Fiber metal laminates (FMLs) exhibit excellent impact and damage resistance, but their failure mechanisms under high-speed loads are complex due to metal-fiber interactions. Therefore, improving the accuracy of finite element simulations of FMLs under high-speed impact is a critical challenge. This study presents an inverse identification approach for the constitutive parameters of fiber metal laminates (FMLs) under high-speed impact loading, aiming to enhance the predictive accuracy of FML simulations under dynamic conditions. The high-speed impact behavior of FMLs was experimentally characterized, and the finite element model was refined by inversely identifying constitutive parameters while accounting for strain-rate effects. The Split Hopkinson Pressure Bar (SHPB) experiment was carried out through orthogonal experimental design to obtain dynamic response data, and the influence of ply design on impact performance was analyzed. A high-speed simulation model of FML was established based on the explicit dynamic finite element method of strain rate effect, and the effectiveness of the model was verified through experimental comparison. In order to reduce the error between the experiment and the simulation, this paper introduced a multi-objective optimization inversion method to perform global inverse analysis on the constitutive parameters to ensure that the model can reflect the real mechanical behavior of the material. The results demonstrate that the proposed method enables accurate characterization and calibration of the mechanical properties of FMLs, offering a robust approach for constitutive modeling and performance optimization. The prediction errors of the inverted constitutive parameters under dynamic loading conditions are significantly reduced, thereby enhancing the model’s reliability for engineering design and practical applications.

The propagation of solar energetic particles (SEPs) through the heliosphere is primarily guided by the interplanetary magnetic field (IMF) which is embedded in the solar wind plasma. Large-scale IMF structures can drive transient variations in SEP intensities. Using Solar Orbiter observations, we identify a distinct class of SEP variations: SEP flux deflections (SFDs), which are commonly detected in SEP events and frequently recur multiple times within a single event. SFDs are characterized by a sudden change in SEP flux directions where the intensities drop in one direction and increase in another direction, without a significant net change in total flux magnitude. These deflections occur dispersionlessly across a broad energy range-from tens of keV to over 100 MeV-and exhibit steep intensity gradients. SFDs are typically associated with magnetic flux tubes with boundary features consistent with tangential discontinuities. We further show that the solar wind inside these structures exhibits distinct plasma properties, and that the SEP streaming direction within SFDs aligns closely to the flux-tube axis. These observations suggest that magnetic flux tubes are a prevalent structural element of the solar wind, and demonstrate that SEPs can serve as an effective diagnostic tool for probing the topology and dynamics of solar wind structures.

Energetic electrons accelerated at coronal reconnection sites during solar flares precipitate into the lower solar atmosphere, generating nonthermal emissions and regulating energy deposition. However, how their transport and precipitation are jointly governed by the three-dimensional (3D) magnetic topology, turbulent scattering, and Coulomb collisions remains unclear. Here, we aim to disentangle these physical processes by using a data-constrained 3D particle transport model for the 2011 August 4 flare. The simulated distribution of precipitated electrons aligns closely with photospheric quasi-separatrix layers and reproduces the observed two-ribbon morphology in 1700 & Aring;. We reveal a strong polarity asymmetry, with the 10 s precipitation fraction about 6 times higher in the weak positive polarity. This arises primarily from distinct mirror ratios of different polarities under the 3D magnetic configuration and can be understood via a modified escape probability for an asymmetric magnetic bottle. Varying strengths of turbulent scattering lead to a rise-then-fall trend and a pronounced energy dependence in the precipitation fraction. Coulomb collisions globally suppress precipitation, especially at low energies, and further amplify the polarity asymmetry. This integrated modeling framework bridges detailed transport physics to observable flare emissions and advances the development of quantitative models for realistic solar flare events.

As tire is the only part of contact between a vehicle and the road, the dynamic response property of a tire under different loads is of great importance to fully understand the tire performance. Simplifying a tire as a ring-shaped structure is a promising method to simulate the dynamic performance of tires. In this study, a flexible ring model that considers the contact characteristics was developed to investigate the in-plane vibrations of a static passenger car tire. The proposed method integrates three key steps in the improved modeling method: the structural modeling of the tire, the implementation of the tire contact boundary condition and the grounded modeling of tire vibration. In this method, the proposed ring model is applied to calculate the radial frequency response functions of the tire under different vertical loads up to 250Hz with low computational cost. The structural and damping parameters of the analytical model are determined using the genetic algorithm method. The accuracy of the radial modal parameters of the free tire—including natural frequency, damping ratio, and frequency response function—as well as the vertical stiffness calculated by the contact model, is validated by experimental modal analysis and tire stiffness tests, respectively. The in-plane mode of the grounded tire in the analytical approach is verified by a finite element model. The results show that the in-plane vibration behavior of the tire can be efficiently evaluated using the proposed analytical method. Furthermore, the influences of vertical loading and inflation pressure on the natural frequencies and dynamic responses of the grounded tire were investigated. It indicates that this improved ring model offers many application scenarios and extension possibilities compared to the classical tire ring model in the analysis of tire in-plane vibrations.

