Alex Mikes, Katherine Edmonds, R. Stone, Bryony DuPont
The purpose of this research is to find the optimum values for threshold variables used in a data mining and prediction algorithm. We also minimize and stratify a training set to find the optimum size based on how well it represents the whole dataset. Our specific focus is automating functional models, but the method can be applied to any dataset with a similar structure. We iterate through different values for two of the threshold variables in this process and cross-validate to calculate the average accuracy and find the optimum values for each variable. We optimize the training set by reducing the size by 78% and stratifying the data, whereby we achieve an accuracy that is 96% as good as the whole training set and takes 50% less time. These optimum values can be used to better predict the functions and flows of any future product based on its constituent components, which can be used to generate a complete functional model.
{"title":"Optimizing an Algorithm for Data Mining a Design Repository to Automate Functional Modeling","authors":"Alex Mikes, Katherine Edmonds, R. Stone, Bryony DuPont","doi":"10.1115/detc2020-22346","DOIUrl":"https://doi.org/10.1115/detc2020-22346","url":null,"abstract":"\u0000 The purpose of this research is to find the optimum values for threshold variables used in a data mining and prediction algorithm. We also minimize and stratify a training set to find the optimum size based on how well it represents the whole dataset. Our specific focus is automating functional models, but the method can be applied to any dataset with a similar structure. We iterate through different values for two of the threshold variables in this process and cross-validate to calculate the average accuracy and find the optimum values for each variable. We optimize the training set by reducing the size by 78% and stratifying the data, whereby we achieve an accuracy that is 96% as good as the whole training set and takes 50% less time. These optimum values can be used to better predict the functions and flows of any future product based on its constituent components, which can be used to generate a complete functional model.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123036305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work introduces a topology optimization method for the design of structures composed of rectangular plates each of which is made of a predetermined anisotropic material. This work builds upon the geometry projection method with two notable additions. First, a novel geometric parameterization of plates represented by offset surfaces is formulated that is simpler than the one used in previous works. Second, the formulation presented herein adds support to the geometry projection method for geometric components with general anisotropic material properties. A design-generation framework is formulated that produces optimal designs composed exclusively of rectangular plates that may be made of a predetermined, generally anisotropic material. The efficacy of the proposed method is demonstrated with a numerical example comparing optimal cantilever beam designs obtained using isotropic- and orthotropic-material plates. For this example, we maximize the stiffness of the structure for a fixed amount of material. The example reveals the importance of considering material anisotropy in the design of plate structures. Moreover, it is demonstrated that an optimally stiff design for plates made of an isotropic material can exhibit detrimental performance if the plates are naively replaced with an anisotropic material. Although the example given in this work is in the context of orthotropic plates, since the formulation presented in this work supports arbitrary anisotropic materials, it may be readily extended to support the design of each component’s material anisotropy as a part of the optimization routine.
{"title":"A Topology Optimization Method for the Design of Orthotropic Plate Structures","authors":"Hollis Smith, J. Norato","doi":"10.1115/detc2020-22400","DOIUrl":"https://doi.org/10.1115/detc2020-22400","url":null,"abstract":"\u0000 This work introduces a topology optimization method for the design of structures composed of rectangular plates each of which is made of a predetermined anisotropic material. This work builds upon the geometry projection method with two notable additions. First, a novel geometric parameterization of plates represented by offset surfaces is formulated that is simpler than the one used in previous works. Second, the formulation presented herein adds support to the geometry projection method for geometric components with general anisotropic material properties. A design-generation framework is formulated that produces optimal designs composed exclusively of rectangular plates that may be made of a predetermined, generally anisotropic material. The efficacy of the proposed method is demonstrated with a numerical example comparing optimal cantilever beam designs obtained using isotropic- and orthotropic-material plates. For this example, we maximize the stiffness of the structure for a fixed amount of material. The example reveals the importance of considering material anisotropy in the design of plate structures. Moreover, it is demonstrated that an optimally stiff design for plates made of an isotropic material can exhibit detrimental performance if the plates are naively replaced with an anisotropic material. Although the example given in this work is in the context of orthotropic plates, since the formulation presented in this work supports arbitrary anisotropic materials, it may be readily extended to support the design of each component’s material anisotropy as a part of the optimization routine.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128416928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Statistical network models allow us to study the co-evolution between the products and the social aspects of a market system, by modeling these components and their interactions as graphs. In this paper, we study competition between different car models using network theory, with a focus on how product attributes (like fuel economy and price) affect which cars are considered together and which cars are finally bought by customers. Unlike past work, where most systems have been studied with the assumption that relationships between competitors are binary (i.e., whether a relationship exists or not), we allow relationships to take strengths (i.e., how strong a relationship is). Specifically, we use valued Exponential Random Graph Models and show that our approach provides a significant improvement over the baselines in predicting product co-considerations as well as in the validation of market share. This is also the first attempt to study aggregated purchase preference and car competition using valued directed networks.
{"title":"A Weighted Network Modeling Approach for Analyzing Product Competition","authors":"Yaxin Cui, Faez Ahmed, Zhenghui Sha, Lijun Wang, Yan Fu, Wei Chen","doi":"10.1115/detc2020-22591","DOIUrl":"https://doi.org/10.1115/detc2020-22591","url":null,"abstract":"\u0000 Statistical network models allow us to study the co-evolution between the products and the social aspects of a market system, by modeling these components and their interactions as graphs. In this paper, we study competition between different car models using network theory, with a focus on how product attributes (like fuel economy and price) affect which cars are considered together and which cars are finally bought by customers. Unlike past work, where most systems have been studied with the assumption that relationships between competitors are binary (i.e., whether a relationship exists or not), we allow relationships to take strengths (i.e., how strong a relationship is). Specifically, we use valued Exponential Random Graph Models and show that our approach provides a significant improvement over the baselines in predicting product co-considerations as well as in the validation of market share. This is also the first attempt to study aggregated purchase preference and car competition using valued directed networks.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125421489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel E. Hulse, C. Hoyle, I. Tumer, K. Goebel, Chetan S. Kulkarni
Resilience models assess a system’s ability to withstand disruption by quantifying the value of metrics (e.g. expected cost or loss) over time. When such a metric is the result of injecting faults in a dynamic model over an interval of time, it is important that it represent the statistical expectation of fault responses rather than a single response. Since fault responses vary over fault injection times, representing the statistical expectation of responses requires sampling a number of points. However, fault models are often built around computationally expensive dynamic simulations, and it is desirable to be able to iterate over designs as quickly as possible to improve system resilience. With this in mind, this paper explores approaches to sample fault injection times to minimize computational cost while accurately representing the expectation of fault resilience metrics over the set possible occurrence times. Two general approaches are presented: an a priori approach that attempts to minimize error without knowing the underlying cost function, and an a posteriori approach that minimizes error when the cost function is known. Among a priori methods, numerical integration minimizes error and computational time compared to Monte Carlo sampling, however both are prone to error when the metric’s fault response curve is discontinuous. While a posteriori approaches can locate and correct for these discontinuities, the resulting error reduction is not robust to design changes that shift the underlying location of discontinuities. The ultimate decision to use an a priori or a posteriori approach to quantify resilience is thus dependent on a number of considerations, including computational cost, the robustness of the approximation to design changes, and the underlying form of the resilience function.
{"title":"Temporal Fault Injection Considerations in Resilience Quantification","authors":"Daniel E. Hulse, C. Hoyle, I. Tumer, K. Goebel, Chetan S. Kulkarni","doi":"10.1115/detc2020-22154","DOIUrl":"https://doi.org/10.1115/detc2020-22154","url":null,"abstract":"\u0000 Resilience models assess a system’s ability to withstand disruption by quantifying the value of metrics (e.g. expected cost or loss) over time. When such a metric is the result of injecting faults in a dynamic model over an interval of time, it is important that it represent the statistical expectation of fault responses rather than a single response. Since fault responses vary over fault injection times, representing the statistical expectation of responses requires sampling a number of points. However, fault models are often built around computationally expensive dynamic simulations, and it is desirable to be able to iterate over designs as quickly as possible to improve system resilience. With this in mind, this paper explores approaches to sample fault injection times to minimize computational cost while accurately representing the expectation of fault resilience metrics over the set possible occurrence times. Two general approaches are presented: an a priori approach that attempts to minimize error without knowing the underlying cost function, and an a posteriori approach that minimizes error when the cost function is known. Among a priori methods, numerical integration minimizes error and computational time compared to Monte Carlo sampling, however both are prone to error when the metric’s fault response curve is discontinuous. While a posteriori approaches can locate and correct for these discontinuities, the resulting error reduction is not robust to design changes that shift the underlying location of discontinuities. The ultimate decision to use an a priori or a posteriori approach to quantify resilience is thus dependent on a number of considerations, including computational cost, the robustness of the approximation to design changes, and the underlying form of the resilience function.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125620014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable towards external disturbances. Hence, managing potential disruptive events during design, operating, and recovery phase of an engineered system therefore improving the system’s resilience is an important yet challenging task. In order to ensure system resilience after the occurrence of failure events, this study proposes a mixed integer linear programming (MILP) based restoration framework using heterogenous dispatchable agents. Scenario based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from the nature. Moreover, different from conventional SO using deterministic equivalent formulations, additional risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves with a large-scale MILP problem and thus an adequate decompaction technique, i.e., modified Langragian Relaxation, is also proposed in order to achieve tractable time complexity. Case study results based on the IEEE 37-buses test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.
