It is urgent to develop advanced materials with high strength, high toughness and good ductility for modern engineering structures. Graphene reinforced metal matrix nanocomposites exhibit significantly enhanced strength and toughness, but their ductility remains relatively low due to the inherent tensile brittleness of graphene. Inspired by the origami concept, we utilize the surface hydrogenation method to develop an armchair-like folded graphene (AFG) structure as reinforcement for metal matrix composites. Molecular dynamics simulations show that the AFG structure can simultaneously enhance the tensile strength, stiffness, ductility, and toughness of copper (Cu) matrix composites. Compared with pristine graphene/Cu nanocomposites, AFG/Cu nanocomposites exhibit better ductility and toughness, while maintaining comparable strength and stiffness. Furthermore, the mechanical properties of AFG/Cu nanocomposites can be tuned by altering the degree of AFG folding and the distances between adjacent hydrogenated zones. The strengthening and toughening mechanism is that mechanically strong AFG can effectively block dislocation propagation across the metal-graphene interface before it unfolds to fracture. Such mechanism can be extended to other 2D nanomaterials reinforced metal matrix nanocomposites, opening up an avenue for developing high-performance nanocomposites.

More and more complex Civil Engineering problems are being considered in computational mechanics with the invention of high-quality computing techniques. In addition, the computational cost and storage requirement for complex and or large structures have increased dramatically, leading to an increased interest in removing the difficulties using any form of parallel computing. The process of applying the preload for parallel computing to any unstable structures is called a stabilising process, such as the Dynamic Relaxation Method (DRM) is one. This method minimises the energy by a simple vector iteration technique, which ultimately leads the structure to a static equilibrium state. The present study aims to highlight the utility of the DRM in the stabilisation process for small structures like building frames and large and or complicated structures such as bridges before actual transient analysis. Therefore, the present manuscript discusses the computational cost, CPU runtime, multiple increases of mass and rigid body displacement of building frames and bridges. The DRM allows an explicit solver to conduct a dynamic analysis by increasing the damping until the kinetic energy drops to a proposed value. The simulation of the DRM starts to find the equilibrium state with minimal dynamic effect, which is required to apply at the beginning of the solution phase to obtain the initial stress and displacement field before the start of the actual analysis.

This study proposes a fast yet reliable optimization framework for the aerodynamic design of transonic aircraft wings. Combining Computational Fluid Dynamics (CFD) and Machine Learning (ML), the framework is successfully applied to the Common Research Model (CRM) benchmark aircraft proposed by NASA. The framework relies on a series of automated CFD simulations, from which no less than 160 planform variations of the CRM wing are assessed from an aerodynamic standpoint. This database is used to educate an ML surrogate model, for which two specific algorithms are explored, namely eXtreme Gradient Boosting (XGB) and Light Gradient Boosting Machine (LGBM). Once trained with 80 % of this database and tested with the remaining 20 %, the ML surrogates are employed to explore a larger design space, their optimum being then inferred using an optimization framework relying on a Multi-Objective Genetic Algorithm (MOGAO). Each ML-based optimal planform is then simulated through CFD to confirm its aerodynamic merits, which are then compared against those of a conventional, fully CFD-based optimization. The comparison is very favourable, the best ML-based optimal planform exhibiting similar performances as its CFD-optimized counterpart (e.g. a 14 % higher lift-to-drag ratio) for only half of the CPU cost. Overall, this study demonstrates the potential of ML-based methods for optimizing aircraft wings, thereby paving the way to the adoption of more disruptive, data-driven aircraft design paradigms.

The transverse and vertical layouts of tendons within prestressed concrete (PC) box girders induce complex mechanical behaviors that necessitate precise evaluation for effective structural design. However, existing investigations often overlook the impact of tendon transverse layout. To address this gap, an improved beam element, designated as B12TS, is developed for shear deformation and shear lag analyses of PC box girders under prestressing effects. The element integrates the tendon transverse layout through non-uniform longitudinal displacements of the tendons modeled as a series of piecewise linear segments. The prestressing forces are converted into equivalent nodal forces acting on the elements. The element shape functions are derived from the homogeneous solutions to the relevant differential equations. Comparative analyses involving various beam element models, available experimental data, and three-dimensional (3D) finite element simulations demonstrate that the B12TS element model significantly enhances the accuracy and efficiency of predicting both deflections and stress distributions. Furthermore, the effects of prestressing on the flange and web tendons of typical PC box beams are examined to quantify the impacts of shear lag, shear deformation, and tendon transverse layout. The findings reveal that the transverse layout of the flange tendons remarkably influences both the magnitude and distribution shape of normal stresses, particularly near anchorage locations. Consequently, the B12TS element model proves to be a valuable analysis tool for designing prismatic and non-prismatic PC box girder bridges with various tendon layouts.

