Chart 1 Application of AD in Turbomachinery Design 19 th European Workshop on Automatic Differentiation Jan Backhaus DLR Cologne

Transcription

1 Chart 1 Application of AD in Turbomachinery Design 19 th European Workshop on Automatic Differentiation Jan Backhaus DLR Cologne

2 Chart 2 CFD based Optimization CRISP 1 rig Gradient-free optimization is possible, but expensive CRISP 2 optimization result

3 Chart 3 Design Process CAD parameters surfaces mesh CFD solution Objectives blade generator mesh generator mesh deformation TRACE POST Bladegenerator Forward Mode AD CodiPack Mesh deformation self-adjoint problem: manually adjoined TRACE Reverse Mode AD: ADOL-C, DCO, CodiPack POST Reverse Mode AD:ADOL-C, DCO, CodiPack

4 Chart 4 Bladegenerator DLRs NURBS based turbomachinery geometry tool differentiated with CodiPack + tinyform in forwardmode (Reverse Mode in progress)

5 Chart 5 Bladegenerator forward AD sensitivities

6 NonLinear TRACE - finite volume method on structured and unstructured meshes - compressible Navier-Stokes, real-gas model - upwind-scheme 2 nd order accurate - implicit time integration scheme - hybrid parallelization (MPI/OpenMP/SSE) - turbulence modelling - Spalart-Allmaras - Wilcox k-ω - Menter SST k-ω - W&J EARSM k-ω (Hellsten) - RSTM Wilcox Stress-ω DES computation of a mixer - transition modelling - non-local: MultiMode γ Re θ Model (Menter & Langtry) - LKE Model

7 Chart 7 TRACE Post-Processing - Data reduction - Massively parallel - Flexible process chains - Topology independet - Based on VTK

8 DLR.de Chart 8 Automatic Differentiation in Gas Turbine Performance Coupling of the aerodynamic and propulsion analysis tools for aircraft design is a necessity in multi- disciplinary design analysis and optimization of new aircraft concepts with highly integrated engines. To the authors knowledge, currently no propulsion system analysis tool is capable of providing analytic derivatives for gradient based optimization. Refactoring of DLR s inhouse gas turbine performance code GTlab- Performance undertaken in order to employ automatic differentiation. Current status: Incorporation of active datatypes for AD (using CodiPack) Introduction of a polynomial gas model Interpolation libraries provide analytic derivatives which are interfaced to the AD datatypes (forward and reverse mode) Testing, testing, testing...;)

9 Chart 9 AD in TRACE Adjoint Method Motivation: new simulation models consistency Continuous Discrete AD knowhow (TU-KL) Manual implementation (+ GMRES) Reverse Mode AD ADadjoint TRACE +Finite differences +Selective forward AD +Optimizations simulation code (DLR) industrial application (MTU) adjointtrace ADadjointTRACE 3 year BMWi project R&E-Turb (completed) 3 year EU project CleanSky 2 (running)

10 TRACE Software Development years of code history - ~20 core developers + external developers at MTU, Universities - Language: C - Philosophy: stable trunk - Coding guidelines - Code reviews - Automated test and validation suite - Branches for substantial developments Task Version Management Continuous Integration Automated testing and validation Bug tracking IDE Tool Subversion Jenkins In-house Python tools Mantis Eclipse

11 Chart 11 AD in TRACE (2) Challenges C is not C++ C is not too strict about types communication / structs printf (tip: tinyformat) unions designated initializers TRACE is optimized for performance no influence on primal performance only moderate changes to primal code developer acceptance restarting/checkpointing computational ressources AD-Tools implemented for TRACE adjoint Parallelization Optimizations ADOL-C DCO CodiPack Adjoint MPI Adjoinable MPI reverse accumulation pre accumulation of Jacobians

12 Chart 12 Implicit Solution Scheme Steady Solution: Euler Forward: Taylor Series: Implicit pseudotime stepping: Fixed point iteration: Reverse Accumulation: dq dt = R(q) = 0 q n+1 = q n + tr n+1 R n+1 ¼ R q + O( t2 ) µ 1 q = R n q n+1 = G(q n ); q = G(q ) Ã n+1 = G adj (q ; Ã n )

13 Chart 13 Implicit Solution Scheme Steady Solution: Euler Forward: Taylor Series: Implicit pseudotime stepping: Fixed point iteration: Reverse Accumulation: What if we keep ³ dq dt = R(q) = 0 q n+1 = q n + tr n+1 R n+1 ¼ R q + O( t2 ) µ 1 q = R n q n+1 = G(q n ); q = G(q ) Ã n+1 = G adj (q ; Ã n ) n constant inside G adj?

14 Chart 14 Constant implicit matrix CRISP 2

15 Chart 15 Testcase CRISP 2 CAD parameters surfaces mesh CFD solution Objectives blade generator mesh deformation TRACE POST

16 Chart 16 Testcase CRISP 2 CAD parameters surfaces mesh CFD solution Objectives blade generator mesh deformation TRACE POST 24 parameter shape changes by deformed surfaces

17 Chart 17 Sensitivities after 1000 adjoint iterations CAD Param Const implicit mat. Updated implicit mat e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e e-04

18 Chart 18 Testcase CRESCENDO: 1st stage LPT 1.8 Mio Cells Wilcox k-w Multi-Mode transition Model Testbed for joint optimizations among MTU and RR distributed onto 72 procs

19 Chart 19 Constant implicit matrix Memory [GB] factor time / iteration factor mem Primal 12, adjoint var impmatrix 219,97 2,62 17,19 adjoint const impmatrix 123,36 1,12 9,64

20 Chart 20 Thanks!