1 / 67

Mark Stockmyer October 5, 2007

Development and Analysis of a Gravity-Simulated Particle-Packing Algorithm for Modeling Optimized Rocket Propellants. Mark Stockmyer October 5, 2007. Approved for public release; distribution is unlimited. Acknowledgments. Dr. Hossein Saiedian Dr. Arvin Agah Dr. Xue-Wen Chen

birch
Download Presentation

Mark Stockmyer October 5, 2007

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Development and Analysis of aGravity-Simulated Particle-Packing Algorithm for Modeling Optimized Rocket Propellants Mark Stockmyer October 5, 2007 Approved for public release; distribution is unlimited.

  2. Acknowledgments • Dr. Hossein Saiedian • Dr. Arvin Agah • Dr. Xue-Wen Chen • Dr. Travis Laker • Kristina Stockmyer • ONR – Office of Naval Research

  3. Outline • Problem • Significance • Methodology/Solution • Results/Evaluation • Conclusion • Further Research

  4. Outline • Problem • Significance • Methodology/Solution • Results/Evaluation • Conclusion • Further Research

  5. China Lake – Naval Air Warfare Center: Weapons Division* • RDT&E: Research, Develop, Test, and Evaluate *http://www.nawcwpns.navy.mil

  6. Energetics Development • Energetics • Explosives • Rocket propellant • Fuzes • Igniters

  7. Rocket Propellant • Properties (Miller 1982) • Thrust • Smoke • Exhaust signature • Heat • Burn rate • Various rocket applications • How to optimize these properties?

  8. Rocket Propellant (continued) • Currently • Research chemist • Think up new formulations • Test out best candidates • Problems • Very expensive • Research community is limited

  9. Combinatorial Chemistry • Computer simulation • Input millions of random combinations • See what the results are • Used in drug synthesis (Furka 1995) • Very difficult • Not currently feasible for energetic materials • More complex than drugs

  10. Steps to Combinatorial Chemistry • PEP (Propellant Equilibrium Program) • Optimizing version written last year at C/L • Determine internal structure of propellant (Knott, Jackson, & Buckmaster 2001) • Done, but slow • Simulate burning of the propellant • Still in progress; very slow

  11. Outline • Problem • Significance • Methodology/Solution • Results/Evaluation • Conclusion • Further Research

  12. Packing: State of the Art • Physical simulation is difficult (Agarwal 2002) • Requires “plausible” motion • Momentum • Parallelization • In a word: Slow • CSAR (Center for Simulation of Advanced Rockets) (Knott et al. 2001) • Kinematic model • 100,000 particles • 64 processors • 100 hours

  13. Kinematic Modeling Is Too Slow • Assembly algorithms are faster • Place particles in best location • Final location is fixed (sticky) • Get close to modeling reality • Without all the slowness • Problem: Can be algorithm specific • Too specific to be of use

  14. What Is a Good Simulation?* • DOD method/modeling and simulation • Subject matter experts • Hierarchy of indicators • Weighting of indicators • Rule-based knowledge base *(Balci 2001)

  15. How Do You Measure Quality of Packing? • Speed • Packing fraction • Randomness • Scalability

  16. Speed • How long does it take? • 100 hours is too long • Faster the better • Target: 100,000 particles in a minute • 86,000 runs/processor/day • 1 Million runs in 12 days

  17. Packing Fraction • How “dense” is the pack? • Relationship between • Volume of the particles • Volume of the empty container

  18. High Density Packing Example

  19. Medium Density Packing Example

  20. Low Density Packing Example

  21. How Dense Is Dense? • Ball bearing experiments (McGeary 1961) • Drop a few ball bearings in graduated cylinders • Shake and vibrate • Repeat until the cylinder is full • Final packing fraction: 0.625 • Kilgore and Scott (1969) • 0.6366 • Our target: 0.63

  22. Randomness • Difficult to measure • Looking for patterns • How far are particles from one specific particle? • RDF (Radial Distribution Function) • Statistical tool • Can be used to measure particle relationships • Direct RDF example later

  23. High Density – Patterns Evident

  24. Scalability • How do the properties change as the number of particles change? • Speed • Packing fraction

  25. Relation to Computer Science • Modeling • Abstraction of reality • Data structures • Algorithm development • Algorithm analysis • Complexity analysis • Solving a real complex problem

  26. Outline • Problem • Significance • Methodology/Solution • Results/Evaluation • Conclusion • Further Research

  27. How Do You Make a Rocket Motor? (Simplified) • Get a rocket case • Pour in propellant • Attach exhaust nozzle Image from http://www.aerospaceweb.org/

  28. What Does Propellant Look Like? • Molecules of propellant • Essentially spheres Image from http://www.aerospaceweb.org/

  29. How Do Things Fall? • Gravity • Falling • Collision • How does a falling particle know where to go? • Simple to the human eye • How do I create an algorithm to do the same thing?

  30. SGMP: Spin Gap Move Protocol • Move a particle downward until there’s a collision • Spin - Move the particle in a circle • Gap - Find out where there’s no collision • Move - Move the particle in the direction of non- collision

  31. SGMP – Visualization • Custom tool • Particle/Primitive system (Ebert 1996) • Demonstration

  32. SGMP – Collision Calculations • Calculated many, many times • Computationally expensive • Use neighbor lists to reduce number of checks

  33. SGMP– Neighbor Lists* • Find a small number of particles • The neighbors • Near the object particle • List will always contain fixed (or less) number of particles • Around 10-20 • A computationally expensive process *(Torquato 2002)

  34. SGMP – Complexity • Specify all possible computations (Hartmanis and Hopcroft 1971) • Repeated steps • Generate neighbor list • Downward drop • Circular sweep • Find largest gap • Move particle into gap • Sweep, Gap, and Move can be grouped

  35. Generate Neighbor List • One-time complexity • O(n) • Compare one to all the rest • Total complexity (entire pack) • O(n2)

  36. Downward Drop • One-time complexity • O(1) • Constant drop distance • Total complexity (entire pack) • O(n)

  37. Spin, Gap, and Move • One-time complexity • O(1) • Remember, fixed # of particles in the neighbor list • Total complexity (entire pack) • O(n log(n)) • n – all particles • log(n) – cross section of pack • Example

  38. Why log(n)?

  39. Outline • Problem • Significance • Methodology/Solution • Results • Evaluation • Conclusion • Further Research

  40. SGMP Starting Arrangements • Single Column • Small Dense • Large Dense • Loose Random • Number of particles tested • 150, 300, 750, 1002, 2001, 3000, 6000, 9000

  41. SGMP – Single Column • Single column of particles above control volume • Demonstration

  42. Results – Single Column • Packing fraction • 300 particles - 0.62 • 9000 particles - 0.60 • Speed • 9000 particles • 41424 seconds (~12 hours)

  43. Results – Single Column

  44. Results – Single Column

  45. SGMP – Small Dense • Densely packed starting grid • Only within the control volume • Demonstration

  46. Small Dense – Results • Packing fraction • 750 particles - 0.61 • 9000 particles - 0.60 • Speed • 9000 particles • 56337 seconds (~15 hours)

  47. Small Dense -- Results

  48. SGMP – Large Dense • Densely packed starting grid • Within the expanded volume • Demonstration

  49. Large Dense – Results • Packing fraction • 9000 particles - 0.59 • Other packs were very similar • Speed • 9000 particles • 19699 seconds (~5.4 hours)

  50. Large Dense -- Results

More Related