This paper presents a simulation-based analysis of a model of a mid-sized vehicle while exploring powertrains of interest. In addition to a baseline conventional vehicle (CV), the explored powertrain architectures include: hybrid electric vehicle (HEV), plugin hybrid electric vehicle (PHEV) and batterW2Wy-only electric vehicle (BEV). The modeling also considers several different all electric driving range (AER) of the PHEVs and BEVs. Fuel economy/energy-efficiency assessment is conducted by with open source software (FASTSim), and by analyzing a large set of real-world driving trips from California Household Travel Survey (CHTS-2013), which contains a record of more than 65 thousand trips with one second interval recording of the vehicle seed. Gas and/or electric energy usage from the analyzed trips are then used to generate greenhouse gas (GHG) statistical distributions (in units of gm-CO2/mile) for a modelled vehicle powertrain. Gas and/or electric energy usage are also utilized in the calculation of the running cost, and ultimately the net average cost (in units of $/mile) for the modelled powertrains. Pareto trade-off analysis (Cost vs GHG) is then conducted for four sub-population segments of CHTS vehicle samples in a baseline scenario as well as four future-looking scenarios where carbon intensity in electric power generation gets lower, gas gets more expensive and batteries get less expensive. While noting limitations of the conducted analysis, key findings suggest that: i) mix of PHEVs and BEVs with various AER that is properly matched to driver needs would be better than one single powertrain design for all drivers, and ii) electrified powertrains do not become cost-competitive in their own right (without incentives or subsidies) until some of the future battery technology goals are attained.
{"title":"A Pareto Trade-Off Analysis of Cost Versus Greenhouse Gas Emissions for a Model of a Mid-Sized Vehicle With Various Powertrains","authors":"K. Hamza, K. Laberteaux, J. Willard, K. Chu","doi":"10.1115/DETC2018-85256","DOIUrl":"https://doi.org/10.1115/DETC2018-85256","url":null,"abstract":"This paper presents a simulation-based analysis of a model of a mid-sized vehicle while exploring powertrains of interest. In addition to a baseline conventional vehicle (CV), the explored powertrain architectures include: hybrid electric vehicle (HEV), plugin hybrid electric vehicle (PHEV) and batterW2Wy-only electric vehicle (BEV). The modeling also considers several different all electric driving range (AER) of the PHEVs and BEVs. Fuel economy/energy-efficiency assessment is conducted by with open source software (FASTSim), and by analyzing a large set of real-world driving trips from California Household Travel Survey (CHTS-2013), which contains a record of more than 65 thousand trips with one second interval recording of the vehicle seed. Gas and/or electric energy usage from the analyzed trips are then used to generate greenhouse gas (GHG) statistical distributions (in units of gm-CO2/mile) for a modelled vehicle powertrain. Gas and/or electric energy usage are also utilized in the calculation of the running cost, and ultimately the net average cost (in units of $/mile) for the modelled powertrains. Pareto trade-off analysis (Cost vs GHG) is then conducted for four sub-population segments of CHTS vehicle samples in a baseline scenario as well as four future-looking scenarios where carbon intensity in electric power generation gets lower, gas gets more expensive and batteries get less expensive. While noting limitations of the conducted analysis, key findings suggest that: i) mix of PHEVs and BEVs with various AER that is properly matched to driver needs would be better than one single powertrain design for all drivers, and ii) electrified powertrains do not become cost-competitive in their own right (without incentives or subsidies) until some of the future battery technology goals are attained.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131899621","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}
Due to their volatile behavior, natural disasters are challenging problems as they often cannot be accurately predicted. An efficient method to gather updated information of the status of a disaster, such as the location of any trapped survivors, is extremely important to properly conduct rescue operations. To accomplish this, an algorithm is presented to control a swarm of UAVs (Unmanned Aerial Vehicles) and optimize the value of the information gathered. For this application, the UAVs are autonomously navigated with a decentralized control method. With sensor technology embedded, this swarm collects information from the environment as it operates. By using the swarm’s location history, areas of the environment that have gone the longest without exploration can be prioritized, ensuring a thorough search. Measures are also developed to prevent redundant or inefficient exploration, which would reduce the value of the gathered information. A case study of a flood scenario is examined and simulated. Through this approach, the value of the proposed swarm algorithm can be tested by tracking the number of survivors found as well as the rate at which they are discovered.
