Optimization of Thermoplastic Elastomer (TPE) Components for Aerospace Structures Using Computerized Data-Driven Design

<div>Continuous rubber track systems for heavy applications are typically designed using multiple iterations of full-scale physical prototypes. This costly and time-consuming approach limits the possibility of exploring the design space and understanding how the design space of that kind of system is governed. A multibody dynamic simulation has recently been developed, but its complexity (due to the number of model’s inputs) makes it difficult to understand and too expensive to be used with multi-objective optimization algorithms (approximately 3 h on a desktop computer).</div> <div>This article aims to propose a first design space exploration of continuous rubber track systems via multi-objective optimization methods. Using an existing expensive multibody dynamic model as original function, surrogate models (artificial neural networks) have been trained to predict the simulation responses. These artificial neural networks are then used to explore the design space efficiently by using optimization algorithms. Sensitivity studies and multi-objective optimization were carried out on surrogate models to identify high-impact design parameters and potential improvements ranging from 2.24% to 17.2% compared to the current design of reference (CTS halftrack system) on wheel load, track pinch, and maximum required torque to cross a bump.</div>

This research work deals with aspects concerned with delamination detection in composite structures as revealed by an approach based on vibration measurements. Variations in vibration characteristics generated in composite laminates indicate the existence of delaminations because degradation due to delamination causes reduction in flexural stiffness and strength of the material and as a result vibration parameters like natural frequency responses are changed. Hence, it is possible to monitor the variation in natural frequencies to identify the presence of delamination, and assess its size and location for online structural health monitoring (SHM). The approach to this paper, therefore, typically depends on undertaking the analysis of structural models implemented by finite element analysis (FEA). The numerical solutions using FE models known as the simulator computes the natural frequencies for the delaminated and undelaminated specimens of composite laminates. However, these FE models are computationally expensive, and surrogate (approximation) models are introduced to curtail the computational expense. The simulator is employed to solve the inverse problem using algorithms based on computational intelligence concepts. An artificial neural network (ANN) model is developed to also solve the inverse problem for delamination detection directly and to provide surrogate models integrated with optimization algorithms (the gradient-based local search and non-dominated sorting genetic algorithm-II) to contain the computationally expensive simulations by FEA. This approach is termed as surrogate assisted optimization and it is seen that the engagement of surrogate models in lieu of the FE models in the optimization loop greatly enhances the accuracy of delamination detection results within an affordable computational cost and provides control over handling different variables. Meanwhile, to aid with the building of effective surrogate models using substantial number of training datasets, K-means clustering algorithm is harnessed and this effectively reduces the large training datasets usually required for ANN training. This paper demonstrated that ANN and optimization algorithms with surrogates show immense potentialities for use in delamination damage detection scenarios. Prediction errors of the algorithms were quantified and they were shown to be satisfactory when applied to previously experimental data. The algorithms in their inverse formulations are capable of predicting accurately delamination parameters. Hence, these algorithms should be employed for application in the domain of SHM where their small computational requirements could be exploited for online damage detection.

Seepage criteria based optimal design of water retaining structures with reliability quantification utilizing surrogate model linked simulation-optimization approach

Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management

In chemical engineering design, process plants are often capable of turndown and flexible operation to maximise profitability depending on demand and price signals, and the optimisation of these processes needs to take into account the variability in the plant operation. By performing the optimisation with flexible operating conditions the overall plant performance can be improved. The optimisation process makes use of two computational modelling techniques: multi-objective optimisation (MOO) and surrogate modelling. Multi-objective optimisation allows for the optimisation of two or more competing variables simultaneously. In the cases studied here, the long individual simulation time coupled with the large number of individual simulations required meant that the total simulation time was prohibitive for dynamic MOO. In order to reduce computation time surrogate models of the processes are developed using artificial neural networks. A statistical dynamic modelling framework within a multi-objective optimisation methodology is developed to optimise a process plant’s performance. Here the dynamic modelling is used to optimise the economic and environmental performance of natural gas combined cycle power plant systems fitted with carbon capture and storage (CCS) and solar thermal energy (STE) in a time-integrated MOO framework. This framework allows for variable process inputs and flexible operation based on external signals, such as product price. By introducing a probabilistic approach to the modelling of flexible operation allows for more data to be included in the analysis. The methodology for dynamic modelling of flexible operation could be applied to other industrial processes that face similar dynamic economic and/or environmental conditions. From a CCS standpoint dynamic MOO was used to show that variable capture can be used to make the CCS plant more profitable over steady state operation. At a capture rate of 80 % the NPV of a gas turbine combined cycle with CCS improved by 38 m$AU by utilising flexible operation of the capture process. This also allows for various economic scenarios to incentivise the reduction of CO₂ emissions from natural gas based power plants, with the use of CCS and STE used to reduce the greenhouse emissions. The economic strategies of a carbon price and governmental grants are investigated to determine the levels of policy help to incentivise implementing CO₂ reduction technologies. In order to achieve a financial incentive for limiting CO₂ emissions from a combined cycle power plants equipped with CCS a government co-investment grant of 50 % of the capital costs and a carbon price of 83 $AU/tCO₂ is required.

