1. Static vector fields Wind can be simulated using vector fields. The specific vectors in the field determine the direction and size of the wind force applied to a given object or particle. Static vector fields is stored in a 2D matrix for a simple wind model. For a more sophisticated simulation a 3D matrix is needed. To get a good result, the resolution of the vector fields must correspond to the resolution of the given scene or object. To create a turbulence around a given center, the vector field could look like the illustration. Real Time Wind Simulation Lennarth Hansen Technical University of Denmark Email: s021651@student.dtu.dk Niels Berg Technical University of Denmark Email: s021788@student.dtu.dk Multiple vector fields In real life wind can be very complex. If only one vector field is used it will be problematic to create fine wind details for a large scene. If all details are stored in only one vector field the resolution has to be very high, resulting in a high memory cost. Instead of storing all data in one vector field it is more convenient to use multiple vector fields. This will satisfy the wanted level of details, and make it possible to attach or translate a specific vector field. As an example two fields can be mixed to create a twister, one to make the spinning and the other to create a drag towards the center. Introduction In virtual reality wind simulation is not getting much attention. Often wind is simulated as a variable force, which in some cases does not give a realistic touch. This poster will introduce some methods to make the simulation better, even at a low costs. Wind direction and deflection To make the wind simulation more realistic, it is not enough to create a vector field for the direction of the drag force. A vector field for the deflection is also needed. An object or function which creates wind should produce a vector field for the wind direction. If the wind interferes with an object, this object must create a deflection field to lead the wind around itself. The strength of deflection depends on the viscosity of the wind. The two screen shots show the deflection of a sphere bombarded with particles. By adding a new sphere with its own deflection, the particles will get affected by both spheres. When moving the spheres the deflection will follow, which gives a nice real time interaction. Functionbased vector fields The use of vector fields results in a quick data gathering; but when it comes to a high level of details the memory consumption could be huge. Functions are a cheaper way to handle deflection fields. This gives us the opportunity to let the function depend on several inputs e.g. position, direction, mass etc. The problem, in this case the particle path, is solved as a stepwise differential equation, which makes it possible to make real time interaction by moving the object or adding objects. Combined with the use of multiple vector fields, particles could be affected by more than one object. A great advantage of functionbased fields is the low cost of memory and processor. Even for large number of objects/particles the method is pretty fast. Object Outer deflection range Inner range Particle DeflectVec Direction Particle path (i,j) (i,j+1)(i-1,j+1) (i-1,j) (i,j-1)(i-1,j-1) (i+1,j) (i+1,j+1) (i+1,j-1) A more correct solution is to look at the wind simulation in a more real life manner. By dividing the scene into pressure cells it is possible to let the nature of the pressure cells solve the problem by themselves. A pressure cell is defined to give away all its pressure to the surrounding cells – and receive pressure from other cells. Kind of the way gas molecules interact. If a cell is placed beside an object the pressure released in that direction will bounce back to the cell itself. In a given scene the wind field will automatically be generated when boundary limits have been defined (High/low pressure). The direction of the wind is now given by the pressure difference between the pressure cells. The pressure will be lead around any obstacles which creates a nice deflection. When using a great number of pressure cells the pressure exchange is pretty slow. This makes the method difficult to use in real time. Although the process can be speeded up when using Fourier trans- formation, similar to the convolution problem in image processing. For a static wind field the method is very usable since the pressure cells can be stopped and used as a simple table of pressures. Conclusion For a better solution to our problem, a combination of the methods is preferred. Pressure cells give a nice look, but the update rate is slow. When a good pressure chart is generated the process can be stopped and combined with the fast functionbased deflection field. This will result in a floating interaction between particles and movable objects. The wind dynamics are pretty similar to general fluid dynamics. This means that the shown methods will work for fluid simulations such as water. Creating an interactive river including obstacles can easily be done. Using the pressure cell method, the river particles will be more robust for a bumpy river bed. Pressure cells