{"title":"Using an Intelligent UAV Swarm in Natural Disaster Environments","authors":"J. Asbach, Souma Chowdhury, K. Lewis","doi":"10.1115/DETC2018-86112","DOIUrl":"https://doi.org/10.1115/DETC2018-86112","url":null,"abstract":"Due to their volatile behavior, natural disasters are challenging problems as they often cannot be accurately predicted. An efficient method to gather updated information of the status of a disaster, such as the location of any trapped survivors, is extremely important to properly conduct rescue operations. To accomplish this, an algorithm is presented to control a swarm of UAVs (Unmanned Aerial Vehicles) and optimize the value of the information gathered. For this application, the UAVs are autonomously navigated with a decentralized control method. With sensor technology embedded, this swarm collects information from the environment as it operates. By using the swarm’s location history, areas of the environment that have gone the longest without exploration can be prioritized, ensuring a thorough search. Measures are also developed to prevent redundant or inefficient exploration, which would reduce the value of the gathered information. A case study of a flood scenario is examined and simulated. Through this approach, the value of the proposed swarm algorithm can be tested by tracking the number of survivors found as well as the rate at which they are discovered.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"288 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131911703","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}
A methodology for the design and control of a variable twist wind turbine blade is presented. The blade is, modular, flexible, and additively manufactured (AM). The AM capabilities have the potential to create a flexible blade with a low torsional-to-longitudinal-stiffness ratio. This enables new design and control capabilities that could be applied to the twist angle distribution. The variable twist distribution can increase the aerodynamic efficiency during Region 2 operation. The suggested blade design includes a rigid spar and flexible AM segments that form the surrounding shells. The stiffness of each segment and the actuator placement define the twist distribution. These values are used to find the optimum free shape for the blade. Given the optimum twist distributions, actuator placement, and free shape, the required amount of actuation could be determined. The proposed design process first determines the twist distribution that maximizes the aerodynamic efficiency in Region 2. A mechanical design algorithm subsequently locates a series of actuators and defines the stiffness ratio between the blade segments. The free shape twist distribution is selected in the next step. It is chosen to minimize the amount of actuation energy required to shape the twist distribution as it changes with Region 2 wind speed. Wind profiles of 20 different sites, gathered over a three-year period, are used to get the free shape. A control framework is then developed to set the twist distribution in relation to wind speed. A case study is performed to demonstrate the suggested procedure. The aerodynamic results show up to 3.8 and 3.3% increase in the efficiency at cut-in and rated speeds, respectively. The cumulative produced energy within three years, improved by up to 1.7%. The mechanical design suggests that the required twist distribution could be achieved by five actuators. Finally, the optimum free shape is selected based on the simulations for the studied sites.
{"title":"Integrative Control and Design Framework for an Actively Variable Twist Wind Turbine Blade to Increase Efficiency","authors":"H. K. Nejadkhaki, John F. Hall","doi":"10.1115/DETC2018-86098","DOIUrl":"https://doi.org/10.1115/DETC2018-86098","url":null,"abstract":"A methodology for the design and control of a variable twist wind turbine blade is presented. The blade is, modular, flexible, and additively manufactured (AM). The AM capabilities have the potential to create a flexible blade with a low torsional-to-longitudinal-stiffness ratio. This enables new design and control capabilities that could be applied to the twist angle distribution. The variable twist distribution can increase the aerodynamic efficiency during Region 2 operation. The suggested blade design includes a rigid spar and flexible AM segments that form the surrounding shells. The stiffness of each segment and the actuator placement define the twist distribution. These values are used to find the optimum free shape for the blade. Given the optimum twist distributions, actuator placement, and free shape, the required amount of actuation could be determined. The proposed design process first determines the twist distribution that maximizes the aerodynamic efficiency in Region 2. A mechanical design algorithm subsequently locates a series of actuators and defines the stiffness ratio between the blade segments. The free shape twist distribution is selected in the next step. It is chosen to minimize the amount of actuation energy required to shape the twist distribution as it changes with Region 2 wind speed. Wind profiles of 20 different sites, gathered over a three-year period, are used to get the free shape. A control framework is then developed to set the twist distribution in relation to wind speed. A case study is performed to demonstrate the suggested procedure. The aerodynamic results show up to 3.8 and 3.3% increase in the efficiency at cut-in and rated speeds, respectively. The cumulative produced energy within three years, improved by up to 1.7%. The mechanical design suggests that the required twist distribution could be achieved by five actuators. Finally, the optimum free shape is selected based on the simulations for the studied sites.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127275057","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}
In this study, we characterize machine learning regression techniques for their ability to predict storm-related transmission outages based on local weather and transmission outage data. To test the machine learning regression techniques, we use data from the central Oregon Coast — which is particularly vulnerable to storm-related transmission outages — for a case study. We test multiple regression methods (linear and polynomial models with varying degrees) as well as support vector regression methods using linear, polynomial, and Radial-Basis-Function kernels. Results indicate relatively poor prediction capability by these methods, but this is attributed to the lack of outage data (characteristic of low-probability, high-risk events), and a cluster of data points representing momentary (<0 seconds) outages. More long-term outage data could lead to better characterization of the models, enabling others to quantify the frequency of storm-related transmission outages based on local weather data. Only by understanding the frequency of these occurrences can a cost-benefit analysis for potential transmission upgrades or generation sources be completed.
