Design Scenarios Methodology Enabling Requirements Driven Design Spaces

Download or read book Design Scenarios Methodology - Enabling Requirements-driven Design Spaces written by Victor Gane and published by Stanford University. This book was released on 2011 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the conceptual design process, the building shape, orientation, materials and other major properties are established, all of which have a substantial impact on multi-aspect performance. In this process, multidisciplinary teams define project objectives, create various alternatives, and try to understand their impacts and value. With non-parametric Computer Aided Design (CAD) methods designers produce and analyze as few as three alternatives, whereas with parametric CAD -- they can generate thousands. However, with current parametric methods, CAD experts lack a comprehensive method to build and analyze multi-objective parametric models. Therefore the resulting models do not effectively encapsulate multi-objective value measures. This research introduces the Design Scenarios Methodology (DS), which builds on research from Systems Engineering, Process Modeling, and Parametric Modeling. With DS, Enablers use Methods to create Elements using five interconnected models to define (1) project stakeholders and their objectives, (2) designer logic used to address objectives, (3) the connection between designer logic and computable models to generate alternatives, (4) the predicted impact and (5) value of the generated alternatives. I implemented DS as a web-based software prototype and tested it on an industry project. The results provide evidence that the DS method provides CAD experts with well-defined logic and parameters for addressing objectives and the process enables creating parametric alternatives with clear multi-objective values that potentially provide clients with better building designs. This thesis lays the foundation for future research on automating the design alternative generation and analyses processes by leveraging such well established methods as Multi-Disciplinary Optimization.