Search Results (152)

This study proposes a fast material parameter evaluation method for multilayer thin-film structures based on machine learning technology to solve the problems of long time and low efficiency in the traditional material parameter inversion process. Nanoindentation experiments are first conducted to establish an experimental basis across film stacks. A two-dimensional elasto-plastic model of the indentation process is then built to generate a large set of load–depth curves, which serve as training data for a machine learning model. Trained on simulated curves and validated against measurements, the model enables fast inverse identification of layer-wise plastic parameters and interfacial cohesive properties. The experimental results show that the method has high accuracy and efficiency in the inversion of interlayer cohesion parameters, and the correlation coefficient R² is 0.99 or more. Compared with traditional methods, the pipeline supports batch analysis of multiple datasets and delivers parameter estimates within 1 h, substantially shortening turnaround time while improving result reliability. This method can not only effectively solve the challenges faced by traditional material evaluation, but also provide a new and effective tool for the performance evaluation and optimization design of multilayer thin-film materials. It has broad application prospects and potential value. Full article

We consider an axysimmetric cylindrical vessel grown by surface deposition at the inner boundary. The residual stress in the vessel can vary, e.g., depending on the loading history during growth. Can we represent and characterize a stress-free material (namely, reference) configuration for the vessel? Extending an idea initially proposed for surface growth occurring on a fixed boundary, the material configuration is introduced as a two-dimensional manifold immersed in a three-dimensional space. The problem is first formulated in fairly general terms for an incompressible neo-Hookean material in plane strain and then specialized to material configurations represented by ruled surfaces. An illustrative example using geometric and material parameters of carotid arteries shows the characterization of different material configurations based on their three-dimensional slope and computes the corresponding residual stress fields. Finally, such a slope is shown to be in a one to one relationship with the customary measure of residual stress in arteries, i.e., the opening angle in response to a cut. The present work introduces a novel framework for residual stress and shows its applicability in a special setting. Several generalizations and extensions are certainly necessary in the following sections to further test and assess the proposed method. Full article

This study proposes a novel egg-crate honeycomb core sandwich panel (SP-EHC) that combines the structural advantages of conventional lattice and grid configurations while mitigating their limitations in stability and mechanical performance. The design employs chamfered intersecting grid walls to create a semi-enclosed honeycomb architecture, enhancing out-of-plane stiffness and buckling resistance and enabling ventilation and drainage. To facilitate efficient and accurate structural analysis, a two-dimensional equivalent plate model (2D-EPM) is developed using the variational asymptotic method (VAM). This model significantly reduces the complexity of three-dimensional elasticity problems while preserving essential microstructural characteristics. A Reissner–Mindlin-type formulation is derived, enabling local field reconstruction for detailed stress and displacement evaluation. Model validation is conducted through experimental testing and three-dimensional finite element simulations. The 2D-EPM demonstrates high accuracy, with static analysis errors in load–displacement response within 10% and a maximum modal frequency error of 10.23% in dynamic analysis. The buckling and bending analyses, with or without initial deformation, show strong agreement with the 3D-FEM results, with deviations in the critical buckling load not exceeding 5.23%. Local field reconstruction achieves stress and displacement prediction errors below 2.7%, confirming the model’s fidelity at both global and local scales. Overall, the VAM-based 2D-EPM provides a robust and computationally efficient framework for the structural analysis and optimization of advanced sandwich panels. Full article

In subway stations constructed using the cut-and-cover method, an increasing number of projects are adopting the form of precast components combined with on-site assembly. However, analysis of the novel structural elements within such overlapped subway stations remains inadequate. To simulate the shear failure mechanism at slab–wall joints, the structural behavior of these joints in overlapped subway stations is idealized as a rigid die stamping problem. An admissible failure mechanism is constructed, comprising a rigid wedge zone and a vertical tensile fracture perpendicular to a smooth base. The limit analysis approach is adopted, a two-dimensional velocity field is constructed, and the upper-bound theorem is applied to determine the bearing capacity of these joints under strip loading, utilizing a modified Coulomb yield criterion incorporating a small tensile stress cutoff. The failure mechanism proposed on the basis of an engineering case is validated through analytical calculations and parametric studies. Finally, a parametric analysis is conducted to investigate the influence of factors such as the geometric configuration of the slab–wall joints and the tensile and compressive strengths of concrete on their ultimate bearing capacity. The results obtained can provide an effective reference for the design and construction of precast slab–wall joints in future overlapped subway station projects. Full article

