This paper proposes an intelligent monitoring method for power machinery failures based on acoustic wave characteristics, which involves the construction of an improvedconvolutional neural network (CNN) and long short-term memory (LSTM) model. From an implementation perspective, the proposed method consists of three key steps: the development of an intrinsic acoustic wave database related to a mechanical equipment failure, the execution of intelligent failure monitoring, and the creation of a visual operation interface. Specifically, when a mechanical failure occurs, acoustic wave data containing information about the failure is collected and processed. The fast Fourier transform (FFT) is employed to extract the acoustic wave characteristics associated with equipment failures. A comprehensive database of these characteristics is compiled from various equipment failures, such as fan blade damage and pump body leakage, and stored as a failure eigen acoustic wave database. Then, during the intelligent monitoring of specific mechanical equipment failures, the intrinsic acoustic wave database acquired from these failures serves as an embedded feature for extracting and outputting the corresponding failure's acoustic wave fragments. This process enables the identification of the failure type and facilitates accurate early warning regarding potential equipment failures. Furthermore, a visual operational interface has been developed based on an improved CNN-LSTM neural network model, which allows for straightforward and precise intelligent monitoring of mechanical failures. This intelligent monitoring approach, grounded in the characteristics of acoustic waves, offers several advantages, including low cost, ease of deployment, and high identification efficiency. These attributes make it particularly well-suited for applications in complex operational environments within power machinery systems across various industries, including aerospace and nuclear power.

A novel solving approach featuring bi-mapping, referred to as B-ICM, has been developed based on the ICM (independent, continuous, and mapping) method of structural topology optimization. B-ICM consists of two distinct steps: the first involves applying linear (L) mapping to the structural topology optimization problem, transforming it into a discrete model, and subsequently constructing the constraint function; the second entails implementing nonlinear (NL) mapping on this discrete model to create a continuous model, while converting continuous elemental topology variables into discrete ones. In contrast to the original ICM method, wherein the first step serves solely as a theoretical derivation, and the construction of constraint functions along with modeling and solving algorithms are all encompassed within the second step, which is categorized as a “one-step” approach, the B-ICM is classified as a “two-step” approach. Despite this distinction, it still employs the sequential dual quadratic programming algorithm commonly utilized in ICM methods for solving optimization models. We demonstrated this modeling and solving process using the structural topology optimization problem of volume minimization with displacement constraints. Results from both single-load and multi-load cases validated the effectiveness of our approach. We compared iteration count, clarity, and optimization capability across three methods for achieving distinct topologies: (1) the SIMP method considering Heaviside projection, (2) the floating projection topology optimization (FPTO) method, (3) the non-penalized method of smooth-edged material distribution for optimizing topology (SEMDOT), as well as the original ICM method. Results indicated that B-ICM outperforms these alternatives. This study not only enhances the modeling strategy and refines the solution approach of the ICM method, but also offers a superior technique for addressing blurry boundary problems. In continuum topology optimization, optimal topologies with blurry boundaries are typically generated through filtering operations designed to mitigate checkerboard patterns and mesh dependency issues. Notably, an increase in the filtering radius results in a more blurred boundary. Our study successfully addressed this challenge by achieving clear boundaries for optimal topologies. Importantly, the key techniques developed here are applicable to all continuous variable optimization methods, including the variable density method.

