Quantifying Uncertainties in Radiative Shock Experiments Carolyn C. Kuranz CRASH Annual Review Fall 2010

Why is it important to understand experimental uncertainty for this project? Creates realistic input parameter space for predictive studies Understanding dominant sources of uncertainty can help us to focus on those areas to reduce the uncertainty Helps us to understand and improve the predictive capability of the model Important for future experiments

Partial list of experimental inputs that have uncertainty associated with them Laser energy Laser pulsewidth Laser spot size Observation time Be disk thickness Be surface roughness Xe gas pressure Diagnostic x-ray signal Background signal Source broadening Target geometry Angle between Be disk and tube Angle between tube and diagnostic Pre and post-shot probability distributions functions (PDFs) for these uncertainties often differ!

Summary of the CRASH calculation X - Experiment parameters θ - Physical Constants N - Numerical Parameters Y S - Results to be analyzed with data by statistical methods CRASH Pre-Processor CRASH Pre-Processor XHXH XHXH Calibration Data (D) Calibration Data (D) CRASH Radiation-Hydrodynamics Simulation Code CRASH Radiation-Hydrodynamics Simulation Code XCXC XCXC θCθC θCθC NCNC NCNC Y HP YCYC YSYS CRASH Post-Processor CRASH Post-Processor XRXR XRXR θRθR θRθR I will be discussing the uncertainties in some of the experimental inputs

Types of PDFs for experimental inputs Tails of PDFs are often complex (details and examples to follow)

Laser Energy is an example of quasi-Gaussian distribution Mean values of experimental days are within 3% of nominal but standard deviation is ~1% or less on individual day

Be disk thickness is an example of a quasi-uniform distribution Several parameters have a “uniform” distribution with low- amplitude, long tails In this case, the tails of the distribution correspond to cases in which there is a malfunction of a simple measuring instrument or disregard of measuring procedures

Understanding experimental uncertainties is very complex: observation time in Y2 experiment Recent experiments measured the amount of time it takes for the shock to move through the Be disk Each experiment used 3 instruments for the measurement The most sensitive instrument had 10 ps resolution Velocity Interferometer

But these instrumental uncertainties were not the dominant uncertainty t0t0 Total uncertainty was ± 50 ps even though instrumental uncertainty was smaller Largest uncertainty came from measuring time interval between the drive laser and diagnostic fiducial laser

Always look behind the curtain… Often the analysis of experimental data focuses on the detail of these small error bars The uncertainty in this measurement is dominated by a larger systematic error

Conclusions Understanding and quantifying the uncertainties in our experiments is complex and sometimes surprising There are 2 types of PDFs for these uncertainties: quasi- Gaussian and quasi-uniform The tails of these PDFs are often complex The PDF for a given parameter can be different pre-shot and post-shot We are continuing to work towards identifying and quantifying uncertainty in our experiments