High-fidelity CFD simulations can easily generate terabytes to petabytes of resulting data. Post-processing of such data is not an easy task. It holds especially for volume rendering, one of the most illustrative but computationally intensive post-processing techniques.This paper presents an HPC-ready workflow for post-processing large-scale CFD data computed on unstructured meshes by volume rendering using matured visual effects tools. The workflow consists of five steps: (1) parallel loading of unstructured data into memory, (2) data load-balancing among available resources, (3) re-sampling unstructured data into a regular grid (voxelisation), (4) storing data to OpenVDB format, and (5) final high-quality volume rendering of the (possibly sparse) regular grid in Blender. The workflow is based on open-source libraries, where we have improved all these steps to build an effective and robust approach. Due to parallel loading and appropriate load balancing, our workflow (a) allows loading sequential databases that do not fit into the memory of a single node and (b) significantly outperforms current scientific visualisation tools in voxelisation scalability. Moreover, due to the connection to professional visual effects tools such as Blender, interactive or photo-realistic volume rendering by path tracing, which includes global illumination effects, is allowed.With the workflow, it is possible to re-sample hundreds of time steps on an unstructured mesh with 1 billion cells (tens of TB of data) to a sparse regular grid with a density of 11 billion voxels and prepare data for interactive visualisation in just a few minutes using thousands of CPU cores.

Due to high tensile strength, light weight, and good flexibility, the steel wire ropes with helical structures are widely used as crucial load-bearing components in various industrial sectors such as civil engineering. They are subjected to significant vibrations caused by multi-axial dynamic loading during the service period which may eventually result in premature failures. This paper presents a refined finite element analysis method for helical wire ropes under multi-axial dynamic loading. The proposed method employs multi-directional dynamic excitations extracted from the analysis of the overall engineering systems to consider actual loading conditions. Refined finite element analysis of the entire steel wire rope under multi-axial dynamic loading is carried out for the first time based on the global-local finite element model to obtain detailed mechanical responses. The critical rope segment is represented by solid elements taking into account the helical structure, inter-wire frictional contact, slippage, and material nonlinearity, among others, and non-critical segments are simulated with beam elements in the established global-local model, which can achieve good balance between computational efficiency and accuracy. The refined finite element modeling strategy is validated via three numerical examples with comparisons against the results in the literature. The proposed method is illustrated on the suspender cable used in suspension bridges. Detailed mechanical responses and their influencing factors are examined to acquire new insights into the dynamic mechanical characteristics of typical double-helical wire rope. The present work can provide an efficient tool for the assessment of in-service engineering systems containing helical wire ropes.

Engineering applications of finite volume Shallow-water Hydro-Sediment-Morphodynamic models (SHSM) have faced limitations due to their high computational demands arising from either extremely large amounts of computational cells or extremely small time steps at some regions and simultaneously the adoption of the globally minimum time step. To this end, we present an engineering-oriented modeling framework by (1) using the GPU-acceleration that overcomes the challenge of extremely large amounts of computational cells and (2) using a hybrid local-time-stepping/global maximum time step (LTS/GMaTS) strategy that mitigates the extremely small local time steps necessitated by locally-refined meshes or non-uniformity of flow conditions. The GPU parallel algorithm is tailored to fully leverage the computational power of GPU, optimizing numerical structure, kernel functions and memory usage, all in conjunction with the hybrid LTS/GMaTS implementation. We demonstrate its computational efficiency by simulating one experimental dam-break flow and a field-scale case in the Xinjiu waterway, Middle Yangtze River. The results show that the scheme performs well in terms of accuracy, efficiency, and robustness in reproducing real-world hydro-sediment-morphological evolution.

Computing position and orientation deviations (PODs) considering non-ideal morphologies and local deformations of mating surfaces is a key challenge in controlling the performances of assembled mechanical products through tolerance analysis. However, existing computational models based on Skin Model Shapes face difficulty in handling the PODs for all types of assembly issues. This paper proposes a novel multi-objective search-based approach for POD computation on a conception of force equilibrium constraints. A multi-objective particle swarm optimization algorithm is employed to solve the multi-objective model, and the efficiency and accuracy of the proposed approach are tested and compared with the NSGA-II and FEM methods. To demonstrate its distinctive capability to deal with assembly issues with multiple non-ideal mating surfaces, the proposed approach is applied to assemblies with multiple planar and cylindrical surfaces. Finally, an application of predicting the coaxial accuracy of a fuel pump is carried out, and the capabilities of the proposed approach are further demonstrated.

The current work introduces a new probability-based fuzzy type-2 decision mechanism to adjust the optimization process during the simultaneous size and topology optimization of the Skeletal structural systems. For the probabilistic part a binomial module is developed that feeds the fuzzy mechanism by forecasting success probability for future topological actions. The proposed fuzzy decision mechanism permanently monitors the optimization process and attends to dynamically tune the balance between size and topology actions. The presented strategy, by reducing the number of ineffective iterations, significantly enhances the efficiency of the optimization process. Since the proposed decision mechanism is designed as an auxiliary separate module it can be integrated with different optimization methods. Accordingly, in this study, it is integrated with four different optimization algorithms and applied to solve distinct size and topology problems. To comprehensively evaluate the effect of the proposed strategy a new performance index is defined and employed. The acquired results demonstrate that the proposed decision mechanism considerably enhances the search performance of the algorithms on handling the structural size and topology optimization problems.