Nanoplate structures are widely used in nanoelectromechanical systems because of their excellent mechanical properties. At the same volume, the specific surface area of the nano-laminates is much larger than that of the single-layer nanomaterials, and the surface effect is more significant. The influence of the surface effect on nanostructures can be regarded as the combination of surface elasticity and surface residual stress. The classical plate-shell theory does not consider the surface effect and is no longer suitable for describing nanostructures such as nanoplate-shells. As a new type of composite materials, functionally graded materials have received more and more attention from researchers. The mechanical properties of micro/nano structural components made of functionally graded materials are completely different from the macroscopic structures made of conventional materials. Plate structure is a basic component in nanoelectromechanical systems, so it is necessary to study the mechanical properties of plates made of functionally graded materials. In this paper, based on Kirchhoff plate theory and Mindlin plate theory considering shear deformation, the buckling and post-buckling behaviors of functionally graded sandwich nanoplates with surface effect are studied. Based on the force balance analysis, the governing equations of buckling and post-buckling are obtained. The analytical solutions of critical buckling loads under uniaxial and biaxial compression are given. By using the Galerkin method, the approximate solutions of critical post-buckling loads under movable and immovable boundary conditions are given. Numerical results show that the influence of surface effects on the stability of functionally graded nano-laminated plates is related to the volume fraction of the materials that make up the plates, as well as to the ratio of structural surface area to volume. Considering that shear deformation will reduce the critical load for buckling and post-buckling of functionally graded nano-laminated plates, the influence of shear deformation can be ignored for thinner nano-laminated plates.

Most existing isotropic metamaterial designs assume that the base material possesses isotropic symmetry. However, 316L steel produced by selective laser melting (SLM) typically exhibits mechanical anisotropy, which strongly depends on the manufacturing process and parameters. The limited experimental studies currently available are insufficient to fully reveal the elastic symmetry of 316L steel under different scanning strategies, and the quantitative impact of varying laser powers on Young's modulus remains unknown. In this study, ultrasonic resonance experiments were conducted to characterize the elastic constants of 316L steel under two typical laser scanning strategies (parallel and orthogonal scanning) and two laser power levels (214.2 W and 274.2 W). The results indicate that under the orthogonal scanning strategy, the symmetry of the steel degraded to transverse isotropy at a power of 214.2 W, while the material remained orthotropic at a power of 274.2 W, indicating that transverse isotropy results from specific laser power. Compared to orthogonal scanning, 316L steel produced using parallel scanning exhibited stronger anisotropy. Within the range of laser powers investigated, Young's modulus was found to be insensitive to power variations. Based on the experimentally obtained elastic constants and finite element simulations, we optimized the elastic isotropy of three types of truss metamaterials: FCC-BCC, SC-OT, and SC-OT-BCC, achieving shape control by adjusting rod dimensions. The optimization results showed that isotropic metamaterials made from SLM 316L steel and cast 316L steel possess nearly identical elastic properties. From an application perspective, this research offers feasible solutions to overcome the technical challenges of producing isotropic metamaterials using SLM additive manufacturing.

Factors such as processing techniques, daily abrasion, and atmospheric corrosion lead to a specific level of surface roughness in component surfaces. This paper examines the tensile and compressive deformation behavior of circular rods with axisymmetric random rough surfaces. First, a digital reconstruction of the rough circular rod model was conducted, where the rough surface was jointly characterized by two statistical parameters: root mean square height and correlation length. Then, the dimensionless governing differential equation for both tensile and compressive cases of the rough circular rod was derived through the infinitesimal element method. Subsequently, in combination with the perturbation method and the fast Fourier transform, the governing equation was solved, yielding the perturbation solutions for both types of deformation of the rough circular rod. The validity of these solutions was verified through comparisons with analytical and finite element solutions. Finally, to explore the influence of the statistical parameters of rough surfaces on tensile and compressive deformation, the contributions of first-order and second-order perturbation solutions were systematically compared, and an empirical formula for the perturbation amplitude was established. The effect of perturbation on the results gradually increased with root mean square height and correlation length; the proportion of the second-order perturbation solution rose with root mean square height but remained unaffected by correlation length. This work not only expands the research scope of traditional problems in mechanics of materials related to tensile and compressive deformation, but also provides an example of the application of mathematical and physical methods in mechanical practice. Moreover, this study offers a theoretical basis for optimizing the manufacturing processes of components and quantitatively assessing the impact of surface defects on the mechanical properties of components.

