Aerospace Plasmas

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Aerospace Plasmas Tech-X Workshop / ICOPS 2012, Edinburgh, UK 8-12 July, 2012 Alexandre Likhanskii , Kris Beckwith Tech-X Corporation

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Aerospace Plasmas. Alexandre Likhanskii , Kris Beckwith Tech-X Corporation. Tech-X Workshop / ICOPS 2012, Edinburgh, UK 8-12 July, 2012. DBD Background. Stall control for up to M=0.4 using AC driven DBDs Stall control for transonic flow using ns-pulse driven DBDs - PowerPoint PPT Presentation

Transcript of Aerospace Plasmas

GEC_09_DBD

Aerospace PlasmasTech-X Workshop / ICOPS 2012,Edinburgh, UK 8-12 July, 2012Alexandre Likhanskii, Kris BeckwithTech-X Corporation

11DBD Background

Stall control for up to M=0.4 using AC driven DBDs Stall control for transonic flow using ns-pulse driven DBDs Bow shock control using ns-pulse driven DBDs SWBLI control using LAFPAAtmospheric pressure plasmas have a broad range of industrial applications

Aerospace

Energy

Plasma Processing

Plasma Medicine

Applications

Why does one need modeling?44Aerospace: DBDs

Applications

. rest

Why does one need modeling?55Aerospace: DBDs

Applications

. rest

Power Supply

geometry, materials,

Why does one need modeling?66Aerospace: DBDs

Applications

. rest

Power Supply

geometry, materials,

PulserWhat is the optimum pulse duration?What is the rise time?What is the repetition rate?What is the power consumption?How heavy is it?

AC, DC, RF..?Why does one need modeling?77 Solve for charged species motion coupled with Poisson Solver Include all relevant plasma processes Resolve all relevant spatial and time scales Use appropriate physical model for plasma description at particular conditions Couple with CFD codeComplete, comprehensive plasma model requires: Ionization Recombination Attachment Detachment Photoionization Detailed air chemistry? Excitation? Fast heating?

The model needs to include complex plasma processes Solve for charged species motion coupled with Poisson Solver Include all relevant plasma processes Resolve all relevant spatial and time scales Use appropriate physical model for plasma description at particular conditions Couple with CFD codePlasma model requirements:Spatial scales:

Plasma sheath size is ~ 10 microns micron grid sizePlasma length is several millimeters millimeter numerical domain for plasma generationSurface charge accumulation centimeter numerical domain for surface charging

106-107 grid points for just 2D

The model needs to resolve plasma/system spatial scales

Time scales:

Electron drift velocity ~ 106 m/s picosecond time step due to CFL The cycle of device operation ~ ms millisecond time interval should be computed

109 time points

Need to use state-of-the-art numerical techniques

The model needs to resolve plasma/system time scalesModel ComplexityCode PerformanceThe model needs both to solve appropriate equations and to be computationally efficient 1313Options / Approaches Non-uniform (unnecessary refinement) or adaptive grids (difficult to make parallel) Variable time steps (validate physical assumptions) Implicit methods (stable, but require validation of grid size and time step choices) High-performance clusters (additional investments)ElectronsPositive ionsChargephotoionizationpotentialElectric field

What physics are we interested in?Quasi-neutral bodySheathConductive channelStrong Efield near head

0.3 ns2.1 ns3.0 nsElectric potential evolution represents classical streamer propagation -> conductive plasma carries the potential of exposed electrodeStreamer is higher and thicker than in the fluid modelsPIC model provides correct electric potential evolution during streamer propagationX, mX, mX, mY, m1616

High density of electrons in streamer bodyLow density of electrons ahead of streamer headAlmost no electrons anywhere elsePIC model provides correct electron distribution within streamer bodyY, mX, m1717Electrons are combined in the region of high electron density (streamer body)Electrons are not combined (accurate resolution) around streamer head

Concept of variable-weight particles allows accurate and efficient streamer simulation in VORPALY, mParticle weightX, m1818Perform validation study of the particle combining algorithm1919

3.3 ns3.3 ns Changes in threshold for combining macroparticles do not change results Efield is lower than in fluid modelingHorizontal component of Efield for the developed streamer is the same for both cases2D Ex, V/mSet 1Set 21D Ex, V/m2020

xz3D DBD simulation - ElectronsZ-component of Efield, top viewzxVORPAL can perform 3D DBD simulations and resolve 3D filamentary structure2121

Efficient in parallel Streamer resolution Using particle combination during breakdown and splitting during plasma decay avoid over- and under-resolution Simulations from first principles, detailed physics Fluid models are generally more efficientWhy can PIC be efficient at high pressures?