Previous studies have demonstrated that in alkylmagnesium-involved retarded anionic polymerization, the actual molecular weight of resulting polymers is significantly lower than the theoretical value; this crucial phenomenon has long lacked in-depth theoretical interpretation. Based on the 1,1-diphenylethyllithium/dibutylmagnesium (DPE-Li/Bu2-Mg) living carbanionic polymerization system, this study has, for the first time, revealed the unique dynamic exchange process between Li-Mg bimetallic active centers and innovatively proposed the "living carbanionic chain-shuttling polymerization" (LCCSP) mechanism. Experimental results confirm that in the DPE-Li/Bu2-Mg polymerization system, the resulting polymer chains simultaneously contain both DPE and Bu initiating chain ends, with the Bu end group content showing a significant increase proportional to the Bu2-Mg feeding amount. Stepwise monomer addition, timed sampling experiments, and ethylene oxide (EO) end-capping experiments verified the characteristic living polymerization behavior of this system. Notably, kinetic studies employing the sterically hindered monomer 9-methylene-9H-thioxanthene (MTAE) provided evidence for a dynamic exchange process between Li-Mg terminal active centers. In both polystyryllithium (PS-Li) and polystyrylethylmagnesium (PS-Mg-Et) polymer chains, the chain exchange process between Li and Mg was demonstrated to be continuously dynamic. Furthermore, the Li-Mg exchange process was corroborated through small-molecule model reactions and molecular simulation calculations. Additionally, we investigated the effects of different metal structures and substituents on the LCCSP process. Experimental results revealed that the diethylzinc/n-butyllithium (Et2-Zn/n-BuLi) polymerization system exhibits distinct chain-shuttling behavior, whereas the sterically hindered dibutylzinc (Bu2-Zn)/DPE-Li system and alkylaluminum systems showed negligible chain-shuttling phenomena. This confirms that the LCCSP is regulated by both substituent steric hindrance and metal species. This study fundamentally advances our understanding of polymetallic mechanisms in living carbanionic polymerization.

Multi-fidelity (MF) surrogate models are increasingly utilized in engineering design due to their abilities to balance computational costs and data accuracy across different fidelity levels. At this stage, there are many multi-fidelity methods that approximate high-fidelity (HF) models by incrementally modelling multiple low-fidelity (LF) models in a layer-by-layer hierarchical manner or fusing non-hierarchical LF datasets. However, modelling accuracy of multi-fidelity models still has room for further improvement. Therefore, we introduce an augmented autoregressive nonlinear mapping multi-fidelity (AANMMF) surrogate model for adaptive multi-fidelity data fusion. First, LF surrogate models are constructed based on the available LF dataset. Second, the predicted LF data and HF data are incorporated into a trend function, and the corresponding regression terms are designed to characterize the high-fidelity function. Finally, the MF model is refined by tuning its hyperparameters. To assess the performance of AANMMF, it is compared against the commonly used bi-fidelity surrogate model and one standard MF surrogate model. Four numerical test functions and one engineering case study demonstrate that AANMMF achieves accurate and robust predictions with minimal computational cost, highlighting its potential for practical applications.

Fiber metal laminates (FMLs), composed of alternately stacked metallic and fiber-reinforced polymer (FRP) layers, exhibit significant heterogeneity and anisotropy, and the constitutive parameters are the key issue to comprehensively characterize their complex mechanical behaviors. This study proposes a coupled optimization-based and experimental data-driven method for constitutive parameter inversion to accurately characterize and optimize the mechanical properties of FMLs. Tensile and bending tests were conducted to obtain mechanical response data. The effects and underlying mechanisms of aluminum thickness, fiber thickness and ply angles on performance were analyzed, and the predictive models were obtained using multivariate linear equations. The metal layer and the fiber material layer were established based on the Johnson-Cook damage rule and the Hashin failure criterion, respectively, and the inter-layer fracture is described by the cohesive zone model (CZM) damage rule, thereby constructing a multiscale constitutive model for FMLs. The parameter inversion for Johnson-Cook damage rule, Hashin failure criterion and the CZM model was performed by employing the intelligent optimization algorithms with the multi-objective of minimizing the discrepancies between the experimental and simulation results under the tensile and bending cases, respectively. The results shows that the proposed method achieves precise characterization and calibration of FMLs’ mechanical properties, providing a robust solution for the constitutive modeling and optimization of FMLs for their design and practical applications.

