Uncertainties are normally unavoidable in engineering practice, which should be taken into account in the structural design and optimization so as to reduce the relevant risks. Yet, the probabilistic models of the uncertainties are often unavailable in the problems due to the lack of samples, and the precision of the conventional non-probabilistic models are not satisfactory when the samples are of multi-cluster distribution. In view of this, an improved method by using a non-probabilistic multi-cluster ellipsoidal model (multi-CEM) for the critical structural reliability analysis is proposed in this paper, which describes the samples in a more accurate and compact way and helps to acquire more satisfactory reliability analysis results. Firstly, a Gaussian mixture model (GMM) is built for the multi-cluster samples with performing expectation maximization (EM) algorithm, based on which the multi-CEM can be constructed. In the structural reliability analysis, two cases, respectively, considering whether the components of the multi-CEM are intersected or not are researched in detail. The non-probabilistic reliability (NPR) indexes for each component of the multi-CEM are computed using the Hasofer–Lind–Rackwitz–Fiessler (HL-RF) algorithm, and then the multidimensional volume ratios of the safe domain to the whole uncertainty domain are computed based on these indexes, indicating the structural NPR. In the end, two numerical examples and a practical application are conducted and analyzed to testify the effectiveness of the method.

By aiming at the common distributed random dynamic loads in engineering practice, an equivalent identification method that is based on K–L decomposition and sparse representation is proposed. Considering that the establishment of a probability model of the distributed random dynamic load is usually unfeasible because of the requirement of a large number of samples, this method describes it by using an interval process model. Through K–L series expansion, the interval process model of the distributed random dynamic load is recast as the sum of the load median function and the load uncertainty. Then, the original load identification problem is transformed into two deterministic ones: the identification of the load median function and the reconstruction of the load covariance matrix, which reveals the load uncertainty characteristics. By integrating the structural modal parameters, and by adopting the Green’s kernel function method and sparse representation, the continuously distributed load median function is equivalently identified as several concentrated dynamic loads that act on the appropriate positions. On the basis of the realization of the first inverse problem, the forward model of the load covariance matrix reconstruction is derived by using K–L series expansion and spectral decomposition. The resolutions to both inverse problems are assisted by the regularization operation so as to overcome the inherent ill-posedness. At the end, a numerical example is presented to show the effectiveness of the proposed method.

At present, the thermal switch for localized heating is becoming a hot research topic in MEMS packaging technology. A method of using a liquid-based thermal switch is proposed in this paper. A plate local heating device based on electric field distribution control is constructed, where a thermal liquid column can be formed under the action of the electric field. The electric field controls the formation and dissipation of the liquid column to realize the on-off and off-on function of the thermal switch. Through the control of the different conductive substrates in the plate, the localized heat transfer can be further realized, and the heat transfer position is precisely regulated. Furthermore, due to the fluidity of the liquid, the prepared thermal switch can also realize movable heating. The experimental results show that the heating time and speed can be controlled precisely by adjusting the driven voltage and the conductive area of the upper substrate.

A fatigue life prediction model with multiaxis load is proposed. The model introduces a new effective cyclic parameter, equivalent stress on the critical surface, to modify the Suntech model. The new damage parameters are not related to empirical constants, hence more applicable for practical application in engineering. The multiaxis fatigue test was carried out with high-strength aluminum alloy 7075-T651, and the multiaxis fatigue life prediction of the test piece was conducted with the finite element software. The experiment result shows that the model proposed is effective for predicting the fatigue life under multiaxis load.