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Multi-Disciplinary Conceptual Design of Multi-Stage Hybrid Rocket using Genetic Algorithm and Data Mining Technique

Associate Professor at Tokyo Metropolitan University um Tokyo Metropolitan University
27. Nov 2012
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Multi-Disciplinary Conceptual Design of Multi-Stage Hybrid Rocket using Genetic Algorithm and Data Mining Technique

  1. 1 6th EUROPEAN CONGRESS ON COMPUTATIONAL METHODS IN APPLIED SCIENCES AND ENGINEERING, the University of Vienna, Austria, September 10-14, 2012. MULTI-DISCIPLINARY CONCEPTUAL DESIGN OF MULTI-STAGE HYBRID ROCKET USING GENETIC ALGORITHM AND DATA MINING TECHNIQUE ○Masahiro Kanazaki Tokyo Metropolitan University Yosuke Kitagawa Tokyo Metropolitan University Koki Kitagawa Japan Aerospace Exploration Agency Masaki Nakamiya Kyoto University Toru Shimada Japan Aerospace Exploration Agency
  2. 2 Contents • Background • Objectives • Design methods – Evaluation procedure of hybrid rocket engine (HRE) – Multi-objective Genetic Algorithm (MOGA) – Analysis of Variance (ANOVA) – Self-organizing Map (SOM) • Formulation (Design problem for LV with HRE) – Design variables – Objective functions • Results – Design and visualization results – Design knowledge • Conclusions
  3. 3 Background  Rockets presently used for space transportation  Solid-propellant rocket engine Advantage:・Simple mechanism and construction ・Easy to maintain the propellant Disadvantage:・Inability to stop combustion after it is ignited ・Low specific impulse (Isp) ・Environment issues (caused by ammonium perchlorate (NH4ClO4), and aluminum oxide (Al2O3))  Liquid-propellant rocket engine Advantage : ・Ability to stop/restart combustion ・High specific impulse (Isp) Disadvantage:・Complex mechanism and construction ・Difficulty to store low temperature propellant ・Risk of explosion
  4. 4 Background  What is hybrid rocket? • Hybrid Rocket Engine(HRE) : propellant stored in two kinds of phases It can adopt the beneficial features of both the liquid and solid rockets. SpaceShipTwo Advantage of HRE  Simple construction and mechanism  Ability to stop/restart combustion  Lower cost HEAT-II  Expectation for private space transportation  Virgin Galactic:SpaceShipTwo  Copenhagen Suborbitals :HEAT-II for “TychoBrahe” launching ⇒HRE are introduced.  Research working group in ISAS/JAXA. →Plan of ground test for 5kN class HRE.
  5. 5 Background HRE research working group (HRErWG) ・Mainly single port type fuel rport (t )  a  Goxi t  ・Several studies are carried out. n (combustion, measurement, simulation, and design optimization) fuel rport (t )  a  Goxi t   n  Important empirical expression for several combustion techniques. rport (t )  a  Goxi t   n Index n and coefficient a are empirically summarized for each combustion techniques. ・Swirling oxidizer type HRE with polypropylene fuel ・Glycidyl Azide Polymer(GAP) fuel rport (tfuel a  Goxi t   ・WAX (paraffin) ) n
  6. 6 Background  Difficulty of hybrid rocket design  Solid rocket:Preliminary mixed solid propellant  Liquid rocket:Control of mass flow of fluid propellant → Easy to maintain a constant oxidizer massand fuel mass ratio (O/F) and to get a stable thrust  HRE:The mixture of fuel and oxidizer is initiated after ignition. Combustion occurs in the boundary layer diffusion flame. → Because O/F is decided in this part of combustion process, the solid fuel geometry and the supply control of the oxidizer have to be optimally combined. ⇔With too much mass flow of oxidizer, the rocket achieves higher thrust, but structural weight should be heavier . Importance to find optimum fuel geometry and oxidizer supply ⇒Multi-disciplinary design which is considered propulsion, structure and trajectory