In practical engineering, a variety of random uncertainties are frequently encountered. To automatically design a structure with desired performance that can deal with these uncertain disturbances, robust topology optimization (RTO) of continuum structures inevitably involves numerous random variables. However, conventional uncertainty quantification methods often result in considerable computational cost or inaccurate results when tackling RTO problems with high-dimensional random variables. To this end, this paper proposes a new efficient framework based on direct probability integral method (DPIM) and sequential approximate integer programming with trust region (SAIP-TR) method for addressing RTO design problems of continuum structures with high-dimensional random variables. Firstly, the expansion optimal linear estimation method is adopted to characterize spatially varying random field. Then, DPIM-based framework is devised to attack the challenging issue of statistical moment estimation with high-dimensional random variables. Finally, the SAIP-TR method is utilized for discrete variable topology optimization to obtain clear topology configurations that are easy to process and manufacture. Four typical examples considering high-dimensional load uncertainty, material uncertainty, and mixed uncertainties are illustrated. The results demonstrate that the proposed framework can efficiently and accurately calculate statistical moments and solve robust discrete variable topology optimization problems with 20 random variables and generate clear black-and-white designs. Its computational efficiency is significantly enhanced compared to the widely used polynomial chaos expansion method. In addition, this paper also reveals that the number of truncation terms in random field has a remarkable impact on the optimal topology design configurations of structures.

The determination of optimal mesh size constitutes a critical factor in the numerical simulation of wave propagation, particularly under high-frequency excitations such as blast or impact loadings. In such contexts, mesh dimension significantly influences both the accuracy and efficiency of computations. Existing meshing strategies predominantly rely on numerical mesh independence analysis, which necessitates extensive computations to identify the requisite mesh size, resulting in diminished computational efficiency and limited generality. This study presents a novel theoretical approach for quantifying meshing-induced errors considering different constitutive models, founded upon wave field propagation theory and frequency-domain analysis. This approach enables the a priori estimation of appropriate mesh size along with its associated frequency truncation error through theoretical formulas, thereby obviating the need for exhaustive mesh sensitive studies and reducing computational expenditure. Furthermore, this study examines the impacts of input waveforms characteristics, geometric attenuation and material nonlinearity upon mesh-induced errors, providing theoretical explanations for empirical findings from previous studies. Numerical simulations of traditional engineering materials demonstrate the accuracy and engineering applicability of the proposed error estimation method. And by combining the proposed error estimation method with mesh refinement techniques, an adaptive meshing strategy that adheres to the error threshold is proposed. This research provides a more universal, efficient, and precise foundation for mesh generation in explosion simulations, potentially driving the advancement of highly refined and computationally efficient numerical modeling techniques.

As a common structural configuration, multi-bolt composite-metal joints exhibit mechanical performance limitations due to the inherently low plastic deformation capacity of composite materials. Due to the load imbalance among the bolts and the limited redistribution capacity, the local damage around a bolt hole can not be effectively shared by neighboring bolts. Consequently, the overall joint is subject to premature failure, resulting in poor load-bearing efficiency and structural reliability.This study proposes a self-spinning structured insert with stress-relief functionality around bolt holes. The insert features a "self-spinning" structure, a term which in this study refers to a torsion-like elastic deformation along a predefined path under loading, rather than continuous rotational motion. Inserts are introduced into the metal plate holes to regulate the local stiffness around the hole edges and improve the load-sharing behavior of CFRTP-metal multi-bolt joints. The results demonstrate that the proposed insert design significantly reduces the load concentration at the most critical hole. Specifically, the load carried by the first hole decreases from 7.7 kN to 2.8 kN, corresponding to a reduction of approximately 63%. Furthermore, the variance of bolt loads in the optimized multi-hole configuration decreases from 0.77 to 0.10, indicating a substantially improved load redistribution capability.

