Acta Mechanica Sinica最新文献

The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring. The research on the state estimation method of flexible multibody system with large deformation and large rotation remains rare. In this investigation, a state estimator based on multiple nonlinear Kalman filtering algorithms was designed for the flexible multibody systems containing large flexibility components that were discretized by absolute nodal coordinate formulation (ANCF). The state variable vector was constructed based on the independent coordinates which are identified through the constraint Jacobian. Three types of Kalman filters were used to compare their performance in the state estimation for ANCF. Three cases including flexible planar rotating beam, flexible four-bar mechanism, and flexible rotating shaft were employed to verify the proposed state estimator. According to the different performances of the three types of Kalman filter, suggestions were given for the construction of the state estimator for the flexible multibody system.

Mesh reflector antennas are the mainstream of large space-borne antennas, and the stretching of the truss achieves their deployment. Currently, the truss is commonly designed to be a single degree of freedom (DOF) deployable mechanism with synchronization constraints. However, each deployable unit’s drive distribution and resistance load are uneven, and the forced synchronization constraints lead to the flexible deformation of rods and difficulties in the deployment scheme design. This paper introduces an asynchronous deployment scheme with a multi-DOF closed-chain deployable truss. The DOF of the truss is calculated, and the kinematic and dynamic models are established, considering the truss’s and cable net’s real-time coupling. An integrated solving algorithm for implicit differential-algebraic equations is proposed to solve the dynamic models. A prototype of a six-unit antenna was fabricated, and the experiment was carried out. The dynamic performances in synchronous and asynchronous deployment schemes are analyzed, and the results show that the cable resistance and truss kinetic energy impact under the asynchronous deployment scheme are minor, and the antenna is more straightforward to deploy. The work provides a new asynchronous deployment scheme and a universal antenna modeling method for dynamic design and performance improvement.

The gear transmission system directly affects the operational performance of high-speed trains (HST). However, current research on gear transmission systems of HST often overlooks the effects of gear eccentricity and running resistance, and the dynamic models of gear transmission system are not sufficiently comprehensive. This paper aims to establish an electromechanical coupling dynamic model of HST traction transmission system and study its electromechanical coupling vibration characteristics, in which the internal excitation factors such as gear eccentricity, time-varying meshing stiffness, backlash, meshing error, and external excitation factors such as electromagnetic torque and running resistance are stressed. The research results indicate that gear eccentricity and running resistance have a significant impact on the stability of the system, and gear eccentricity leads to intensified system vibration and decreased anti-interference ability. In addition, the characteristic frequency of gear eccentricity can be extracted from mechanical signals and current signals as a preliminary basis for eccentricity detection, and electrical signals can also be used to monitor changes in train running resistance in real time. The results of this study provide some useful insights into designing dynamic performance parameters for HST transmission systems and monitoring train operational states.

T-carbon is a new allotrope of carbon materials, and it displays high hardness and low density. Nevertheless, the hardening mechanisms of T-carbon thin films under nanoindentation remain elusive. This work utilizes molecular dynamics simulation to explore the hardening mechanisms of T-carbon thin films under nanoindentation with variations of loading velocities and temperatures. The results reveal that a loading velocity increase at a given temperature raises the nanoindentation force. The increase in nanoindentation force is due to graphitization, which is related to the fracture of tetrahedral structures in T-carbon thin films. However, increased graphitization caused by an increased temperature lowers the nanoindentation force at a given loading velocity. The increased graphitization is influenced by both the fractured tetrahedrons and the deformation of inter-tetrahedron bond angles. This is attributed to the loss of thermal stability and the lower density of T-carbon thin films as the temperature increases. These findings have significant implications for the design of nanodevices for specific application requirements.

Failure of thermal barrier coatings (TBCs) can reduce the safety of aero-engines. Predicting the lifetime of TBCs on turbine blades under real service conditions is challenging due to the complex multiscale computation required and the chemo-thermo-mechanically coupled mechanisms involved. This paper proposes a multiscale deep-learning method for TBC failure prediction under typical thermal shock conditions involving calcium-magnesium-alumina-silicate (CMAS) corrosion. A micro-scale model is used to describe local stress and damage with consideration of the TBC microstructure and CMAS infiltration and corrosion mechanisms. A deep learning network is developed to reveal the effect of microscale corrosion on TBC lifetime. The modeled spalling mechanism and area are consistent with the experimental results, with the predicted lifetime being within 20% of that observed. This work provides an effective method for predicting the lifetime of TBCs under real service conditions.

