Volume 2B: 45th Design Automation Conference最新文献_第3页

Feasibility of Pairing a Low-Cost Positive Displacement Pump With Low-Energy Pressure Compensating Drip Irrigation Emitters for Smallholder Farms in Africa 非洲小农农场低成本容积泵与低能压力补偿滴灌装置配对的可行性

Volume 2B: 45th Design Automation Conference Pub Date : 2019-11-25 DOI: 10.1115/detc2019-98128

Seiji Engelkemier, F. Grant, J. Landis, Carolyn Sheline, Hannah M. Varner, R. Zubajlo, J. Sokol, A. Winter

引用次数: 1

System Reliability Analysis Using Hybrid Gaussian Process Model 基于混合高斯过程模型的系统可靠性分析

Volume 2B: 45th Design Automation Conference Pub Date : 2019-11-25 DOI: 10.1115/detc2019-98173

Meng Li, Mohammadkazem Sadoughi, Zhen Hu, Chao Hu

{"title":"System Reliability Analysis Using Hybrid Gaussian Process Model","authors":"Meng Li, Mohammadkazem Sadoughi, Zhen Hu, Chao Hu","doi":"10.1115/detc2019-98173","DOIUrl":"https://doi.org/10.1115/detc2019-98173","url":null,"abstract":"\u0000 This paper proposes a system reliability analysis method based on the hybrid of multivariate Gaussian process (MGP) and univariate Gaussian process (UGP) models, named as hybrid Gaussian process-based system reliability analysis (HGP-SRA). MGP and UGP models are selectively constructed for the components of a complex engineered system: MGP models are constructed over the groups of highly interdependent components and the individual UGP models are built over the components which are relatively independent of one another. A nonlinear-dependence measure, namely the randomized dependence coefficient, is adopted to adaptively learn and quantify the pairwise dependencies of the components with both linear and nonlinear dependency patterns. In the proposed HGP-SRA method, initial hybrid Gaussian process (HGP) models are first constructed with a set of near-random samples and these surrogate models are then updated with new samples that are sequentially identified based on the acquisition function named as multivariate probability of improvement (MPI). The results of two mathematical and a real-world engineering case studies suggest that the proposed method can achieve better accuracy and efficiency in system reliability estimation than the benchmark surrogate-based methods.","PeriodicalId":198662,"journal":{"name":"Volume 2B: 45th Design Automation Conference","volume":"17 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":"122635454","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}

引用次数: 1

An Automated Approach to Recording and Analyzing Design Activities Using a Graphical User Interface 使用图形用户界面自动记录和分析设计活动的方法

Volume 2B: 45th Design Automation Conference Pub Date : 2019-11-25 DOI: 10.1115/detc2019-98226

F. M. A. Taha, Ramez M. Adly Taha, Keegan West, M. Fazelpour, J. Herrmann, Matthew Anthony Polvinale

引用次数: 2

A New Robustness Metric for Robust Design Optimization Under Time- and Space-Dependent Uncertainty Through Metamodeling 基于元建模的时空不确定性下稳健设计优化的鲁棒性度量

Volume 2B: 45th Design Automation Conference Pub Date : 2019-11-25 DOI: 10.1115/detc2019-97611

Xinpeng Wei, Xiaoping Du

{"title":"A New Robustness Metric for Robust Design Optimization Under Time- and Space-Dependent Uncertainty Through Metamodeling","authors":"Xinpeng Wei, Xiaoping Du","doi":"10.1115/detc2019-97611","DOIUrl":"https://doi.org/10.1115/detc2019-97611","url":null,"abstract":"\u0000 Product performance varies with respect to time and space in many engineering applications. This work discusses how to measure and evaluate the robustness of a product or component when its quality characteristics are functions of random variables, random fields, temporal variables, and spatial variables. At first, the existing time-dependent robustness metric is extended to the present time- and space-dependent problem. The robustness metric is derived using the extreme value of the quality characteristics with respect to temporal and spatial variables for the nominal-the-better type quality characteristics. Then a metamodel-based numerical procedure is developed to evaluate the new robustness metric. The procedure employs a Gaussian Process regression method to estimate the expected quality loss that involves the extreme quality characteristics. The expected quality loss is obtained directly during the regression model building process. Three examples are used to demonstrate the robustness analysis method. The proposed method can be used for robustness assessment during robust design optimization under time- and space-dependent uncertainty.","PeriodicalId":198662,"journal":{"name":"Volume 2B: 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":"129027399","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}

