Inverse design methods for indoor ventilation systems using CFD-based multi-objective genetic algorithm

Zhiqiang John Zhai, Yu Xue, Qingyan Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

42 Citations (Scopus)

Abstract

Conventional designers typically count on thermal equilibrium and require ventilation rates of a space to design ventilation systems for the space. This design, however, may not provide a conformable and healthy micro-environment for each occupant due to the non-uniformity in airflow, temperature and ventilation effectiveness as well as potential conflicts in thermal comfort, indoor air quality (IAQ) and energy consumption. This study proposes two new design methods: the constraint method and the optimization method, by using advanced simulation techniques-computational fluid dynamics (CFD) based multi-objective genetic algorithm (MOGA). Using predicted mean vote (PMV), percentage dissatisfied of draft (PD) and age of air around occupants as the design goals, the simulations predict the performance curves for the three indices that can thus determine the optimal solutions. A simple 2D office and a 3D aircraft cabin were evaluated, as demonstrations, which reveal both methods have superior performance in system design. The optimization method provides more accurate results while the constraint method needs less computation efforts.

Original languageEnglish
Pages (from-to)661-669
Number of pages9
JournalBuilding Simulation
Volume7
Issue number6
DOIs
Publication statusPublished - Dec 2014

Keywords

  • age of air
  • computational fluid dynamics
  • inverse modeling
  • multi-objective genetic algorithm
  • percent dissatisfied
  • predicted mean vote

ASJC Scopus subject areas

  • Building and Construction
  • Energy (miscellaneous)

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