1. Progress in UCSD Chamber Simulation Experiments Farrokh Najmabadi Sophia Chen, Andres Gaeris, John Pulsifer HAPL Meeting December 5-6, 2002 Naval Research Lab, Washington, DC Electronic copy: http://aries.ucsd.edu/najmabadi/TALKS UCSD IFE Web Site: http://aries.ucsd.edu/IFE

2. Thermo-mechanical Response of the Wall Is Mainly Dictated by Wall Temperature Evolution  Most phenomena encountered depend on wall temperature evolution (temporal and spatial) and chamber environment. Only sputtering and radiation (ion & neutron) damage effects depend on “how” the energy is delivered.  Wall response depends strongly on the “spectrum” of incidence energy from the target blast. Target designs are not finalized and target spectrum is not known; There is no simulation source that can completely simulate the energy spectrum from the target.  In order to develop predictive capability: Focus on achieving temperature profiles similar to those expected in a laser-IFE wall (Laser, ion source and X-rays can all do this). Measure and understand the wall response in the relevant range of wall temperature profiles and in real time.  In order to develop predictive capability: Focus on achieving temperature profiles similar to those expected in a laser-IFE wall (Laser, ion source and X-rays can all do this). Measure and understand the wall response in the relevant range of wall temperature profiles and in real time.

3. Thermo-Mechanical Response of Chamber Wall Can Be Explored in Simulation Facilities Capability to simulate a variety of wall temperature profiles Requirements: Capability to isolate ejecta and simulate a variety of chamber environments & constituents Laser pulse simulates temperature evolution Vacuum Chamber provides a controlled environment A suite of diagnostics:  Real-time temperature  Per-shot ejecta mass and constituents  Rep-rated experiments to simulate fatigue and material response Relevant equilibrium temperature A suite of diagnostics:  Real-time temperature  Per-shot ejecta mass and constituents  Rep-rated experiments to simulate fatigue and material response Relevant equilibrium temperature

4. Components of Simulation Experiment  Optical trainOperational. YAG laser is upgraded with injection seeding for reproducible and smooth temporal profile.  Vacuum ChamberOperational. Capable to below 10 -8 Torr.  High-Temperature Operational. Specimen “equilibrium” temperature Specimen Holdercan be maintained at up to 1,200 o C. Endurance runs of several hours have been made at 1,000 o C.  Master Timing Operational (~100ps gitter). Capable of single shot to Control System10 Hz.  Data Acquisition SystemOperational (1GHz, 1.25 GS/s, upgradeable to 2.5GS/s).  Diagnostics: PIMAX & SpectrographOperational. ThermometerCalibrated externally (< 2% error). In-chamber shake-down and tests are in progress. IR CameraPurchase is deferred (exploring alternatives). Quartz MicrobalancingPurchase order is being placed. RGAPurchase order is being placed.  Specimen preparationProcedure in place. Need measurement of thermophysical properties at elevated temperatures.

5. Specimen holder operating at 1000 o C High Temperature Specimen Holder is tested to 1200 o C with endurance runs at 1000 o C. Stainless steel vacuum seal Specimen holder is made of Mo Thermocouple feed through Power feed through Specimen is heated by radiation from a tungsten element located behind the specimen. Specimen

6. Real-time Temperature Measurements Can Be Made With Fast Optical Thermometry Temperature deduction by measuring radiance at fixed  One-color: Use tables/estimates for  ( ) Typical error < 25%  Two colors: Assume  ( 1 ) =  ( 2 ) Typical error < 5 %  Three colors: Assume d 2  /d   [usually a linear interpolation of Ln(  ) is used] Typical error < 1 % Temperature deduction by measuring radiance at fixed  One-color: Use tables/estimates for  ( ) Typical error < 25%  Two colors: Assume  ( 1 ) =  ( 2 ) Typical error < 5 %  Three colors: Assume d 2  /d   [usually a linear interpolation of Ln(  ) is used] Typical error < 1 %  Spectral radiance is given by Planck’s Law (Wien’s approximation): L(,T) = C 1  (,T) -5 exp(-C 2 / T)  Since emittance is a strong function of, T, surface roughness, etc., deduction of temperature from total radiated power has large errors.  Spectral radiance is given by Planck’s Law (Wien’s approximation): L(,T) = C 1  (,T) -5 exp(-C 2 / T)  Since emittance is a strong function of, T, surface roughness, etc., deduction of temperature from total radiated power has large errors.

