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Freshwater scarcity has emerged as a major challenge of our time. Under this context, the importance of an efficient and energy-saving water desalination method is highlighted. In recent years, carbon nanotube (CNT) membrane characterizing with high permeability has attracted much attention in research, and it is regarded as a promising alternative to the conventional reverse osmosis technology. This work aims at numerically investigating the water desalination ability of a novel type of CNT membrane structure, namely the double-walled carbon nanotube (DWCNT) with Moiré pattern. After establishing the physical CNT models and running the molecular dynamics (MD) simulation of the water desalination system, it is found that both the single-walled carbon nanotube (SWCNT) and DWCNT can desalinate the seawater successfully while the water permeability of DWCNT is at least 18.9% higher than that of SWCNT within the same time. As far as the Moiŕe pattern adopted in this study is concerned, the water permeability of DWCNT without Moiŕe pattern is 18.6% higher than that with Moiré pattern.

The different elastic properties of tension and compression are obvious in many engineering materials, especially new materials. Materials with this characteristic, such as graphite, ceramics, and composite materials, are called bi-modulus materials. Their mechanical properties such as Young’s modulus have randomness in tension and compression due to different porosity, microstructure, etc. To calibrate the mechanical properties of bi-modulus materials by bridging finite element method (FEM) simulation results and scarce experimental data, the paper presents a data-fusion computational method. The FEM simulation is implemented based on parametric variational principle (PVP), while the experimental result is obtained by digital image correlation (DIC) technology. To deal with scarce experimental data, maximum entropy principle (MEP) is employed for the uncertainty quantification (UQ) and calibration of material parameters and responses, which can retain the original probabilistic property of a priori data. The non-parametric p-box is used as a constraint for data fusion. The method presented in this paper can quantify the mechanical properties of materials with high uncertainty, which is verified by a typical example of bi-modulus graphite. It is possible to find applications in the real-time estimation of structural reliability by combining with digital twin technology in the future.

The benefits of tracking, identifying, measuring features of interest from structure responses have endless applications for saving cost, time and improving safety. To date, structural health monitoring (SHM) has been extensively applied in several fields, such as aerospace, automotive, and mechanical engineering. However, the focus of this paper is to provide a comprehensive up-to-date review of civil engineering structures such as buildings, bridges, and other infrastructures. For this reason, this article commences with a concise introduction to the fundamental definitions of SHM. The next section presents the general concepts and factors that determine the best strategy to be employed for SHM. Afterward, a thorough review of the most prevalent anomaly detection approaches, from classic techniques to advanced methods, is presented. Subsequently, some popular benchmarks, including laboratory specimens and real structures for validating the proposed methodologies, are demonstrated and discussed. Finally, the advantages and disadvantages of each method are summarized, which can be helpful in future studies.

In recent years, two types of nonlocal differential operators and their theories and formulations have been proposed and used in numerical modeling and computations, particularly simulations of material and structural failures, such as fracture and crack propagations in solids. Since the differences of these nonlocal operators are subtle, and they often cause confusion and misunderstandings. The first type of nonlocal differential operators is derived from the Taylor series expansion of nonlocal interpolation, e.g., Bergel and Li (Comput Mech 58(2):351–370, 2016), Madenci et al. (Comput Methods Appl Mech Eng 304:408–451, 2016) and Ren et al. (Comput Methods Appl Mech Eng 358:112621, 2020). The second type of nonlocal operators is based on the nonlocal operator theory in peridynamic theory, which is a class of antisymmetric nonlocal operators stemming from the nonlocal balance laws, e.g., Gunzburger and Lehoucq (Multiscale Model Simul 8(5):1581–1598, 2010) and Du et al. (SIAM Rev 54(4):667–696, 2012; Math Models Methods Appl Sci 23(03):493–540, 2013a). In this work, a comparative study is conducted for evaluating the computational performances of these two types of nonlocal differential operators. It is found that the first type of nonlocal differential operators can yield convergent results in both uniform and non-uniform particle distributions. In contrast, the second type of nonlocal differential operators can only converge in uniform particle distributions. Specifically, we have evaluated the performance of the two types of nonlocal differential operators in three crack propagation simulation examples. The results show that the second type of nonlocal differential operators is more suitable to deal with complex crack branching patterns than the first type of nonlocal differential operators, and the simulation results obtained by using the second type of nonlocal differential operators have better agreement with experimental observations. For modelings of simple crack growth and conventional elastic deformation problems, both nonlocal operators can provide good results in simulations.

