TR195: CAD-Centric Attribution Methodology for Multidisciplinary Optimization (CAMMO): Enabling Designers to Efficiently Formulate and Evaluate Large Design Spaces
Abstract/Contents
- Abstract
- Research has demonstrated that multidisciplinary design optimization (MDO) processes that automate the workflow from a parametric product model to performance simulation engines can compress design cycle time, increase design knowledge, and yield substantive product quality and performance gains. However, the accuracy and cost-effectiveness of an MDO process is highly dependent on designers’ ability to structure the optimization problem for specific challenges, particularly when specifying how building attributes and their associated geometry are configured for an optimization process. To fit current workflows efficiently, designers need flexible CAD-centric attribution methods for MDO environments. These methods are not addressed in literature, or defined in available methods. This paper fills these gaps by developing a CAD-Centric Attribution Methodology for Multidisciplinary Optimization (CAMMO). The authors demonstrate the potential power and generality of CAMMO with two industry case studies.
Description
Type of resource | text |
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Date created | March 2012 |
Creators/Contributors
Author | Welle, Benjamin | |
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Author | Haymaker, John | |
Author | Fischer, Martin | |
Author | Bazjanac, Vladimir |
Subjects
Subject | CIFE |
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Subject | Center for Integrated Facility Engineering |
Subject | Stanford University |
Subject | CAD-centric attribution |
Subject | Conceptual Building Design |
Subject | daylighting simulation |
Subject | Energy Simulation |
Subject | MDO |
Subject | Multidisciplinary Design Optimization |
Subject | process integration and design automation |
Genre | Technical report |
Bibliographic information
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Preferred citation
- Preferred Citation
- Welle, Benjamin and Haymaker, John and Fischer, Martin and Bazjanac, Vladimir . (2012). TR195: CAD-Centric Attribution Methodology for Multidisciplinary Optimization (CAMMO): Enabling Designers to Efficiently Formulate and Evaluate Large Design Spaces. Stanford Digital Repository. Available at: http://purl.stanford.edu/rp997qb4230
Collection
CIFE Publications
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- Contact
- fischer@stanford.edu
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