Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

PDF papers
References

Hits 1 to 3 of 3

Reformat results as: short short into frame detailed detailed into frame

_id	ecaade2013_180
id	ecaade2013_180
authors	Mueller, Volker and Strobbe, Tiemen
year	2013
title	Cloud-Based Design Analysis and Optimization Framework
doi	https://doi.org/10.52842/conf.ecaade.2013.2.185
source	Stouffs, Rudi and Sariyildiz, Sevil (eds.), Computation and Performance – Proceedings of the 31st eCAADe Conference – Volume 2, Faculty of Architecture, Delft University of Technology, Delft, The Netherlands, 18-20 September 2013, pp. 185-194
summary	Integration of analysis into early design phases in support of improved building performance has become increasingly important. It is considered a required response to demands on contemporary building design to meet environmental concerns. The goal is to assist designers in their decision making throughout the design of a building but with growing focus on the earlier phases in design during which design changes consume less effort than similar changes would in later design phases or during construction and occupation.Multi-disciplinary optimization has the potential of providing design teams with information about the potential trade-offs between various goals, some of which may be in conflict with each other. A commonly used class of optimization algorithms is the class of genetic algorithms which mimic the evolutionary process. For effective parallelization of the cascading processes occurring in the application of genetic algorithms in multi-disciplinary optimization we propose a cloud implementation and describe its architecture designed to handle the cascading tasks as efficiently as possible.
wos	WOS:000340643600018
keywords	Cloud computing; design analysis; optimization; generative design; building performance.
series	eCAADe
email
last changed	2022/06/07 07:58

_id	caadria2013_173
id	caadria2013_173
authors	Mueller, Volker; Drury B. Crawley and Xun Zhou
year	2013
title	Prototype Implementation of a Loosely Coupled Design Performance Optimisation Framework
doi	https://doi.org/10.52842/conf.caadria.2013.675
source	Open Systems: Proceedings of the 18th International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2013) / Singapore 15-18 May 2013, pp. 675-684
summary	Integration of analyses into early design phases poses several challenges. An experimental implementation of an analysis framework in conjunction with an optimization framework ties authoring and analysis tools together under one umbrella. As a prototype it served intensive use-testing in the context of the SmartGeometry 2012 workshop in Troy, NY. In this prototype the data flow uses a mix of proprietary and publicised file formats, exchanged through publicly accessible interfaces. The analysis framework brokers between the parametric authoring tool and the analysis tools. The optimization framework controls the processes between the authoring tool and parametric engine on one side and the optimization algorithm on the other. In addition to some user-implemented analyses inside the parametric design model the prototype makes energy analysis and structural analysis available. The prototype allows testing assumptions about work flow, implementation, usability and general feasibility of the pursued approach.
wos	WOS:000351496100066
keywords	Design-analysis integration, Design refinement, Optimization
series	CAADRIA
email
last changed	2022/06/07 07:58

_id	ecaade2013_090
id	ecaade2013_090
authors	Wilkinson, Samuel; Hanna, Sean; Hesselgren, Lars and Mueller, Volker
year	2013
title	Inductive Aerodynamics
doi	https://doi.org/10.52842/conf.ecaade.2013.2.039
source	Stouffs, Rudi and Sariyildiz, Sevil (eds.), Computation and Performance – Proceedings of the 31st eCAADe Conference – Volume 2, Faculty of Architecture, Delft University of Technology, Delft, The Netherlands, 18-20 September 2013, pp. 39-48
summary	A novel approach is presented to predict wind pressure on tall buildings for early-stage generative design exploration and optimisation. The method provides instantaneous surface pressure data, reducing performance feedback time whilst maintaining accuracy. This is achieved through the use of a machine learning algorithm trained on procedurally generated towers and steady-state CFD simulation to evaluate the training set of models. Local shape features are then calculated for every vertex in each model, and a regression function is generated as a mapping between this shape description and wind pressure. We present a background literature review, general approach, and results for a number of cases of increasing complexity.
wos	WOS:000340643600003
keywords	Machine learning; CFD; tall buildings; wind loads; procedural modelling.
series	eCAADe
email
last changed	2022/06/07 07:57

No more hits.