K. Deng, H. K. Nejadkhaki, F. M. Pasquali, A. Amaria, J. Armstrong, John F. Hall
A model to compute the elastic modulus and tensile properties of 3D printed Carbon Fiber Reinforced Polymers (CFRP) is presented. The material under consideration is Carbon Fiber Reinforced Nylon (CFRN) produced in a Fused Deposition Modeling (FDM) process. A relationship between the nylon raster in each layer and the carbon fiber volume fraction was devised with the help of a scanning electron microscope (SEM). Thirteen groups with different layer configurations and carbon-fiber percentages were formulated and tested to obtain the elastic modulus and tensile strength. This study focused only on the properties along the printed fiber direction. The results from these tests were analyzed within the rule of mixtures framework. The results suggest that the rule of mixtures can be successfully applied to unidirectional CFRP fabricated using additive manufacturing.
{"title":"Rule of Mixtures Model to Determine Elastic Modulus and Tensile Strength of 3D Printed Carbon Fiber Reinforced Nylon","authors":"K. Deng, H. K. Nejadkhaki, F. M. Pasquali, A. Amaria, J. Armstrong, John F. Hall","doi":"10.1115/detc2019-98024","DOIUrl":"https://doi.org/10.1115/detc2019-98024","url":null,"abstract":"\u0000 A model to compute the elastic modulus and tensile properties of 3D printed Carbon Fiber Reinforced Polymers (CFRP) is presented. The material under consideration is Carbon Fiber Reinforced Nylon (CFRN) produced in a Fused Deposition Modeling (FDM) process. A relationship between the nylon raster in each layer and the carbon fiber volume fraction was devised with the help of a scanning electron microscope (SEM). Thirteen groups with different layer configurations and carbon-fiber percentages were formulated and tested to obtain the elastic modulus and tensile strength. This study focused only on the properties along the printed fiber direction. The results from these tests were analyzed within the rule of mixtures framework. The results suggest that the rule of mixtures can be successfully applied to unidirectional CFRP fabricated using additive manufacturing.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130447284","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}
Anand Balu Nellippallil, P. Mohan, J. Allen, F. Mistree
The production of steel products involves a series of manufacturing processes. The material Thermo-Mechanical Processing (TMP) history at each process affects the final properties and performances of the product. Experiments and plant trials to predict these properties and performance of steel products are expensive and time consuming. This has resulted in the need for computational design methods and tools that support a human designer in realizing such complex systems involving the material, product and manufacturing processes from a simulation-based design perspective. In this paper, we present a Goal-oriented Inverse Design method to achieve the integrated design exploration of materials, products and manufacturing processes. The key functionality offered is the capability to carry out a microstructure-mediated design satisficing specific processing requirements and performance goals of the product. Given models to establish the information flow chain, a designer can use the method for the decision-based design exploration of material microstructure and processing paths to realize products in a manufacturing process chain. The efficacy of the method is tested using an industry-inspired hot rolling problem to inversely design the thermo-mechanical processing of a steel rod. The focus here is the method and associated design constructs which are generic and support the formulation and decision-based design of similar problems involving materials, products and associated manufacturing processes.
{"title":"Inverse Thermo-Mechanical Processing (ITMP) Design of a Steel Rod During Hot Rolling Process","authors":"Anand Balu Nellippallil, P. Mohan, J. Allen, F. Mistree","doi":"10.1115/detc2019-97390","DOIUrl":"https://doi.org/10.1115/detc2019-97390","url":null,"abstract":"\u0000 The production of steel products involves a series of manufacturing processes. The material Thermo-Mechanical Processing (TMP) history at each process affects the final properties and performances of the product. Experiments and plant trials to predict these properties and performance of steel products are expensive and time consuming. This has resulted in the need for computational design methods and tools that support a human designer in realizing such complex systems involving the material, product and manufacturing processes from a simulation-based design perspective.\u0000 In this paper, we present a Goal-oriented Inverse Design method to achieve the integrated design exploration of materials, products and manufacturing processes. The key functionality offered is the capability to carry out a microstructure-mediated design satisficing specific processing requirements and performance goals of the product. Given models to establish the information flow chain, a designer can use the method for the decision-based design exploration of material microstructure and processing paths to realize products in a manufacturing process chain. The efficacy of the method is tested using an industry-inspired hot rolling problem to inversely design the thermo-mechanical processing of a steel rod. The focus here is the method and associated design constructs which are generic and support the formulation and decision-based design of similar problems involving materials, products and associated manufacturing processes.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"457 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114960194","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}
Demand Response (DR) is the adjustment of consumer electricity demand through the deployment of one or more strategies, e.g. direct load control, policy implementation, dynamic pricing, or other economic incentives. Widespread implementation of DR is a promising solution for addressing energy challenges such as the integration of intermittent renewable energy resources, reducing capacity cost, and improving grid reliability. Understanding residential consumer preferences for shifting product usage and how these preferences are distributed amongst a population are key to predicting the effectiveness of different DR strategies. In addition, there is a need for a better understanding of how different DR programs, system level objectives, and preference distributions will impact different segments of consumers within a population. Specifically, the impacts on their product use behavior and electricity bill. To address this challenge, a product based approach to modeling consumer decisions about altering their electricity consumption is proposed, which links consumer value to their products, instead of directly to the amount of electricity they consume. This model is then used to demonstrate how population level preference distributions for altering product use impact system level objectives.