Dissimilar metal welds in special vehicles and equipment are essential parts that significantly influence their performance. This study focuses on how different working temperatures affect crack propagation behavior in these welds. The research employs the extended finite element method (XFEM) as its numerical analysis tool. By refining the local mesh at the crack tip, it avoids complex crack-tip field enhancement function found in traditional methods. Temperature effects are integrated into the interaction integral, and its form is simplified to isolate terms related to material properties and temperature. The method for extracting mode I and mode II stress intensity factors is provided. Thus, a numerical calculation method for the stress intensity factor at the crack tip of dissimilar metal welds in special vehicles has been established. Taking a two-dimensional infinite plate with a crack as an example, stress intensity factors are calculated under various temperature loads and crack lengths and compared with the analytical solution. It is found that the relative error between the two is less than 1.3%, and changes in different integration regions have minimal impact on results. The correctness and integral region independence of this method have been verified. Then, based on the maximum circumferential stress criterion and the above research content, a quasi-static crack growth numerical simulation method is established for different temperatures. Simulations are conducted at low, normal, and high temperatures to examine crack propagation paths at various locations. Initial crack growth angles and paths reveal that crack propagation in the welding zone is predominantly mode I at normal temperatures. However, low and high temperatures have opposite effects on crack direction. Additionally, cracks may propagate across weld boundaries. This finding significantly advances the prediction of performance at different working temperatures in dissimilar metal welds of special vehicles and provides crucial guidance for durability and safety design in engineering applications.

Most methods for solving plane stress problems currently rely on the finite element method (FEM), which is widely accepted for its established effectiveness in various engineering applications. However, FEM often suffers from shear locking during the solution process. To address this challenge and improve the solution of plane stress problems without the risk of shear locking, this paper adopts a physics-informed neural network (PINN) approach. The PINN framework integrates physical laws directly into the neural network training, allowing for the bypassing of traditional mesh requirements, which presents a considerable advantage. In this approach, internal and boundary points are randomly generated within the specified domain[x, y], serving as the basis for calculations. The geometric equations, constitutive equations, and balance equations that describe the behavior of the materials involved are incorporated into the model. Additionally, the physical constraints of boundary conditions for the boundary points are included in the loss function of the neural network model. This integration ensures that the model accurately reflects physical reality and adheres to the governing equations. By minimizing the loss function, the model effectively approximates the solution of the partial differential equations (PDEs) associated with plane stress problems. Importantly, this method does not require mesh generation, simplifying the computational process. Instead, it focuses solely on optimizing the loss functions for the internal and boundary points. To validate the proposed approach, further analysis is conducted to compare the PINN method with traditional FEM. The results demonstrate that the PINN method can solve plane stress problems without the need for labeled data. Furthermore, it effectively addresses finite element defects arising from spurious shear deformation due to mesh generation, specifically shear locking. Additionally, case studies indicate that the PINN method maintains high accuracy even with complex boundaries and varying stress conditions. This feature suggests that the method has significant potential for practical engineering applications in the future.

This paper derives the analytical solution for the buckling load of extension-twist multicoupled laminates. The optimization targets both the buckling load and coupling effect, with verification through simulations and experiments. Using the double trigonometric series method, the analytical solution for the buckling load of the extension-twist multicoupled laminate, simply supported on all four sides and subjected to in-plane compressive loads, is obtained. Based on this, a multi-objective optimization design model for the extension-twist multicoupled laminate is established, with the optimization objectives of maximal buckling load and coupling effect. The optimization is achieved using the sequential quadratic programming (SQP) algorithm, and the laminate with enhanced coupling effect and buckling load is designed. Based on the ply angle sequence of the optimal laminate, numerical simulations and robustness analyses confirm the buckling model. Compared to the simulation results, the analytical solution error for the buckling load of the laminate is within 5%, validating the theoretical framework. Experimental measurements using a multi-directional loading test machine reveal that the discrepancy between measured and theoretical buckling loads is within 3%, further verifying the theory's accuracy. This research provides valuable insights for improving the load-bearing capacity of structures such as wings and wind turbine blades.