When to use PIC:Validate fluid modelsResolve physics which fluid codes cannot handle2222Fluid DBD model in VorpalTime-dependent plasma dynamics in drift-diffusion approximation coupled with 2D Poisson solver for electric potential distributionAir: neutrals, electrons, positive and negative ionsElectron temperature, ionization, recombination, attachment, detachment and transport parameters: functions of E/NProper boundary conditions (incl. charge build-up on dielectric surface, surface recombination and secondary electron emission)Subnanosecond time scales and micron geometrical scales are properly resolved for accurate plasma modeling Background plasma density Plasma model provides force and heating terms for Navier-Stokes solver

Positive ionspotentialElectric field20*log(Np)VORPAL can reproduce major physical phenomena for streamer propagation Plasma is in streamer form Potential is quasi-uniform within streamer body Electric field is strong at the streamer headDBD Property Experimental Results (3kV, 5ns)(Princeton)Numerical Results(3kV, 4ns)Qualitative Comparison ResultPlasma length~ 2 mm~ 0.5 mmFair agreementPlasma thickness150-200 microns100 microns for fluid approach250 microns for kinetic approachGood agreementConsumed Energy per plasma volume~20 kJ/m3~18 kJ/m3Excellent agreementVORPAL is quantitatively validated against experimental data

Obtain spatial and temporal distribution of force and heating terms from VORPALInsert them as RHS into Navier-Stokes equationsStudy DBD-flow interactionVORPAL output can later be coupled with CFD toolsairfoilExample of flow separation simulation in Nautilus, Tech-Xs CFD/MHD code on unstructured meshesApplication of DBDs to Shock-Wave Boundary Layer Interaction problem

Control using snow plow arcs by momentum transfer (Princeton) Control using LAFPLA by heat deposition (Ohio State) Can we control SWBLI using pulsed DBD? Proposed experimental setup at Princeton(M=3 wind tunnel)

What can modeling do?VORPAL has an experimentally validated capability to compute heat deposition by high-V ns pulses

Need an accurate CFD tool to compute SWBLI Fluid code Nautilus General purpose fluid plasma modeling code Supports shock capturing methods for MHD, Hall MHD, Two-Fluid plasma, Navier Stokes and Maxwells equations Bodyfitted and unstructured grids in 1, 2 and 3 dimensions Ability to model the plasma device as part of a circuit Massively parallel and has been run on up to 4000 processors on NERSC facilities. Recent applications of Nautilus have included modeling merging plasma jets, laboratory accretion disk experiments, weakly ionized hypersonic flow modeling, magnetic nozzles and capillary discharges. Multi-platform tool: Windows, Mac and LinuxModels for SWBLI(similar to Shneiders model) Dimensionally unsplit MUSCL-Hancock integrator (``Van-Leer'') using second order spatial reconstruction in the primitive variables Prandtl-Boussinesq turbulence model Super time stepping method to use hyperbolic time step for CFD simulations Compute steady-state solution for SWBLI without DBD Obtain gas parameters in BL for DBD model in Vorpal Compute pulsed DBD heat deposition in Vorpal Use Vorpal data as a heat source for Nautilus CFD simulationsNumerical Grid

Coarse resolution96 x 36dx = 2.38125 mmdymin = 198.4375 mMedium resolution192 x 72dx = 1.19062 mmdymin = 99.21875 mFine resolution384 x 144dx = 0.79375 mmdymin = 49.609375 mGrid resolution study / no plasma case

CoarseMediumFineSchlieren ImageHorizontal component of velocityDBD simulation for the boundary layer

Applied Votage :7kV, 5ns pulseNumerical domain: 2cm x 1mmGrid size: 2x2 micronsRunning on 64 coreTypical run time: ~ - 1 dayOutput: streamer dimensions: ~1cm x 200 microns, propagating ~500 microns above the surfaceOutput: temporal and spatial distribution of instant and integrated energy releaseOutput: total energy (E*J) release = 8mJ/m

DBD placement

Simulation cases 1MHz pulses:Case A (realistic)Plasma is OFFNo energy deposition(base line)Case BPlasma is ON20 mJ (per pulse) is deposited within 5ns each 1 microsecond(100% instant heat deposition)Case C (realistic)Plasma is ON8 mJ (from DBD simulations):3 mJ (~35%) is deposited within 5ns each 1 microsend5 mJ is deposited uniformly in time between pulsesSchlieren: SWBLI control with pulsed DBDs

Case A Plasma OFFBaselineCase B Plasma ONInstantaneousheat depositionCase C Plasma ONRealisticheat deposition 3737Vx: SWBLI control with pulsed DBDs

Case A Plasma OFFBaselineCase B Plasma ONInstantaneousheat depositionCase C Plasma ONRealisticheat deposition 3838Observations: Shock wave moves upstream (similar observation to Samimys experiments)variables Additional mixing in boundary layer Main influence by upstream DBD - good placement is at the free flow / boundary layer interface to induce mixing DBDs deep inside BL do almost nothing, but heat the BL Overall, DBD can effect SWBLI but more optimization studies are necessary - mainly DBD placement and pulse repetition rate3939Acknowledgements: NASA Glenn Research Center (Dr. David Ashpis) NASA Langley Research Center (Dr. Fang-Jenq Chen) Wright-Patterson AFRL (Dr. Jon Poggie)

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