

This bibliography lists 399 reports, articles and other documents introduced into the NASA scientific and technical information system between January 1, 1985 and June 30, 1985. Its purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system, interactive analysis and design, structural and thermal analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems

Aeronautical engineering: A cumulative index to a continuing bibliography (supplement 235)

This publication is a cummulative index to the abstracts contained in Supplements 223 through 234 of Aeronautical Engineering: A Continuing Bibliography. The bibliographic series is compiled through the cooperative efforts of the American Institute of Aeronautics and Astronautics (AIAA) and the National Aeronautics and Space Administration (NASA). Seven indexes are included -- subject, personal author, corporate source, foreign technology, contract number, report number and accession number

Bibliography of Lewis Research Center technical publications announced in 1992

This compilation of abstracts describes and indexes the technical reporting that resulted from the scientific and engineering work performed and managed by the Lewis Research Center in 1992. All the publications were announced in the 1992 issues of STAR (Scientific and Technical Aerospace Reports) and/or IAA (International Aerospace Abstracts). Included are research reports, journal articles, conference presentations, patents and patent applications, and theses

A Technique for Real-Time Detection of Defects in Composite Structure using Carbon nanotubes, and Transfer Learning

ABSTRACT A Technique for Real-Time Detection of Defects in Composite Structure using Carbon nanotubes, Machine Learning Including Transfer Learning Farzad Kashefinishabouri Fiber-reinforced polymer composites have garnered interest in a range of industrial applications due to their outstanding mechanical characteristics and lightweight. Monitoring the health of polymer composite structures in real-time is one of the most challenging issues in the practical use of composites due to their susceptibility to many types of damage. Conventional non-destructive tests (NDT) methods such as X-ray tomography and ultrasonic may be used to assess composite materials but the drawback of conventional NDT techniques is that they cannot be implemented when the composite part is in use. Composite plates’ electrical characteristics can be to do real-time health monitoring. Carbon nanotubes (CNTs) because of their excellent conductivity characteristics, are used in composites to enhance the conductivity of resins within composites. It is shown that by monitoring the electrical behavior of composites with CNT embedded resins, defects can be detected. The issue with this approach is that numerous wires and connections are required. The weight of composite structures is greatly influenced by the number of wires and connections, which also makes the system more prone to errors because numerous connections must function well for the system to respond as intended. To tackle this problem, in this study, the goal is to reduce the number of required probes and connections by limiting the probes to the edges of composite plates rather than throughout the plate. By having the probes on the edges of the plate, there may not be a direct correlation between defects at different locations within the plates to the measurements as it was in the previous cases. There are two approaches to tackle this problem:1. To develop a physics-based model that can precisely model the electrical behavior of composite with CNTs embedded in the resin. 2. To develop a data-driven model that can relate the measurements to the location of the defect. As the first approach is expensive and time-consuming the second approach is picked in this study. Neural network (NN) is used to find the pattern between measurements on the edges and the location of the defect. The problem with using neural network (NN) models is that they require numerous numbers of labeled examples. To tackle this problem Transfer Learning (TL) and data augmentation is used. TL is used to reduce the number of labeled data points required for the training process as in composites it is too expensive and time-consuming to generate a huge number of data points. In the TL method that is used, the training is done in two stages, first stage the training is done based on the data generated from a similar problem that the data can be abundant, and the second stage of training is done using experimental data of the exact problem. Data augmentation is used on experimental data to increase the data points for training NN. The performance of the trained neural network in locating defects by having the probes only on the edges of the samples is promising (accuracy of 78.57% on the test set). Also, the performance of the neural network models for different precisions and sample sizes were studied. The precision is defined as the area in which the defects can be located within. KEYWORDS Real-time defect detection, Neural Network, Transfer learning, Data augmentatio

Shear-promoted drug encapsulation into red blood cells: a CFD model and μ-PIV analysis

The present work focuses on the main parameters that influence shear-promoted encapsulation of drugs into erythrocytes. A CFD model was built to investigate the fluid dynamics of a suspension of particles flowing in a commercial micro channel. Micro Particle Image Velocimetry (μ-PIV) allowed to take into account for the real properties of the red blood cell (RBC), thus having a deeper understanding of the process. Coupling these results with an analytical diffusion model, suitable working conditions were defined for different values of haematocrit