The non-probabilistic convex model requires only the boundaries of structurally uncertain parameters, making it suitable for dealing with engineering problems with limited samples. However, existing convex models primarily focus on regular mathematical models, potentially leading to an excessive expansion of the uncertainty domain. This paper introduces a new type of convex model, namely the interval and ellipsoidal intersection model, to more accurately constrain the uncertainty domain, and examines its application in structural uncertainty propagation analysis. Firstly, the interval and ellipsoidal intersection model is proposed to describe the uncertainty domain, which is constructed by taking the intersection of the interval model and the ellipsoidal model. Subsequently, the proposed model is applied to structural uncertainty propagation analysis with two cases of nonlinear response functions. For the weakly nonlinear response function, a linear approximation is derived using the first-order Taylor series expansion, and then a semi-analytical method is developed to predict its structural response interval. For the strongly nonlinear response function, a nonlinear approximation is achieved using the second-order Taylor series expansion, and the sequential quadratic programming (SQP) method is adopted to predict its structural response interval. Finally, results from four numerical examples indicate that the proposed model generally offers a smaller uncertainty domain and narrower structural response interval compared to the traditional interval and ellipsoidal models. Additionally, the semi-analytical method is more efficient than the SQP method and the Monte Carlo simulation (MCS) method.

The distance-minimizing data-driven method introduces a new computing paradigm and focuses on computational solid mechanics research. This method enables direct input of discrete material data sets (stress-strain pairs), bypassing the empirical constitutive modeling process and reducing modeling errors and uncertainties. To apply this method to boundary value problems, it is necessary to define a distance functional from the solution set to the material data set, seeking the functional extremum that satisfies the strain-displacement relationship and equilibrium equation from the material data set. In this study, we extend the method to structural dynamics, using the structural dynamic equilibrium equation as a constraint for the distance functional. We derive data-driven computing formulas, analyze the value of the constant matrix in the formula, and develop an algorithm for solving structural dynamic responses. The accuracy and efficiency of the proposed method are validated through linear and nonlinear dynamic response analyses of single-degree-of-freedom systems and multi-degree-of-freedom truss structures. Within this theory, the final value from the previous moment serves as the initial value for the current moment, facilitating faster numerical solutions and reducing computational time. Additionally, the material data set accommodates both linear and nonlinear material behaviors. It is also found that when the amount of material data exceeds 100, the amount of material data and excitation step minimally impact computational accuracy, with the signal-noise ratio (SNR) becoming the primary factor. Under the same conditions, the amount of material data and excitation step significantly affect computational efficiency, while the influence of SNR can be ignored. This study provides theoretical support for the development of data-driven dynamic solvers.

Shallow spherical shells are widely used in large structures, such as launch vehicles, nuclear containments, and gasholders. Studying scaled models of geometric and material distortion in the free vibration of large, thin-walled clamped shallow spherical shells is essential. First, frequency equations and natural frequency solutions for clamped shallow spherical shells are utilized to apply similarity transformation, leading to the derivation of generalized similarity conditions and scaling laws for the free vibration of these shells. The numerical approach is then validated by comparing the theoretical natural frequencies of clamped shallow spherical shells. Finally, geometric and material distortion similarity predictions for the free vibration of clamped shallow spherical shells with different rise-to-thickness ratios are investigated by numerical simulations. Results indicate that scaled models for clamped shallow spherical shells with different rise-to-thickness ratios, designed according to similarity conditions and incorporating the corresponding generalized similarity condition of mode shape and the natural frequency scaling relationship, can accurately predict the free vibration characteristics of the prototypes. For the free vibration of clamped shallow spherical shells with medium, small, and large rise-to-thickness ratios, in addition to meet the same mode shape, the distorted scaling laws also need to ensure that the scaling factors of rise-to-thickness ratio and Poisson's ratio equal 1, the scaling factor of Poisson's ratio is 1, and the rise-to-thickness ratios of scaled models and the prototype are no less than 25. The method and results of this study offer valuable insights for the design and experiments of scaled models for similar shells with thickness and material distortion.