An original comprehensive guide on computational nanomechanics discussing basic concepts and implications in areas such as computational physics, materials, mechanics and engineering as well as several other interdisciplinary avenues. This book makes the underlying theory accessible to readers without specialised training or extensive background in quantum physics, statistical mechanics, or theoretical chemistry. It combines a careful treatment of theoretical concepts with a detailed tutorial on computer software and computing implementation, including multiscale simulation and computational statistical theory. Multidisciplinary perspectives are provided, yielding a true insight on the applications of computational nanomechanics across diverse engineering fields. The book can serve as a practical guide with step-by-step discussion of coding, example problems and case studies. This book will be essential reading for students new to the subject, as well as an excellent reference for graduates and researchers.

The conventional flow rule and plasticity constitutive models implicitly assume that the principal stress and plastic strain rate are coaxial, which often result in various setbacks in modeling, for example, the bifurcation and the evolution of shear band cannot be accurately and adequately captured. In this work, we investigate the non-coaxial effects of an elastoplastic constitutive model on strain localization in the framework of micropolar plasticity theory. The vertex-like yield surface is introduced in the flow rule, and a non-coaxial Drucker–Prager model is implemented in a user-defined finite element subroutine (UEL) in ABAQUS. The simple shear test and the plane strain compression test have been conducted to study the non-coaxial stress–strain response and the strain localization behavior. Effects of boundary condition on bifurcation, shear band orientation and thickness are investigated. Results indicate that the proposed model shows a good performance in the simulation of the non-coaxial behavior of geomaterials. On the premise of overcoming mesh dependency, the shear band orientation and width predicted by the non-coaxial model possess higher values than those predicted by the coaxial model, and a better agreement with the experimental results are achieved by the non-coaxial model. Moreover, by analyzing stress–strain responses, we have found that material strength may be systematically overestimated by the coaxial model.

Accurately estimating the interfacial bond capacity of the near-surface mounted (NSM) carbon fiber-reinforced polymer (CFRP) to concrete joint is a fundamental task in the strengthening and retrofit of existing reinforced concrete (RC) structures. The machine learning (ML) approach may provide an alternative to the commonly used semi-empirical or semi-analytical methods. Therefore, in this work we have developed a predictive model based on an artificial neural network (ANN) approach, i.e. using a back propagation neural network (BPNN), to map the complex data pattern obtained from an NSM CFRP to concrete joint. It involves a set of nine material and geometric input parameters and one output value. Moreover, by employing the neural interpretation diagram (NID) technique, the BPNN model becomes interpretable, as the influence of each input variable on the model can be tracked and quantified based on the connection weights of the neural network. An extensive database including 163 pull-out testing samples, collected from the authors’ research group and from published results in the literature, is used to train and verify the ANN. Our results show that the prediction given by the BPNN model agrees well with the experimental data and yields a coefficient of determination of 0.957 on the whole database. After removing one non-significant feature, the BPNN becomes even more computationally efficient and accurate. in addition, compared with the existed semi-analytical model, the ANN-based approach demonstrates a more accurate estimation. Therefore, the proposed ML method may be a promising alternative for predicting the bond strength of NSM CFRP to concrete joint for structural engineers.

Solid object water entry is a common problem in various natural, industrial and military applications, which involves large deformation of free surfaces and violent fluid–structure interactions. In computational fluid dynamics (CFD), this is a type of problem that tests the robustness and capacity of any CFD numerical algorithm. In this work, the newly-developed updated Lagrangian particle hydrodynamics (ULPH) method, which is a fluid version of peridynamics, is enhanced and applied to simulate solid object water entry problems. ULPH method is Lagrangian meshfree particle method that can ensure the specific free surface conditions automatically satisfied. The density filter and artificial viscosity diffusion are adopted in the ULPH scheme to stabilize and smooth the pressure field. In the process of a rigid body entering water, it may induce negative pressure in some areas of impact region, which can cause spurious tensile instability in some meshfree particle simulations, such as SPH and ULPH. A tensile instability control technique based on the ULPH framework has been developed to overcome the numerical instability. To validate the stability and accuracy of the ULPH approach in simulating water entry problems, several 2D and 3D examples of water entry have been carried out in this work. The necking and cavity pinch-off phenomena are visible in the numerical results. The simulation results of the ULPH method are well compared with experimental data and other numerical solutions. The water crown, cavity shapes and stream pattern formed around the rigid body entering the water can be well captured. The computation results show that the ULPH method has the ability to simulate the complex solid object water entry precess accurately.

