First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
Course unit
INN1027679, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
IN0518, Degree course structure A.Y. 2011/12, A.Y. 2019/20
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Number of ECTS credits allocated 9.0
Type of assessment Mark
Department of reference Department of Industrial Engineering
E-Learning website
Mandatory attendance No
Language of instruction English
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge ERNESTO BENINI ING-IND/08

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-IND/08 Fluid Machines 9.0

Course unit organization
Period First semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 9.0 72 153.0 No turn

Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2011 course timetable

Examination board
Board From To Members of the board
8 A.A. 2018/19 01/10/2018 30/11/2019 BENINI ERNESTO (Presidente)
ARDIZZON GUIDO (Membro Effettivo)

Prerequisites: For a better understanding of the topics covered it is recommended that the student has attended basic courses on fluid machines.
Target skills and knowledge: Acquisition of fundamental knowledge related to modern methods for single and multiobjective optimization, with particular reference to fluid machines. Use of specific computer codes for dealing with optimization problems in fluid machinery .
Examination methods: Conventional viva
Assessment criteria: Assessment on the course program and discussion of the project developed during the course.
Course unit contents: Mathematical calculus, maxima and minima in bounded vector functions of several variables. Single- and multi-objective optimality. Classical and advanced optimization methods: deterministic, stochastic and pseudo-stochastic algorithms. Gradient algorithms, genetic-evolutionary algorithms, simulated annealing, fuzzy logic. Hybrid methods. Constraints in optimization problems. Functional optimization of fluid machines and their components. Calculation models for rotodynamic machines. Numerical models and experimental models. Interface between optimization algorithms and performance codes in turbomachines. Applications: optimization of compressible and incompressible flow turbo machinery; optimization of 2D cascades, optimization of rotor - stator interaction; 3D optimization of both rotating and stationary blades; optimization of internal combustion engines.
Planned learning activities and teaching methods: After a general discussion concerning the methods for single and multi-objective numerical optimization (both traditional and advanced), students are provided with the necessary knowledge for proper formulation, implementation and troubleshooting in optimized design of fluid machines (with particular reference to turbomachinery).
The student is guided step-by-step in the development of an optimization procedure according to two- and three-dimensional approaches; This procedure includes: (i) the geometric parameterization of a machine or one of its component, (ii) the analysis thereof by CFD (computational fluid dynamics) and (iii) interfacing with appropriate recursive algorithms of operational research. The procedure has the peculiarity of being fully automated and to enable, starting from a baseline design, to obtain optimized shapes after a finite number of iterations.
Additional notes about suggested reading: Lecture notes
Textbooks (and optional supplementary readings)
  • --, Dispense delle lezioni. --: --, --. Cerca nel catalogo
  • Kwang-Yong Kim, Abdus Samad, Ernesto Benini, Design Optimization of Fluid Machinery: Applying Computational Fluid Dynamics and Numerical Optimization. --: Wiley, 2019. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Laboratory
  • Case study
  • Working in group
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • One Note (digital ink)
  • Matlab

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure