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This paper aims to propose a gradient-based shape optimization framework in which traditional time-consuming conversions between computer-aided design and computer-aided engineering and the mesh update procedure are avoided/eliminated. The scheme is general so that it can be used in all cases as a black box, no matter what the objective and/or design variables are, whilst the efficiency and accuracy are guaranteed.

The authors integrated CAD and CAE by using isogeometric analysis (IGA), enabling the present methodology to be robust and accurate. To overcome the difficulty in evaluating the sensitivities of objective and/or constraint functions by analytic method in some cases, the authors adopt the finite difference method to calculate these sensitivities, thereby providing a universal approach. Moreover, to further eliminate the inefficiency caused by the finite difference method, the authors advance the exact reanalysis method, the indirect factorization updating (IFU), to exactly and efficiently calculate functions and their sensitivities, which guarantees its generality and efficiency at the same time.

The proposed isogeometric gradient-based shape optimization using our IFU approach is reliable and accurate, as well as general and efficient.

The authors proposed a gradient-based shape optimization framework in which they first integrate IGA and the proposed exact reanalysis method for applicability to structural response and sensitivity analysis.

The optimal material microstructures in pure material design are no longer efficient or optimal when accounting macroscopic structure performance with specific boundary conditions. Therefore, it is important to provide a novel multiscale topology optimization framework to tailor the topology of structure and the material to achieve specific applications. In comparison with porous materials, composites consisting of two or more phase materials are more attractive and advantageous from the perspective of engineering application. This paper aims to provide a novel concurrent topological design of structures and microscopic materials for thermal conductivity involving multi-material topology optimization (material distribution) at the lower scale.

In this work, the effective thermal conductivity properties of microscopic three or more phase materials are obtained via homogenization theory, which serves as a bridge of the macrostructure and the periodic material microstructures. The optimization problem, including the topological design of macrostructures and inverse homogenization of microscopic materials, are solved by bi-directional evolutionary structure optimization method.

As a result, the presented framework shows high stability during the optimization process and requires little iterations for convergence. A number of interesting and valid macrostructures and material microstructures are obtained in terms of optimal thermal conductive path, which verify the effectiveness of the proposed mutliscale topology optimization method. Numerical examples adequately consider effects of initial guesses of the representative unit cell and of the volume constraints of adopted base materials at the microscopic scale on the final design. The resultant structures at both the scales with clear and distinctive boundary between different phases, making the manufacturing straightforward.

This paper presents a novel multiscale concurrent topology optimization method for structures and the underlying multi-phase materials for thermal conductivity. The authors have carried out the concurrent multi-phase topology optimization for both 2D and 3D cases, which makes this work distinguished from existing references. In addition, some interesting and efficient multi-phase material microstructures and macrostructures have been obtained in terms of optimal thermal conductive path.

This paper aims to investigate the friction and wear properties of Cr-Ni-Mo-V steel against 440C stainless steel under both water and water–silica mixture lubricant.

The Cr-Ni-Mo-V steel specimens were taken from a forged steel brake disc with the process of quenching at 900°C and tempering at 600°C. The tribological testing was performed using a contact configuration of ball-on-flat with a liquid cell according to the ASTM standard. Detailed examinations on the worn surface were analyzed using a scanning electron microscope.

The results indicate that the friction coefficient and friction damage of the steel sliding under water–silica mixture are higher than those under water. The friction coefficient decreases with increasing load and increases with the sliding speed for the two lubricants. The mass wear rate presents a rising trend with both sliding load and speed. The wear mechanisms of the Cr-Ni-Mo-V steel sliding under the two lubricants are oxidation wear, abrasive wear and fatigue wear.

Because of the chosen tribological testing approach, the research results could not describe the tribological performance of the brake disc accurately during actual braking process of the high-speed train. Therefore, researchers are encouraged to test the proposed propositions further.

This study shows that the tribology behavior of the Cr-Ni-Mo-V steel with water or water–silica mixture lubrications helps the industrial firms and academicians to work on the wear of the brake disc in rainwater or wet environment.

Power loss is an important index to evaluate the transmission performance of a gear pair. In some cases, the starved lubrication exists on the gear contact interface. The purpose of this paper is to reveal the mechanical power loss of a helical gear pair under starved lubrication.

A starved thermal-elastohydrodynamic lubrication (EHL) model is proposed to evaluate the tribological properties of a helical gear pair. The numerical result has been validated against the published simulation data. Based on the proposed model, the influence of thermal effect, working conditions, inlet oil-supply layer and surface roughness on the mechanical power loss and lubrication performance has been discussed.

Results show that the thermal effect has a significant effect on the tribological properties of helical gear pair, especially on mechanical power loss. For a specified working condition, there is an optimal oil supply for gear lubrication to obtain the state of full film lubrication. Meanwhile, it reveals that the mechanical power loss increases with the increase of the surface roughness amplitude.