引用次数: 2

Robust Optimization With Mixed Interval and Probabilistic Parameter Uncertainties, Model Uncertainty, and Metamodeling Uncertainty 具有混合区间和概率参数不确定性、模型不确定性和元建模不确定性的鲁棒优化

Volume 2B: 45th Design Automation Conference Pub Date : 2019-11-25 DOI: 10.1115/detc2019-97495

Yanjun Zhang, Tingting Xia, Mian Li

{"title":"Robust Optimization With Mixed Interval and Probabilistic Parameter Uncertainties, Model Uncertainty, and Metamodeling Uncertainty","authors":"Yanjun Zhang, Tingting Xia, Mian Li","doi":"10.1115/detc2019-97495","DOIUrl":"https://doi.org/10.1115/detc2019-97495","url":null,"abstract":"\u0000 Various types of uncertainties, such as parameter uncertainty, model uncertainty, metamodeling uncertainty may lead to low robustness. Parameter uncertainty can be either epistemic or aleatory in physical systems, which have been widely represented by intervals and probability distributions respectively. Model uncertainty is formally defined as the difference between the true value of the real-world process and the code output of the simulation model at the same value of inputs. Additionally, metamodeling uncertainty is introduced due to the usage of metamodels. To reduce the effects of uncertainties, robust optimization (RO) algorithms have been developed to obtain solutions being not only optimal but also less sensitive to uncertainties. Based on how parameter uncertainty is modeled, there are two categories of RO approaches: interval-based and probability-based. In real-world engineering problems, both interval and probabilistic parameter uncertainties are likely to exist simultaneously in a single problem. However, few works have considered mixed interval and probabilistic parameter uncertainties together with other types of uncertainties. In this work, a general RO framework is proposed to deal with mixed interval and probabilistic parameter uncertainties, model uncertainty, and metamodeling uncertainty simultaneously in design optimization problems using the intervals-of-statistics approaches. The consideration of multiple types of uncertainties will improve the robustness of optimal designs and reduce the risk of inappropriate decision-making, low robustness and low reliability in engineering design. Two test examples are utilized to demonstrate the applicability and effectiveness of the proposed RO approach.","PeriodicalId":198662,"journal":{"name":"Volume 2B: 45th Design Automation Conference","volume":"93 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":"126389731","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}

引用次数: 0

Complexity Reduction in Mass Customization to Facilitate Better Decision Support 降低大规模定制的复杂性以促进更好的决策支持

Volume 2B: 45th Design Automation Conference Pub Date : 2019-08-18 DOI: 10.1115/detc2019-97369

Jai Nath Banerjee, Madhura Mundale, Adnan Sachche, Christopher McComb

引用次数: 0

The Technology/Tactics (TEC/TAC) Plot: Explicit Representation of User Actions in the Product Design Space 技术/策略(TEC/TAC)情节:产品设计空间中用户行为的显式表示

Volume 2B: 45th Design Automation Conference Pub Date : 2019-08-18 DOI: 10.1115/detc2019-98400

Tyler Stapleton, Trent Owens, C. Mattson, C. Sorensen, Michael L. Anderson

{"title":"The Technology/Tactics (TEC/TAC) Plot: Explicit Representation of User Actions in the Product Design Space","authors":"Tyler Stapleton, Trent Owens, C. Mattson, C. Sorensen, Michael L. Anderson","doi":"10.1115/detc2019-98400","DOIUrl":"https://doi.org/10.1115/detc2019-98400","url":null,"abstract":"\u0000 The initial phases of the design process — including interactions with stakeholders, ideation of concept candidates, and the selection of the best candidates — have a large impact on the success of a project as a whole. They also tend to be the most unstructured portion of the project, and are often marginalized by teams who assume they already understand stakeholder needs and the best solution paths to pursue. Design researchers have developed methods shown to increase the creativity and divergent thinking of the design team during these initial phases of design. Nevertheless, these methods often rely on only a vague or amorphous representation of the design space (the set of all possible concepts the design team could feasibly select to meet the objective of the project). In this paper, we introduce a particular design-space structure that can help teams ideate and evaluate their ideation, thus improving the early phases of the design process. The design space presented here is a vector space with a basis of technology (the physical product people will use) and tactics (the procedure for using the product). Also presented are definitions, principles, and sub-theories that facilitate the creation and use of technology-tactics plots to represent the design space. Considering the design space in this structured way, the design team can gain valuable insights that improve the effectiveness of the initial stages of design, and may yield additional benefits to the design process as a whole, if further developed.","PeriodicalId":198662,"journal":{"name":"Volume 2B: 45th Design Automation Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128798572","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}