Energy generation from renewable sources has increased exponentially worldwide, particularly wind energy, which is converted into electricity through wind turbines. The growing demand for renewable energy has driven the development of horizontal-axis wind turbines with larger dimensions, as the energy captured is proportional to the area swept by the rotor blades. In this context, the dynamic loads typically observed in wind turbine towers include vibrations caused by rotating blades at the top of the tower, wind pressure, and earthquakes (less common). In offshore wind farms, wind turbine towers are also subjected to dynamic loads from waves and ocean currents. Vortex-induced vibration can be an undesirable phenomenon, as it may lead to significant adverse effects on wind turbine structures. This study presents a two-dimensional transient model for a rigid body anchored by a torsional spring subjected to a constant velocity flow. We applied a coupling of the Fourier pseudospectral method (FPM) and immersed boundary method (IBM), referred to in this study as IMERSPEC, for a two-dimensional, incompressible, and isothermal flow with constant properties—the FPM to solve the Navier–Stokes equations, and IBM to represent the geometries. Computational simulations, solved at an aspect ratio of $\varphi =4.0$, were analyzed, considering Reynolds numbers ranging from $Re=150$ to Re = 1000 when the cylinder is stationary, and $Re=250$ when the cylinder is in motion. In addition to evaluating vortex shedding and Strouhal number, the study focuses on the characterization of space–time symmetry during the galloping response. The results show a spatial symmetry breaking in the flow patterns, while the oscillatory motion of the rigid body preserves temporal symmetry. The numerical accuracy suggested that the IMERSPEC methodology can effectively solve complex problems. Moreover, the proposed IMERSPEC approach demonstrates notable advantages over conventional techniques, particularly in terms of spectral accuracy, low numerical diffusion, and ease of implementation for moving boundaries. These features make the model especially efficient and suitable for capturing intricate fluid–structure interactions, offering a promising tool for analyzing wind turbine dynamics and other similar systems. Full article

Addressing the problem that energy supply and load demand cannot be matched due to the difference in inertia effects among multiple energy sources, and taking into account the thermoelectric load, this paper designs a two-stage operation optimization model of IES considering multi-dimensional thermal inertia and constructs an intelligent adaptive solution method based on a time scale-model base. Validation is conducted through an arithmetic example. Scenario 2 has 15.3% fewer CO₂ emissions than Scenario 1, 19.7% less purchased electricity, and 20.0% less purchased electricity cost. The optimal algorithm for the day-ahead phase is GA, and the optimal algorithm for the intraday phase is PSO, which is able to produce optimization results in a few minutes. Full article

The short-term scheduling of pumped-storage hydropower plants is characterised by high dimensionality and nonlinearity and is subject to multiple operational constraints. This study proposes an intelligent scheduling framework that integrates an Atomic Orbital Search (AOS)-optimised Long Short-Term Memory (LSTM) network with the Deep Deterministic Policy Gradient (DDPG) algorithm to minimise water consumption during the generation period while satisfying constraints such as system load and safety states. Firstly, the AOS-LSTM model simultaneously optimises the number of hidden neurons, batch size, and training epochs to achieve high-precision fitting of unit flow–efficiency characteristic curves, reducing the fitting error by more than 65.35% compared with traditional methods. Subsequently, the high-precision fitted curves are embedded into a Markov decision process to guide DDPG in performing constraint-aware load scheduling. Under a typical daily load scenario, the proposed scheduling framework achieves fast inference decisions within 1 s, reducing water consumption by 0.85%, 1.78%, and 2.36% compared to standard DDPG, Particle Swarm Optimisation, and Dynamic Programming methods, respectively. In addition, only two vibration-zone operations and two vibration-zone crossings are recorded, representing a reduction of more than 90% compared with the above two traditional optimisation methods, significantly improving scheduling safety and operational stability. The results validate the proposed method’s economic efficiency and reliability in high-dimensional, multi-constraint pumped-storage scheduling problems and provide strong technical support for intelligent scheduling systems. Full article