{"title":"Comparing Machine Learning Regression Techniques for Transmission-Related Storm Outages","authors":"Caitlyn E. Clark, Bryony DuPont","doi":"10.1115/DETC2018-85127","DOIUrl":"https://doi.org/10.1115/DETC2018-85127","url":null,"abstract":"In this study, we characterize machine learning regression techniques for their ability to predict storm-related transmission outages based on local weather and transmission outage data. To test the machine learning regression techniques, we use data from the central Oregon Coast — which is particularly vulnerable to storm-related transmission outages — for a case study. We test multiple regression methods (linear and polynomial models with varying degrees) as well as support vector regression methods using linear, polynomial, and Radial-Basis-Function kernels. Results indicate relatively poor prediction capability by these methods, but this is attributed to the lack of outage data (characteristic of low-probability, high-risk events), and a cluster of data points representing momentary (<0 seconds) outages. More long-term outage data could lead to better characterization of the models, enabling others to quantify the frequency of storm-related transmission outages based on local weather data. Only by understanding the frequency of these occurrences can a cost-benefit analysis for potential transmission upgrades or generation sources be completed.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133935039","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}
Though academic research for identifying and considering the social impact of products is emerging, the actual use of these processes in industry is undeclared in the literature. The gap between academic research and the industry adoption of these theories and methodologies can have real consequences. This paper explores current practices in industry that design engineers use to consider the social impact of products during the customer use stage. 30 people from nineteen different companies were interviewed to discover what disconnects exist between academia and industry when considering a product’s social impact. Although social impact assessments (SIA) and social life cycle assessments (SLCA) are two of the most common evaluative processes discussed in the literature, not a single company interviewed used either of these processes despite affirming that they do consider social impact in product design. Predictive processes were discussed by the respondents that tended to be developed within the company and often related to government regulations.
{"title":"Social Impact in Product Design: An Exploration of Current Industry Practices","authors":"Andrew T. Pack, E. Phipps, C. Mattson, E. Dahlin","doi":"10.1115/DETC2018-86170","DOIUrl":"https://doi.org/10.1115/DETC2018-86170","url":null,"abstract":"Though academic research for identifying and considering the social impact of products is emerging, the actual use of these processes in industry is undeclared in the literature. The gap between academic research and the industry adoption of these theories and methodologies can have real consequences. This paper explores current practices in industry that design engineers use to consider the social impact of products during the customer use stage. 30 people from nineteen different companies were interviewed to discover what disconnects exist between academia and industry when considering a product’s social impact. Although social impact assessments (SIA) and social life cycle assessments (SLCA) are two of the most common evaluative processes discussed in the literature, not a single company interviewed used either of these processes despite affirming that they do consider social impact in product design. Predictive processes were discussed by the respondents that tended to be developed within the company and often related to government regulations.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132130420","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}
Early stages of the engineering design process are vital to shaping the final design; each subsequent step builds from the initial concept. Innovation-driven engineering problems require designers to focus heavily on early-stage design generation, with constant application and evaluation of design changes. Strategies to reduce the amount of time and effort designers spend in this phase could improve the efficiency of the design process as a whole. This paper seeks to create and demonstrate a two-tiered design grammar that encodes heuristic strategies to aid in the generation of early solution concepts. Specifically, this two-tiered grammar mimics the combination of heuristic-based strategic actions and parametric modifications employed by human designers. Rules in the higher-tier are abstract and potentially applicable to multiple design problems across a number of fields. These abstract rules are translated into a series of lower-tier rule applications in a spatial design grammar, which are inherently domain-specific. This grammar is implemented within the HSAT agent-based algorithm. Agents iteratively select actions from either the higher-tier or lower-tier. This algorithm is applied to the design of wave energy converters, devices which use the motion of ocean waves to generate electrical power. Comparisons are made between designs generated using only lower-tier rules and those generated using only higher-tier rules.
