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In present study, a new type of three-dimensional pin–fin plate with elliptical cross-section was proposed. Steam condensation on the proposed plates with different geometric parameter was experimentally studied at atmospheric pressure. The effects of horizontal pin spacing, pin height and inclination angle on condensation heat transfer were investigated. The results showed that all the elliptical pin-fin plates exhibited substantially better performance than the flat plate. The highest enhancement ratio of 2.33 was achieved when the elliptical pin-fin plate was vertically placed. The geometric parameter and inclination angle showed a strong influence on the performance of elliptical pin-fin plate. The performance of elliptical pin-fin plate was enhanced when the pin height increased from 1.2 mm to 2.0 mm or the horizontal pin spacing increased from 1.2 mm to 2.0 mm. The strengthening effect of pin height became weaker with the increase of horizontal pin spacing. When the inclination angle increased from 0° to 60°, the heat flux and average condensation heat transfer coefficient decreased by 16 and 25%, respectively. With the increase of inclination angle, the gravity effect in the direction of condensate flow decreased, resulted in the deterioration of heat transfer. The results can provide reference for practical engineering applications and the further optimization design of pin-fin enhanced condensation structure.

This paper studied the effect of ultrasound on distribution characteristics of condensate droplets on a vertical metal surface. The surface was made of aluminum and coated with PVC film to obtain durable condensate droplets. Visualization of the condensation process was carried out under the action of ultrasonic vibration with a constant frequency of 20 kHz. The effects of ultrasonic power on surface coverage of condensate droplets, first shedding time of condensate droplets, total number of shedding, heat flux and condensation heat transfer coefficient were analyzed. Furthermore, the mechanism of ultrasonic vibration on accelerating the shedding of condensate droplets was discussed. The results indicated that the shedding of condensate droplets was accelerated by ultrasound compared with those without ultrasound. In addition, the shedding period of condensate droplets was decreased with the increase of ultrasonic power. Contrarily, the heat flux and the condensation heat transfer coefficient were increased with the increase of ultrasonic power. The maximum enhancement ratio of heat transfer coefficient reached 2.67 compared with that without applying ultrasound. This study shows that ultrasound has a good application prospect in strengthening condensation heat transfer, particularly for space applications in microgravity environment.

Moment independent sensitivity index is widely concerned and used since it can reflect the influence of model input uncertainty on the entire distribution of model output instead of a specific moment. In this paper, a novel analytical expression to estimate the Borgonovo moment independent sensitivity index is derived by use of the Gaussian radial basis function and the Edgeworth expansion. Firstly, the analytical expressions of the unconditional and conditional first four-order moments are established by the training points and the widths of the Gaussian radial basis function. Secondly, the Edgeworth expansion is used to express the unconditional and conditional probability density functions of model output by the unconditional and conditional first four-order moments, respectively. Finally, the index can be readily computed by measuring the shifts between the obtained unconditional and conditional probability density functions of model output, where this process doesn't need any extra calls of model evaluation. The computational cost of the proposed method is independent of the dimensionality of model inputs and it only depends on the training points and the widths which are involved in the Gaussian radial basis function meta-model. Results of several case studies demonstrate the effectiveness of the proposed method.

For estimating the effect of uncertain distribution parameter on the variance of failure probability function (FPF), the map from distribution parameters to FPF is built and the high efficient approximation form is extended to solve the parametric variance-based sensitivity index. Then the parametric variance-based sensitivity index can be firstly expressed as the moments of the FPF, and the FPF is approximated by a product of the univariate functions of the distribution parameters, on which the moments of the FPF approximated by the univariate functions can be easily evaluated by the Gaussian integration using the values of the FPF at the Gaussian nodes. Thus the primary task of evaluating the parametric variance-based sensitivity is transformed to calculate the FPF at Gaussian nodes of the univariate functions, for which Monte Carlo (MC), Extended Monte Carlo (EMC) and Rejection Sampling (RS) are employed and compared here. Only one set of samples of inputs are needed in either EMC or RS. Several numerical and engineering examples are presented to verify the accuracy and efficiency of the proposed approximate methods. Additionally, the results also reveal the virtue of RS which can be more accurate and more unlimited than EMC.

