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The purpose of this study is to propose a fast method for predicting flow fields with periodic behavior with verification in the context of a radial turbine to meet the urgent requirement to effectively capture the unsteady flow characteristics in turbomachinery. Aiming at meeting the urgent requirement to effectively capture the unsteady flow characteristics in turbomachinery, a fast method for predicting flow fields with periodic behavior is proposed here, with verification in the context of a radial turbine (RT).

Sparsity-promoting dynamic mode decomposition is used to determine the dominant coherent structures of the unsteady flow for mode selection, and for flow-field prediction, the characteristic parameters including amplitude and frequency are predicted using one-dimensional Gaussian fitting with flow rate and two-dimensional triangulation-based cubic interpolation with both flow rate and rotation speed. The flow field can be rebuilt using the predicted characteristic parameters and the chosen model.

Under single flow-rate variation conditions, the turbine flow field can be recovered using the first seven modes and fitted amplitude modulus and frequency with less than 5% error in the pressure field and less than 9.7% error in the velocity field. For the operating conditions with concurrent flow-rate and rotation-speed fluctuations, the relative error in the anticipated pressure field is likewise within an acceptable range. Compared to traditional numerical simulations, the method requires a lot less time while maintaining the accuracy of the prediction.

It would be challenging and interesting work to extend the current method to nonlinear problems.

The method presented herein provides an effective solution for the fast prediction of unsteady flow fields in the design of turbomachinery.

A flow prediction method based on sparsity-promoting dynamic mode decomposition was proposed and applied into a RT to predict the flow field under various operating conditions (both rotation speed and flow rate change) with reasonable prediction accuracy. Compared with numerical calculations or experiments, the proposed method can greatly reduce time and resource consumption for flow field visualization at design stage. Most of the physics information of the unsteady flow was maintained by reconstructing the flow modes in the prediction method, which may contribute to a deeper understanding of physical mechanisms.

The purpose of this paper is to investigate the wear resistance behavior of the striated tool for cross wedge rolling (CWR).

A mechanical-thermal coupled, temperature-dependent FE wear model was developed to explore the wear behaviors for striated CWR tools. To verify the proposed FE model, a newly developed measuring device was also developed to measure wear on the tool ridge. To find the impact order of the parameters of striate unit, orthogonal experiment was carried out.

The experimental and numerical results both indicate that the wear resistance of striated tool is better than that of smooth tool. Minimum tool ridge wear can be achieved by choosing proper tool contact temperature with striated units on crossed ridge. The order of the striation geometrical factors’ impact on ridge wear is striation width > striation interval > striation length.

Because of the specified tool, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

It is shown that the wear resistance of striated CWR tool is better than that of smooth tool. The information may help CWR manufactures to design and produce tools with less wear.

Remanufacturing of worn blades with various defects normally requires processes such as scanning, regenerating a geometrical reference model, additive manufacturing (AM) through laser cladding, adaptive machining and polishing and quality inspection. Unlike the manufacturing process of a new part, the most difficult problem for remanufacturing such a complex surface part is that the reference model adaptive to the worn part is no longer available or useful. The worn parts may suffer from geometrical deformation, distortion and other defects because of the effects of harsh operating conditions, thereby making their original computer aided design (CAD) models inadequate for the repair process. This paper aims to regenerate the geometric models for the worn parts, which is a key issue for implementing AM to build up the parts and adaptive machining to reform the parts. Unlike straight blades with similar cross sections, the tip geometry of the worn tip of a twist blade needs to be regenerated by a different method.

This paper proposes a surface extension algorithm for the reconstruction of a twist blade tip through the extremum parameterization of a B-spline basis function. Based on the cross sections of the scanned worn blade model, the given control points and knot vectors are firstly reconstructed into a B-spline curve D. After the extremum of each control point is calculated by extremum parameterization of a B-spline basis function, the unknown control points are calculated by substituting the extremum into the curve D. Once all control points are determined, the B-spline surface of the worn blade tip can be regenerated. Finally, the extension algorithm is implemented and validated with several examples.

The proposed algorithm was implemented and verified through the exampled blades. Through the extension algorithm, the tip geometry of the worn tip of a twist blade can be regenerated. This method solved a key problem for the repair of a twist blade tip. It provides an appropriate reference model for repairing worn blade tips through AM to build up the blade tip and adaptive machining/polishing processes to reform the blade geometry.

The extension errors for different repair models are compared and analyzed. The authors found that there are several factors affecting the accuracy of the regenerated model. When the cross-section interval and the extension length are set properly, the restoration accuracy for the blade tip can be improved, which is acceptable for the repairing.

The lack of a reference geometric model for worn blades is a significant problem when implementing blade repair through AM and adaptive machining processes. Because the geometric reference model is unavailable for the repair process, reconstruction of the geometry of a worn blade tip is the first crucial step. The authors proposed a surface extension algorithm for the reconstruction of a twist blade tip. Through the implementation of the proposed algorithm, the blade tip model can be regenerated.