  7. 7 Objectives • Development of the evaluation tool for conceptual design of launch vehicle (LV) with HRE – Evaluation based on the empirical model • Multi-disciplinary design exploration for concept of three stage LV – Solutions of multi-objective problem obtained by MOGA – Knowledge discovery using data mining
  8. 8 Flow chart of HRE evaluation Calculation of engine specifications - fuel size, time variation of O/F, Design variables pressure of combustion chamber, etc.. ・ Initial value of oxidizer mass flow ・Initial value of O/F Estimation of structural weight ・Coefficient a of regression rate ・Combustion chamber, Oxidizer tank, ・Initial value of mass flux of oxidizer Pressurizing tank, nozzle ・Combustion time ・ Initial pressure of combustion chamber ・ Initial pressure of pressurizing tank Engine specifications ・Aperture ratio of nozzle Thrust by NASA-CEA* *NASA Chemical Equilibrium with Applications (Gordon, S., et al, “Computer Program for Calculation of Complex Chemical Equilibrium Compositions and Trajectory Applications I. Analysis,” NASA RP-1311, 1994.) No Kosugi, Y., Oyama, A., Fujii, K., and Kanazaki, M.: Multidisciplinary t>combustion time? and Multi-objective Design Exploration Methodology for Conceptual Yes Design of a Hybrid Rocket, AIAA 2011-1634a, 2011. Altitude, velocity, .. after mth stage combustion
  9. 9 Flight Sequence Ignition of 3rd stage Separation of 2nd stage Coasting Target altitude (perigee 250km) Separation of 1st stage Launch
  10. 10 Design exploration  Heuristic search:Multi-objective genetic algorithm (MOGA)  Inspired by evolution of life  Selection, crossover, mutation  Searching global non-dominated  NSGA2 is employed.  BLX0.5 for cross over Arbitral evaluation Minimize f1 Minimize f2 Ranking by NSGA2 Crossover (BLX-α) Flowchart of GA
  11. 11 Design methods Knowledge management1 Integrate Analysis of Variance One of multi-valiate analysis for quantitative information The main effect of design variable xi: i ( xi )     y( x1 ,....., xn )dx1 ,..., dxi 1 , dxi 1 ,.., dxn   ˆ variance where:      y( x1 ,....., xn )dx1 ,....., dxn ˆ μ1 Total proportion to the total variance:  i  xi  dxi 2 pi    y ( x1 ,....,xn )   dx1 ...dxn 2   ˆ where, εis the variance due to design variable xi. Proportion (Main effect)
  12. 12 Design methods Knowledge management2 Self-organizing map for qualititative information – Proposed by Prof. Kohonen – Unsupervised learning – Nonlinear projection algorithm from high to two dimensional map Design-objective Multi-objective Each cell represents vector which has same number of components as input. Two-dimensional map (Colored by an component, N component plane, for N dimensional input.) Multi-dimensional data *modeFrontier ®v4.0 is used.
  13. 13 Design space a can control by changing intensity of the oxidizer swirling.*( r  a  Gon )  * Hikone,S., et al, “Regression Rate Characteristics and Combustion Mechanism of Some Hybrid Rocket Fuels ,”Asian Joint Conference on Propulsion and Power 2010.
