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Using CUDA for Solar Thermal Plant Computation

Using CUDA for Solar Thermal Plant Computation. Capstone Spring 2009 – Team 5. Using CUDA for Solar Thermal Plant Computation. Solar Thermal Plants Background Problem Energy Solution Algorithm Polygon Clipping Why CUDA? Progress. Our Team. Claus Nilsson Sahithi Chalasani

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Using CUDA for Solar Thermal Plant Computation

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  1. Using CUDA for Solar Thermal Plant Computation Capstone Spring 2009 – Team 5

  2. Using CUDA for Solar Thermal Plant Computation. • Solar Thermal Plants • Background • Problem • Energy • Solution • Algorithm • Polygon Clipping • Why CUDA? • Progress

  3. Our Team. • Claus Nilsson • Sahithi Chalasani • Pranav Mantini • Arun Kumar Subramanian

  4. Instructor: Dr.Bun yue Mentor: Dr.Michel Izygon Mr. Peter Armstrong

  5. SOLAR THERMAL PLANT • Solar Thermal power plants are used to generate electricity from the energy of the sun.

  6. Background • Structure • 1. Central receiver • A type of solar furnace. • Receives the sunlight redirected by Heliostats. • 2. Heliostat • A type of mirror. • redirects sunlight towards the central receiver.

  7. Background. • Field • Generally huge • Heliostats are placed in a radial stagger formation. • For the purpose of computation the field is broken into grids containing cells.

  8. Shading and Blocking. • Shading and Blocking. • The sunlight being received by one heliostat can be blocked by the adjacent heliostats, causing shading and blocking. • A field of heliostats suffers loss in efficiency caused by shading and blocking. • For our purpose we assume that shading and blocking occurs only within a cell.

  9. Shading. • Shading is the loss of illumination on a given mirror due to the interception of the incident sunlight by a neighbouring mirror. [3]

  10. SHADING

  11. Blocking. • Blocking is the loss of illumination on the central receiver due to the interception of reflected sunlight by another neighbouring mirror. [3]

  12. BLOCKING

  13. Energy. • The energy generated by a heliostat depends on many factors, one of them being the area of the heliostats. • The energy generated from a solar thermal plant is directly proportional to the amount of sunlight reflected on to the central receiver.

  14. Energy • The amount of sunlight reflected onto the central receiver depends on the total area of the heliostats that is neither shaded nor blocked. • To optimize the energy generated from a solar thermal plant, the total area of the heliostats that is shaded and blocked should be calculated.

  15. Algorithm. • An algorithm was designed by Mr. Peter Armstrong of Tietronix Software, Inc., to calculate the shading and blocking among the heliostats.[4] • This algorithm makes use of Vector Mathematics, including vector projection and a polygon clipping algorithm.

  16. Algorithm. • The algorithm calculates the co-ordinates of the heliostats for a given configuration of the grid at a certain location of the sun. • These co-ordinates are used to find the interactions of the heliostats. • The algorithm uses vector projections to find, if one heliostat shades or blocks another heliostat.

  17. Algorithm. • If a heliostat shades or blocks another heliostat, a polygon clipping algorithm is used to find the vertices of the unshaded region of the representative heliostat. • This algorithm is applied for all the neighboring heliostats and the area is added to the running total of the area calculated.

  18. Polygon Clipping. • The area of the representative Heliostat that is shaded or blocked does not contribute towards the power generated. • This area should be subtracted using a polygon clipper. • The Original program designed by Tietronix Software, Inc. makes call to a general polygon clipping(gpc) library.

  19. Polygon Clipping • This gpc is a huge library designed at The University of Manchester. • It has about 2500 lines of code. • Most of the processing time for calculating the co-ordinates is taken by the gpc.

  20. Problem. • Solar thermal fields in general have considerably large number of heliostats. • This computation algorithm takes significant amount of time to calculate shading and blocking for thousands of heliostat. • The objective is to decrease this computation time.

  21. Solution. • To increase the efficiency of this computation algorithm, Tietronix Software, Inc. has proposed to create an application that computes the shading and blocking among the heliostats simultaneously. • For this purpose, CUDA(Compute Unified Device Architecture), a parallel computing architecture was chosen.

  22. Design • As CUDA is extension to language c, the whole computation algorithm and the polygon clipping algorithm was first implemented in C and then later converted to CUDA.

  23. Polygon Clipping Issue • The gpc library used for the original computation could not be used for our purpose. • As, the gpc library are located on the host, a call to the function on the host from the device is lot more time consuming. • A polygon clipping algorithm that is more specific to the computation algorithm has to be designed.

  24. Polygon Clipping • The polygon clipping algorithm used for our design is a paper, Efficient clipping of arbitrary polygons proposed by GUNTHER GREINER and KAI HORMANN. • This algorithm is chosen because, it is relatively more efficient than Sutherland Hodgman algorithm which is more commonly used. • The data structures used for the polygons are very simple.

  25. Polygon Clipping • A doubly linked list is used in the algorithm to represent the polygons. • The clipping algorithm involves the calculation of all the intersection points and the choosing among these points to create the desired polygon.

  26. CUDA • What is CUDA • Scalable programming model and • Software environment for parallel computing [2] • Extension to the C programming language

  27. CUDA • What CUDA does • Allows utilization of GPU • Allows parallel execution of code • Manages threads automatically • CUDA Requires • One or more Nvidia GPUs (Graphics Processing Unit) • Nvidia’s CUDA API

  28. CUDA Example Source: “Parallel Processing With CUDA” by Tom R. Halfhill [1]

  29. CUDA

  30. CUDA • Challenges with CUDA • No communication from device to host • No dynamic memory allocation on device during run • Only Single Precision on our devices

  31. References [1] Tom R. Halfhill. Parallel Processing With CUDA. Microprocessor, 01/28/08-01, www.nvidia.com/docs/IO/55972/220401_Reprint.pdf [2] Greg Ruetsch, Brent Oster. Getting Started with CUDA. http://www.nvidia.com/content/cudazone/download/Getting_Started_w_CUDA_Training_NVISION08.pdf

  32. References [3] Lipps, F. W.; vant-Hull, L. L., Shading and blocking geometry for a solar tower concentrator with rectangular mirrors, American Society of Mechanical Engineers, Winter Annual Meeting, New York, N.Y., Nov. 17-22, 1974, 7 p. NSF-supported research. [4] Peter Armstrong, An Algorithm For Shading And Blocking Computation Of A Field Of Heliostats Arranged In A Grid Layout.

  33. Thank You!Any Questions?

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