Experimentation, Errors, and Uncertainty - Errors and Uncertainties in a Measured Variable - General Uncertainty Analysis: Planning an Experiment and Applications in Validation - Detailed Uncertainty Analysis: Designing, Debugging, and Executing an Experiment - Validation of Simulations - Data Analysis, Regression, and Reporting of Results.
M.Sc. in Mechanical Engineering
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| content serial | Description |
|---|
| 1 | Experimentation and Experimental Approach, Basic Concepts and Definitions. |
| 2 | Experimental Results Determined from Multiple Measured Variables. |
| 3 | Statistical Distributions and Gaussian Distribution. |
| 4 | Samples from Gaussian Parent Population |
| 5 | Statistical Rejection of Outliers from a Sample. |
| 6 | Uncertainty of a Measured Variable. |
| 7 | Taylor Series and Monte Carlo Method for Propagation of Uncertainties. |
| 8 | Overview: Using Uncertainty Propagation in Experiments and Validation. |
| 9 | General Uncertainty Analysis Using the Taylor Series Method. |
| 10 | Using TSM Uncertainty Analysis in Planning an Experiment. |
| 11 | Examples of Presentation of Results from Actual Applications. |
| 12 | Application in Validation: Estimating Uncertainty in Simulation Result Due to Uncertainties in Inputs |
| 13 | Determining Random and Systematic Uncertainty of Experimental Result. |
| 14 | Sample-to-Sample Experiment and Debugging and Qualification of a Time wise Experiment. |
| 15 | Least-Squares Estimation and Classical Linear Regression Uncertainty: Random Uncertainty |
| 16 | Final Examination. |
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