State-of-the-art methods for inverse design of an enclosed environment

Wei Liu; Tengfei Zhang; Yu Xue; Zhiqiang John Zhai; Jihong Wang; Yun Wei; Qingyan Chen

doi:10.1016/j.buildenv.2015.02.041

State-of-the-art methods for inverse design of an enclosed environment

Build Environ. 2015 Sep:91:91-100. doi: 10.1016/j.buildenv.2015.02.041. Epub 2015 Mar 17.

Authors

Wei Liu^{1

2}, Tengfei Zhang³, Yu Xue^{2

4}, Zhiqiang John Zhai⁴, Jihong Wang³, Yun Wei³, Qingyan Chen^{1

2}

Affiliations

¹ School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.
² Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China.
³ School of Civil Engineering, Dalian University of Technology, Dalian 116024, China.
⁴ Department of Civil, Environmental, and Architectural Engineering, University of Colorado at Boulder, CO 80309, USA.

Abstract

The conventional design of enclosed environments uses a trial-and-error approach that is time consuming and may not meet the design objective. Inverse design concept uses the desired enclosed environment as the design objective and inversely determines the systems required to achieve the objective. This paper discusses a number of backward and forward methods for inverse design. Backward methods, such as the quasi-reversibility method, pseudo-reversibility method, and regularized inverse matrix method, can be used to identify contaminant sources in an enclosed environment. However, these methods cannot be used to inversely design a desired indoor environment. Forward methods, such as the CFD-based adjoint method, CFD-based genetic algorithm method, and proper orthogonal decomposition method, show the promise in the inverse design of airflow and heat transfer in an enclosed environment. The CFD-based adjoint method is accurate and can handle many design parameters without increasing computing costs, but the method may find a locally optimal design that could meet the design objective with constrains. The CFD-based genetic algorithm method, on the other hand, can provide the global optimal design that can meet the design objective without constraints, but the computing cost can increase dramatically with the number of design parameters. The proper orthogonal decomposition method is a reduced-order method that can significantly lower computing costs, but at the expense of reduced accuracy. This paper also discusses the possibility to reduce the computing costs of CFD-based design methods.

Keywords: Backward method; Enclosed environment; Forward method; Inverse design.