Remanufacturing of worn blades with various defects is highly demeaned for the aerospace enterprises considering sustainable development. Unlike straight blades, repair of twist blades encountered a very difficult problem because the geometric reference model is unavailable for the repair processes. This paper proposed a different method to generate the reference model for the repair of a twist blade tip. With this model, repair of twist blades can be implemented through AM to build up the blade tip and adaptive machining to subtract the extra material.

The authors proposed a surface extension algorithm to reconstruct the geometric model for repair of twist blades.

Fitting evenness is one key characteristic for three-dimensional objects' optimal fit. The weighted Gaussian imaging method is developed for fitting evenness of auto body taillight fitting optimization.

Fitting boundary contours are extracted from scanning data points. Optimal fitting target is represented with gap and flushness between taillight and auto body. By optimizing the fitting position of the projected boundary contours on the Gaussian sphere, the weighted Gaussian imaging method accomplishes optimal requirements of gap and flushness. A scanning system is established, and the fitting contour of the taillight assembly model is extracted to analyse the quality of the fitting process.

The proposed method accomplishes the fitting optimization for taillight fitting with higher efficiency.

The weighted Gaussian imaging method is used to optimize the taillight fitting. The proposed method optimized the fitting objects' 3-D space, while the traditional fitting methods are based on 2-D algorithm. Its time complexity is O(n3), while those of the traditional methods are O(n5). The results of this research will enhance the understanding of the 3-D optimal fitting and help in systematically improving the productivity and the fitting quality in automotive industry.

Using the case of social reading via WeChat in China, this paper aims to explore adolescents’ social reading motivation and behaviour. It also examines how the specific dimensions of reading motivation contribute to the different aspects of social reading behaviour.

This study used survey approach, which gathered 1,039 valid responses from a cluster sampling in 14 middle and high schools in Shanghai, China.

The results indicated that social reading motivation was a multidimensional construct, which included the dimensions of social interaction, self-development, peer recognition, information acquisition, personal interests and time killing. The research also found that different motivational dimensions exerted different influences on adolescents’ social reading activities. Specifically, the motivations of time killing and self-development are significant predictors of both the reading act and socializing act. However, information acquisition and personal interests were significant predictors only of the reading act, while social interaction and peer recognition significantly predicted the socializing act.

The findings would be valuable for those who develop reading programs or administer adolescents’ reading practice. This study can help them understand the complexity of adolescents’ social reading motivation and distinguish between its different dimensions.

The study provides important insights into the nature of adolescents’ social reading motivation and how it relates to their social reading behaviour. It not only confirmed the multidimensionality of social reading motivation as a construct but also expanded the exploration of reading motivation and behaviour to the social media arena.

The purpose of this paper is to develop a standardized, psychometrically sound instrument for the emerging construct of change recipient proactivity (CRP), using a deductive approach.

Using a systematic item-development framework as a guide (i.e. item generation, questionnaire administration, item reduction and scale evaluation) and based on a sample of 414 white-collar employees, this paper discusses the development and validation of an instrument that can be used to measure change recipient’s proactive behavioral responses to planned change efforts.

Results suggest that our proposed CRP scale is internally consistent (reliable) and valid in that it is conceptually distinct from, yet empirically correlated with neighboring constructs such as affective commitment to change, readiness for change and proactive personality.

The findings illustrate that change recipients can demonstrate proactive behaviors in response to change efforts. However, this study’s contribution is only a first step, requiring further theoretical and methodological refinement of the scale in different contexts.

The deductive nature of our study resulted in a comprehensive and domain-specific scale assessing recipients’ proactive responses to organizational change efforts. This opens doors to empirical studies on examining the conditions under which change recipients “may” step outside the boundaries of passivity to respond positively and proactivity to organizational change efforts.

The purpose of this paper is to investigate the manufacturer’s production, pricing and green technology investment decision problem when strategic customer behavior and carbon emissions-sensitive random demand is taken into consideration and discuss the impact of carbon emissions-sensitive demand on the manufacturer’s operation strategies, total carbon emissions and maximum expected profit.

The authors formulate a model to introduce carbon emissions-sensitive demand into the newsvendor framework with strategic customer behavior. The authors characterize the rational expectations equilibrium to derive the optimal solutions to the manufacturer. The authors analyze the effects of carbon emissions-sensitive demand on the manufacturer’s optimal strategies, total carbon emissions and maximum expected profit by comparative analysis.

The authors obtain the manufacturer’s optimal production, pricing and green technology investment strategies under rational expectations equilibrium in scenario of price-sensitive demand and that of carbon emissions-sensitive demand, respectively. The authors find that as customer demand changes from price-sensitive demand to carbon emissions-sensitive demand, the manufacturer’s optimal prices are the same but optimal production quantity, optimal unit carbon emissions and maximum expected profit go down. Though the total emissions decrease, the carbon emissions reduction would not increase as the demand is more carbon emissions-sensitive. Whether it increases or decreases depends on the model parameters.

Carbon emissions-sensitive demand and strategic customer behavior are considered simultaneously in an integrated model. The result can guide the manufacturer decision-making. The proposed model are hoped to shed light to the future works in the field of sustainable supply chain management.