In this course the students learn the basics of numerical optimization techniques, and their application to solve multidisciplinary optimization problem in engineering. The course includes hands-on tutorials, where the students learn how to use numerical optimization methods to solve engineering problems. Topics included in this course are: Design parametrization techniques, design structure matrix, numerical optimization methods, unconstrained and constrained optimization, sensitivity analysis methods, gradient free optimization methods, MDO architectures, multi-objective optimization, approximation methods in MDO.
In this course the students learn the basics and applications of topology optimization in engineering problems. Topics included are: Introduction to numerical optimization methods, density-based methods for topology optimization, numerical challenges and instabilities, topology optimization in dynamic problems, stress constraints in topology optimization, buckling problems in topology optimization, Topology optimization considering body force, Ground structure method for topology optimization. The course includes hands-on tutorials, where the students learn how to use computer programs to solve engineering topology optimization problems.
In this course advanced methods in design of transport aircraft are discussed. It is shown how multidisciplinary design optimization can be used to design the next generation of transport aircraft. Besides, advanced topics in aircraft aerodynamics, structure and performance are discussed. After completion of this course the students are able to formulate an aircraft design as a multidisciplinary design optimization problem, and then they are able to solve it using physics-based simulation and numerical optimization methods to find the optimum design solution. The course includes a comprehensive design optimization assignment to give the students hands on experience to apply the theoretical knowledge they gained during the lecture to solve an aircraft design and optimization problem. The included topics are: aircraft aerodynamics in transonic regime, aircraft drag estimation and drag reduction, advanced methods for structural weight estimation, advanced methods for aircraft mission analysis and computing aircraft fuel consumption, and aircraft aerostructural optimization.
In the course Advanced Aircraft Design I, the advanced methods for aircraft design are discussed. In this course advanced technologies as well as advanced aircraft configurations are discussed. By following this course students get familiar with the promising technologies to be used in the future transport aircraft, as well as novel aircraft configurations. Besides, they will learn how to design an aircraft with novel configuration (e.g. blended wing body), novel propulsion system (e.g. electric propulsion) and novel technologies (e.g. active flow control). The topics include: active flow control, boundary layer ingestion, active load alleviation, blended wing body aircraft configuration, box wing aircraft configuration, forward swept wings, and design of electric and hybrid electric aircraft.
In this lecture, the students gain insight into current issues of applied multidisciplinary design optimization (MDO) of commercial aircraft, familiarize themselves with the state of the art in the field of MDO and understand how modern and advanced MDO techniques support the engineering design of complex products. The course includes tutorials, hands-on exercises, seminars and guest lectures. Topics addressed in this course include: collaborative MDO, novel and advanced MDO architectures, pros and cons of different MDO formulations, optimization frameworks and software to solve large nonlinear optimization problems, issues in benchmarking MDO architectures, real-world applications and associated challenges.
Students will have comprehensive machine learning techniques and the ability to formulate and solve complex probabilistic models by using the sum and product rules of probability. Through the machine learning techniques acquired in this course, in engineering design optimization students have ability to generate models which enable to explore the solutions actively and efficiently by exploiting the uncertainties obtained in the learning process. In addition, preprocessing such as feature extraction, which is often used in image recognition technology, can also be performed by the machine learning techniques acquired in this course. These contribute to the simplification of problems and cost efficiency in engineering problems in general, and also enable automatic sample generation, so to say, constructing new images in the above example. Furthermore, in scientific problems, it is used as a key technology to reveal essential physical quantities. Overall by viewing globally and unifiedly the theory of probability from Bayesian perspectives students enable to formulate probabilistic models actively and to acquire proper machine learning approaches on each problem setting. The course includes practical tutorials using computer programs. Recent applications of machine learning techniques to aerospace engineering are introduced as examples.