Modeling And Optimization Decision Of Green Design Scheme Of Product Integrating Life Cycle Scenario

The manufacturing and use of electromechanical products consume vast amounts of energy and resources,while generating significant amounts of pollutant emissions,resulting in severe environmental pollution problems.Therefore,designers need to comprehensively consider the entire life cycle of the product,including information about raw material acquisition,processing and manufacturing,use,transportation,and recycling.Incorporating this information into the design process can effectively support green design and improve environmental performance.The use of whole life cycle design methods can minimize the negative environmental impact of products,and ultimately improve product quality and sustainability.Therefore,this thesis focuses on the research of modeling and optimization decision of green design scheme.The specific research work is as follows:(1)The expression model of product green design scheme integrated with life cycle scenario is established.A method of expression of green design scheme based on directed network structure is proposed to realize effective integration of functional domain,structural domain,process domain and material domain information related to life cycle design and multidomain correlation mapping with the whole life cycle stage.Based on the adjacency list structure,the design network model of product is constructed by the hierarchical traversal.A life cycle scenario representation method based on set theory is proposed and the representation model of design scheme integrating life cycle scenario is constructed by designing node interface step by step matching.The life cycle list data of the whole design scheme is obtained by summating the list data of each scene through the method of obtaining the list data based on the scene.Based on the LCA environmental impact calculation process,the environmental impact value of the design scheme can be obtained.Through the proposed model and method,the design network model of 2.5MW wind turbine is constructed.(2)The optimization method of green design scheme of product based on improved differential evolution algorithm is proposed.According to the characteristics of the established network model of product design.the depth-first search algorithm with the pruning strategy is used to generate individual initial design schemes by traversing each design node in the design network model.A coding strategy based on the adjacent real number matrix is developed for the correlation between design nodes in the individual design scheme.By setting the differential evolution operator,the green design scheme of product is optimized under the condition of meeting the environmental constraints of the product life cycle.Finally,five design schemes of 2.5MW wind turbine with small environmental impact results are obtained through optimization and the design schemes are qualitatively analyzed.The improved differential evolution algorithm is compared with conventional differential evolution algorithm.(3)A decision method of green design scheme of product based on intuitionistic fuzzyimproved TOPSIS is proposed.The principles and process of constructing the evaluation system for design schemes are explored,and a multi-objective evaluation index system for design schemes is established based on the green demand characteristic of the product.The intuitionistic fuzzy semantic variable transformation method is utilized to transform expert semantic variables into standardized and unified intuitionistic fuzzy numbers,while hesitation is taken into account.With the application of this method,powerful support for the green design and sustainable development of products can be provided.Considering the difference and uncertainty of different expert evaluation results,the expert’s hesitation and similarity weight are integrated.Using the design decision method based on the improved TOPSIS,the ranking of five 2.5MW wind turbine design schemes is completed.Finally,the stability of the proposed method is verified by the sensitivity analysis of the resolution coefficient and the comparison analysis of different expert weight determination methods and different decision methods.