{"title":"Risk-Averse Optimization for Resilience Enhancement Under Uncertainty","authors":"Jiaxin Wu, Pingfeng Wang","doi":"10.1115/detc2020-22226","DOIUrl":"https://doi.org/10.1115/detc2020-22226","url":null,"abstract":"\u0000 With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable towards external disturbances. Hence, managing potential disruptive events during design, operating, and recovery phase of an engineered system therefore improving the system’s resilience is an important yet challenging task. In order to ensure system resilience after the occurrence of failure events, this study proposes a mixed integer linear programming (MILP) based restoration framework using heterogenous dispatchable agents. Scenario based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from the nature. Moreover, different from conventional SO using deterministic equivalent formulations, additional risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves with a large-scale MILP problem and thus an adequate decompaction technique, i.e., modified Langragian Relaxation, is also proposed in order to achieve tractable time complexity. Case study results based on the IEEE 37-buses test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115955389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposed a design support method based on structuralization and analysis of various design candidates of product architecture design. The product architecture is a basic scheme that assigns the function of the product to physical components. In the conventional modular design method, a concise model, i.e., a graph or a matrix, is used to express the interactions of the system’s components and aims to support the designer grasping the system behavior. The Design Structure Matrix (DSM) is a representative model of system architecture and enables quantitative evaluation of design candidates. While various design candidates are generated through mathematical operations, it is difficult to understand their relationships from simple comparisons because of discrete behavior and the size of the problem. It must be a critical issue at the stage of selecting and interpreting the design candidates. In the proposed method, the design candidates are classified and structuralized as a dendrogram by the hierarchical clustering method. The comparison of clusters of each branch of dendrogram clarifies the system leverage points. The information of the system is summarized into the hierarchical tree-shaped graph that corresponds to the dendrogram. The designer can explore the design candidates with such a graph-based based interpretation of underlying structures effectively.
{"title":"Structural Design Exploration for Product Architecture Design","authors":"Masato Toi, Yutaka Nomaguchi, K. Fujita","doi":"10.1115/detc2020-22599","DOIUrl":"https://doi.org/10.1115/detc2020-22599","url":null,"abstract":"\u0000 This paper proposed a design support method based on structuralization and analysis of various design candidates of product architecture design. The product architecture is a basic scheme that assigns the function of the product to physical components. In the conventional modular design method, a concise model, i.e., a graph or a matrix, is used to express the interactions of the system’s components and aims to support the designer grasping the system behavior. The Design Structure Matrix (DSM) is a representative model of system architecture and enables quantitative evaluation of design candidates. While various design candidates are generated through mathematical operations, it is difficult to understand their relationships from simple comparisons because of discrete behavior and the size of the problem. It must be a critical issue at the stage of selecting and interpreting the design candidates. In the proposed method, the design candidates are classified and structuralized as a dendrogram by the hierarchical clustering method. The comparison of clusters of each branch of dendrogram clarifies the system leverage points. The information of the system is summarized into the hierarchical tree-shaped graph that corresponds to the dendrogram. The designer can explore the design candidates with such a graph-based based interpretation of underlying structures effectively.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130986903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Aoyama, Yoshihiro Uchibori, K. Oizumi, Shigeki Hiramatsu, Hiroshi Unesaki, Shyuichi Kondo
In this study, following the concept of set-based design, after preparing global calculation results, we introduced the approach of setting the design solution area that satisfies the product performance goals of the system design. In this approach, from the viewpoint of considering uncertainty, we aimed to develop an analysis method that can get the organic relationship between target variables and design variables. And more, under the assumption that it is difficult to comprehend the full picture of products that are becoming more sophisticated and complex with the knowledge that has been fostered by skilled engineers, the proposed system uses the objective calculation indices that is provided knowledge of the designer. Specifically, the following method are proposed to solve the problem. - Implementation of meta-modeling of design space. - Classified solution space using a density-based clustering method to detect that the solution spaces are divided into multiple disconnected space. - Defined an index called distribution concentration and expressed the possibility of dealing with the uncertainty of the solution domain. - The network diagram based on the calculated index values was proposed to confirm the change in the characteristics of the solution space when the performance target of the product was changed. Finally, the effectiveness of the proposed method was verified by applying it to actual simulation results.
