Module also offered within study programmes:
General information:
Name:
Uncertainty analysis in engineering
Course of study:
2013/2014
Code:
RMS-2-219-MD-s
Faculty of:
Mechanical Engineering and Robotics
Study level:
Second-cycle studies
Specialty:
Mechatronic Design
Field of study:
Mechatronics with English as instruction languagege
Semester:
2
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Responsible teacher:
dr hab. inż. Gallina Alberto (agallina@agh.edu.pl)
Academic teachers:
dr hab. inż. Gallina Alberto (agallina@agh.edu.pl)
Module summary

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence
M_K001 Awareness of the responsibility for own work and readiness to comply with the rules of team work and accepting responsibility for tasks performed collectively MS2A_K02, MS2A_U02 Involvement in teamwork,
Execution of laboratory classes
Skills
M_U001 Improving software programming skills and ability to integrate different simulation environments MS2A_U11, MS2A_U14, MS2A_U05, MS2A_U10, MS2A_U07 Execution of laboratory classes
M_U002 Student is able to present his own work and justify his/her choices made in the execution of the work. MS2A_U04, MS2A_U05, MS2A_U03 Presentation
Knowledge
M_W001 Awareness of the importance of uncertainty analysis in engineering problems. Understanding of the most common non-deterministic methods and optimization methods used in engineering. MS2A_W07, MS2A_W04, MS2A_W03 Execution of laboratory classes,
Execution of a project,
Completion of laboratory classes
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Others
Zaj. terenowe
Zaj. warsztatowe
E-learning
Social competence
M_K001 Awareness of the responsibility for own work and readiness to comply with the rules of team work and accepting responsibility for tasks performed collectively - - + + - - - - - - -
Skills
M_U001 Improving software programming skills and ability to integrate different simulation environments - - + - - - - - - - -
M_U002 Student is able to present his own work and justify his/her choices made in the execution of the work. - - - + - - - - - - -
Knowledge
M_W001 Awareness of the importance of uncertainty analysis in engineering problems. Understanding of the most common non-deterministic methods and optimization methods used in engineering. + - - - - - - - - - -
Module content
Lectures:
  1. Overview of uncertainty descriptors

    • random variables. fuzzy numbers, interval analysis
    • uncertainty analyses: uncertainty quantification, sensitivity analysis, reliability analysis, robustness analysis

  2. Calculus of probability

    • basic concepts
    • discrete and continuous random variables
    • fundamental properties
    • conditional properties
    • important distributions
    • elements of statistics
    • maximum likelihood estimator
    • maximum a posteriori

  3. Sensitivity analysis

    • regression analysis
    • Morris’ method
    • Sobol method
    • other methods

  4. Optimization

    • local methods
    • global methods

  5. Regression models

    • linear regression
    • Bayesian linear regression
    • Gaussian process linear regression

  6. Reliability analysis

    • First order reliability method
    • Important sampling

  7. Propagation of uncertainty

    • analytical method
    • first order second moment
    • Monte Carlo method
    • sampling strategies

Project classes:
Development of the project

  • selection of the model and analysis
  • implementation in MATLAB
  • presentation of results

Laboratory classes:
MATLAB labs

  • description of specific MATLAB commands and toolboxes
  • creation of scripts for uncertainty propagation

Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 132 h
Module ECTS credits 5 ECTS
Participation in lectures 14 h
Participation in project classes 14 h
Participation in laboratory classes 14 h
Preparation for classes 30 h
Contact hours 5 h
Preparation of a report, presentation, written work, etc. 15 h
Completion of a project 40 h
Additional information
Method of calculating the final grade:
  • Preparation of laboratory tasks
  • Preparation and presentation of a project
  • Short colloquium
Prerequisites and additional requirements:
  • Fundamentals of MATLAB
  • Fundamentals of FEM
Recommended literature and teaching resources:

Basic:

  • Notes provided by the lecturer:
    Additional:
  • Grinstead, Introduction to probability
  • Meyers and Montgomery, Applied statistics and probability for engineers
  • Bishop, Pattern recognition and machine learning.
Scientific publications of module course instructors related to the topic of the module:

Additional scientific publications not specified

Additional information:

Subject web-site : http://home.agh.edu.pl/~agallina/?UNCERTAINTY_ANALYSIS_IN_ENGINEERING_%28UA%29