In this paper, a starved thermal-EHL model has been developed for the helical gear pair based on the finite line contact theory. This model can be used to analyze the tribological properties of gear pair from full film lubrication to mixed lubrication. The results can provide the tribological guidance for design of a helical gear pair.

The purpose of this study is to adopt the perspective of congruence to explore how organizational unlearning facilitates knowledge transfer in cross-border mergers and acquisitions (M&A).

Drawing on the congruence theory, this study built a theoretical model and examined it with survey data from 212 firms in China.

Organizational unlearning has no direct influence on knowledge transfer. In contrast, it promotes knowledge and routine compatibility that facilitate knowledge transfer. Routine and knowledge compatibility have different mechanisms on knowledge transfer. Specifically, the higher routine compatibility, the more effective is knowledge transfer. When knowledge compatibility is at a medium level, the effectiveness of knowledge transfer is optimal.

Firms should regard organizational unlearning as a crucial facilitator to knowledge and routine compatibility that promote knowledge transfer.

This study provides a specific understanding of the relationships between organizational unlearning and knowledge transfer by focusing on knowledge and routine compatibility as the crucial links, and enriches existing literature regarding knowledge transfer.

Pick-and-place tasks are common across many industrial sectors, and many rigid-linked robots have been proposed for this application. This paper aims to alternatively present the development of a continuum robot for low-load medium-speed pick-and-place tasks.

An inversion of a previously proposed dual continuum mechanism, as a key design element, was used to realize the horizontal movements of the CurviPicker’s end effector. A flexible shaft was inserted to realize rotation and translation about a vertical axis. The design concept, kinematics, system descriptions and proof-of-concept experimental characterizations are elaborated.

Experimental characterizations show that the CurviPicker can achieve satisfactory accuracy after motion calibration. The CurviPicker is easy to control due to its simple kinematics, while its structural compliance makes it safe to work with, as well as less sensitive to possible target picking position errors to avoid damaging itself or the to-be-picked objects.

The vertical translation of the CurviPicker is currently realized by moving the flexible shaft. Insertion of the flexible shaft introduces possible disturbances. It is desired to explore other form of variations to use structural deformation to realize the vertical translation.

The proposed CurviPicker realizes the Schöenflies motions via a simple structure. Such a robot can be used to increase robot presence and automation in small businesses for low-load medium-speed pick-and-place tasks.

To the best of the authors’ knowledge, the CurviPicker is the first continuum robot designed and constructed for pick-and-place tasks. The originality stems from the concept, kinematics, development and proof-of-concept experimental characterizations of the CurviPicker.

Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

This paper aims to identify the factors that are constraining technological innovation to support the development of coalbed methane in China.

The analysis applies ideas relating to national and sector systems of innovation to explain why China’s strategies to support research and technological innovation have failed to stimulate the desired progress in coalbed methane production. It also provides a counter-example of the USA that implemented a number of measures in the 1970s that proved very effective.

The deficiencies of China’s research and development strategies in support of coalbed methane development reflect the national and sectoral systems of innovation. They are exacerbated by the structure of the national oil and gas industry. Key constraints include the excessively top-down management of the national R&D agenda, insufficient support for basic research, limited collaboration networks between companies, research institutes and universities and weak mechanisms for diffusion of knowledge. The success of the USA was based on entirely different systems for innovation and in quite a different industrial setting.

The originality of this analysis lies in placing the challenges facing research and innovation for China’s coalbed methane development in the context of the national and sectoral systems for innovation and comparing with the approach and success of the USA.

Surrogate models are extensively used to substitute real models which are expensive to evaluate in the time-dependent reliability analysis. Normally, different surrogate models have different scopes of application. However, information is often insufficient for analysts to select the most appropriate surrogate model for a specific application. Thus, the result precited by individual surrogate model tends to be suboptimal or even inaccurate. Ensemble model can effectively deal with the above concern. This work aims to study the application of ensemble model for reliability analysis of time-independent problems.

In this work, a method of reliability analysis for time-dependent problems based on ensemble learning of surrogate models is developed. The ensemble of surrogate models includes Kriging, radial basis function, and support vector machine. The prediction is approximated by the weighted average model. The ensemble learning of surrogate models is updated by finding and adding the sample points with large prediction errors throughout the entire procedure.

The effectiveness of the proposed method is verified by several examples. The results show that the ensemble of surrogate models can effectively propagate the uncertainty of time-varying problems, and evaluate the reliability with high prediction accuracy and computational efficiency.

This work proposes an adaptive learning framework for the uncertainty propagation of time-dependent problems based on the ensemble of surrogate models. Compared with individual surrogate models, the ensemble model not only saves the effort of selecting an appropriate surrogate model especially when the knowledge of unknown problem is lacking, but also improves the prediction accuracy and computational efficiency.