Adaptive Sampling Assisted Surrogate Modeling of Initial Failure Envelopes of Composite Structures

2021 ◽  
pp. 113985
Author(s):  
Su Tian ◽  
Wenbin Yu
Keyword(s):  
Adaptive Sampling ◽  
Composite Structures ◽  
Surrogate Modeling ◽  
Initial Failure ◽  
Failure Envelopes

scholarly journals ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5332
Author(s):  
Carlos A. Duchanoy ◽  
Hiram Calvo ◽  
Marco A. Moreno-Armendáriz
Keyword(s):  
Artificial Intelligence ◽  
Model Selection ◽  
Adaptive Sampling ◽  
Search Algorithm ◽  
Sequential Sampling ◽  
Surrogate Modeling ◽  
Kriging Model ◽  
Surrogate Models ◽  
Physical Experiments ◽  
Leave One Out

Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies represent a new challenge for the generation of surrogate models since they require the implementation of elaborated artificial intelligence algorithms and minimize the number of physical experiments measured. To reduce the assessment of a physical system, several existing adaptive sequential sampling methodologies have been developed; however, they are limited in most part to the Kriging models and Kriging-model-based Monte Carlo Simulation. In this paper, we integrate a distinct adaptive sampling methodology to an automated machine learning methodology (AutoML) to help in the process of model selection while minimizing the system evaluation and maximizing the system performance for surrogate models based on artificial intelligence algorithms. In each iteration, this framework uses a grid search algorithm to determine the best candidate models and perform a leave-one-out cross-validation to calculate the performance of each sampled point. A Voronoi diagram is applied to partition the sampling region into some local cells, and the Voronoi vertexes are considered as new candidate points. The performance of the sample points is used to estimate the accuracy of the model for a set of candidate points to select those that will improve more the model’s accuracy. Then, the number of candidate models is reduced. Finally, the performance of the framework is tested using two examples to demonstrate the applicability of the proposed method.

scholarly journals An extended invariant approach to laminate failure of fibre-reinforced polymer structures

2022 ◽  
pp. 1-24
Author(s):  
G. Corrado ◽  
A. Arteiro ◽  
A.T. Marques ◽  
J. Reinoso ◽  
F. Daoud ◽  
...  
Keyword(s):  
Composite Laminates ◽  
Composite Structures ◽  
Failure Modes ◽  
Three Dimensional ◽  
Failure Criteria ◽  
Design Tool ◽  
Advanced Composite Materials ◽  
Failure Envelopes ◽  
Out Of Plane ◽  
Stress States

Abstract This paper presents the extension and validation of omni-failure envelopes for first-ply failure (FPF) and last-ply failure (LPF) analysis of advanced composite materials under general three-dimensional (3D) stress states. Phenomenological failure criteria based on invariant structural tensors are implemented to address failure events in multidirectional laminates using the “omni strain failure envelope” concept. This concept enables the generation of safe predictions of FPF and LPF of composite laminates, providing reliable and fast laminate failure indications that can be particularly useful as a design tool for conceptual and preliminary design of composite structures. The proposed extended omni strain failure envelopes allow not only identification of the controlling plies for FPF and LPF, but also of the controlling failure modes. FPF/LPF surfaces for general 3D stress states can be obtained using only the material properties extracted from the unidirectional (UD) material, and can predict membrane FPF or LPF of any laminate independently of lay-up, while considering the effect of out-of-plane stresses. The predictions of the LPF envelopes and surfaces are compared with experimental data on multidirectional laminates from the first and second World-Wide Failure Exercise (WWFE), showing a satisfactory agreement and validating the conservative character of omni-failure envelopes also in the presence of high levels of triaxiality.

Surrogate Modeling of Fugacity Coefficients Using Adaptive Sampling

2019 ◽  
Vol 58 (40) ◽  
pp. 18703-18716 ◽  
Author(s):  
Corina Nentwich ◽  
Joschka Winz ◽  
Sebastian Engell
Keyword(s):  
Adaptive Sampling ◽  
Surrogate Modeling ◽  
Fugacity Coefficients

Three-Dimensional Underwater Shock Response of Composite Marine Structures

2011 ◽  
Vol 78 (6) ◽  
Author(s):  
Michael R. Motley ◽  
Yin L. Young ◽  
Zhanke Liu
Keyword(s):  
Composite Structures ◽  
Turbine Blades ◽  
Three Dimensional ◽  
Aluminum Bronze ◽  
Marine Structures ◽  
Initial Failure ◽  
Shock Response ◽  
Failure Loads ◽  
Structural Responses ◽  
Marine Applications

In recent years, there has been an increased interest in the use of advanced composites in marine applications. It has been shown that by exploiting the inherent anisotropic nature of the material, fiber-reinforced composite structures can be tailored to allow automatic, passive, three-dimensional (3D) adaptive/morphing capabilities such that they outperform their rigid counterparts both hydrodynamically and structurally. Much of the current research on the shock response of composite structures focuses on air-backed structures with fixed-fixed or simply supported boundary conditions. Nevertheless, many critical components of marine structures where adaptive/morphing capabilities are needed are cantilevered-type structures, including propeller and turbine blades, hydrofoils, and rudders. This paper investigates the 3D transient response of cantilevered, anisotropic, composite marine structures, namely, fully submerged cantilevered plates, subject to a range of shock loads. Structural responses and the initial failure loads of a composite plate are compared with a nickel-aluminum-bronze plate. Discussions of the fluid and structural responses of both materials are presented, and the initial failure loads of both materials are compared.

Adaptive Sampling-Based Artificial Neural Network for Surrogate Modeling

2022 ◽  
Author(s):  
Subham Gupta ◽  
Achyut Paudel ◽  
Mishal Thapa ◽  
Sameer B. Mulani ◽  
Robert Walters
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Sampling ◽  
Surrogate Modeling ◽  
Artificial Neural

Concurrent surrogate modeling and adaptive sampling in load-pull measurements

2015 ◽  
Author(s):  
Pawel Barmuta ◽  
Francesco Ferranti ◽  
Konstanty Lukasik ◽  
Arkadiusz Lewandowski ◽  
Dominique Schreurs
Keyword(s):  
Adaptive Sampling ◽  
Surrogate Modeling ◽  
Load Pull
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