The course aims at providing the students with modern instruments to enable shape optimization in fluid dynamics. In the first part of the course different methods are presented, such as Deterministic optimization, Design of Experiment, Response Surface Modelling, Stochastic Optimization and Robust Design Optimization. In the second part the students will learn some industrial open source codes (Dakota and OpenFOAM) and perform shape optimization of realistic cases. The final exam is a project.
The objective of the course is to provide students with useful and modern tools to make shape optimization in the field of fluid dynamics. In the first part of the course the theory of the various optimization methods are presented, including deterministic optimization, Design of Experiment (DoE), surface response (RSM), stochastic optimization and robust design. In the second part of the course, practical examples, such as optimization of a wing profile and a convergent / divergent conducts, are analyzed with open source industrial tools (Dakota and OpenFOAM)
In the first part of the course the theory of the various optimization methods are presented, including deterministic optimization, Design of Experiment (DoE), surface response (RSM), stochastic optimization and robust design. In the second part of the course, practical examples, such as optimization of a wing profile and a convergent / divergent conducts, are analyzed with open source industrial tools (Dakota and OpenFOAM)
Aerodynamics, Transition and Turbulence
The course will be based on a series of conventional lectures, and numerical examples in relation to respective lecture, for the students to set in practice what they learn
The course is roughly divided into three parts; sensitivity analysis, constrained optimization and nonmodal stability analysis. The different lectures include both a theoretical part and practical numerical examples in which the students will put into practice what they learn. In order to facilitate the practical part regarding numerical examples, the initial lectures of the course comprise a short repetition regarding basic numerical analysis. At the beginning of the course the students will choose, together with the lecturer, a topic related to the content of the course that they will study both theoretically and numerically. This "mini" project shall be summarized in a report and finally presented at the end of the course. A sample document regarding the report style will be handed out and discussed in the beginning of the course.
Notes and other material will be provided by the instructor and the following textbooks are suggested:
Nocedal, J. & Wright, S.J.,1999, "Numerical optimization", Springer
Henningson, D.S. & Schmid, P.J., 2001, "Stability and transition in shear flows", Springer
LeVeque, R.J.,1998, "Finite Difference Methods for Differential Equations", University of Washington
Ricevimento: The teacher receives on appointment at DICCA, Via Montallegro 1, hydraulic laboratory, Genoa. Interviews can be made, even via skype (janpralits) or Teams For appointments send an email to: jan.pralits@unige.it.
JAN OSCAR PRALITS (President)
ANDREA MAZZINO
ALESSANDRO BOTTARO (President Substitute)
https://corsi.unige.it/9270/p/studenti-orario
ACOMPUTATIONAL OPTIMIZATION IN FLUID DYNAMIC
A written examination will be performed at two occasions during the course. The final mark will be based on both the project and the two exams.
Details on how to prepare for the exam and the degree of depth of each topic will be given during the lessons.
The written exam will verify the actual acquisition of basic knowledge on some methodologies and their applications for analysis.
The project can be carried out after the end of the course and the presentation agreed with the teacher before the end of the academic year.
Pre-requisites :
Students with SLD, disability or other special educational needs certification are advised to contact the teacher at the beginning of the course to agree on teaching and exam methods that, in compliance with the teaching objectives, take into account the modalities learning opportunities and provide suitable compensatory tools.