{"title":"The Impact of Consumer Preference Distributions on Dynamic Electricity Pricing for Residential Demand Response","authors":"Samuel Dunbar, S. Ferguson","doi":"10.1115/detc2019-98219","DOIUrl":"https://doi.org/10.1115/detc2019-98219","url":null,"abstract":"\u0000 Demand Response (DR) is the adjustment of consumer electricity demand through the deployment of one or more strategies, e.g. direct load control, policy implementation, dynamic pricing, or other economic incentives. Widespread implementation of DR is a promising solution for addressing energy challenges such as the integration of intermittent renewable energy resources, reducing capacity cost, and improving grid reliability. Understanding residential consumer preferences for shifting product usage and how these preferences are distributed amongst a population are key to predicting the effectiveness of different DR strategies. In addition, there is a need for a better understanding of how different DR programs, system level objectives, and preference distributions will impact different segments of consumers within a population. Specifically, the impacts on their product use behavior and electricity bill. To address this challenge, a product based approach to modeling consumer decisions about altering their electricity consumption is proposed, which links consumer value to their products, instead of directly to the amount of electricity they consume. This model is then used to demonstrate how population level preference distributions for altering product use impact system level objectives.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130642150","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 develops a self-adaptive control strategy for a newly-proposed J-type air-based battery thermal management system (BTMS) for electric vehicles (EVs). The structure of the J-type BTMS is first optimized through surrogate-based optimization in conjunction with computational fluid dynamics (CFD) simulations, with the aim of minimizing temperature rise and maximizing temperature uniformity. Based on the optimized J-type BTMS, an artificial neural network (ANN)-based model predictive control (MPC) strategy is set up to perform real-time control of mass flow rate and BTMS mode switch among J-, Z-, and U-mode. The ANN-based MCP strategy is tested with the Urban Dynamometer Driving Schedule (UDDS) driving cycle. With a genetic algorithm optimizer, the control system is able to optimize the mass flow rate by considering several steps ahead. The results show that the ANN-based MPC strategy is able to constrain the battery temperature difference within a narrow range, and to satisfy light-duty daily operations like the UDDS driving cycle for EVs.
{"title":"Self-Adapting Intelligent Battery Thermal Management System via Artificial Neural Network Based Model Predictive Control","authors":"Yuan Liu, Jie Zhang","doi":"10.1115/detc2019-98205","DOIUrl":"https://doi.org/10.1115/detc2019-98205","url":null,"abstract":"\u0000 This paper develops a self-adaptive control strategy for a newly-proposed J-type air-based battery thermal management system (BTMS) for electric vehicles (EVs). The structure of the J-type BTMS is first optimized through surrogate-based optimization in conjunction with computational fluid dynamics (CFD) simulations, with the aim of minimizing temperature rise and maximizing temperature uniformity. Based on the optimized J-type BTMS, an artificial neural network (ANN)-based model predictive control (MPC) strategy is set up to perform real-time control of mass flow rate and BTMS mode switch among J-, Z-, and U-mode. The ANN-based MCP strategy is tested with the Urban Dynamometer Driving Schedule (UDDS) driving cycle. With a genetic algorithm optimizer, the control system is able to optimize the mass flow rate by considering several steps ahead. The results show that the ANN-based MPC strategy is able to constrain the battery temperature difference within a narrow range, and to satisfy light-duty daily operations like the UDDS driving cycle for EVs.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127367091","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}
Bingtao Hu, Yixiong Feng, Yicong Gao, Hao Zheng, Jianrong Tan
Brazing materials can be made into different shapes to meet the requirements of different scenarios and the welding rod is a very common form. The rough-processed welding rods must be properly finished by the drawing bench to remove the oxide film on the surface and made into a uniform diameter. However, the continuous welding rod often breaks resulting low production efficiency. To reduce the frequency of workers’ reconnection operation of broken welding rods, we proposed a digital twin-based approach to improve the design of the structure of the drawing bench. First, we established a full life cycle digital twin model for the welding rod from the formulation stage to the finishing stage. The product ontology of the welding rod was built and the key process parameters were collected. Second, based on the product ontology, the key structural parameter of the drawing bench that affects the internal stress of the welding rod was determined by means of analytic hierarchy process. Third, we modified the key structural parameter in the digital twin model and simulated the finishing process. A near-optimal parameter was found. Last, we improved the structure of the actual drawing bench accordingly and carried out some experiments. The results matched well with the simulation prediction and the frequency of welding rod breaking is significantly reduced, which proved the effectiveness of our proposed improved design approach.
{"title":"A Digital Twin-Driven Improved Design Approach of Drawing Bench for Brazing Material","authors":"Bingtao Hu, Yixiong Feng, Yicong Gao, Hao Zheng, Jianrong Tan","doi":"10.1115/detc2019-97437","DOIUrl":"https://doi.org/10.1115/detc2019-97437","url":null,"abstract":"\u0000 Brazing materials can be made into different shapes to meet the requirements of different scenarios and the welding rod is a very common form. The rough-processed welding rods must be properly finished by the drawing bench to remove the oxide film on the surface and made into a uniform diameter. However, the continuous welding rod often breaks resulting low production efficiency. To reduce the frequency of workers’ reconnection operation of broken welding rods, we proposed a digital twin-based approach to improve the design of the structure of the drawing bench. First, we established a full life cycle digital twin model for the welding rod from the formulation stage to the finishing stage. The product ontology of the welding rod was built and the key process parameters were collected. Second, based on the product ontology, the key structural parameter of the drawing bench that affects the internal stress of the welding rod was determined by means of analytic hierarchy process. Third, we modified the key structural parameter in the digital twin model and simulated the finishing process. A near-optimal parameter was found. Last, we improved the structure of the actual drawing bench accordingly and carried out some experiments. The results matched well with the simulation prediction and the frequency of welding rod breaking is significantly reduced, which proved the effectiveness of our proposed improved design approach.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129134034","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}
Stochastic search methods are widely used when it comes to design synthesis and optimization of response-based objective functions. In engineering applications, the objective function is typically expensive to evaluate, and stochastic search methods lack efficiency, resulting in the necessity of extensive design evaluations. In order to improve stochastic search methods, we propose a Machine Learning (ML)-based augmentation, consisting of three modules: a design archiver, a data modeler, and a modification advisor. These three modules cooperatively work together to store the gathered data during the design process, build up a representative model of the observations made, and advise the search for further sequences of modifications to apply. The proposed method is benchmarked against its unaugmented parent method in placing cooling channels in a die casting mold. The results show that the efficiency of the method is significantly improved when augmented with ML, i.e. similar results are obtained with 25–50% fewer evaluations. Additionally, the robustness and reliability of the optimization process is improved with a standard deviation of the obtained results that is 60–85% smaller. It is shown that the search strategy can be significantly improved with the proposed method, resulting in shorter running times and more reliable convergence behavior.
{"title":"Machine Learning-Augmented Stochastic Search for the Automated Synthesis and Optimization of Cooling Channels","authors":"Jonas Schwarz, K. Shea","doi":"10.1115/detc2019-97830","DOIUrl":"https://doi.org/10.1115/detc2019-97830","url":null,"abstract":"\u0000 Stochastic search methods are widely used when it comes to design synthesis and optimization of response-based objective functions. In engineering applications, the objective function is typically expensive to evaluate, and stochastic search methods lack efficiency, resulting in the necessity of extensive design evaluations. In order to improve stochastic search methods, we propose a Machine Learning (ML)-based augmentation, consisting of three modules: a design archiver, a data modeler, and a modification advisor. These three modules cooperatively work together to store the gathered data during the design process, build up a representative model of the observations made, and advise the search for further sequences of modifications to apply. The proposed method is benchmarked against its unaugmented parent method in placing cooling channels in a die casting mold. The results show that the efficiency of the method is significantly improved when augmented with ML, i.e. similar results are obtained with 25–50% fewer evaluations. Additionally, the robustness and reliability of the optimization process is improved with a standard deviation of the obtained results that is 60–85% smaller. It is shown that the search strategy can be significantly improved with the proposed method, resulting in shorter running times and more reliable convergence behavior.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127816190","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}
4D printed structures can change their properties and functionalities as a response to a change in the environmental conditions, such as a change in the temperature. A heat stimulus can be used to trigger a transition between two states of a shape memory polymer. Specially designed structures made from these materials can transform into different shapes at different temperatures and can be useful for applications in morphing wings or car panels. Most of these structures, however, are still designed by hand and possess limited load carrying capabilities in at least one of their states. Here, it is shown how complex lightweight structures with multiple stable states can be designed using material modeling and structural optimization methods. By distributing different materials to different parts of the structure, local stiffness gradients are introduced, giving rise to architected global deformations under a single, locally applied load. The shape deformations can be either continuous over the whole structure or discrete only in small regions. The results demonstrate how active materials can be used in a new way to design shape morphing, lightweight lattice structures with different stable states and without sacrificing their structural capabilities.
{"title":"Computational Design of 4D Printed Shape Morphing Multi-State Lattice Structures","authors":"Thomas S. Lumpe, K. Shea","doi":"10.1115/detc2019-97774","DOIUrl":"https://doi.org/10.1115/detc2019-97774","url":null,"abstract":"\u0000 4D printed structures can change their properties and functionalities as a response to a change in the environmental conditions, such as a change in the temperature. A heat stimulus can be used to trigger a transition between two states of a shape memory polymer. Specially designed structures made from these materials can transform into different shapes at different temperatures and can be useful for applications in morphing wings or car panels. Most of these structures, however, are still designed by hand and possess limited load carrying capabilities in at least one of their states. Here, it is shown how complex lightweight structures with multiple stable states can be designed using material modeling and structural optimization methods. By distributing different materials to different parts of the structure, local stiffness gradients are introduced, giving rise to architected global deformations under a single, locally applied load. The shape deformations can be either continuous over the whole structure or discrete only in small regions. The results demonstrate how active materials can be used in a new way to design shape morphing, lightweight lattice structures with different stable states and without sacrificing their structural capabilities.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117248817","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}
Engineering design is usually an iterative procedure where many different configurations are tested to yield a desirable end performance. When the design objective can only be measured by costly operations such as experiments or cumbersome computer simulations, a thorough design procedure can be limited. The design problem in these cases is a high cost optimization problem. Meta model-based approaches (e.g. Bayesian optimization) and transfer optimization are methods that can be used to facilitate more efficient designs. Transfer optimization is a technique that enables using previous design knowledge instead of starting from scratch in a new task. In this work, we study a transfer optimization framework based on Bayesian optimization using Gaussian Processes. The similarity among the tasks is determined via a similarity metric. The framework is applied to a particular design problem of thin film solar cells. Planar multilayer solar cells with different sets of materials are optimized to obtain the best opto-electrical efficiency. Solar cells with amorphous silicon and organic absorber layers are studied and the results are presented.
{"title":"Using Bayesian Optimization With Knowledge Transfer for High Computational Cost Design: A Case Study in Photovoltaics","authors":"Mine Kaya, S. Hajimirza","doi":"10.1115/detc2019-98111","DOIUrl":"https://doi.org/10.1115/detc2019-98111","url":null,"abstract":"Engineering design is usually an iterative procedure where many different configurations are tested to yield a desirable end performance. When the design objective can only be measured by costly operations such as experiments or cumbersome computer simulations, a thorough design procedure can be limited. The design problem in these cases is a high cost optimization problem. Meta model-based approaches (e.g. Bayesian optimization) and transfer optimization are methods that can be used to facilitate more efficient designs. Transfer optimization is a technique that enables using previous design knowledge instead of starting from scratch in a new task. In this work, we study a transfer optimization framework based on Bayesian optimization using Gaussian Processes. The similarity among the tasks is determined via a similarity metric. The framework is applied to a particular design problem of thin film solar cells. Planar multilayer solar cells with different sets of materials are optimized to obtain the best opto-electrical efficiency. Solar cells with amorphous silicon and organic absorber layers are studied and the results are presented.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126551696","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}
Zero energy building (ZEB) is an important concept for sustainable building design. This paper introduces a holistic design approach for residential net-zero energy buildings (NZEB) by adopting the Triple Bottom Line (TBL) principles: social, environmental, and financial. The proposed approach optimizes social need by maximizing thermal comfort time of natural cooling, and visual comfort time of daylighting. The environmental need is addressed by optimizing energy efficiency, and the financial need is addressed by optimizing life cycle cost (LCC). Multi-objective optimizations are conducted in two phases: the first phase optimizes the utilization rate of natural cooling and daylighting, and the second phase optimizes energy efficiency and LCC. Sensitivity analysis is conducted to identify the most influential variables in the optimization process. The approach is applied to the design of a landed house in a tropical country, Singapore. The results provide a framework and modeled cases for parametric design and trade-off analysis toward sustainable and livable built environment.
