Optimization of air supply location, size, and parameters in enclosed environments using a computational fluid dynamics-based adjoint method

Liu, Wei; Jin, Mingang; Chen, Chun; Chen, Qingyan

doi:10.1080/19401493.2015.1006525

Cited by 30 publications

(16 citation statements)

References 29 publications

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“…This study found that different initial inlet air conditions led to different optimal inlet air conditions, which implies the existence of multiple solutions. Later, Liu et al [48] included the air supply location and size as design variables. In a two-dimensional non-isothermal ventilated cavity case [49] as shown in Fig.…”

Section: Cfd-based Adjoint Methodsmentioning

confidence: 99%

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

Liu

Zhang

Xue

et al. 2015

Building and Environment

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a b s t r a c tThe 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.

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Section: Cfd-based Adjoint Methodsmentioning

confidence: 99%

“…Demonstration of the CFD-based adjoint method in inverse reconstruction of a two-dimensional non-isothermal cavity case: (a) Sketch of the cavity[49] and (b) flow with the initial supply location and (c) flow with the final supply location[48].…”

mentioning

confidence: 99%

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

Liu

Zhang

Xue

et al. 2015

Building and Environment

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“…may not be desirable. Recently, inverse or optimal design processes have emerged, such as the computational fluid dynamics (CFD)‐based genetic algorithm (GA) method, CFD‐based proper orthogonal decomposition (POD) method, CFD‐based artificial neural network (ANN) method, and adjoint method …”

Section: Introductionmentioning

confidence: 99%

Inverse design of indoor environment using an adjointRNGk‐ε turbulence model

Zhao

Chen

2019

Indoor Air

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The adjoint method can determine design variables of an indoor environment according to the optimal design objective, such as minimal predicted mean vote (PMV) for thermal comfort. The method calculates the gradient of the objective function over the design variables so that the objective function can be minimized along the fastest direction using an optimization algorithm. Since the objective function is controlled by the Reynolds‐averaged Navier‐Stokes (RANS) equations with the RNG k‐ε model during the optimization process, all the corresponding adjoint equations should be solved, rather than the “frozen turbulence” assumption used in previous studies. This investigation developed adjoint equations for the RNG k‐ε turbulence model and applied it to a two‐dimensional ventilated cavity and a three‐dimensional, two‐person office. Design processes with the adjoint RNG k‐ε turbulence model led to a near‐zero design function for the two cases, while those with the “frozen turbulence” assumption did not. This investigation has successfully used the new method to design a two‐person office with optimal thermal comfort level around the two occupants.

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“…Kato et al 1992;Jiang and Chen 2002;Kurabuchi et al 2004;Hu et al 2005;van Hooff and Blocken 2010a,b;Novoselac 2011, 2013;Ramponi and Blocken 2012a,b;Ai and Mak 2014a,b;Perén et al 2015;Tong et al 2016a,b), for indoor airflow studies (e.g. Gan and Awbi 1994;Chen 1995;Nielsen 1998;Zhang et al 2007;Wang and Chen 2009;Cao and Meyers 2013;Liu et al 2013Liu et al , 2016aYou et al 2016) and for indoor pollutant concentration studies (e.g. Chung and Hsu 2001;Rouaud and Havet 2005;Chen et al 2014Chen et al , 2015.…”

Section: Introductionmentioning

confidence: 99%

Low-Reynolds number mixing ventilation flows: Impact of physical and numerical diffusion on flow and dispersion

Hooff

Blocken

2017

Build. Simul.

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Quality assurance in computational fluid dynamics (CFD) is essential for an accurate and reliable assessment of complex indoor airflow. Two important aspects are the limitation of numerical diffusion and the appropriate choice of inlet conditions to ensure the correct amount of physical diffusion. This paper presents an assessment of the impact of both numerical and physical diffusion on the predicted flow patterns and contaminant distribution in steady Reynolds-averaged NavierStokes (RANS) CFD simulations of mixing ventilation at a low slot Reynolds number (Re≈2,500). The simulations are performed on five different grids and with three different spatial discretization schemes; i.e. first-order upwind (FOU), second-order upwind (SOU) and QUICK. The impact of physical diffusion is assessed by varying the inlet turbulence intensity (TI) that is often less known in practice. The analysis shows that: (1) excessive numerical and physical diffusion leads to erroneous results in terms of delayed detachment of the wall jet and locally decreased velocity gradients; (2) excessive numerical diffusion by FOU schemes leads to deviations (up to 100%) in mean velocity and concentration, even on very high-resolution grids; (3) difference between SOU and FOU on the coarsest grid is larger than difference between SOU on coarsest grid and SOU on 22 times finer grid; (4) imposing TI values from 1% to 100% at the inlet results in very different flow patterns (enhanced or delayed detachment of wall jet) and different contaminant concentrations (deviations up to 40%); (5) impact of physical diffusion on contaminant transport can markedly differ from that of numerical diffusion.

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Optimization of air supply location, size, and parameters in enclosed environments using a computational fluid dynamics-based adjoint method

Cited by 30 publications

References 29 publications

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

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

Inverse design of indoor environment using an adjointRNGk‐ε turbulence model

Low-Reynolds number mixing ventilation flows: Impact of physical and numerical diffusion on flow and dispersion

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