7.  Published work with Multi-color Optical Thermometry: Complicated optical designUse fiber optics Response time of > 10-100  sUse fast PD or PMT  Published work with Multi-color Optical Thermometry: Complicated optical designUse fiber optics Response time of > 10-100  sUse fast PD or PMT MCFOT Is a Natural Extension of Multi-color Optical Thermometry MCFOT — Multi-Color Fiber Optical Thermometry  Simple design, construction, operation and analysis.  Can be easily mounted inside a vacuum vessel.  Easy selection of spectral ranges, via filter changes. MCFOT — Multi-Color Fiber Optical Thermometry  Simple design, construction, operation and analysis.  Can be easily mounted inside a vacuum vessel.  Easy selection of spectral ranges, via filter changes.

8. Schematic of Multi-Color Fiber Optical Thermometer

9. Sensor Head can be focused to < 1mm 2 spots.  Rugged design with accurate swivel controls allows sensor head to be focused to < 1mm 2 spots with a high degree of position accuracy.  Steel foil protects the sensor head from thermal radiation.

10. Thermal radiation is injected by the Sensor Head into four bundled low-OH Silica fibers and relayed into fast PMTs

11. Each branch of the fiber bundle is filtered in a narrow spectral band by an interference filter and connected to a fast PMT

12. MCFOT is calibrated using the Optronics UL-45U lamp with a total error of < 3%  14 Calibration points  One adjustable parameter (c 1 c 3 /c 2 2 ) c i =V i (PMT) / L i (Sensor head)  14 Calibration points  One adjustable parameter (c 1 c 3 /c 2 2 ) c i =V i (PMT) / L i (Sensor head)

13. MCFOT is installed in the chamber and shake-down tests are in progress System Improvements:  Issue: Limited range of voltage from PMTs (need > ~ 5 mV for good SN ratio, PMT output saturates around 200 mV) Channel balancing by using neutral density filters in each channel Balance between voltages from calibration lamp filament and specimen to be handled by a neutral density filter in sensor head. Reliability Issues:  Calibration: How long it is dependable?  Speed: Individual PMT response is better than 700 ps. Would thermometer achieve the same response time?  Sensitivity: what is the minimal T measurable?  Ease of use: mounting, alignment, interference, vacuum.  Maintenance: Is it foolproof? PMTs electrode degradation? Heat/vacuum damage to optics/mechanicals? Fiber breakage? System Improvements:  Issue: Limited range of voltage from PMTs (need > ~ 5 mV for good SN ratio, PMT output saturates around 200 mV) Channel balancing by using neutral density filters in each channel Balance between voltages from calibration lamp filament and specimen to be handled by a neutral density filter in sensor head. Reliability Issues:  Calibration: How long it is dependable?  Speed: Individual PMT response is better than 700 ps. Would thermometer achieve the same response time?  Sensitivity: what is the minimal T measurable?  Ease of use: mounting, alignment, interference, vacuum.  Maintenance: Is it foolproof? PMTs electrode degradation? Heat/vacuum damage to optics/mechanicals? Fiber breakage?