{"title":"Design Space Analysis Method for Support of System Design Under the Consideration of Uncertainties in the Early Design Stage","authors":"K. Aoyama, Yoshihiro Uchibori, K. Oizumi, Shigeki Hiramatsu, Hiroshi Unesaki, Shyuichi Kondo","doi":"10.1115/detc2020-22649","DOIUrl":"https://doi.org/10.1115/detc2020-22649","url":null,"abstract":"\u0000 In this study, following the concept of set-based design, after preparing global calculation results, we introduced the approach of setting the design solution area that satisfies the product performance goals of the system design. In this approach, from the viewpoint of considering uncertainty, we aimed to develop an analysis method that can get the organic relationship between target variables and design variables. And more, under the assumption that it is difficult to comprehend the full picture of products that are becoming more sophisticated and complex with the knowledge that has been fostered by skilled engineers, the proposed system uses the objective calculation indices that is provided knowledge of the designer. Specifically, the following method are proposed to solve the problem.\u0000 - Implementation of meta-modeling of design space.\u0000 - Classified solution space using a density-based clustering method to detect that the solution spaces are divided into multiple disconnected space.\u0000 - Defined an index called distribution concentration and expressed the possibility of dealing with the uncertainty of the solution domain.\u0000 - The network diagram based on the calculated index values was proposed to confirm the change in the characteristics of the solution space when the performance target of the product was changed.\u0000 Finally, the effectiveness of the proposed method was verified by applying it to actual simulation results.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"409 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124338491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meng Li, Sheng Shen, Vahid Barzegar, Mohammadkazem Sadoughi, S. Laflamme, Chao Hu
Several acquisition functions have been proposed to identify an optimal sequence of samples in sequential kriging-based reliability analysis. However, no single acquisition function provides better performance over the others in all cases. To address this problem, this paper proposes a new acquisition function, namely expected uncertainty reduction (EUR), that serves as a meta-criterion to select the best sample from a set of optimal samples, each identified from a large number of candidate samples according to the criterion of an acquisition function. EUR directly quantifies the expected reduction of the uncertainty in the prediction of limit-state function by adding an optimal sample. The uncertainty reduction is quantified by sampling over the kriging posterior. In the proposed EUR-based sequential sampling framework, a portfolio that consists of four acquisition functions is first employed to suggest four optimal samples at each iteration of sequential sampling. Then, EUR is employed as the meta-criterion to identify the best sample among those optimal samples. The results from two mathematical case studies show that (1) EUR-based sequential sampling can perform as well as or outperform the single use of any acquisition function in the portfolio, and (2) the best-performing acquisition function may change from one problem to another or even from one iteration to the next within a problem.
{"title":"Expected Uncertainty Reduction for Sequential Kriging-Based Reliability Analysis","authors":"Meng Li, Sheng Shen, Vahid Barzegar, Mohammadkazem Sadoughi, S. Laflamme, Chao Hu","doi":"10.1115/detc2020-22680","DOIUrl":"https://doi.org/10.1115/detc2020-22680","url":null,"abstract":"\u0000 Several acquisition functions have been proposed to identify an optimal sequence of samples in sequential kriging-based reliability analysis. However, no single acquisition function provides better performance over the others in all cases. To address this problem, this paper proposes a new acquisition function, namely expected uncertainty reduction (EUR), that serves as a meta-criterion to select the best sample from a set of optimal samples, each identified from a large number of candidate samples according to the criterion of an acquisition function. EUR directly quantifies the expected reduction of the uncertainty in the prediction of limit-state function by adding an optimal sample. The uncertainty reduction is quantified by sampling over the kriging posterior. In the proposed EUR-based sequential sampling framework, a portfolio that consists of four acquisition functions is first employed to suggest four optimal samples at each iteration of sequential sampling. Then, EUR is employed as the meta-criterion to identify the best sample among those optimal samples. The results from two mathematical case studies show that (1) EUR-based sequential sampling can perform as well as or outperform the single use of any acquisition function in the portfolio, and (2) the best-performing acquisition function may change from one problem to another or even from one iteration to the next within a problem.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131314740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Microvascular composite offers a variety of multi-functionality based on the choice of fluid flowing through the embedded microchannels. The design of the microchannel network in microvascular composites is quite challenging. Indeed, the design is often expected to have high cooling efficiency, satisfy the manufacturing and operating constraints, and also have redundancy to increase the temperature uniformity and alleviate the destructive effects of potential microchannel blockage. In this study, we present a design optimization framework to satisfy these requirements. We use the Hybrid Topology/Shape (HyTopS) optimization scheme to design a redundant blockage-tolerant cooling network. In this method, the optimizer can change the topology of the design during the shape optimization process. Being able to modify the topology of the network enables the optimizer to provide network redundancy to effectively optimize the design for blockage tolerance. We also solve several numerical examples to show the unique features of the proposed method.