Reliability analysis becomes increasingly complex when facing the complicated expensive-to-evaluate engineering applications, especially problems involve the implicit finite element models. In order to balance the accuracy and efficiency of implementing reliability analysis, an advanced Kriging method is proposed for efficiently analyzing the structural reliability. The method starts with an incipient Kriging model built from a very small number of samples generated by the simple random sampling method, then determines the most probable region in the probabilistic viewpoint and chooses the subsequent samples located in this region by employing the probabilistic classification function. Besides, the leave-one-out technique is used to update the current model. By locating samples in the probabilistic most probable region, only a small number of samples are used to build a precise surrogate model in the end, and only a few actual limit state function evaluations are required correspondingly. After the high quality surrogate of the implicit limit state is available by the advanced Kriging model, the Monte Carlo simulation method is employed to implement reliability analysis. Some engineering examples are introduced to demonstrate the accuracy and efficiency of the proposed method.

The moment independent importance measure is a popular global sensitivity analysis technique, and aims at evaluating contributions of the inputs to the whole output distribution. In this work, moment independent sensitivity analysis is performed for models with correlated inputs, by decomposing the importance measure into the uncorrelated part and correlated part. The correlated input variables are first orthogonalized, then the moment independent sensitivity analysis of the newly generated independent variables is performed. Discussions indicate that the moment independent importance measures, so obtained, can be interpreted as the full, correlated and uncorrelated contributions of the original inputs to the whole output distribution. Procedure of the proposed approach has been generalized. By the decomposition, the information provided by the moment independent importance analysis is enriched, which has been demonstrated by the application to several examples.

In order to measure how the different distribution parameters of basic variables affect the statistical characteristics of the structure or system output, the mixed sensitivities of failure probability and the statistical moments of the performance function with respect to the distribution parameters of input variables are defined, and the corresponding mixed kernel functions are defined. The expressions of the mixed kernel functions for a two-parameter distribution are derived, and the universal properties of the mixed kernel functions are analyzed as well. Furthermore, based on the properties of the mixed kernel functions, and by taking a quadratic polynomial without cross-terms as an example of a performance function, the analytical mixed sensitivity results of the failure probability are derived for the normal variables. Comparing the analytical results with the numerical simulation results, it demonstrates that, the derived analytical mixed sensitivity expressions of the failure probability are precise enough.

For consideration of the wide applications of concordance and order information in global sensitivity analysis (SA), a new sensitivity index based on the spearman correlation coefficient (S-CC) is presented in this article. S-CC can reflect the linear order correlation between variables; thus the proposed sensitivity index can be used to measure the influence of input on the linear order of output. Then the main task becomes efficiently estimating the defined index. Here the authors introduce the basic unscented transformation (UT) to compute the index with high efficiency, and high order unscented transformation (HOUT) is also employed to further improve the computational accuracy. Several examples, including the commonly used Ishigami test function and other engineering examples, are used to demonstrate the validity of the proposed sensitivity index and the efficiency of the proposed UT-based methods.

Sensitivity analysis is frequently considered an essential component in engineering design. In the design process of engineered structures, the output is implicitly related with the input variables. The Kriging model, one of the most commonly used emulator models, is sometimes used for structure analysis. In order to efficiently estimate the sensitivities of failure probability or statistical moments of performance function with respect to distribution parameters of input variables, the analytical solutions are derived based on the Kriging model. Generally, the Kriging model can be expressed as a tensor product basis function, thus the multivariate integrals can be decomposed into the sum of univariate integrals, which makes it possible to solve the sensitivity of statistical moments with respect to distribution parameters of normal input variables by the properties of kernel functions. Next, the fourth-moment reliability sensitivity method is applied to compute the sensitivity of failure probabil...