In this paper, the dynamic amplification factors (DAFs) of high-speed railway continuous girder bridges are studied. The vehicle-bridge interactions (VBIs) of 13 concrete continuous girder bridges with spans ranging from 48 to 130 m are analyzed, the influences of the train speed, the train marshalling and the bridge fundamental frequency on the DAF are investigated, and the DAF design standard for high-speed railway bridges is discussed. The results indicate that for the continuous beam bridge whose fundamental frequency is less than 3.0 Hz, the maximum DAF is no more than 1.15; while for the bridge examples with a fundamental frequency larger than 3.0 Hz, the maximum DAF reaches 1.25 because the resonance occurs at high train speed. The empirical formulas of the DAFs in the Japan Railway Technical Research Institute (JRTRI) code could provide a conservative estimation of the DAFs of high-speed railway continuous bridges.

In this work, we have developed a data-driven artificial intelligence (AI) solution to assist the ship hull design process. Specifically, we have developed and implemented an AI-based multiple-input neural network model to realize the real-time prediction of the total resistance of the ship hull structure while avoiding the inconsistent estimates from different types of design input parameters. It is demonstrated that the developed AI-based machine learning algorithm as a prediction tool can assist the ship hull design process by accurately providing the total resistance of ship hulls in real time. Moreover, we have conducted design tasks to validate the proposed method, and the validation results show that a well-trained artificial neural network model can avoid the problem of different sensitivities due to the different degrees of influence of the input parameters on the output parameter. The proposed AI-based data-driven solution provides a real-time hydrodynamic performance calculation, which can predict the hyperdynamic performances of ship hulls based on their geometry modification parameters. This approach gives a consistent prediction in terms of accuracy when facing different geometry modification parameters, and it in turn provides a fast and accurate AI-based method to assist ship hull design to achieve an optimum forecast accuracy in the entire design space, making an advance to artificial intelligence assist design in naval architecture engineering.

Unformatted post-print version of the accepted article The inclusion of polymer-grafted nanoparticles (PGNPs) can impart various functional properties to polymer nanocomposites (PNCs). For semicrystalline polymers, we can control the spatial dispersion of PGNPs and presumably use it to modulate the nucleation rate of the polymer. In this work, the correlation between the dispersion quality of poly(ethylene glycol) (PEG) grafted silica (PEG-g-SiO2) nanoparticles and the crystallization ability of poly(ethylene oxide) (PEO) nanocomposites is systematically investigated by varying the grafting density (σ, chains/nm2) and the value of P/N (P: molecular weight of matrix chains, N: molecular weight of grafted chains). The variation of PEG-g-SiO2 dispersion state was studied by morphological characterization and small-angle X-ray scattering (SAXS). It was found that, in contrast to the unmodified SiO2 and poly(methyl methacrylate) grafted silica (PMMA-g-SiO2), PEG-g-SiO2 (high σ and low P/N) can increase the nucleation rate of PEO even under conditions where they are well dispersed in the PEO matrix. Evidently, the nature of the graft, i.e., amorphous PMMA vs. crystallizable PEO, has profound consequences in this context, a novel result that has not been anticipated based on previous work. NP aggregation occurs at higher P/N values and limits the effectiveness of the grafted PEG on the crystallization ability of PEO nanocomposites. Based on differential scanning calorimetry (DSC) and polarized light optical microscopy (PLOM) characterization, we deduced that the increased nucleation density at high σ and low P/N has a strong impact on accelerating the overall crystallization of PEO nanocomposites. This project is supported by the Ministry of Science and Technology of China (2017YFE0117800) and the National Natural Science Foundation of China (21574141). We would like to thank the financial support provided by the BIODEST project; this project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 778092. The authors thank Prof. Yongfen Men (CIAC) for providing the SAXS beam time and helps during experiments. The discussion of TEM data with Andrew M. Jimenez (Columbia University, New York, NY) is gratefully acknowledged. We also thank B. Guan, J. L. Yue and K. A. Liu for help in cryo-TEM experiments.