Design Digital Recipes
Facilitating Collaboration for Facade Design
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Precision in digital workflow is necessary to deliver facade projects where there is a high design aesthetic or structural performance requirement. This process was essential in the design of the structural skin of a stadium proposed for Minnesota United MLS team. A philosophy of “digital recipes” was used for documentation, collaboration, and scalability of digital workflow among the design team. Efficient and effective digital communication between team members allowed the project to constantly evolve based on design pressure from the architect.
For this project, the overall digital workflow was condensed into four main recipes. Each recipe contained information on fundamental steps to generate either engineering or design results. This paper will go in depth into each of the recipes and its relationship to the project, describing the value it brought to the project. It will also present connections between a recipe and the team member working on it. This paper will describe a generation process of two types of models: one for analysis and one for design. Each of these models played a key role in overall development of the project. Creation of these models depended on the digital recipes designed by the team.
The digital approach also provided great value downstream to the fabrication process. The level of precision of the information embedded within the “master menu” of digital recipes made the production of a digital fabrication model much quicker, and lead to successful collaboration between the design and engineering team at Walter P Moore and the architect Populous through the use of transparent digital tools and their distribution among the team members.
Digital workflow has been assisting designers and engineers to conquer intricate design and bring stunning architecture to urban skylines. This process should be more of a team collaboration than an
A digital recipe is a set of instructions needed to perform a specific task for a project. It can also be described as a visual history of steps needed to
Recipe 1 - Setting up Secondary Structure Geometry Parametrically
Façade design was the key architectural aspect for the Minnesota United Stadium. The secondary support structure behind the façade is a system made up of HSS rolled
Embedding Index in the Model
A high level of geometric complexity and large number of structural members required an additional layer of information associated with each model element. Indexing as a type of naming convention
Custom Grasshopper Component
Besides general components, custom GH components were created to assist in establishing the design recipes specifically for this project. These included components to read or write data in GSA/SAP2000 and
Design Model to Analysis Model
With the secondary structural geometry and indexing information summarized in the Grasshopper cluster, the information could be passed down to set up the engineering analysis model in GSA (structural analysis
Recipe 2 - Combining Secondary Steel Information into SAP
As WPM-Los Angeles Office was fully in control of the secondary structural geometry (except for the driver tube, which was issued by Populous), the armature’s information needed
Recipe 3 - Visualizing Analysis Results
Demand-capacity ratio (DCR) is one of the most important indicators to understand if a structural element has enough capacity under the prescribed loading. However, the traditional approach
Recipe 4 - Documentation in Revit
Recipe 004 is a documentation process which combined all the information gathered throughout the analysis and design phase. Revit performed as the main documentation tool. In addition
Generation of Tekla Model
The undulating driver pipes created the backbone for the dynamic façade at Minnesota United Stadium. Each of the driver pipes had a varying radius, length, and different start-end coordinates in
A number of highlights in the project’s design process are:Digital workflow was documented in a bespoke menu, which consisted of various design recipes.Attributes assigned according to native object geometry allowed
The authors would like to thank the project team for their collaboration on this project:
Phil Kolbol – Populous (Kansans City, MO)
Justin Barton, David Landis, Aditya Potdar – Walter P Moore (Kansas City, MO), Structural Engineering
Marty Augustyniak - Walter P Moore (Buffalo, NY), Facade Engineering
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"EYAS." Food4Rhino. October 25, 2016. Accessed January 12, 2018. http://www.food4rhino.com/app/eyas.