Learning Outcomes:
Upon completion of this module, students should be able to:
1. Demonstrate a practical and analytical understanding of the principles of engineering measurement (i.e. temperature, pressure, flow, fluid rheology, particle size distribution) as applied to heat transfer, mass transfer, fluid flow and particulate processes;
2. Collate and meaningfully present/visualize engineering information and data;
3. Critically analyse engineering data and apply data science techniques (characterization of distributions, linear regression analysis, confidence intervals, hypothesis testing), by using EXCEL and/or SPSS, where appropriate;
4. Quantitatively estimate the uncertainty in experimentally determined quantities;
5. Effectively undertake independent study, on a discipline-related topic;
6. Describe and apply basic principles of Statistical Process Control.
Indicative Module Content:
• Introduction to Engineering Measurement
- Review of units
- Measurement principles: accuracy, precision, reproducibility, instrument calibration
- Introduction to the quantitative transfer of materials
- Use of the steam tables
• Basic Principles of Measuring Devices
- Pressure measurement
- Temperature measurement
- Particle size measurements
- Introduction to the use of spectrophotometers
- Flow measurement in pipelines
- Rheological characterization of fluids
• Introduction to Engineering Applications
- Pumping Fluids
• Data Science
- The engineering method
- Basic presentation and analysis of engineering data
- Linear regression (simple and multivariate)
- Quantitative evaluation of experimental uncertainties
- Basics of probability
- Probability density functions
- Descriptive statistics
- The normal and Log-normal distribution
- Confidence intervals on the mean and variance
- Sampling distributions
- Hypothesis testing: t-test, chi-squared test, f-test, p-values
- Statistical errors (Type I&II)
- Analysis of variance (ANOVA)
- Statistical process control