{"title":"Sustainable Design of Residential Net-Zero Energy Buildings: A Multi-Phase and Multi-Objective Optimization Approach","authors":"Lan Lan, K. Wood, C. Yuen","doi":"10.1115/detc2019-97171","DOIUrl":"https://doi.org/10.1115/detc2019-97171","url":null,"abstract":"\u0000 Zero energy building (ZEB) is an important concept for sustainable building design. This paper introduces a holistic design approach for residential net-zero energy buildings (NZEB) by adopting the Triple Bottom Line (TBL) principles: social, environmental, and financial. The proposed approach optimizes social need by maximizing thermal comfort time of natural cooling, and visual comfort time of daylighting. The environmental need is addressed by optimizing energy efficiency, and the financial need is addressed by optimizing life cycle cost (LCC). Multi-objective optimizations are conducted in two phases: the first phase optimizes the utilization rate of natural cooling and daylighting, and the second phase optimizes energy efficiency and LCC. Sensitivity analysis is conducted to identify the most influential variables in the optimization process. The approach is applied to the design of a landed house in a tropical country, Singapore. The results provide a framework and modeled cases for parametric design and trade-off analysis toward sustainable and livable built environment.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129854800","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}
From engineering analysis and topology optimization to generative design and machine learning, many modern computational design approaches require either large amounts of data or a method to generate that data. This paper addresses key issues with automatically generating such data through automating the construction of Finite Element Method (FEM) simulations from Dirichlet boundary conditions. Most past work on automating FEM assumes prior knowledge of the physics to be run or is limited to a small number of governing equations. In contrast, we propose three improvements to current methods of automating the FEM: (1) completeness labels that guarantee viability of a simulation under specific conditions, (2) type-based labels for solution fields that robustly generate and identify solution fields, and (3) type-based labels for variational forms of governing equations that map the three components of a simulation set — specifically, boundary conditions, solution fields, and a variational form — to each other to form a viable FEM simulation. We implement these improvements using the FEniCS library as an example case. We show that our improvements increase the percent of viable simulations that are run automatically from a given list of boundary conditions. This paper’s procedures ultimately allow for the automatic — i.e., fully computer-controlled — construction of FEM multi-physics simulations and data collection required to run data-driven models of physics phenomena or automate the exploration of topology optimization under many physics.
{"title":"Checking the Automated Construction of Finite Element Simulations From Dirichlet Boundary Conditions","authors":"Kevin N. Chiu, M. Fuge","doi":"10.1115/detc2019-98000","DOIUrl":"https://doi.org/10.1115/detc2019-98000","url":null,"abstract":"\u0000 From engineering analysis and topology optimization to generative design and machine learning, many modern computational design approaches require either large amounts of data or a method to generate that data. This paper addresses key issues with automatically generating such data through automating the construction of Finite Element Method (FEM) simulations from Dirichlet boundary conditions. Most past work on automating FEM assumes prior knowledge of the physics to be run or is limited to a small number of governing equations.\u0000 In contrast, we propose three improvements to current methods of automating the FEM: (1) completeness labels that guarantee viability of a simulation under specific conditions, (2) type-based labels for solution fields that robustly generate and identify solution fields, and (3) type-based labels for variational forms of governing equations that map the three components of a simulation set — specifically, boundary conditions, solution fields, and a variational form — to each other to form a viable FEM simulation. We implement these improvements using the FEniCS library as an example case. We show that our improvements increase the percent of viable simulations that are run automatically from a given list of boundary conditions. This paper’s procedures ultimately allow for the automatic — i.e., fully computer-controlled — construction of FEM multi-physics simulations and data collection required to run data-driven models of physics phenomena or automate the exploration of topology optimization under many physics.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126876562","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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