Groundwater management involves a complex decision‐making process, often with the need to balance the trade‐off between meeting society's demand for water and environmental protection. Therefore effective management of groundwater resources often involves some form of multi‐objective optimization (MOO). Many existing software tools offer simulation model‐enabled optimization, including evolutionary algorithms, for solving MOO problems. However, such analyses involve a huge amount of numerical process‐based model runs, which require significant computational effort, depending on the nonlinearity and dimensionality of the problem, in order to seek the optimal trade‐off function known as the Pareto front. Surrogate modeling, through techniques such as Gaussian Process Regression (GPR), is an emerging approach to significantly reduce the number of these model evaluations thereby speeding up the optimization process. Yet, surrogate model predictive uncertainty remains a profound challenge for MOO, as it could mislead surrogate‐assisted optimization, which may result in either little computational savings from excessive retraining, or lead to suboptimal and/or infeasible solutions. In this work, we present probabilistic Pareto dominance criteria that considers the uncertainty of GPR emulation during MOO, producing a “cloudy” Pareto front which provides an efficient decision space sampling mechanism for retraining the GPR. We then developed a novel acquisition strategy to manage the solution repository from this cloud and generate an ensemble of infill points for retraining. We demonstrate the capabilities of the algorithm through benchmark test functions and a typical density‐dependent coastal groundwater management problem.

In this paper, we propose a Multi-objective Five-element Cycle Optimization algorithm based on Multi-strategy fusion (MOFECO-MS) to address the Bi-objective Traveling Thief Problem (BITTP), an extension of the Traveling Thief Problem that incorporates two conflicting objectives. The novelty of our approach lies in a unique individual selection strategy coupled with an innovative element update mechanism rooted in the Five-element Cycle Model. To balance global exploration and local exploitation, the algorithm categorizes the population into distinct groups and applies crossover operations both within and between these groups, while also employing a mutation operator for local searches on the best individuals. This coordinated approach optimizes parameter settings and enhances the search capabilities of the algorithm. Extensive experiments were conducted on nine BITTP instances, comparing MOFECO-MS against eight state-of-the-art multi-objective optimization algorithms. The results show that MOFECO-MS excels in both Hypervolume (HV) and Spread (SP) indicators, while also maintaining a high level of Pure Diversity (PD). Overall, MOFECO-MS outperformed the other algorithms in most instances, demonstrating its superiority and robustness in solving complex multi-objective optimization problems.

Recently, there has been an interest in the incorporation of buckypaper (BP), or carbon nanotube (CNT) membranes, in composite laminates. Research has shown that using BP in contrast to nanotube doped resin enables the introduction of a higher CNT weight fraction which offers multiple benefits including higher piezo resistivity for health monitoring applications and enhanced mechanical response for structural applications. However, their impact on the deformation and failure mechanisms of composite laminates has not been investigated thoroughly. Understanding these issues experimentally would require a carefully executed test plan involving a multitude of design parameters such as BP geometry and placement, material anisotropy and variability, and laminate stacking sequence. Computational investigations can also be conducted to 1reduce the labor and cost associated with testing; however, for these results to be meaningful, high-fidelity physics-based simulation tools, accounting for scale-dependent variability, constitutive laws, and damage mechanisms and their evolution across the length scales, must be used. These methodologies are computationally intensive and their implementation in the analysis of complex heterogeneous structural systems can be prohibitive. This paper presents a deep learning (DL)-based surrogate model for studying the mechanical response of hybrid carbon fiber reinforced polymer (CFRP) composite laminates with BP interleaves under various mechanical loads. The surrogate model utilizes a long short-term memory (LSTM) architecture implemented within a DL framework and predicts the laminate global response for a given configuration, geometry, and loading condition. The DL framework training and cross-validation are performed via data acquisition from a series of three-point bend tests conducted through finite element analysis (FEA) and in-house experiments, respectively. The results show that the surrogate model is capable of predicting damage induced inelastic response without the computationally expensive task of solving nonlinear equations such as continuum damage mechanics and fracture mechanics-based equations. The model predictions show good agreement with FEA simulations and experimental results, where CFRP with two BP interleaves showed enhanced flexural strength and modulus over pristine samples. This enhancement can be attributed to the excellent crack retardation capabilities of CNTs, particularly in the interlaminar region. Finally, confocal microscopy images of experimentally tested specimens were analysed to interpret the predicted stress-strain response. Early damage initiation was observed in the 90° ply, resulting in the onset of nonlinearity and stiffness degradation. These micrographs also confirmed the role of BP in preventing through-thickness crack propagation.