引用次数: 1

Testing Design Optimization for Uncertainty Reduction in Generating Off-Road Mobility Map 越野机动地图生成中降低不确定性的测试设计优化

Volume 2B: 45th Design Automation Conference Pub Date : 2019-08-18 DOI: 10.1115/detc2019-97685

Zhen Hu, Z. Mourelatos, D. Gorsich, P. Jayakumar, Monica Majcher

{"title":"Testing Design Optimization for Uncertainty Reduction in Generating Off-Road Mobility Map","authors":"Zhen Hu, Z. Mourelatos, D. Gorsich, P. Jayakumar, Monica Majcher","doi":"10.1115/detc2019-97685","DOIUrl":"https://doi.org/10.1115/detc2019-97685","url":null,"abstract":"\u0000 The Next Generation NATO Reference Mobility Model (NG-NRMM) plays a vital role in vehicle mobility prediction and mission planning. The complicated vehicle-terrain interactions and the presence of heterogeneous uncertainty sources in the modeling and simulation (M&S) result in epistemic uncertainty/errors in the vehicle mobility prediction for given terrain and soil conditions. In this paper, the uncertainty sources that cause the uncertainty in mobility prediction are first partitioned into two levels, namely uncertainty in the M&S and uncertainty in terrain and soil maps. With a focus on the epistemic uncertainty in the M&S, this paper presents a testing design optimization framework to effectively reduce the uncertainty in the M&S and thus increase the confidence in generating off-road mobility maps. A Bayesian updating approach is developed to reduce the epistemic uncertainty/errors in the M&S using mobility testing data collected under controllable terrain and soil conditions. The updated models are then employed to generate off-road mobility maps for any given terrain and soil maps. Two types of design strategies, namely testing design for model selection and testing design for uncertainty reduction, are investigated in the testing design framework to maximize the information gain subject to limited resources. Results of a numerical example demonstrate the effectiveness of the proposed mobility testing design optimization framework.","PeriodicalId":198662,"journal":{"name":"Volume 2B: 45th Design Automation Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129147587","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}

引用次数: 0

Decomposing Incentives for Early Resilient Design: Method and Validation 早期弹性设计的分解激励:方法和验证

Volume 2B: 45th Design Automation Conference Pub Date : 2019-08-18 DOI: 10.1115/detc2019-97466

Daniel E. Hulse, C. Hoyle, I. Tumer, K. Goebel

{"title":"Decomposing Incentives for Early Resilient Design: Method and Validation","authors":"Daniel E. Hulse, C. Hoyle, I. Tumer, K. Goebel","doi":"10.1115/detc2019-97466","DOIUrl":"https://doi.org/10.1115/detc2019-97466","url":null,"abstract":"\u0000 Due to the expansive, time-consuming nature of risk analyses, it is important to be able to assign the minimization of risk (and, in general, optimization of resilience) to responsible teams that can work in parallel. However, while methods exist for minimization of risk in conventional design processes, research has not yet shown how it should be performed in a model-based design context in early design phase, when the design representation is relatively high-level and there are uncertainties in parameter values. This paper presents a value-driven design approach to minimize risk by decomposing the design, operational, and expected failure costs to individual functions in a system failure model. This process is demonstrated in a case study considering the redundancy of components to fulfill overall functions in an electric power system, where it is shown to increase design value significantly. An uncertainty-based process is additionally provided to enable the designer to test the sensitivity of the chosen design solution to uncertain parameter values. In this limited case study it is shown that the sensitivity of the choice to parameter value uncertainty is low, provided the range of uncertainty for each parameter is within a reasonable range. In situations like this, presented expected cost metrics provide meaningful information to justify system-architectural design decisions made on the basis of resilience.","PeriodicalId":198662,"journal":{"name":"Volume 2B: 45th Design Automation Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124461327","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}

引用次数: 2