International Journal of Mechanics and Materials in Design最新文献

The growing demand for energy has led to significant attention being given to the energy harvesting process from vibrations using piezoelectric materials. Given the limited energy available for conversion, robust designs that minimize sensitivity to parameter uncertainties or external variations are essential. To ensure project quality, multi-objective optimizations are necessary to maximize the mean and minimize the standard deviation of the response, but the computational cost increases with the number of uncertain parameters, requiring more efficient approaches. In this way, with metamodels, which are computational tools, it is possible to provide a faster and less costly evaluation of such computationally expensive models. This study proposes the use of a Kriging metamodel to design robust cantilever beam energy harvesting devices, combined with Monte Carlo Simulation to estimate the mean and standard deviation of the Frequency Response Function of power output. Multi-objective optimization and sensitivity analysis are applied. Results indicate that using more design variables leads to a metamodel with higher computational cost due to the larger number of experimental samples required. Nevertheless, this cost remains low compared to direct model optimization, with a satisfactory time reduction in the optimization process.

The detonation of landmines poses a significant threat to armored vehicles and their crews on battlefield. To enhance the resistance of vehicles to shallow-buried explosives, sandwich structures are commonly employed. This paper employs an uncertain design approach with the optimization objective of rear panel displacement to conduct reliability optimization of sandwich anti-explosion structures and perform parameter sensitivity analysis. In order to improve the computational efficiency and the robustness of the algorithm, the single-objective optimization problem of minimizing the weight of the structure under the reliability constraints is transformed into a bi-objective optimization problem in terms of the structural areal density and the probability of failure, and is solved using the NSGA-II optimization algorithm. In local sensitivity analysis, the thickness of the front and rear panels, as well as the core thickness, exhibits a substantial influence on rear panel displacement. Regarding global reliability sensitivity analysis, the displacement of the front and rear panels exerts a more significant impact on the failure probability of rear panel displacement. The reliability optimization method proposed in this paper holds considerable engineering significance for optimizing sandwich panels under explosive loads. This offers a valuable framework for researchers and engineers involved in the design of sandwich structures for efficient energy absorption in the context of shallow-buried landmine scenarios.

This paper presents a numerical simulation using ANSYS Fluent to investigate the performance of two water desalination systems under identical conditions. Realistic environmental conditions such as ambient pressure, temperature, fluid inlet velocity, and temperature of the fluid entering the tank and the heat source (both being water) were considered. Both models were tested under the same conditions: a pressure of 1 atm, an ambient temperature of 27 °C, an inlet fluid velocity in the tank of 0.08 m/s, an inlet fluid temperature to the tank of 27 °C, an inlet fluid velocity to the heat source of 0.01 m/s, and an inlet fluid temperature to the heat source of 67 °C. The tank and heat source were made of aluminum and copper, respectively. The obtained results showed significant differences and will be discussed in detail in the following sections.

Magnetic gears show important potential applications in the field of wind power generation equipment due to their low maintenance cost, low noise, no lubrication, and overload protection. However, the existing dual-modulated magnetic ring double modulated axial magnetic gears suffer from weak modulation effect, susceptibility to magnetic saturation, and magnetic leakage. In response to these problems, a single modulator modulated axial field flux focusing magnetic gear is proposed in this paper. This axial field flux focusing magnetic gear (AFFMG) combines an H-type modulated stator and an array of Halbach permanent magnets (PMs) and is designed to replace mechanical gearboxes in wind power generation systems. By introducing an H-type modulated stator in the middle of the high and low-speed rotor of the AFFMG, the magnetic field of the PMs is double modulated in the axial and transverse directions, and the hybrid double modulated utilization is realized. This design effectively suppresses the magnetic leakage and magnetic saturation effect of the PMs and improves the torque density and utilization rate of the PMs. In addition, the high-speed rotor PMs are magnetized with Halbach arrays, which significantly improves the magnetic flux density of the air gap and reduces the non-operating harmonics, thereby effectively improving the torque density. In this study, the topology and working principle of the proposed AFFMG are introduced in detail, and the proposed AFFMG finite element model is established. Based on the results of the comprehensive sensitivity analysis, the response surface method and the multi-objective whale optimization algorithm were used to optimize the design, and the optimal structure size parameters were determined. The performance comparison analysis verifies the effectiveness of the optimized design method. The results show that the proposed AFFMG can effectively reduce the magnetic flux leakage at the end, suppress the magnetic saturation effect, increase the torque density by 140.67%, and significantly enhance the magnetic field modulation effect. By observing the starting torque and starting speed curves, it is found that the proposed AFFMG can provide stable torque output during the start-up phase. At the same time, the torque ripple of the high-speed rotor and the external rotor is reduced by 12.31 and 16.16% respectively, and the transmission reliability is significantly improved. This study provides a useful reference for the design of high-performance new double modulated flux focusing axial magnetic gear.

