Shape Optimisation of Axisymmetric Scramjets

Transcription

1 Shape Optimisation of Axisymmetric Scramjets Hideaki Ogawa 11 th International Workshop on Shock Tube Technology Australian Hypersonics Workshop 2011 University of Queensland, Brisbane 23 rd March 2011

2 Overview Introduction Axisymmetric Scramjet Engines Approach (Optimisation & CFD) Results Inlet Design Optimisation Nozzle Design Optimisation Full Flow-Path Optimisation Conclusions

3 Shape Optimisation of Axisymmetric Scramjets Introduction

4 Axisymmetric Scramjet Flowfield shock / boundary layer interactions shock-shock interactions chemical reactions

5 Design Optimisation Methodology Design optimisation capability developed at Evolutionary algorithms (elitist non-dominated sorting genetic algorithm NSGA-II) Population with individuals over generations Probabilities of recombination operators: Simulated binary crossover = 100%, Polynomial mutation = 10% Surrogate prediction (using 90% of solutions in archive) response surface models, kriging approximation, radial basis functions

6 Computational Fluid Dynamics Solver CFD++ (Metacomp, Inc.) Implicit algorithm + multigrid acceleration 2 nd order spatial accuracy Convergence order = Assumptions Boundary layer: fully turbulent (2-equation SST k-ω RANS model) Gas: Equilibrium air (inlet) / Evans & Schexnayder s model 12 species and 25 elementary reactions) Surface: isothermal cold wall (300K)

7 Computational Mesh Mesh Pointwise grid generator Fully structured mesh Min. cell width = 10-5 m (y + =0.32) resolutions (mx, my) wall vertical cells (1/2, 1/2) ,943 (1/4, 1) ,222 (1/2, 1) ,643 (1, 1) 1, ,485 (2, 2) 3, ,421,769 M = 8

8 Shape Optimisation of Axisymmetric Scramjets Results

9 Inlet Design Optimisation

10 Design Parameters 8 parameters: l 1, l 2, l 3, θ 1, Δθ 2, Δθ 3, r c, r t inlet area and r t fixed 6 decision variables: l 2, l 3, θ 1, Δθ 2, Δθ 3, r c

11 Freestream Conditions Inflow: equilibrium air Mach number M 8 Altitude h Static pressure p Static temperature T Dynamic pressure q 30 km 1197 Pa 227 K 53.6 kpa Reynolds number Re

12 Minimise: Scramjet based Access to Space Systems Inlet drag = D inlet Design Objectives Compression efficiency loss = 1-η B Maximum adverse pressure gradient = dp / ds max Subject to: Exit temperature = T 2ave 850K (Stream-thrust averaged) M = 8 T 2ave 2

13 Pareto Optimal Front better

14 Pareto Optimal Front

15 Geometries Evaluated in Optimisation

16 Inlet Drag and Exit Temperature

17 Inlet Drag and Exit Temperature analytical D = F in m 2(h t h(t)) + RT 2(h t h(t ))

18 Design Parameters

19 Decision Variables 9 decision variables

20 Boundary Conditions Pressure profiles at nozzle entrance

21 Optimisation Progression

22 Thrust Comparison

23 Thrust and Nozzle radius Thrust =F( r n /r c, F in, m, h t )

24 Full Flow-Path Optimisation 24 design parameters Geometric representation of axisymmetric scramjet engine

25 Design Objectives 24-5 = 19 decision variables Maximise: net thrust (= minimise total drag F x ) Subject to: CFD convergence 10-2 Design variables: x Li x i x Ui (i = 1,,19)

26 Freestream Conditions Inflow: premixed fuel (H2) / air (equivalence ratio Φ = 80%) Mach number M 8 Altitude h Static pressure p Static temperature T Dynamic pressure q 30 km 1197 Pa 227 K 53.6 kpa Reynolds number Re

27 Optimum Geometry Progression

28 Evaluated Geometries

29 Flowfield Comparison optimum optimum baseline baseline Mach number temperature

30 Flowfield Comparison optimum optimum optimum baseline baseline baseline H2 mass fraction H2O mass fraction

31 Overall Performance η c 1 c H 2 c H 2 i = for premixed fuel / air of Φ = 0.8 Δ I sp = Δ F = F x x (fuel on) F x (fuel off) m g H 2 i combustion efficiency incremental specific impulse

32 Sensitivity Analysis η c F x

33 Scramjet based Access to Space Systems Full Flow-Path Optimisation with Injection Profile scaling 0.06 penetration 0.05 ch2, T r [m] injection 0.02 premixed ch

34 Optimum Flowfield Comparison injection profile injection profile premixed fuel / air premixed fuel / air Mach number temperature

35 Conclusions The capability of coupled CFD / MDO approach with surrogateassisted evolutionary algorithms has been demonstrated in design optimisation problems of axisymmetric scramjets. 3-objective optimisation has been performed for the inlet and single objective optimisation (for maximum thrust) has been performed for the nozzle and full flow-path configuration. The performance has been improved considerably as a result of optimisation and flowfield analysis has led to significant physical insight including correlations for the inlet drag and nozzle thrust. New optimisation capabilities to be employed in forthcoming studies: Surrogate-assisted robust design optimisation Infeasiblility-driven evolutionary algorithms (IDEA) Hybrid design space search (global + local search)