The work comparing the Yamada-Ota and Xue models for nanoparticle flow across a stretching surface has benefits in nanotechnology, medicinal treatments, environmental engineering, renewable energy, and heat exchangers. Most published nanofluid flow models assumed constant thermal conductivity and viscosity. With such great physiognomies in mind, the novelty of this work focuses on comparing the performance of the nanofluid models, Xue, and Yamada-Ota models on a stretched sheet with variable thickness under the influence of a magnetic field and quadratic thermal radiation. The altered boundary layer equations for momentum and temperature, subject to adequate boundary conditions, are numerically solved using an optimized, efficient, and extensive bvp-4c approach. The effects of non-dimensional constraints such as magnetic field, power index of velocity, wall thickness parameter, and quadratic radiation parameter on momentum and temperature profile in the boundary layer area are analyzed thoroughly and outcomes were illustrated graphically. Additionally, the consequences of certain distinctive parameters over engineering factors are also examined and results were presented in tabular form. From the outcomes, it is seen that fluid velocity slows down in the presence of a magnetic field but the opposite nature is observed in the case of temperature profile. With a higher index of velocity, the velocity profile decreases and the temperature field elevates. It has been found that the presence of quadratic convection improves the temperature field. The outcomes of the two models are compared. The Yamada-Ota model performed far better than the Xue model in the heat transfer analysis.

Numerical simulation is dominant in solving partial differential equations (PDEs), but balancing fine-grained grids with low computational costs is challenging. Recently, solving PDEs with neural networks (NNs) has gained interest, yet cost-effectiveness and high accuracy remain a challenge. This work introduces a novel paradigm for solving PDEs, called multi-scale neural computing (MSNC), considering spectral bias of NNs and local approximation properties in the finite difference method (FDM). The MSNC decomposes the solution with a NN for efficient capture of global scale and the FDM for detailed description of local scale, aiming to balance costs and accuracy. Demonstrated advantages include higher accuracy (10 times for 1D PDEs, 20 times for 2D PDEs) and lower costs (4 times for 1D PDEs, 16 times for 2D PDEs) than the standard FDM. The MSNC also exhibits stable convergence and rigorous boundary condition satisfaction, showcasing the potential for hybrid of NN and numerical method.

Legendre polynomial method is well-known in modeling acoustic wave characteristics. This method uses for the mechanical displacements a single polynomial expansion over the entire sandwich layers. This results in a limitation in the accuracy of the field profile restitution. Thus, it can deal with the guided waves in layered sandwich only when the material properties of adjacent layers do not change significantly. Despite the great efforts regarding this issue in the literature, there remain open questions. One of them is: “what is the exact threshold of contrasting material properties of adjacent layers for which this polynomial method cannot correctly restitute the roots of guided waves?” We investigated this numerical issue using the calculated guided phase velocities in 0°/φ/0°-carbon fibre reinforced plastics (CFRP) sandwich plates with gradually increasing angle φ. Then, we approached this numerical problem by varying the middle layer thickness h_90° for the 0°/90°/0°-CFRP sandwich structure, and we proposed an exact thickness threshold of the middle layer for the Legendre polynomial method limitations. We showed that the polynomial method fails to calculate the quasi-symmetric Lamb mode in 0°/φ/0°-CFRP when φ > 25°. Moreover, we introduced a new Lamb mode so-called minimum-group-velocity that has never been addressed in literature.

Engineering tests can yield inaccurate data due to instrument errors, human factors, and environmental interference, introducing uncertainty in numerical model updating. This study employs the probability-box (p-box) method for representing observational uncertainty and develops a two-step approximate Bayesian computation (ABC) framework using time-series data. Within the ABC framework, Euclidean and Bhattacharyya distances are employed as uncertainty quantification metrics to delineate approximate likelihood functions in the initial and subsequent steps, respectively. A novel variational Bayesian Monte Carlo method is introduced to efficiently apply the ABC framework amidst observational uncertainty, resulting in rapid convergence and accurate parameter estimation with minimal iterations. The efficacy of the proposed updating strategy is validated by its application to a shear frame model excited by seismic wave and an aviation pump force sensor for thermal output analysis. The results affirm the efficiency, robustness, and practical applicability of the proposed method.