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Modelling electro-mechanical behaviour in piezoelectric composites: Current status and perspectives on homogenisation

Piezoelectric composites have emerged as a versatile platform with immense potential for tailoring electro-mechanical properties to cater to a wide spectrum of applications. Central to employing their capabilities are modelling and homogenisation techniques, both analytical and numerical, which serve as the cornerstone of analysing and optimising these materials applications. As technology continues to evolve, the development of sophisticated models and innovative composite designs promises to drive further advancements in the realm of piezoelectric composites. This comprehensive review explores the analytical and numerical models employed for homogenising piezoelectric composites. It systematically presents and scrutinises these models, shedding light on their distinct advantages and limitations, thereby aiding researchers in selecting the most appropriate one for their specific needs. This review highlights challenges in modelling long-fibre composites, citing limitations in Eshelby-Type Models. Simplifying micromechanics-based models encounters challenges when dealing with transverse properties, while Asymptotic Homogenization-Based Models excel in regular patterns. Limited experimental validation exists, particularly in metallic matrices. In conclusion, this comprehensive review navigates the diverse landscape of modelling strategies for Representative Volume Elements, each with its unique strengths and limitations. Researchers in this field must judiciously select modelling techniques based on their piezoelectric characteristics and desired accuracy levels. Additionally, the pressing need for further experimental validation, especially concerning metallic matrices, stands out as a critical avenue for enhancing the reliability and real-world applicability of these modelling techniques.

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Numerical generation and verification of a two-dimensional virtual asphalt mixture

This paper aims at finding a robust method to generate a two-dimensional virtual asphalt mixture and to verify the suitability of the generated virtual mixture. First, three methods namely stereological method, Walraven formula and phase separation method are adopted to convert the actual three-dimensional gradation curves into two-dimensional ones. Then, an approach called semi whole scale cross section (SWSCS) method is developed to verify the results obtained and to determine the most robust procedure to follow. Third, the numerical two-dimensional geometry shape data library (GSDL) of different aggregates and voids and a robust method to assemble representative volume elements (RVEs) are developed using Matlab. Finally, a gradation curve from the literature is considered to generate a virtual asphalt mixture specimen, which is then imported in the finite element (FE) software Abaqus to simulate the dynamic modulus test. The virtual test results are further verified using available literature and another homogenization numerical prediction method based on the Mori-Tanaka's approach. The results show that the phase separation method is effective to convert the three-dimensional gradation curve to a two-dimensional one. The simulation results of the virtual asphalt mixture developed in this study turn out to be in good agreement with both previously presented literature data and Mori-Tanaka's theory.

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Accelerating the shooting and bouncing ray based electromagnetic scattering calculations via CPU and GPU Implementations: Application to PREDICS tool

In this paper, we present the parallel computation of physical electromagnetic (EM) solvers, namely physical optics (PO), shooting and bouncing rays (SBR), and the physical theory of diffraction (PTD), which are used for EM scattering/diffraction calculations. The accelerated solvers have been tested on CPU and GPU kernels for significantly faster calculation times. As an application, the developed implementation is employed to recently developed tool of PREDICS that uses combined techniques PO, SBR, and PTD for the calculation of EM scattering/diffraction and radar cross section (RCS) from electrically large, complex objects. First, the CPU parallelization of these three combined techniques on the PREDICS code is explained. The parallelization implementation is specially tailored for the nature of SBR technique such that the CPU iteration loop is accomplished over triangular facets of the CAD file by employing a read/write spin lock task dispatching technique. Next, a lock-free implementation of the PO, SBR, and PTD techniques on the GPU threads is presented. Thanks to availability of many GPU cores, the parallelization procedure is widened such that triangular facets, observation angles of elevation and azimuth are used for a much faster calculation set-up. Numerical examples for the EM/RCS calculation of simple canonical and electrically big, complex objects are shared to demonstrate the effectiveness, accuracy and the computational acceleration based on the multi-CPU and multi-GPU implementations. The key metric of the contribution of the study is the speed-up value of the implementations. The acceleration gained via multi-CPU realization yielded directly in parallel with the number of available CPU cores. On the other hand, the acceleration factor can reach to hundreds when multi-GPUs are used. In fact, the acceleration factor has reached to 4.7 for a machine with 4 Core 8 Thread CPU processor, and 141.3 for a 2560 CUDA GPU Core system.