Robust topology optimization under uncertain transient loads faces the challenge of high computational cost due to the repeated execution of structural dynamic responses and worst-case scenarios during the optimization process. In this study, unit impulse response functions are introduced for linear dynamic systems to leverage the superposition principle for the explicit computation of structural responses and interval variable sensitivities. The computational complexity analysis shows that, when the objective function involves only a small number of degrees of freedom, the speedup achieved by the proposed method increases significantly and may approach its theoretical upper bound. Under such conditions, the worst-case scenario can be efficiently approached numerically using the multi-start local search method. Additional constraints are introduced as necessary conditions for maintaining a scenario as the worst-case scenario, aiming to mitigate potential convergence issues caused by neglecting the sensitivity of the worst-case scenario with respect to the design variables. During the optimization, the additional constraints are updated based on clusters obtained using the density-based spatial clustering of applications with noise algorithm. Numerical examples are presented to validate the effectiveness and efficiency of the proposed method. The results support the validity of the speedup derivation and indicate that, once unit impulse response functions are available, the cost of identifying the worst-case scenario can be significantly reduced. When local structural performance is used as the objective, the additional constraints help improve optimization convergence. Compared with deterministic designs, the proposed method shows a noticeable reduction in the upper bound of the objective function.

It is argued that Kolmogorov cascades, including those affecting space weather in the solar wind plasma, can effectively be described by realizations of a continuous-time random walk. The nature of the random walk is ruled by the nonlinearity of the structure function scaling exponent characterizing intermittency and multifractality. The scale-dependent probability density functions satisfy the Montroll-Weiss equation in Fourier-Laplace space. They are solutions of a linear Boltzmann equation in the Markovian approximation for cascades subordinated to a renewal Poisson process. The cascade paths become continuous in a properly scaled transition to the diffusion limit of the random walk describing the change of the logarithm of the turbulent fluctuation amplitudes among scales.

This paper investigates the epistemic uncertainty associated with dependence modeling and its impact on structural reliability analysis when data for vine structure selection is limited. Conventionally, the vine copula model characterizing dependent input variables in reliability analysis is typically assumed to be either a single model or the optimal fit model obtained from the data. However, subjective assumptions and data scarcity may introduce bias into correlation measurements, causing significant deviations between the vine structure and the actual dependence structure, as well as substantial errors in reliability analysis. In this paper, we address model uncertainties in vine structures arising from sparse data by introducing Bayesian multi-model inference. This approach identifies the corresponding Bayesian posterior probabilities for an ensemble of candidate vine structures based on their overall dependence modeling performance. Utilizing the Bayes factor to screen effective candidate vine structures, a reweighted model set is formed to quantify model uncertainty in dependence modeling. The integrated first-order reliability method for regular vine copulas provides both precision and high efficiency in reliability analysis. Finally, numerical and engineering examples demonstrate that the proposed method yields more accurate and robust reliability analysis results across various sample sizes in contrast to a single optimal vine copula model.

We investigate the variability of the east–west asymmetry in energetic storm particle (ESP) heavy ion intensities at interplanetary shocks driven by coronal mass ejections (CMEs) during solar cycles (SCs) 23 and 24. We analyze helium (He), oxygen (O), and iron (Fe) intensities in the energy range of ∼0.13–3 MeV/nucleon, using observations from NASA’s ACE and STEREO missions. We examine the longitudinal distribution of ESP intensities and their correlation with the near-Sun CME speed and average transit CME speed, distinguishing between eastern and western events. Our results reveal significant differences in the east–west asymmetry of ESP intensities between SC 23 and SC 24. This shift is linked to changes in the heliolongitude distribution of the CME Speed Ratio (the ratio of CME average transit speed to near-Sun speed), which transitions from peaking predominantly in the western heliosphere in SC 23 to the eastern heliosphere in SC 24. This shift suggests a systematic difference in CME deflection between the two cycles, with CMEs in SC 23 being, on average, deflected eastward, while those in SC 24 exhibit a tendency for westward deflection.

The singularity structure of the electromagnetic field fluctuations in strong Alfv & eacute;nic turbulence is addressed. Owing to the transverse polarization of the fluctuations, the turbulent cascade is essentially perpendicular to the direction of the guiding magnetic field. A scaling analysis of the local energy transfer rates suggests that the most intense singularities are the sites of inertial dissipation of the energy. The lack of smoothness of the electromagnetic fields can give rise to dissipative processes unrelated to collisional transport phenomena in the magnetized plasma. A log-Poisson cascade model is developed. The random multiplicative cascade process is quantized and acts on the Els & auml;sser field increments. In a multifractal interpretation, the log-Poisson cascade is characterized by three parameters: the most probable singularity strength, the strongest singularity, and its fractal codimension. The resulting probability distribution functions are derived from discrete superpositions of dilated integral scale distributions weighted by scale-dependent Poisson distributions. In addition to the three parameters mentioned above, they depend on the standard deviation of the integral scale fluctuations. The four-parameter family of normal log-Poisson mixture distributions is sensitive to the strongest singularities responsible for inertial dissipation of the electromagnetic energy in collisionless space plasma environments.