{"title":"A Two-Tiered Grammatical Approach for Agent-Based Computational Design","authors":"Christopher McComb, J. Cagan, L. Puentes","doi":"10.1115/DETC2018-85648","DOIUrl":"https://doi.org/10.1115/DETC2018-85648","url":null,"abstract":"Early stages of the engineering design process are vital to shaping the final design; each subsequent step builds from the initial concept. Innovation-driven engineering problems require designers to focus heavily on early-stage design generation, with constant application and evaluation of design changes. Strategies to reduce the amount of time and effort designers spend in this phase could improve the efficiency of the design process as a whole. This paper seeks to create and demonstrate a two-tiered design grammar that encodes heuristic strategies to aid in the generation of early solution concepts. Specifically, this two-tiered grammar mimics the combination of heuristic-based strategic actions and parametric modifications employed by human designers. Rules in the higher-tier are abstract and potentially applicable to multiple design problems across a number of fields. These abstract rules are translated into a series of lower-tier rule applications in a spatial design grammar, which are inherently domain-specific. This grammar is implemented within the HSAT agent-based algorithm. Agents iteratively select actions from either the higher-tier or lower-tier. This algorithm is applied to the design of wave energy converters, devices which use the motion of ocean waves to generate electrical power. Comparisons are made between designs generated using only lower-tier rules and those generated using only higher-tier rules.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132513049","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}
Planning and strategizing are essential parts of the design process and are based on the designer’s skill. Further, planning is an abstract skill that can be transferred between similar problems. However, planning and strategy transfer within design have not been effectively modeled within computational agents. This paper presents an approach to represent this strategizing behavior using a probabilistic model. This model is employed to select the operations that computational agents should perform while solving configuration design tasks. This work also demonstrates that this probabilistic model can be used to transfer strategies from human data to computational agents in a way that is general and useful. This study shows a successful transfer of design strategy from human-to-computer agents, opening up the possibility of deriving high-performing behavior from designers and using it to guide computational design agents. Finally, a quintessential behavior of transfer learning is illustrated by agents while transferring design strategies across different problems, improving agent performance significantly. The work presented in this study leverages a computational framework built by embedding cognitive characteristics into agents, which has shown to mimic human problem-solving in configuration design problems.
{"title":"Design Strategy Transfer in Cognitively-Inspired Agents","authors":"Christopher McComb, J. Cagan, A. Raina","doi":"10.1115/DETC2018-85599","DOIUrl":"https://doi.org/10.1115/DETC2018-85599","url":null,"abstract":"Planning and strategizing are essential parts of the design process and are based on the designer’s skill. Further, planning is an abstract skill that can be transferred between similar problems. However, planning and strategy transfer within design have not been effectively modeled within computational agents. This paper presents an approach to represent this strategizing behavior using a probabilistic model. This model is employed to select the operations that computational agents should perform while solving configuration design tasks. This work also demonstrates that this probabilistic model can be used to transfer strategies from human data to computational agents in a way that is general and useful. This study shows a successful transfer of design strategy from human-to-computer agents, opening up the possibility of deriving high-performing behavior from designers and using it to guide computational design agents. Finally, a quintessential behavior of transfer learning is illustrated by agents while transferring design strategies across different problems, improving agent performance significantly. The work presented in this study leverages a computational framework built by embedding cognitive characteristics into agents, which has shown to mimic human problem-solving in configuration design problems.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133867745","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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