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Optimization design of cement mixing columns supported height embankment using Plaxis remote scripting and Gene-expression programming technique

This paper is going to present the advanced feature in Plaxis using remote scripting with Python wrapper. Python allows engineers to create Plaxis models, calculate construction phases, and plot the results automatically. The settlement estimation of a high embankment, supported by cement mixing columns (CMC), is presented as a case study. Approximately 500 models were created and analyzed within a few hours with different embankment heights, soft soil thickness, CMC spacing, and CMC diameter. This original database was used to develop a regression model using a Gene-expression programming (GEP) algorithm. The proposed GEP-based model with high accuracy could be applied to optimize the CMC design. In detail, the coefficient of correlation (R-value) of all phases is high and fluctuates from 0.967 to 0.976, while the mean absolute error of the model is lower than 0.009 m. The parametric study indicates that increasing the height of the fill embankment, the thickness of the peat layer, or the spacing between CMCs causes high settlement, while increasing the CMC diameter could significantly reduce the settlement of the embankment. The research results demonstrate that using Plaxis remote scripting and the GEP technique could help engineers run the numerical analysis much faster, leading to a more in-depth analysis to create better decision-making.

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Slope stability prediction based on GSOEM-SV: A mobile application practicably deploy in engineering verification

Slope stability evaluation is a complex and uncertain system problem, and carrying out slope stability prediction is the prerequisite and foundation for slope disaster prevention. In order to achieve fast and accurate prediction of slope stability, this paper considers height, total slope angle, unit weight, cohesion, internal friction angle, and pore water pressure ratio as input features and proposes an intelligent slope stability prediction method based on grid search optimization ensemble learning model by soft voting (GSOEM-SV). First, 390 sets of on-site data were collected to form a dataset, and analyses including correlation coefficients, density estimates, and box lines were carried out. Then, the grid search optimization algorithm is used to optimize the hyperparameters of five algorithms—Gradient Boosting Decision Trees, Light Gradient Boosting Machine, Categorical Boosting, Support Vector Machine, and Random Forest, and integrates them through soft voting. Furthermore, this paper optimizes the hyperparameters of the above five algorithms based on grid search, particle swarm and simulated annealing algorithms, builds 15 improved models and 2 ensemble models and conducts comparison. The results reveal that the GSOEM-SV has the highest slope stability prediction accuracy, up to 91 %, the area under the curve (AUC) is 0.950, and its F1 score 0.917, which are better than the 15 improved and 2 integrated models. In addition, a set of slope stability prediction app based on uni-app is developed in the paper. It provides a technical foundation and an open and shareable information service platform for slope hazard prediction in geotechnical engineering.

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A novel train–bridge interaction computational framework based on a meshless box girder model

In traditional train–bridge coupled system (TBCS), simply supported box girder bridges are often modeled using Euler beam elements, neglecting their spatial structure. This simplification may yield inaccurate results, impacting the running safety analysis of high-speed railway. To address this issue, a novel train–bridge interaction computational framework based on first-order shear deformation theory (FSDT) and radial point interpolation method (RPIM) for the box girder bridge model is proposed. In this model, the displacement fields of top, bottom, and web plates are represented using FSDT and numerically discretized by RPIM. The traditional TBCS is upgraded by replacing the Euler beam model with the novel model. This is the first time that the framework has been applied to TBCS field. Several numerical examples are presented to highlight the accuracy of this novel model, and illustrate the differences, and advantages of it over the traditional model. The results indicate that the proposed model closely matches the accuracy of the solid element (C3D20) model; it can provide a comprehensive bridge response compared to traditional TBCS, and the latter may underestimate the dynamic response of the structure. The proposed model holds significant potential for the simulation of box structures and widespread application in the field of TBCS.

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Aircraft Engine Remaining Useful Life Prediction using neural networks and real-life engine operational data

Aircraft Engine Remaining Useful Life is a key factor which strongly affects flight operations safety and flight operators business decisions. In the article authors decided to present the concept of engine remaining useful life prediction. Proposed method was created as a result of the analysis of the real turbofan engine operational data collected for a few years which was used as an input data for the deep neural network, in order to train, validate and test machine learning algorithms. Two architectures of deep neural networks were created: multilayered deep convolutional neural networks and a long short-term memory network with regression output. Both neural networks were trained, validated and tested on the same engine data and with a various network training options. Results were compared with the neural network performance metrics and figures presenting network prediction convergence. To present how the real-life engine dataset differs the results from the simulated data, both datasets were validated on the same neural network architectures. The main purpose of this article was to present the idea and method of how the artificial neural networks could be used to predict aircraft remaining useful life indicator on the real-life engine operational data not the simulated one.

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