This paper presents a novel thermoelastic model designed to analyze the behavior of porous materials containing voids. The proposed model extends the two-phase lag theory (TPL) by incorporating inherent delays in thermal responses specific to such materials. A significant advancement over traditional elastic models is the inclusion of both spatial and temporal nonlocal effects, which are essential for accurately capturing the intricate microscopic interactions characteristic of porous structures. Furthermore, the integration of fractional Caputo-tempered derivatives into the heat conduction equation enhances the representation of memory effects, offering deeper insights into how prior deformations and thermal influences shape material behavior. The validity and applicability of the model were demonstrated through a detailed analysis of the transient thermo-mechanical response of an infinite porous body with a cylindrical cavity subjected to a time-dependent heat flux. Results were compared with findings from existing literature, enabling an evaluation of the effects of nonlocal interactions, phase delays, and fractional parameters on the observed responses. This comprehensive approach provides a more refined understanding of the dynamics of porous materials under combined thermal and mechanical loads, advancing the theoretical framework for such materials.

This study presents a novel spatiotemporal nonlocal elasticity model based on the Klein–Gordon-type theory to investigate size- and time-dependent mechanical and thermal behaviors in perfectly conducting isotropic micropolar thermoelastic materials at micro- and nanoscales. The proposed model integrates internal length and time scales to account for nonlocal interactions and long-range forces, which are essential for accurately describing material behavior at reduced scales where classical continuum theories fail. This framework is seamlessly coupled with the dual-phase-lag (DPL) generalized thermoelasticity to capture finite-speed heat propagation, overcoming the limitations of Fourier’s law. To analyze the coupled thermoelastic responses, we apply the normal mode analysis technique, which allows for the derivation of exact analytical solutions for critical field variables—including temperature, displacement, microrotation, thermal stresses, and carrier density —under arbitrary loading conditions in a two-dimensional half-space domain. The governing equations incorporate micropolar effects, magneto-thermoelastic coupling, and nonlocal constitutive relations, providing a comprehensive description of the system's dynamic behavior. Numerical simulations are performed for a hypothetical magnesium crystal-like material, chosen for its relevance in advanced engineering applications. The results reveal that the inclusion of micropolarity, DPL phase lags, and spatiotemporal nonlocal parameters significantly enhances the accuracy of predicted thermal and mechanical responses, yielding smoother and more damped profiles compared to classical and generalized thermoelasticity models. Graphical representations illustrate finite-speed wave propagation, nonlocal effects, and the influence of phase lag parameters, emphasizing the model's applicability in nanotechnology, microelectronics, and advanced composite design. The present work not only advances the theoretical understanding of micropolar magneto-thermoelasticity but also provides a robust modeling framework for predicting the behavior of micro- and nano-scale systems under complex thermal and magnetic environments. This enhanced predictive capability is crucial for the design and optimization of high-performance materials and devices operating at small scales.

