{"title":"基于dnn的梯度折射率声子晶体形状优化及其可调焦位和鲁棒能量收集的灵敏度分析","authors":"Mary Kim , Sangryun Lee","doi":"10.1016/j.matdes.2025.114723","DOIUrl":null,"url":null,"abstract":"<div><div>Gradient-index (GRIN) phononic crystals (PnCs) enable energy harvesting (EH) by focusing elastic waves into electrical energy. Efficient EH requires maximizing focused wave intensity, typically achieved by tuning the GRIN PnCs unit-cell shape. However, existing designs often exhibit energy concentration near the GRIN lens boundary and incorporate narrow gaps and sharp corners, making them susceptible to manufacturing errors and limiting their practical applicability. Understanding the potential performance changes caused by manufacturing errors is important because geometrical alterations can compromise wave-focusing performance. Therefore, this study aims to optimize the unit-cell shape toward maximum focused intensity at the desired locations for EH devices. To assess manufacturability, the effects of minor geometric variations on the focal position and focused intensity are evaluated via a sensitivity analysis. The optimal shape is derived using a deep neural network (DNN) surrogate model trained to predict focal position and focused intensity. This model accelerates a genetic algorithm (GA) used to perform the optimization. Our optimized designs exhibit 1.5 to 2.0 times higher focused intensity across the target focal positions compared with the conventional design. Thus, these optimal shapes, along with their sensitivity analysis results, provide practical guidelines for defining manufacturing tolerances and achieving consistent, efficient EH performance.</div></div>","PeriodicalId":383,"journal":{"name":"Materials & Design","volume":"259 ","pages":"Article 114723"},"PeriodicalIF":7.9000,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DNN-based shape optimization of gradient-index phononic crystals with sensitivity analysis for tunable focal position and robust energy harvesting\",\"authors\":\"Mary Kim , Sangryun Lee\",\"doi\":\"10.1016/j.matdes.2025.114723\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Gradient-index (GRIN) phononic crystals (PnCs) enable energy harvesting (EH) by focusing elastic waves into electrical energy. Efficient EH requires maximizing focused wave intensity, typically achieved by tuning the GRIN PnCs unit-cell shape. However, existing designs often exhibit energy concentration near the GRIN lens boundary and incorporate narrow gaps and sharp corners, making them susceptible to manufacturing errors and limiting their practical applicability. Understanding the potential performance changes caused by manufacturing errors is important because geometrical alterations can compromise wave-focusing performance. Therefore, this study aims to optimize the unit-cell shape toward maximum focused intensity at the desired locations for EH devices. To assess manufacturability, the effects of minor geometric variations on the focal position and focused intensity are evaluated via a sensitivity analysis. The optimal shape is derived using a deep neural network (DNN) surrogate model trained to predict focal position and focused intensity. This model accelerates a genetic algorithm (GA) used to perform the optimization. Our optimized designs exhibit 1.5 to 2.0 times higher focused intensity across the target focal positions compared with the conventional design. Thus, these optimal shapes, along with their sensitivity analysis results, provide practical guidelines for defining manufacturing tolerances and achieving consistent, efficient EH performance.</div></div>\",\"PeriodicalId\":383,\"journal\":{\"name\":\"Materials & Design\",\"volume\":\"259 \",\"pages\":\"Article 114723\"},\"PeriodicalIF\":7.9000,\"publicationDate\":\"2025-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials & Design\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0264127525011438\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials & Design","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0264127525011438","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
DNN-based shape optimization of gradient-index phononic crystals with sensitivity analysis for tunable focal position and robust energy harvesting
Gradient-index (GRIN) phononic crystals (PnCs) enable energy harvesting (EH) by focusing elastic waves into electrical energy. Efficient EH requires maximizing focused wave intensity, typically achieved by tuning the GRIN PnCs unit-cell shape. However, existing designs often exhibit energy concentration near the GRIN lens boundary and incorporate narrow gaps and sharp corners, making them susceptible to manufacturing errors and limiting their practical applicability. Understanding the potential performance changes caused by manufacturing errors is important because geometrical alterations can compromise wave-focusing performance. Therefore, this study aims to optimize the unit-cell shape toward maximum focused intensity at the desired locations for EH devices. To assess manufacturability, the effects of minor geometric variations on the focal position and focused intensity are evaluated via a sensitivity analysis. The optimal shape is derived using a deep neural network (DNN) surrogate model trained to predict focal position and focused intensity. This model accelerates a genetic algorithm (GA) used to perform the optimization. Our optimized designs exhibit 1.5 to 2.0 times higher focused intensity across the target focal positions compared with the conventional design. Thus, these optimal shapes, along with their sensitivity analysis results, provide practical guidelines for defining manufacturing tolerances and achieving consistent, efficient EH performance.
期刊介绍:
Materials and Design is a multi-disciplinary journal that publishes original research reports, review articles, and express communications. The journal focuses on studying the structure and properties of inorganic and organic materials, advancements in synthesis, processing, characterization, and testing, the design of materials and engineering systems, and their applications in technology. It aims to bring together various aspects of materials science, engineering, physics, and chemistry.
The journal explores themes ranging from materials to design and aims to reveal the connections between natural and artificial materials, as well as experiment and modeling. Manuscripts submitted to Materials and Design should contain elements of discovery and surprise, as they often contribute new insights into the architecture and function of matter.