The concept of variable-stiffness composites allows the stiffness properties to vary spatially in the material. In the case of fiber-reinforced composites, the mechanical properties of the composite can be improved by tailoring the fiber orientations in a spatially optimal manner. In this paper, the problem of optimal spatial orientation of fibers in a two-dimensional composite structure under in-plane loading is studied, using the strain energy-minimizing method. The fiber orientation is assumed to be constant within each element of the model but varies from element to element. The optimal design problem is solved numerically using a global optimization method based on a genetic algorithm. Some numerical examples illustrate the efficiency and applicability of the method. Full article

We consider a two-dimensional parabolic problem subject to both Neumann and Dirichlet boundary conditions, along with an integral constraint. Based on the integral observation, we solve the inverse problem of a recovering time-dependent right-hand side. By exploiting the structure of the boundary conditions, we reduce the original inverse problem to a one-dimensional formulation. We conduct a detailed analysis of the existence and uniqueness of the solution to the resulting one-dimensional loaded initial-boundary value problem. Furthermore, we derive estimates for both the solution and the unknown function. The direct and inverse problems are numerically solved by finite difference schemes. Numerical verification of the theoretical results is provided. Full article

Aiming at addressing the problem of insufficient fusion of multi-source heterogeneous features in short-term power load forecasting, this paper proposes a three-channel LSTM-CNN hybrid forecasting model. This method extracts the temporal characteristics of time, weather, and historical loads through independent LSTM channels and realizes cross-modal spatial correlation mining by using a Convolutional Neural Network (CNN). The time channel takes hour, week, and holiday codes as input to capture the daily/weekly cycle patterns. The meteorological channel integrates real-time data such as temperature and humidity and models the nonlinear delay effect between them and the load. The historical load channel sequence of the past 24 h is analyzed to interpret the internal trend and fluctuation characteristics. The output of the three channels is concatenated and then input into a one-dimensional convolutional layer. Cross-modal cooperative features are extracted through local perception. Finally, the 24 h load prediction value is output through the fully connected layer. The experimental results show that the prediction model based on the three-channel LSTM-CNN has a better prediction effect compared with the existing models, and its average absolute percentage error on the two datasets is reduced to 1.367% and 0.974%, respectively. The research results provide an expandable framework for multi-source time series data modeling, supporting the precise dispatching of smart grids and optimal energy allocation. Full article

To duly and correctly deliver parcels, both the capacity and the delivery route of a delivery vehicle need to be considered. Thus, the delivery process of a delivery vehicle can be characterized as a capacitated vehicle routing problem with three-dimensional loading constraints (3L-CVRP), which is an NP-hard problem. To solve the problem, a mathematical model is established in this paper to minimize the total delivery distance and maximize the loading rate, simultaneously. Additionally, a hybrid algorithm that combines a three-dimensional (3D) packing algorithm based on the residual space optimized (RSO) strategy and an improved genetic algorithm (IGA) is proposed. Initially, the proposed hybrid algorithm employs a modified Clarke–Wright savings algorithm to generate a feasible set of route solutions. Furthermore, building upon the traditional genetic algorithm, an elite retention strategy is introduced, and an enhanced order crossover method is utilized to improve the stability of the hybrid algorithm and its global search capability for optimal solutions. Finally, during each iteration of the algorithm, the RSO algorithm is integrated to verify the feasibility of 3D packing scheme. Two comparative experiments are conducted on 22 modified benchmark instances and actual logistics data of a university against two other algorithms, demonstrating that the proposed RSO-IGA algorithm achieves superior solutions in delivery efficiency. Full article

Aimed at solving the problem of insulation failure caused by the local overheating of the oil-immersed converter transformer, this paper investigates the heat transfer characteristics of the 500 kV converter transformer based on the electromagnetic-flow-heat coupling model. Firstly, this paper used the finite element method to calculate the core and winding loss. Then, a two-dimensional fluid-heat coupling model was used to investigate the effects of the inlet flow rate and the radius of the oil pipe on the heat transfer characteristics. The results show that the larger the inlet flow rate, the smaller the specific gravity of high-temperature transformer oil at the upper end of the tank. Increasing the pipe radius can reduce the temperature of the heat dissipation of the transformer in relative equilibrium. Still, the pipe radius is too large to lead to the reflux of the transformer oil in the oil outlet. Increasing the central and sub-winding turn distance, the oil flow diffusion area and flow velocity increase. Thus, the temperature near the winding is reduced by about 9%, and the upper and lower wall temperature is also reduced by about 4%. Based on the analysis of the sensitivity weight indicators of the above indicators, it is found that the oil flow rate has the largest share of influence on the hot spot temperature of the transformer. Finally, the surface temperature of the oil tank when the converter transformer is at full load is measured. In the paper, the heat transfer characteristics of the converter transformer are investigated through simulation and measurement, which can provide a certain reference value for the study of the insulation performance of the converter transformer. Full article