This study examined the nonlinear dynamics of an electrostatically excited microbeam with two thin PZT layers. The design utilized an initially curved microbeam to achieve a wider stable travel range under electrostatic excitation. Analytical model was formulated to optimize the beam’s dimensions and analyze its static and dynamic behavior, such as deflection profiles, resonant frequencies, and vibration responses. The findings reveal several nonlinear effects, including snap-through mechanism, a softening effect near the first natural frequency, and a hardening effect near the third resonance. Additionally, applying a DC voltage to the PZT layers induces an axial force either tensile or compressive based on the voltage polarity that modifies the microbeam’s stiffness. This enables active tuning of the natural frequency and dynamic characteristics.

Topology optimization is a method that achieves optimal structural performance by optimizing material distribution and has been widely applied in fields such as aerospace, automotive manufacturing, and biomedical engineering. Although various methods have been developed to address numerical instability issues in topology optimization, such as checkerboard patterns, gray-scale phenomena, and mesh dependence, effectively selecting an appropriate filtering radius remains a key challenge. To address this, this paper proposes a quantitative method based on gray-scale analysis, conducting frequency domain analysis via 2D discrete Fourier transform (DFT) and combining clustering ratio and clustering index. This method systematically investigates the impact of the filtering radius on numerical instability issues and precisely determines the optimal filtering radius. The effectiveness of the proposed method is validated through numerical experiments, where a comprehensive evaluation index S is defined to determine the optimal filtering radius value under different application scenarios. Unlike traditional empirical rules, the method proposed in this paper improves the precision of filtering radius selection through frequency domain feature analysis, significantly reduces numerical instability, and ensures the accuracy and stability of the optimization results. The research results show that the filtering radius selection method based on gray-scale analysis enhances computational efficiency, optimizes structural performance and manufacturability, and avoids the additional costs that may arise from improper filtering radius selection. This study provides a theoretical foundation and quantitative guidance for the parameter selection of filtering techniques in topology optimization, offering significant engineering application value.

Homogenization-based topology optimization methods used for designing graded lattice structures require multiple scaling laws because of the anisotropic elastic properties of cubic lattice cells. In this study, an isotropy-conditioned density mapping (ICDM) approach is presented to define lattice cells with isotropic elastic properties across the full range of relative densities, enabling the use of a single scaling law in density-based topology optimization. Strut radii for different groups within a cubic lattice cell are determined to satisfy an isotropy condition by evaluating homogenized elastic properties over the entire relative density range required for topology optimization. The resulting isotropy-conditioned lattice cells are used for density mapping in topology optimization based on the solid isotropic material with penalization (SIMP) method. The proposed approach is computationally efficient because it enables macroscopic optimization using the standard SIMP method while ensuring that spatially varying mesoscale lattice configurations satisfy isotropy using a single scaling law. The method is demonstrated through two three-dimensional numerical examples to show its efficacy. The improved structural performance of the optimized designs with the isotropy-conditioned lattice cells is shown by comparing their results with the existing designs.

This work presents an innovative framework for thermoelastic-plastic reliability-based topology optimization, tackling challenges related to material uncertainties, geometric imperfections, and variations in volume fractions. An enhanced Bi-directional Evolutionary Structural Optimization (BESO) method is developed. It integrates thermoelastic-plastic finite element analysis with stochastic reliability constraints to achieve robust and efficient structural designs under combined thermal and mechanical loading. The framework incorporates advanced modeling techniques, including temperature-dependent material properties, elasto-plastic behavior, and eigenmode-based imperfection modeling. A key innovation lies in formulating reliability constraints by treating volume fraction as a random variable to model material usage uncertainty. This ensures compliance with target safety indices. The proposed methodology is verified through detailed numerical examples, including steel beam and shell structures subjected to temperatures up to 800 °C. Results show that the probabilistic designs achieved up to 30% higher load-bearing capacity compared to deterministic ones and demonstrated improved stress distribution and thermal resilience. These enhancements confirm the method’s effectiveness in achieving optimal layouts that balance material efficiency, structural stability, and reliability.