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Cu Indicator Layer. Return Currents. e -. Field. Preformed Plasma. Magnetic field. Connecting Simulations and Experiments in HEDP. Richard Stephens - General Atomics. Nova PW, ~500J. Scientific opportunities in HEDP Workshop Washington, D.C. 25-27 August 2008.
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Cu Indicator Layer Return Currents e- Field Preformed Plasma Magnetic field Connecting Simulations and Experiments in HEDP Richard Stephens - General Atomics Nova PW, ~500J Scientific opportunities in HEDP Workshop Washington, D.C. 25-27 August 2008 This work was performed under the auspices of the U.S. DOE under contracts No.DE-FG02-05ER54834, DE-FC0204ER54789 and DE-AC52-07NA27344. IFT\P2008-071
Collaborators K. Akli M.S. Wei,J. King,T. Ma, T. Bartal, S. Chawla, F.N. Beg J. Pasley RAC R. Mason Y. Sentoku L.V. Van Woerkom, R.R. Freeman, A. Link, D. Offerman, V.M. Ovchinnikov A.J. MacKinnon, M. H. Key, A.G. Macphee, H. Chen, P. K. Patel, D. Hey C. Chen
Code development and validation needed in HEDP regime • Simulations are a critical component of HEDP experiments for analysis and extrapolation • High intensities access new physics • Relativistic particles • Self-generated intense fields • Radiation driven structures • Dense, hot plasmas • All components strongly coupled • Codes must be validated in this regime • This requires a deliberate effort and specific experiments
Modeling capabilities for HEDP physics • Kinetic PIC codes modeling laser plasma interaction • collisionless • ~ ps simulation time • down-scaled plasma dimensions with simple profiles • Hybrid/fluid PIC codes modeling fast electron transport • fixed ionization • simple EOS • Spitzer collision rate • limited capabilities of self-consistent fast electron generation
Benchmarking imposes conditions Objective - no unknown parameters Known experiment: • Laser • Target • Diagnostic Completely included in the model: • Relevant physics • Space • Time =>Design simple experiments specifically for benchmarking
K 25 m Fills cone with preplasma 500 μm Determines electron energy spectrum Measure parameters on every shot Prepulse ~ 0.01-0.1J • Pulse length • Prepulse • Intensity distribution Focused Intensity distribution
100 m Critical surface (on axis) Initial target surface Ne~1020 cm-3 (on axis) 200 mg/cc 1 mg/cc Target - properly characterized • Small, simple geometry • Account for prepulse effects 250 mJ, 3 ns 15 m rad
Laser 1020W/cm2 Simulation (1D) of dynamic ionization by intense laser irradiation Ionization in gold target PICLS: electron energy density at 0.825 ps (fully ionized deuterium plasma, 10 µm wire, laser energy 40 J in the spot) 0 y [µm] 250 0 X [µm] Laser 80 Codes must completely describe experiment • Laser creation of electrons • Local heating changing ionization • Resistivity slowing hot electrons • Self-generated magnetic fields • Then have to provide a space large enough for the experiment • Dimensions > 200 m • And present output comparable to diagnostic • Escaped electrons | • X-ray fluorescence |> >5 ps • Bremsstrahlung |
PICLS: LSP: 1023 1022 number density [1/cm3] 1 ps 1021 1.5 ps 2 ps 1020 1019 0 100 200 300 400 Z (µm) Model volume is affected by long time • Hot electrons have long lifetime 1MeV=> 10 ps • Models show population decrease by 1/3 after 3 ps • ~5 ps to get total fluorescence (increasing with energy) • increasing problems with boundary effects Experimental Fluorescence: On-axis fast electron density 660um Electrons extend far beyond model boundaries
Code capability to be tested • Predicted by code: • Re-distribution of preplasma • Magnetic bottlenecking • Magnetic collimation of electron transport • Hot electron energy spectrum • Resistive losses in transport • Filamentation • Experiments to test their predictions • simple enough to make quantitative comparisons
Validated modeling necessary Facilities • Shot-time beam diagnostics on CPA beams • On-target laser parameter characterization Models • Sufficient time and space --> 5 ps,>> 100x100 m • Self-consistent hot electron creation • Dynamic ionization • Simulate experimental diagnostic Targets • Design that can be modeled • Minimize imperfections • Account for prepulse • Successful benchmarking allows: • confident interpretation of experiments • extrapolation beyond current experimental capability