{"title":"Network Redundancy: A Key Design Factor for Cooling Networks","authors":"Reza Pejman, A. Najafi","doi":"10.1115/detc2020-22107","DOIUrl":"https://doi.org/10.1115/detc2020-22107","url":null,"abstract":"\u0000 Microvascular composite offers a variety of multi-functionality based on the choice of fluid flowing through the embedded microchannels. The design of the microchannel network in microvascular composites is quite challenging. Indeed, the design is often expected to have high cooling efficiency, satisfy the manufacturing and operating constraints, and also have redundancy to increase the temperature uniformity and alleviate the destructive effects of potential microchannel blockage. In this study, we present a design optimization framework to satisfy these requirements. We use the Hybrid Topology/Shape (HyTopS) optimization scheme to design a redundant blockage-tolerant cooling network. In this method, the optimizer can change the topology of the design during the shape optimization process. Being able to modify the topology of the network enables the optimizer to provide network redundancy to effectively optimize the design for blockage tolerance. We also solve several numerical examples to show the unique features of the proposed method.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126471058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuqing Zhou, T. Nomura, Enpei Zhao, Wei Zhang, K. Saitou
Variable-axial fiber-reinforced composites allow for local customization of fiber orientation and thicknesses. Despite their significant potential for performance improvement over the conventional multiaxial composites and metals, they pose challenges in design optimization due to the vastly increased design freedom in material orientations. This paper presents an anisotropic topology optimization (TO) method for designing large-scale, 3D variable-axial composite structures. The computational challenge for large-scale 3D TO with extremely low volume fraction is addressed by a tensor-based representation of 3D orientation that would avoid the 2π periodicity of angular representation such as Eular angles, and an adaptive meshing scheme, which, in conjunction with PDE regularization of the density variables, refines the mesh where structural members appear and coarsens where there is void. The proposed method is applied to designing a heavy-duty drone frame subject to complex multi-loading conditions. Finally, the manufacturability gaps between the optimized design and the fabrication-ready design for Tailored Fiber Placement (TFP) is discussed, which motivates future work toward fully-automated design synthesis.
{"title":"Large-Scale Three-Dimensional Anisotropic Topology Optimization of Variable-Axial Composite Structures","authors":"Yuqing Zhou, T. Nomura, Enpei Zhao, Wei Zhang, K. Saitou","doi":"10.1115/detc2020-22509","DOIUrl":"https://doi.org/10.1115/detc2020-22509","url":null,"abstract":"\u0000 Variable-axial fiber-reinforced composites allow for local customization of fiber orientation and thicknesses. Despite their significant potential for performance improvement over the conventional multiaxial composites and metals, they pose challenges in design optimization due to the vastly increased design freedom in material orientations. This paper presents an anisotropic topology optimization (TO) method for designing large-scale, 3D variable-axial composite structures. The computational challenge for large-scale 3D TO with extremely low volume fraction is addressed by a tensor-based representation of 3D orientation that would avoid the 2π periodicity of angular representation such as Eular angles, and an adaptive meshing scheme, which, in conjunction with PDE regularization of the density variables, refines the mesh where structural members appear and coarsens where there is void. The proposed method is applied to designing a heavy-duty drone frame subject to complex multi-loading conditions. Finally, the manufacturability gaps between the optimized design and the fabrication-ready design for Tailored Fiber Placement (TFP) is discussed, which motivates future work toward fully-automated design synthesis.","PeriodicalId":415040,"journal":{"name":"Volume 11A: 46th Design Automation Conference (DAC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122590405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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