Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An approach for building design optimization using design of experiments
Abstract Building simulation based optimization involves direct coupling of the optimization algorithm to a simulation model, making it computationally intensive. To overcome this issue, an approach is proposed using a combination of experimental design techniques (fractional factorial design and response surface methodology). These techniques approximate the simulation model behavior using surrogate models, which are several orders of magnitude faster than the simulation model. Fractional factorial design is used to identify the significant design variables. Response surface methodology is used to create surrogate models for the annual cooling and lighting energy with the screened significant variables. The error for these models is less than 10%, validating their effectiveness. These surrogate models speed up optimization with genetic algorithms, for single- and multi-objective optimization problems and scenario analyses, resulting in a better solution. Thus, optimization becomes possible within reasonable computational time with the proposed methodology. This framework is illustrated using the case study of a three-storey office building for New Delhi.
An approach for building design optimization using design of experiments
Abstract Building simulation based optimization involves direct coupling of the optimization algorithm to a simulation model, making it computationally intensive. To overcome this issue, an approach is proposed using a combination of experimental design techniques (fractional factorial design and response surface methodology). These techniques approximate the simulation model behavior using surrogate models, which are several orders of magnitude faster than the simulation model. Fractional factorial design is used to identify the significant design variables. Response surface methodology is used to create surrogate models for the annual cooling and lighting energy with the screened significant variables. The error for these models is less than 10%, validating their effectiveness. These surrogate models speed up optimization with genetic algorithms, for single- and multi-objective optimization problems and scenario analyses, resulting in a better solution. Thus, optimization becomes possible within reasonable computational time with the proposed methodology. This framework is illustrated using the case study of a three-storey office building for New Delhi.
An approach for building design optimization using design of experiments
Dhariwal, Jay (Autor:in) / Banerjee, Rangan (Autor:in)
Building Simulation ; 10 ; 323-336
08.11.2016
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
factorial design , sensitivity analysis , response surface methodology , surrogate modelling , genetic algorithms , optimization Engineering , Building Construction and Design , Engineering Thermodynamics, Heat and Mass Transfer , Atmospheric Protection/Air Quality Control/Air Pollution , Monitoring/Environmental Analysis
An approach for building design optimization using design of experiments
| Online Contents | 2017
An approach for building design optimization using design of experiments
| DSpace@MIT | 2016
| British Library Online Contents | 2003
Installations by architects : experiments in building and design
| UB Braunschweig | 2009