Solving city and building microclimates by fast fluid dynamics with large timesteps and coarse meshes

Building and Environment

Hooff

et al. 2020

It is crucial to accurately and efficiently predict transient particle transport in indoor environments to improve air distribution design and reduce health risks. For steady-state indoor airflow, fast fluid dynamics (FFD) + Markov chain model increased the calculation speed by around seven times compared to computational fluid dynamics (CFD) + Eulerian model and CFD + Lagrangian model, while achieving the same level of accuracy. However, the indoor airflow could be transient, if there were human behaviors involved like coughing or sneezing and air was supplied periodically. Therefore, this study developed an FFD + Markov chain model solver for predicting transient particle transport in transient indoor airflow. This investigation used two cases, transient particle transport in a ventilated two-zone chamber and a chamber with periodic air supplies, for validation. Case 1 had experimental data for validation and the results showed that the predicted particle concentration by FFD + Markov chain model matched well with the experimental data. Besides, it had similar accuracy as the CFD + Eulerian model. In the second case, the prediction by large eddy simulation (LES) was used for validating the FFD. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The simulated particle concentrations by the Markov chain model and the Eulerian model were similar. The computational time of the FFD + Markov chain model was 7.8 times less than that of the CFD + Eulerian model.

Section: Discussionmentioning

confidence: 99%

Modeling transient particle transport in transient indoor airflow by fast fluid dynamics with the Markov chain method

Building and Environment

Hooff

et al. 2020

“…It is worth noting that the FFD model, in the case of the study by Mortezazadeh and Wang (2020), took approximately 1.5 hours to simulate a validation case with 17 million cells, which could be considered significantly faster even than using the SRANS approach. Furthermore, their subsequent application case studied the transient PLW flow field in a complex urban area (9 km 2 ), in which the unsteady simulation with 35 million cells took less than 2 hours of computing time.…”

Section: Fast Fluid Dynamicsmentioning

confidence: 99%

Recent advances in modeling turbulent wind flow at pedestrian-level in the built environment

Zhong

Zhao

et al. 2022

ARIN

Pressing problems in urban ventilation and thermal comfort affecting pedestrians related to current urban development and densification are increasingly dealt with from the perspective of climate change adaptation strategies. In recent research efforts, the prime objective is to accurately assess pedestrian-level wind (PLW) environments by using different simulation approaches that have reasonable computational time. This review aims to provide insights into the most recent PLW studies that use both established and data-driven simulation approaches during the last 5 years, covering 215 articles using computational fluid dynamics (CFD) and typical data-driven models. We observe that steady-state Reynolds-averaged Navier-Stokes (SRANS) simulations are still the most dominantly used approach. Due to the model uncertainty embedded in the SRANS approach, a sensitivity test is recommended as a remedial measure for using SRANS. Another noted thriving trend is conducting unsteady-state simulations using high-efficiency methods. Specifically, both the massively parallelized large-eddy simulation (LES) and hybrid LES-RANS offer high computational efficiency and accuracy. While data-driven models are in general believed to be more computationally efficient in predicting PLW dynamics, they in fact still call for substantial computational resources and efforts if the time for development, training and validation of a data-driven model is taken into account. The synthesized understanding of these modeling approaches is expected to facilitate the choosing of proper simulation approaches for PLW environment studies, to ultimately serving urban planning and building designs with respect to pedestrian comfort and urban ventilation assessment.

“…Many studies focus on developing fast numerical simulation method, such as the FFD (Fast Fluid Dynamics) method. The FFD method based on the semi-Lagrangian (SL) scheme was first proposed by Stam [3] and has been widely used to quickly simulate the indoor and outdoor airflow distributions [4][5][6][7]. Zuo et al [4] used the FFD to fast simulate the isothermal airflow distribution in a 2D cavity with a computational speed of about 30-50 times faster than the conventional CFD method.…”

Section: Introductionmentioning

confidence: 99%

“…However, the FFD method adopted by Zuo et al uses numerical viscosity as a substitute for turbulent viscosity [4], which cannot accurately predict the vortex region on the leeward side of the buildings when applying it to natural ventilation simulation [5]. Mortezazadeh and Wang [6] improved the accuracy of FFD method using a fourthorder scheme and combined it with LES to simulate the microclimate of a city [7]. However, the SL scheme does * Corresponding author: gaonaiping@tongji.edu.cn not guarantee the overall quantity conservation and may produce large numerical dissipation errors [6].…”

Section: Introductionmentioning

confidence: 99%

Fast Fluid Dynamics Simulation of the Airflow Around a Single Bluff Body

Huang

et al. 2022

E3S Web Conf.

Fast and accurate simulation of the outdoor airflow distribution is important for studying urban microclimate. In this paper, two pressure-correction schemes (i.e., NIPC and NSPF) for solving the N-S equation item by item are implemented in OpenFOAM and their differences from the PISO algorithm in simulating the airflow around a single 1:1:2 bluff body are analyzed. The RNG k-ε turbulence model is chosen to study the airflow disturbance, while the second-order discretization scheme of Gauss limitedLinear is used to solve the advection term in the N-S equation. The results show that the NIPC can accurately predict the main airflow characteristics around the bluff body, while the NSPF cannot predict the recirculation region on its top. The two pressure-correction schemes underestimate the TKE distribution on the top and leeward sides of the bluff body when applying the RNG k-ε turbulence model, and the maximum relative error is about 30%. However, they are consistent with the results of the PISO algorithm under the same conditions. The two schemes are about 2.5-3.0 times faster than the PSIO algorithm when run on a CPU, and the NSPF is about 12% faster than the NIPC scheme.