By the knowledge transferring in different areas, analogical design has been considered as a powerful approach to promote the generation of novel ideas in product conceptual design. An efficient representation scheme for design knowledge is vital to implement analogical transferring. In this article, inspired from the structure mapping mechanism of analogical reasoning, a structure mapping–based representation was proposed to support designers to search and use design analogies. This representation can provide designers with insights into the structural information of knowledge situations, and consequently designers are able to implement the corresponding design analogy search at the level of the structural similarity, rather than the functional or superficial similarity. Based on this new representation scheme, a structure mapping–based analogical design framework was developed. In this framework, patents are used as the source of analogical knowledge, and the relational structure–based representation for the patent knowledge is created using the advanced natural language processing tools/algorithms. Next, the search of design analogies is implemented by means of the vector space model, and a new structure mapping–based concept generation model can finally guide the designers to use design analogies. An industrial case and a compared experiment were carried out to verify the feasibility and effectiveness of the proposed framework.

Former research has proved that the design knowledge involved in the conceptual design stage has multiple attributes/perspectives, which can be further abstracted into the concepts with different abstract/granularity levels, from coarse to fine. Based on this, we believe that it is necessary to use multiple attributes to describe design knowledge during the knowledge retrieval process. Firstly, an attribute ontology with multi-perspective and multi-granularity is built in the new retrieval model. Therefore, the knowledge documents can be abstracted by the concepts of the ontology. Based on the correlation of the concepts, the clustering theory is introduced into the new model to cluster knowledge documents, and the combined keyword concepts of the clusters are generated as well. The keyword concepts of the clusters, rather than the keyword concepts of documents, are used as the index of the retrieval. During the knowledge matching process, the keywords and their semantic extension of design problems are extracted, and the similarity between the abstract descriptions of the knowledge clusters and design problems is calculated, so the best cluster can be found by the calculated results. Based on the granularity levels of the keywords (concepts) the design problems, finally the documents in the selected cluster are ranked in the order of granularity levels. The selected document by the model is that with the highest relevance and most suitable granularity level about the design problem. In the last section of the paper, we used a simple real case to evaluate the new model, and also develop the scheme of the whole design knowledge retrieval system for the future work.

This study aims to integrate electric vehicles, photovoltaic and battery energy storage systems, and distribution network information in a microgrid to achieve decarbonized optimal operation. Under the different operating states of distribution networks, the paper proposes a decarbonized two-stage deeply integrated operational mode for a photovoltaic, battery energy storage system, and electric vehicles integrated microgrid, incorporating the electricity market to optimize overall revenue. In the optimization process, this paper introduces statistical node carbon emission factors and dynamic thermal capacity constraints to explore microgrids’ decarbonized potential and maximize system equipment’s utilization efficiency. This study proposes a two-stage day-ahead robust optimization framework for the microgrids, integrating tri-level min-max-min modeling to address multidimensional uncertainties through parameterized uncertainty sets to balance conservatism and optimize costs under worst-case scenarios. This work develops an adaptive threshold tightening mechanism for Column-and-Constraint Generation algorithms to balance computational efficiency and solution precision. Additionally, this paper proposes an adaptive dynamic pricing framework that integrates electric vehicle users’ cost-adaptive behavior with supply-demand fluctuations, using incentive-driven temporal optimization to maintain real-time grid equilibrium and enhance microgrid flexibility. Simulation results show that the proposed approach improves system profitability by up to 55.2% while enhancing transformer safety and operational flexibility.

In this paper, a space-driven two-stage spur gear system is taken as the research object, and a 10 DOF dynamic model is established. Considering the high load characteristics of the space drive system and the time-varying stiffness and tooth clearance of the gear system, a nonlinear dynamic response analysis was performed. The characteristics of the vibration acceleration, shock and transmission error of the gear system are studied in this paper. This paper analyzes the relationship between backlash and return difference, and derives the theoretical formula between the two. The time-varying stiffness was corrected to make the theoretical model closer to reality. The research in this paper enriches the study on space drive systems and high load gear systems.