Based on previous analyses of data from a fleet of space exploration missions, we argue that plasma fluctuations in the turbulent solar wind are consistently described by normal variance mixtures on a broad range of scales encompassing the inertial and kinetic scales. In the inertial range, the normal variance mixture distributions are derived from conditioning the Elsässer field increments either on fluctuations of the energy transfer rate or on fluctuations of the singularity strength. The two kinds of description can be linked to each other via bridging relations. An exact analytical expression for the even-order structure functions is obtained from a normal inverse Gaussian fit, accurate from inertial to dissipative scales in the plasma. The self-similar kappa distributions describing the kinetic scales result from an inverse χ ^2 distribution of the variance in the Gaussian mixture model.

In this study, a joint repair method by resin thermal reshaping is introduced to repair damaged carbon fiber reinforced thermoplastic (CFRTP) bolted joints. A metal insert at the resin melting temperature is inserted into the damaged CFRTP hole with interference fit during the repair process. The matrix resin melts and fills the gap caused by the damage under the action of temperature and pressure. This process results in a densified joint structure, significantly restoring the mechanical properties and overall structural integrity of the joint. Joints with varying degrees of damage are designed, and the proposed repair method is applied. Single shear joints are prepared and subjected to quasi-static tensile and cyclic loading tests, with the interface observed using optical microscopy. Experimental results show that resin thermal reshaping significantly enhances the repair performance of damaged joints, with a 15.20 % increase in tensile load and a 32.90 % reduction in hole elongation. Finite element simulations further confirm these results and successfully replicate key feature points in the load-displacement curve.

The uptake of hydrogen (H-2) from natural gas pipelines (CH4) through membrane separation technology is an efficient avenue of obtaining H-2 resources. Carbon membranes have the potential to be an attractive option for energy-efficient gas separations in the next generation of membranes due to their precise molecular discrimination ability and easy scalability. Here, we report a carbon membrane composed of a mesoporous substrate, seed layer and sieving layer, achieving a one-step uptake with 55.3 x 10(-9) mol.m(-2).s(-1) Pa-1 permeance and a separation factor for H-2/CH4 of 359.1 by feeding hydrogen (20 vol%). As revealed by the kinetic diffusion experiment, H-2 is transported in mesoporous substrate via Knudsen flow, differing from the transition flow taken by CH4, thus H-2 diffusion rates exceed CH4 by an order of magnitude, which substantially increased H-2 purity from 20 vol% to higher purity as gas mixtures passed through the substrate. Then, the H-2 passed through the seed layer and sieving layer, resulting in a final H-2 purity of similar to 99 vol%, a sharp increase from the initial low concentration. This carbon membrane integrates three functions of capture, pre-separation and purification into one material and provides a new solution for the uptake of H-2 from CH4.

Transient dynamic response topology optimization methods always face the challenge of high computational costs due to the need for repeated time-domain discrete structural response calculations. To address this issue, the Equivalent Static Loads Method (ESLM) calculates structural responses with Equivalent Static Loads (ESLs), and solves a sequence of static response optimization problems to approximate the original dynamic problem. However, it has been noted that ESLM may not always identify a Karush-Kuhn-Tucker (KKT) point, and ESLM may produce nonnegligible errors due to using the static response sensitivity to approximate the dynamic response sensitivity, unless the dynamic characteristics of problems are weak enough. In this paper, we proposed a dynamic topology optimization method with the approximate dynamic response sensitivity by the Adjoint Variable Method (AVM) using ESLs, to replace the static response sensitivity used in ESLM. After conducting the dynamic structural analysis, the approximate sensitivity is equivalent to the dynamic sensitivity obtained by AVM. Nevertheless, the errors between them may occur and increase with increasing iterations of the static structural analysis, as the differential relationships between displacement, velocity and acceleration are relaxed. Thus, the similarity assessment criteria using necessary and sufficient conditions were proposed, which control the errors of sensitivities within an acceptable range and simplify the double-loop algorithm of ESLM into a single-loop. And the approximate structural responses can also be calculated in parallel using ESLs. Several numerical examples are presented to demonstrate the effectiveness and efficiency of the proposed method. It is shown that the proposed method exhibits similar capabilities to AVM in achieving optimized objectives and convergence rates. The proposed method also benefits from the efficiency of parallel computing, and the numerical example demonstrates that the overall optimization process can achieve a maximum speedup of up to around 4, with a structural response speedup of 12.7. The formula for estimating the upper limit of the overall speedup based on the structural response speedup is provided at the end.