Fast Fluid Dynamics Model Research Articles

An open source fast fluid dynamics model for data center thermal management

Although computational fluid dynamics (CFD) has been widely adopted to improve data center thermal management, the high computational demand limits its applications, such as multivariate optimal design and operation. Fast fluid dynamics (FFD), which has been applied for fast airflow simulation, shows great potential. However, few research applied FFD for optimal design and operation of data center thermal management. This research improves the FFD model for data centers and conducts a comprehensive evaluation and demonstration. First, the FFD model is improved by solving the advection and diffusion equations together using an upwind scheme instead of a semi-Lagrangian advection solver in the conventional FFD model. Second, new features for data centers are added, such as a pressure correction method to simulate plenum airflow and dynamic boundary conditions for IT racks. The new FFD model is first validated with two indoor environment cases and the results show that the new FFD model has slightly better overall prediction accuracy and faster speed compared to the conventional FFD model. It is also observed that both FFD models achieve acceptable accuracy, except for a few localized disparities with experimental data, which might be due to simplified handling of turbulence viscosity near the boundaries. Furthermore, validation with a real data center shows that the FFD model achieves a similar level of accuracy as CFD when compared to the experimental measurements with some level of uncertainties. It is then demonstrated for data center optimal design and operation, which saves 53.4–58.8% of annual energy while still meeting the thermal requirements. With a much faster speed and comparable accuracy compared to CFD, the FFD model parallelized on a graphics processing unit is promising for practical model-based data center early design and operation.

Reduction of Numerical Diffusion in FFD Model

Fast flow simulations are needed for some applications in building industry, such as the conceptual design of indoor environment or teaching of Heating Ventilation and Air Conditioning (HVAC) system design in classroom. Instead of pursuing high accuracy, those applications require only conceptual distributions of the flow but within a short computing time. To meet these special needs, a Fast Fluid Dynamics (FFD) method was proposed to provide fast airflow simulation with some compromise in accuracy. This study is to further improve the FFD method by reducing the numerical viscosity that is caused by a linear interpolation in its semi-Lagrangian solver. We propose a hybrid scheme of a linear and a third-order interpolation to reduce the numerical diffusion in low order scheme and the numerical dispersion in high order scheme. The FFD model with both linear and hybrid interpolations are evaluated by simulating four different indoor flows. The results show that the hybrid interpolation can significantly improve the accuracy of the FFD model with a small amount of extra computing time.

Simulations of Air Distributions in Buildings by FFD on GPU

Building design and operation often requires real-time or faster-than-real-time simulations for detailed information on air distributions. By solving the Navier-Stokes equations and transportation equations for energy and species, Fast Fluid Dynamics (FFD) model can provide detailed information as a Computational Fluid Dynamics (CFD) model. Compared to the CFD, the FFD is 50 times faster with some compromise in accuracy. But the accuracy and speed of the FFD model can be further enhanced by improving its numerical schemes. In addition, it was found that the computing time of the FFD program can be reduced up to 30 times by executing on a Graphics Processing Unit (GPU) instead of a Central Processing Unit (CPU). Furthermore, the FFD simulation can be accelerated by optimizing the GPU code and by using multiple GPUs. As a whole, it is possible to perform real-time simulation for a moderate size building with 107 grids and Δt = 0.1s using the FFD on GPUs

Improvements in FFD Modeling by Using Different Numerical Schemes

Indoor environment design and air management in buildings requires fast simulation of air distribution. A fast fluid dynamics (FFD) model seems very promising. This work was to develop the FFD by improving its speed and accuracy. Enhancement of computing speed can be realized by modifying the time-splitting method. Improvements in accuracy were achieved by replacing the finite-difference scheme by the finite-volume method and by proposing a correction function for mass conservation. Using the new FFD model for several indoor air flows, the results show significant reduction in computing time and great improvements on accuracy.

Fast and informative flow simulations in a building by using fast fluid dynamics model on graphics processing unit

Fast indoor airflow simulations are necessary for building emergency management, preliminary design of sustainable buildings, and real-time indoor environment control. The simulation should also be informative since the airflow motion, temperature distribution, and contaminant concentration are important. Unfortunately, none of the current indoor airflow simulation techniques can satisfy both requirements at the same time. Our previous study proposed a Fast Fluid Dynamics (FFD) model for indoor flow simulation. The FFD is an intermediate method between the Computational Fluid Dynamics (CFD) and multizone/zonal models. It can efficiently solve Navier–Stokes equations and other transportation equations for energy and species at a speed of 50 times faster than the CFD. However, this speed is still not fast enough to do real-time simulation for a whole building. This paper reports our efforts on further accelerating FFD simulation by running it in parallel on a Graphics Processing Unit (GPU). This study validated the FFD on the GPU by simulating the flow in a lid-driven cavity, channel flow, forced convective flow, and natural convective flow. The results show that the FFD on the GPU can produce reasonable results for those indoor flows. In addition, the FFD on the GPU is 10–30 times faster than that on a Central Processing Unit (CPU). As a whole, the FFD on a GPU can be 500–1500 times faster than the CFD on a CPU. By applying the FFD to the GPU, it is possible to do real-time informative airflow simulation for a small building.