14. Several Tungsten samples have been prepared for initial simulation experiments.  Each sample has its own pedigree.  Samples have been prepared with different initial polishing and cleaning method.  Pre-shot surface examination has been performed.  Similar samples are also prepared for testing at RHEPP.  Each sample has its own pedigree.  Samples have been prepared with different initial polishing and cleaning method.  Pre-shot surface examination has been performed.  Similar samples are also prepared for testing at RHEPP. WYKO 500X Microscope Surface photograph of samples polished with 1  m grit

15. Development of predictable capability for the thermo-mechanical response of the chamber wall is the goal of UCSD simulation facility Experiment Assembly:  Thermometer shakedown should be completed by Mid January.  Quartz Micro-balancing and RGA should be also operational in January. Experiment Assembly:  Thermometer shakedown should be completed by Mid January.  Quartz Micro-balancing and RGA should be also operational in January. Experimental Studies:  Temperature Response studies Impact of surface morphology and impurities/contaminants, etc.  Thermal Fatigue Studies Different temperature gradients, “equilibrium temperature,” etc.  Material Loss Studies Survey of impact of surface temperature, surface morphology, impurities, etc. NEED: Characterization of thermophysical properties of specimen at high Ts Experimental Studies:  Temperature Response studies Impact of surface morphology and impurities/contaminants, etc.  Thermal Fatigue Studies Different temperature gradients, “equilibrium temperature,” etc.  Material Loss Studies Survey of impact of surface temperature, surface morphology, impurities, etc. NEED: Characterization of thermophysical properties of specimen at high Ts

16. Backup Slides

17. QCM Measures Single-Shot Mass Ablation Rates With High Accuracy QCM: Quartz Crystal Microbalance  Measures the drift in oscillation frequency of the quartz crystal.  QCM has extreme mass sensitivity: 10 -9 to 10 -12 g/cm 2.  Time resolution is < 0.1 ms (each single shot).  Quartz crystal is inexpensive. It can be detached after several shots. Composition of the ablated ejecta can be analyzed by surface examination.

18. Prediction of chamber condition at long time scale is the goal of chamber simulation research.  Chamber dynamics simulation program is on schedule. Program is based on: Staged development of Spartan simulation code. Periodic release of the code and extensive simulations while development of next-stage code is in progress.  Chamber dynamics simulation program is on schedule. Program is based on: Staged development of Spartan simulation code. Periodic release of the code and extensive simulations while development of next-stage code is in progress.  Documentation and Release of Spartan (v1.0) Two papers are under preparation  Exercise Spartan (v1.x) Code Use hybrid models for viscosity and thermal conduction. Parametric survey of chamber conditions for different initial conditions (gas constituent, pressure, temperature, etc.)  Need a series of Bucky runs as initial conditions for these cases.  We should run Bucky using Spartan results to model the following shot and see real “equilibrium” condition. Investigate scaling effects to define simulation experiments.  Documentation and Release of Spartan (v1.0) Two papers are under preparation  Exercise Spartan (v1.x) Code Use hybrid models for viscosity and thermal conduction. Parametric survey of chamber conditions for different initial conditions (gas constituent, pressure, temperature, etc.)  Need a series of Bucky runs as initial conditions for these cases.  We should run Bucky using Spartan results to model the following shot and see real “equilibrium” condition. Investigate scaling effects to define simulation experiments.

19. Several upgrades are planned for Spartan (v2.0) Numeric:  Implementation of multi-species capability: Neutral gases, ions, and electrons to account for different thermal conductivity, viscosity, and radiative losses. Physics:  Evaluation of long-term transport of various species in the chamber (e.g., material deposition on the wall, beam tubes, mirrors) Atomics and particulate release from the wall; Particulates and aerosol formation and transport in the chamber.  Improved modeling of temperature/pressure evolution in the chamber: Radiation heat transport; Equation of state; Turbulence models. Numeric:  Implementation of multi-species capability: Neutral gases, ions, and electrons to account for different thermal conductivity, viscosity, and radiative losses. Physics:  Evaluation of long-term transport of various species in the chamber (e.g., material deposition on the wall, beam tubes, mirrors) Atomics and particulate release from the wall; Particulates and aerosol formation and transport in the chamber.  Improved modeling of temperature/pressure evolution in the chamber: Radiation heat transport; Equation of state; Turbulence models.