Uncertainty Quantification in Laminated Composites: A Meta-model Based Approach

This book provides computationally efficient uncertainty quantification schemes for stochasticity in laminated composite structures, based on meta-model-based approaches. It studies different metamodels and presents comparative results for static and dynamic responses, including sensitivity analyses.

Format: Paperback / softback
Length: 366 pages
Publication date: 31 March 2021
Publisher: Taylor & Francis Ltd

Over the past few decades, uncertainty quantification in composite materials and structures has garnered significant attention from the research community due to industrial demands. This book offers computationally efficient uncertainty quantification schemes following meta-model-based approaches for stochasticity in material and geometric parameters of laminated composite structures. Numerous metamodels have been investigated, and comparative results have been presented for diverse static and dynamic responses. Results for sensitivity analyses are provided to comprehensively assess the relative significance of different material and geometric parameters in the overall structural responses.

Uncertainty quantification in composite materials and structures has become increasingly important in recent years due to the growing demand from industries. This book presents computationally efficient uncertainty quantification schemes that utilize meta-model-based approaches to account for stochasticity in material and geometric parameters of laminated composite structures. Numerous metamodels have been studied, and comparative results have been presented for various static and dynamic responses. Sensitivity analyses have also been conducted to assess the relative importance of different material and geometric parameters in the global structural responses.

The importance of uncertainty quantification in composite materials and structures cannot be overstated. In the past few decades, there has been a significant increase in the demand for products that are lightweight, durable, and cost-effective. To meet these demands, researchers have turned to composite materials, which are made up of two or more different materials combined to create a material with unique properties. However, the use of composite materials also introduces uncertainties, such as variations in material properties, manufacturing processes, and environmental conditions.

Uncertainty quantification is a process that helps engineers and scientists to understand and manage these uncertainties. It involves the use of mathematical models and computer simulations to predict the behavior of composite materials and structures under different conditions. By quantifying the uncertainties, engineers can design and optimize composite materials and structures to minimize their impact on performance and cost.

One of the key challenges in uncertainty quantification is the development of accurate and reliable metamodels. Metamodels are used to represent the behavior of composite materials and structures at the macroscopic level. They are based on a combination of experimental data, theoretical models, and machine learning algorithms. The development of accurate metamodels is essential for the accurate prediction of the behavior of composite materials and structures.

Another challenge is the selection of appropriate uncertainty quantification methods. There are many different methods available, such as Monte Carlo simulations, finite element analysis, and Bayesian inference. Each method has its own advantages and disadvantages, and the selection of the appropriate method depends on the specific application and the level of uncertainty that needs to be quantified.

In this book, we will introduce computationally efficient uncertainty quantification schemes that follow meta-model-based approaches for stochasticity in material and geometric parameters of laminated composite structures. We will discuss numerous metamodels that have been studied and compare their performance for different static and dynamic responses. We will also provide results for sensitivity analyses to assess the relative importance of different material and geometric parameters in the global structural responses.

Uncertainty quantification is not only important for the design and optimization of composite materials and structures, but it is also important for safety and reliability. In many industries, composite materials are used in critical applications, such as aerospace, automotive, and construction. Uncertainties in the behavior of these materials can have significant consequences, such as failure, injury, and even loss of life.

By quantifying the uncertainties, engineers can identify potential risks and develop mitigation strategies. For example, they can design composite materials and structures with redundant components or use sensors to monitor the behavior of the materials in real-time. This can help to ensure that composite materials and structures are safe and reliable in their intended applications.

In conclusion, uncertainty quantification in composite materials and structures has gained significant attention in recent years due to the growing demand from industries. This book presents computationally efficient uncertainty quantification schemes that follow meta-model-based approaches for stochasticity in material and geometric parameters of laminated composite structures. By quantifying the uncertainties, engineers can design and optimize composite materials and structures to minimize their impact on performance and cost. Uncertainty quantification is also important for safety and reliability, and by identifying potential risks and developing mitigation strategies, engineers can ensure that composite materials and structures are safe and reliable in their intended applications.

Weight: 689g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367780791