The Birmingham Canal Navigation Challenge
Win the Birmingham Canal Navigation Challenge, in which boats have 24 hours of continuous cruising to accrue points awarded on the basis of locks used, miles traveled and remoteness of the canal and locations visited.
Predictions - Their relationship to optimization
"Why are we so bad predicting?" Starting from the most striking prediction failures, Ciro Soto wonders about the role of real phenomenon predictions in the engineering analysis and how RSM methods can help reducing the related errors.
Towards Handling Many Objectives Initial Results Using NSGA-III
Existing Evolutionary Multi-objective Optimization (EMO) methods encounter some difficulties
in handling design problems characterized by more than three conflicting objectives. The invited lecture present the promising initial results of the NSGA III adaptive algorithm in dealing with such problems.
Reliability-based Design Optimization applying Polynomial Chaos Expansion- Theory and Applications
Reliability-based Design Optimization is achieving more and more agreement in the industrial design community. In fact, most of the industrial processes are permeated by uncertainties: the manufactured product is generally different, from a geometric point of view, from the product design because of the dimensional tolerances, and, more frequently, the working point is not fixed, but is characterized by some fluctuations in the operating variables.
Pluggable Analysis Viewpoints for Design Space Exploration
Viewpoint modeling is an effective approach for analyzing and designing complex systems. Splitting various elements and corresponding constraints into different perspectives of interests, enables separation of concerns such as domains of expertise, levels of abstraction, and stages in lifecycle. Specifically, in Systems Engineering different viewpoints could include functional requirements, physical architecture, safety, geometry, timing, scenarios, etc. Despite partial inter-dependencies, the models are usually developed independently by different parties, using different tools and languages
Designing Materials with modeFRONTIER - An application of modeFRONTIER to the optimization of physical and chemical properties of polymers
The following presentation proposes an application of modeFRONTIER to the optimization of physical and chemical properties of polymers
Light beam reﬂectance measurement of droplets diameter distribution in crude oil emulsions
Stable oil ﬁeld emulsions create challenging conditions in the petroleum production industry. One important characteristic affecting the stability and rheology of emulsions is the Droplet Diameter Distribution
(DDD). A few techniques are available to determine the DDD in clear and dark liquid/liquid mixtures. Most of them, like the traditional light scattering techniques, require sampling and dilution.
Design and Optimization under Uncertainties A Simulation and Surrogate Model
This thesis deals with development of complex products via modeling and simulation, and especially the use of surrogate models to decrease the computational efforts when probabilistic optimizations are performed. Many methods that can be used to perform probabilistic optimizations exist and this thesis strives to present and demonstrate the capabilities of several of them. A probabilistic optimization requires different kinds of knowledge.
Comparison between Multiobjective Population-based Optimization Algorithms in Mechanical Problem
The objective of this paper was to perform a comparative study among multi-objective optimization methods on practical problem by using modeFRONTIER optimization software, to determine the efficiency of each method. In order to measure the effectiveness and competence of each method, the lifting arm problem was chosen from the literature.
modeFRONTIER optimisation on financial markets
The basic portfolio optimization theory hinges on the discrete time, continuous outcome paradigm otherwise known as the mean-variance or Markowitz paradigm. In 1952, Harry Markowitz introduced this approach, which is widely used in applications involving investment portfolios. Mean-variance theory assumes that among portfolios with the same standard deviation, the one with the greatest expected value is the most efficient.