  14. 14 Design Problem  Design target: Design of three-stage rocket which can deliver micro-satellites to the Sun-synchronous orbit (SSO) (perigee is 250km, apogee is 800km)  Objective functions • maximize Payload mass/Gross mass (Mpay/Mtot) • minimize Gross mass (Mtot)  Constraints • After combustion of third stage,  Height > 250km  Angular momentum > 52413.5km2/s  -0.5deg. < Flight path angle < 0.5deg. • Rocket aspect ratio < 20 • Radius of nozzle exit < Radius of rocket • Area of grain port > 2・(Area of nozzle throat)  Combustion type • Swirling oxidizer type engine • Oxidizer:LOX, Fuel:WAX (FT-0070)
  15. 15 Result MOGA exploration Optimum direction • Trade-off between objective functions • Mpay/Mtot of “Epsilon rocket” planed by JAXA is about 1.3% ⇒Lower cost than existent LVs
  16. 16 Selected Design from Non-dominated Solutions  Selected rocket size Length of rocket [m] 20.8 Diameter of rocket [m] 1.46 Aspect ratio of rocket [-] 14.3 1st stage 2nd stage 3rd stage Length [m] 8.22 6.57 6.06 Diameter [m] 1.45 1.46 1.07 Gross mass [ton] 8.07 4.09 0.70 Structural mass [ton] 1.78 0.49 0.10 Structural mass ratio [%] 22.1 11.9 14.5 20.8 8.22 6.57 6.06 1.35 1.36 0.97 1.46 What kind of design can be high performance? 1.21 2.18 3.21 1.61 2.29 1.06 2.11 1.11 2.06 0.35 0.99 0.64 2.02 ⇒Design knowledge discovery by means of data mining
  17. 17 Knowledge discovery Contribution ratio estimated by analysis of variance Mtot Mpay/Mtot • dv1, 9 (oxidizer mass flow ratios of 1st and 2nd stages) influence on Mtot. • dv6, 14 (combustion pressure of 1st and 2nd stages) influence on Mpay/Mtot. • dv1, 9, 17 (oxidizer mass flow ratios of all stages) influence on Mpay/Mtot. – dv17 (oxidizer mass flow ratio of 3rd stage) remarkably influences on Mpay/Mtot. ⇒ Design of the engine for 3rd stage is important for low cost rocket.
  18. 18 Knowledge discovery • SOM visualization • Trade-off between Mpay/Mtot and Mtot • LV which can deriver high Mpay can not always achieve high Mpay. – Mpay maximization is not always explore high efficient LV.
  19. 19 Knowledge discovery • Selection of design variables based on similarity of SOMs’ colored map and contribution ratios by ANOVA. • dv3, 11, 19 (coefficient a of regression rate) are also checked. r 0  a  Go 0  n
  20. 20 Knowledge discovery • Comparison among objective functions and design variables dv5 dv6 dv1 2.420 139.5 56.01 To obtain higher Mpay/Mtot 60.4 46.01 1.900 • Moderate value of dv5(Combustion time of 1st stage) dv9 dv14 1.600 dv17 3.677 18.30 • Smaller dv6, 14 (Pressure of combustion chamber of 1st and 2nd stage) • Moderate value of dv1(Oxidizer mass flow of 1st stage) • Larger dv9, 17(Oxidizer mass flow of 2nd and 3rd stage) 1.215 1.710 9.36
  21. 21 Knowledge discovery • Coefficient a of regression rate r 0  a  Go 0  n To obtain higher Mpay/Mtot • Lower regression rate at 1st stage • Moderate a at 2nd stage • Higher regression rate at 3rd stage
  22. 22 Conclusions Design exploration of multi-stage launch vehicle with hybrid rocket engines  Empirical expression based evaluation of HRE • Engine size, Thrust, Structure, and Flight  Global Exploration employing MOGA • Type of HRE using FT0070 fuel with swirling oxidizer for all stages • Trade-off between total mass ratio and payload-total mass ratio  Design knowledge using ANOVA and SOM • Oxidizer mass flow of 1st and 2nd stages have predominant effect to total mass. • Pressure of combustion chamber of 1st and 2nd stages influence on payload-total mass ratio • Knowledge about what kind of engine design is promising for each stage.  Further study: Design exploration of LV which has different fuel in each stage to found out better solution.
  23. 23 Acknowledgement • We thank members of the hybrid rocket engine research working group in ISAS/JAXA for giving their experimental data and their valuable advices. This paper and presentation was supported by ISAS/JAXA. Thank you very much for your kind attention.
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