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Lecture

Design Definition and Multidisciplinary Optimization

In course

ENG-421: Fundamentals in systems engineering

Introduction to systems engineering using the classical V-model. Topics include stakeholder analysis, requirements definition, concept selection, design definition and optimization, system integration and verification and validation. This class is part of ...

Description

This lecture covers the NASA Design Definition Process, Multidisciplinary Design Optimization, Concurrent Design Facilities (CDF), and Critical Design Review (CDR). It delves into the importance of design solution definition, multidisciplinary design optimization, and the challenges faced in system design optimization. The instructor discusses the roots and motivation behind Multidisciplinary Design Optimization, as well as the role of constraints in the design process. The lecture also explores the application of optimization techniques in the design of optimal structures, such as a soda can. Furthermore, it addresses the challenges and lessons learned in Concurrent Design Facilities (CDF) and the Critical Design Review (CDR) process.

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Instructor

Volker Gass

After completing his master’s degree in Microtechnology at the Federal Institute of Technology in Lausanne in 1989 he worked as a Project Manager at Mecanex SA, a Swiss High-Tech company active in the field of Aerospace Mechanisms. While working at Mecanex he completed a PhD in Science in the field of applied Micro-Systems Technologies at the University of Neuchâtel, Switzerland in 1994. In 1995, Volker Gass participated in the Management Buy Out of Mecanex SA. In 2000, Mecanex was acquired by the Swiss Defence and Technology group RUAG . He was appointed to lead Customer Relations and Business Development of the newly formed Systems & Space Division at RUAG Aerospace in 2004. In the same year he was appointed member of the Swiss Academy of Engineering Sciences for his contribution in the field of High-Technology Space applications. From November 2006 to June 2007, Volker Gass successfully graduated RUAG’s Top Leadership Course, and held the position of General Manager, Sales & Marketing in the Space Division of RUAG in Switzerland as well as Member of the Board of Directors (President) of Mecanex USA Inc., Berlin, CT, until December 2009.In 2008 he successfully led the acquisition of SAAB Space and its subsidiary Austrian Aerospace. From January to June 2009 Volker Gass led the business team in the successful acquisition of Oerlikon Space. From mid-2009 to September 2011 he was responsible for Special Projects in the Marketing & Sales Organization of RUAG Space Switzerland. Since October 2011, Dr. Volker Gass is nominated Director of the Swiss Space Center at the Ecole Politechnique Fédérale de Lausanne. In March 2012, he is awarded the title of Adjunct Professor at the School of Engineering (STI) of the EPFL. From January 2014 to December 2017, he was nominated member of ESA’s Human Spaceflight and Exploration Science Advisory Committee (HESAC) and from spring 2015 to December 2018, observer at ESA’s Earth Science Advisory Committee (ESAC).As of January 1, 2021, The Swiss Space Center is renamed "Space Innovation"

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Related concepts (30)

Multidisciplinary design optimization

Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines. It is also known as multidisciplinary system design optimization (MSDO), and Multidisciplinary Design Analysis and Optimization (MDAO). MDO allows designers to incorporate all relevant disciplines simultaneously. The optimum of the simultaneous problem is superior to the design found by optimizing each discipline sequentially, since it can exploit the interactions between the disciplines.

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

Global optimization

Global optimization is a branch of applied mathematics and numerical analysis that attempts to find the global minima or maxima of a function or a set of functions on a given set. It is usually described as a minimization problem because the maximization of the real-valued function is equivalent to the minimization of the function . Given a possibly nonlinear and non-convex continuous function with the global minima and the set of all global minimizers in , the standard minimization problem can be given as that is, finding and a global minimizer in ; where is a (not necessarily convex) compact set defined by inequalities .

Ant colony optimization algorithms

In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.

Multi-objective optimization

Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective is a type of vector optimization that has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.

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