OpenVSPIntegration within SUAVE

OpenVSP Integration within SUAVE

OpenVSP Workshop 2019September 18th 2019

EMIL IO BOTERO

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Outline

SUAVE Background

Visualization

Importation

Analysis

Future

Summary

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What is SUAVEConceptual Design Environment

Analyze/Design/Optimize

Collection of analyses and methods

Multifidelity

Interfaces with other tools: AVL, SU2, OpenVSP…

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History of SUAVEStarted in 2013 in the Aerospace Design Lab

There were many other tools….

New vehicle types

Flexible architecture

Modern Code

Open Source since the beginning

LGPL 2.1

Python3 using Open Source packages

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How SUAVE Works (in general)Vehicle Instantiate

Geometric Parameters

Configurations

Family of Aircraft to Cruise/Landing

Analyses

Missions

Results

Can also Optimize!

Nexus

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Why OpenVSP?

Visualization

Analysis

Importation

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VisualizationExport to VSP

Segmented Wings and Airfoils

Fuselages

Turbojets and Turbofans

Stacks or flow through fuselage

Internal Fuel Tanks

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Generative Design Example“Kangaroo Route” Airliners

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ImportationFrom VSP

Segmented Wings and Airfoils

Fuselage Shapes

Propellers through BEM Files

Chris Silva

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AnalysesWetted area calculations

SUAVE has no built in geometry engine

Accurate drag estimations

Wave Drag

Fuel CG

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CFDOpenVSP

Wetted area computation

Surface mesh

Gmsh

Open-source meshing tool

Create volume mesh

SU2

Open-source CFD solver

Use Euler to solve lift

Full open-source toolchain

SUAVE Setup

Run CFD

Run Mission

Build Aero Surrogate

Generate Surface

Mesh

Write VSP Vehicle

Results

Generate Volume

Mesh

Calculate Wetted Areas

Code UtilizedSUAVEOpenVSPGmshSU2

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MeshingOpenVSP Surface Mesh

Creates a vehicle surface mesh for CFD

Far field and symmetry plane meshes also created by default

Default sources automatically enabled

Custom sources can be used for refinement

Gmsh Volume Mesh

Volume mesh in SU2 format generated from surface mesh

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SU2Reads surrogate initialization data

SU2 configuration files created for each point selected

Euler computations run with SU2

SU2 results used to build a surrogate with scikit-learn’s Gaussian Process function

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Multifidelity using OpenVSPSupersonic business jet

Loosely based on Aerion AS21

Altitude: 51,000 ft

Mach 1.4

NACA 65-203 airfoil

Evaluated at single design point

1“AS2 Performance Objectives and Specifications,” http://www.aerionsupersonic.com/technical-specifications, May 2017.

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Optimization ConsiderationsTwo fidelity levels

Correlation based wave drag

Area rule based wave drag (OpenVSP)

Two optimization methods

Additive with expected improvement

Trust Region Model Management

No constraints

Initial values given by baseline design

Variable Wing Area (m2)

Aspect Ratio

Initial Value 125 3.3

Lower Bound 120 2.0

Upper Bound 180 6.0

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Fidelity Levels

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Volume Wave Drag Coefficient

Fuel Burn

(scaled to initial area)

Baseline OpenVSP

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Additive Results

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Final Area 144.3

Final AR 4.204

No. Initial Samples 10

No. Additional VSP Evaluations 5

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Trust Region Results

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Final Area 142.9

Final AR 4.147

No. Iterations 14

No. Total VSP Evaluations 42

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FutureStructural Layout

Refine wave drag interface,

Improve extraction of wave drag information

Hard to extract info from slices

Control Surfaces with VSPAero

Vortex Lift

Wave Drag – Inlet and Exhaust Streamtubes

Exporting Propellers

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SummaryVisualization

Check our work

Compare concepts

Import

Share models

Analysis

Aerodynamics

Questions?E B OT E R O@ S TA N F O R D. E D U