This study aims to show the implementation of an optimization procedure by characterizing the material’s hyperelastic behavior in harsh environments experienced in facility testing and in real world automotive conditions. The procedure consists of conducting material tests under various simulated harsh environments, determining the co-efficients for hyperelastic material modeling and using FEA to predict and correlate the nature of failure observed in facility testing. Two commercially available and commonly employed thermoplastic elastomers (TPE), Santoprene (Ethylene-Propylene-Diene-Monomer rubber and Polypropylene blend) and Desmopan (Thermoplastic Polyurethane), were tested. The harsh environments simulated are fluid immersion tests in automobile grease. Material aging characteristics in controlled thermal conditions were also documented. Compression and tension tests were conducted in order to determine the co-oefficients of the Mooney Rivlin hyperelastic material model. Finite Element Analysis (FEA) simulations were conducted on LS-DYNA software, to determine the quasi-static stress distributions on an overslam bumper part, a typical application of automotive elastomers. Shape and topological variations were investigated in the FEA tests. It was found that certain shape and topological changes to the part result in minimizing the stress concentrations. It is hypothesized that such changes to the rubber component would result in a lower failure rate in facility testing.

In this paper, the problems of fuel cost and emission reduction based on multi-objective optimization are studied and solved. Various multi-objective optimization algorithms, such as the multi-objective ant lion optimization (MOALO), the multi-objective multiverse optimization (MOMVO), the multi-objective particle swarm optimization (MOPSO), the multi-objective thermal exchange optimization (MOTEO), and the multi-objective grasshopper optimization algorithm (MOGOA), are used to reduce the cost and emissions of generation fuel. With equality and inequality restrictions, the suggested work is carried out in an IEEE 30 bus system. To determine the lowest system-wide minimization of generating cost and emission, the results of several optimization strategies are compared. In terms of lowering fuel costs and emissions, the MOGOA performs better than any other multi-objective algorithms.

The warpage of individual IC panel/package or whole molded strips keeps changing with temperature during the assembly processes from die bonding, wire bonding, molding, post-mold cure to soldering reflow. How to optimize package design and properties of all assembly materials to obtain acceptable warpage level both at room temperature and reflow temperature becomes more challenging. In this study, finite element analysis (FEA) simulation was performed in warpage prediction of three types of advanced laminated IC packages (PBGA, CSP BGA and PoP BGA). Shadow Morie system was used in monitoring warpage deviation under temperature profile from -55degC to 260degC. Calibration or correction of FEA results can be performed by introducing experimental results from Shadow Morie measurement to fine-tune the modeling so as to obtain more accurate prediction

A stock price index measures the change in several share prices, which can describe the market and assist investors in deciding on a specific investment. Thus, foreseeing the stock price index benefits investors in creating a better investment strategy. However, forecasting the stock price index can be challenging due to its non-linearity, non-stationary and high uncertainty. Gaussian process regression (GPR) is an attractive and powerful approach for prediction, especially when the data fluctuates over time with fewer restrictions. Besides, the GPR gains advantages over other forecasting techniques as it can offer predictions with uncertainty to provide margin errors. In this study, we evaluate the use of GPR to predict the stock price of Thailand (SET). The SET data are divided into 2 datasets; the data in the year 2015 - 2020 and the data in the year 2020 due to the massive change during the COVID-19 pandemic. The prediction results from the GPR are then compared to the machine learning approaches, artificial neural network (ANN) and recurrent neural network (RNN) using evaluation scores; the root mean square error (RMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE) and the Nash-Sutcliffe efficiency (NSE). The results indicate that the GPR is superior to the ANN and RNN for both datasets as it provides a high prediction accuracy. Moreover, the results suggest that the GPR is less sensitive to the number of input lags in the model. Therefore, the GPR is more favorable for the prediction of SET than the ANN and RNN.
 HIGHLIGHTS
 
 The Gaussian process regression (GPR) was applied to predict the stock price index of Thailand (SET)
 The predictive performance of the GPR was compared to artificial neural networks (ANNs) and recurrent neural networks (RNNs)
 The results indicate that GPR outperformed the other methods as it provided a high prediction accuracy along with prediction intervals
 
 GRAPHICAL ABSTRACT

Application of surrogate models in estimation of storm surge:A comparative assessment

There are three materials that are commonly used in commercial glazing seals: EPDM, silicone and thermoplastic vulcanizates (TPVs). TPVs are a high performance class of thermoplastic elastomers (TPEs), where TPEs have elastomeric properties with thermoplastic processability. TPVs have emerged as materials well suited for use in glazing seals due to ease of processing, economics and part design flexibility. The part design and development process is critical to ensure that the chosen TPV provides economics, quality and function in demanding environments. In the design and development process, there is great value in utilizing dual durometer systems to capitalize on the benefits of soft and rigid materials. Computer-aided design tools, such as Finite Element Analysis (FEA), are effective in minimizing development time and predicting system performance. Examples of TPV glazing seals will illustrate the benefits of utilizing FEA to take full advantage of the material characteristics, which results in functional performance and quality while reducing development iterations. FEA will be performed on two glazing seal profiles to confirm optimum geometry.

This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.