This work shows a three-dimensional (3D) layerwise model for static and free vibration analyses of functionally graded piezomagnetic materials (FGPM) spherical shell structures where magnetic and elastic fields are completely coupled. The 3D magneto-elastic governing equations for spherical shells are made of the three equations of equilibrium in three-dimensional form and the three-dimensional divergence equation for the magnetic induction. Governing equations are written in the orthogonal mixed curvilinear reference system ($\alpha$, $\beta$, z) allowing the analysis of several curved and flat geometries (plates, cylindrical shells and spherical shells) thanks to proper considerations of the radii of curvature. The static cases, actuator and sensor configurations and free vibration investigations are proposed. The resolution method uses the imposition of the Navier’s harmonic forms in the two in-plane directions and the exponential matrix methodology in the transverse normal direction. Single-layered and multilayered simply-supported FGPM structures have been investigated. In order to understand the behavior of FGPM structures, numerical values and trends along the thickness direction for displacements, stresses, magnetic potential, magnetic induction and free vibration modes are proposed. In the results section, a first assessment phase is proposed to demonstrate the validity of the formulation and to fix proper values for the convergence of results. Therefore, a new benchmark section is presented. Different cases are proposed for several material configurations, load boundary conditions and geometries. The possible effects involved in this problem (magneto-elastic coupling and effects related to embedded materials and thickness values of the layers) are discussed in depth for each thickness ratio. The innovative feature proposed in the present paper is the exact 3D study of magneto-elastic coupling effects in FGPM plates and shells for static and free vibration analyses by means of a unique and general formulation. Full article

Uneven inter-stage pressure drops of the common two-stage brush seal (CBS) lead to a problem that the second stage bristles bear excessive pressure load, and this problem leads to the premature failure of the brush seal. In this paper, a novel two-stage brush seal (NBS) with the backing plate holes of the second stage was proposed, and a three-dimensional numerical model of the NBS was established. Then, the effects of the pressure-equalizing (PE) hole on the inter-stage pressure drop distribution of the NBS were numerically analyzed, and an optimal structure was obtained. Finally, the leakage flow characteristics of this optimal structure were studied. The results showed that the NBS with PE hole increased the passage area of the downstream, and so effectively improved the uneven pressure drops of the CBS, and the pressure drop balance ratio of the NBS was obviously smaller than that of the CBS. For the structural parameters studied in this paper, the pressure drop balance ratio of the NBS was improved by 45.6~67.9% compared to the CBS. Moreover, when PE holes were 0.4 mm in diameter, 5.95 mm in height, and the number of rows was 1, the NBS had the best pressure drop balance and its leakage was only 8.7% higher than that of the CBS. Full article

With the growing global focus on sustainable development and climate change mitigation, promoting the low carbonization of energy systems has become an inevitable trend. Power load monitoring is crucial to achieving efficient power management, and load identification is the key link. The traditional load identification method has the problem of low accuracy. It is assumed that the technique of fusing harmonic features through color coding can improve the accuracy of load identification. In this paper, the load’s instantaneous reactive power, power factor and current sequence distribution characteristics are used as the mapping characteristics of the R, G and B channels of the two-dimensional V–I trajectory color image of the load using color coding technology. The harmonic amplitude characteristics are integrated to construct the mixed-color image of the load. The void residual shrinkage neural network is selected as the classification training model. The advantages and disadvantages of two residual shrinkage construction units, RSBU-CS and RSBU-CW, are analyzed. A single-load identification model with three RSBU-CWs is built. Different datasets verify the performance of the model. Compared with the test results of the ordinary color image dataset, the accuracy of the mixed-color image dataset is above 98%, and the accuracy of load identification is improved. Full article