Typical Short Course Schedule – Day 1 SESSION 1 Introduction, Concepts, Definitions Introductions, discussion of attendee backgrounds. Experimentation, validation, errors, uncertainty; uncertainty nomenclature-Type A/Type B, random/systematic, aleatory/epistemic categorizations; calibration effect on invariant (systematic) errors. SESSION 2 Errors, Uncertainties, and Statistical Considerations Uncertainty intervals; levels of confidence or coverage. Errors in measurements of a single variable. Statistics of parent populations. Statistics of sample populations. Definitions of random uncertainty, systematic uncertainty, overall uncertainty in a measurement. Experimental uncertainty vs. measurement uncertainty. Estimating uncertainties at zeroth, first and nth order replication levels. SESSION 3 Uncertainty in an Experimental Result Propagation of uncertainties using (1) the Monte Carlo Method (MCM) and (2) the Taylor Series Method (TSM). MCM: propagation of uncertainties using assumed error distributions. TSM: propagation of standard uncertainties in multiple measured variables into an experimental result; inherent assumptions; large sample approximation. SESSION 4 Applying General UA – Experimental Planning Phase Introduction to general UA; application to simple cases. The special data reduction equation form. Case study examples of use of TSM general UA in planning an experiment. Numerical approximation of sensitivity coefficients. MCM general UA example of pipe flow -- showing implementation of MCM, comments on effects of large U and of nonlinearities on MCM vs TSM results. SESSION 5 Applying Detailed UA – Post-Planning Phases Overview of detailed UA and its use in experiments. Propagation of systematic uncertainties into an experimental result. Example: Identifying and estimating elemental systematic uncertainties in a measured variable – strategies for making defensible systematic standard uncertainty estimates.
Typical Short Course Schedule – Day 2 SESSION 6 Systematic Uncertainties: Correlation Effects Correlated systematic errors; effects; modeling using Monte Carlo approach, Taylor Series approach. Series of examples of increasing complexity illustrating application of concepts.
SESSION 7 Engineering Estimates of Uncertainties in an Experimental Result Comparative testing – importance of correlated systematic errors and case-specific definition of random uncertainty. Uncertainties used in balance checks. Additional comments on estimating systematic uncertainties. Direct determination of random uncertainty of a result in a single test and in multiple tests. Propagation of random uncertainties into an experimental result. Estimating random uncertainties.
SESSION 8 Comprehensive Example: Time-wise Experiment Description of problem, planning and design of experiment. Development of the Data Reduction Equation incorporating models for asymmetric systematic errors. Estimation of selected systematic uncertainties. Debugging and Qualification. Comments on Transient Tests
SESSION 9 Comprehensive Example: Sample-to-sample Experiment Comprehensive example of the use of detailed UA in a sample-to-sample experiment.
SESSION 10 Uncertainties and Regressions The general methodology for determining the uncertainty associated with the use of regressions, considering both random and systematic standard uncertainties in the experimental data on which the regression is based. TSM and MCM approaches.
SESSION 11 Verification and Validation (V&V) of Simulations Consideration of V&V concepts applied to validation points and application (prediction) points in a validation domain. Discussion of current ASME V&V Standards. An overview of verification and validation of simulations considering the uncertainties in experimental data and the uncertainties in the simulation -- the approach in the ASME V&V 20 Standard.
SESSION 12 Open Discussion/Application to Local Problems