Accelerating Fluids Simulation Using SPH and Implementation on GPU

Aditya Hendra
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology
Uppsala University, 2015

   title={Accelerating Fluids Simulation Using SPH and Implementation on GPU},

   author={Hendra, Aditya},



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Fluids simulation is usually done with CFD methods which offers high precision but needs days/weeks/months to compute on desktop CPUs which limits the practical use in industrial control systems. In order to reduce the computation time Smoothed Particle Hydrodynamics (SPH) method is used. SPH is commonly used to simulate fluids in computer graphics field, especially in gaming. It offers faster computation at the cost of lesser accuracy. The goal of this work is to determine the feasibility of using SPH method with GPU parallel programming to provide fluids simulation which is fast enough for real-time feedback control simulation. A previous master thesis work about accelerating fluids simulation using SPH method was done by Ann Johansson at ABB. Her work in Matlab using intel i7 – 2.4Ghz needs 7089 seconds to compute a water-jet simulation with 40000 particles and time-step of 0.006 second. Our work utilizes GPU parallel programs implemented in Fluidsv3, an open-source software as the base code. With CUDA C/C++ and Nvidia GTX980, we need 18 seconds to compute a water-jet simulation using 1000000 particles and time-step of 0.0001 second. Currently, our work lacks of validation method to measure the accuracy of the fluids simulation and more work needs to be done about this. However it only takes 80 msec to compute one iteration which opens an opportunity to be used together with any real-time systems, such as a feedback control system, that has a period of 100msec. This mean it could model industry processes that utilize water such as the cooling process in a hot rolling mill. The next question, which is not addressed in this study, would be how to satisfy application dependent needs such as: simulation accuracy, required parameters, simulations duration in real-time, etc.
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