Multidisciplinary and Multiobjective Optimization in Conceptual Design for Mixed-Stream Turbofan Engines

Despite major advances in design tools such as engine cycle analysis software and computer aided design, conceptual gas turbine engine design is essentially a trial-and-error process based on the experience of engineers. Modern optimization concepts, such as multidisciplinary optimization (MDO), and multiobjective optimization (MOO), linked with sequential quadratic programming (SQP) methods and genetic algorithms (GA), were applied to the conceptual engine design process to automate the conceptual design phase. Robust integrated computer codes were created to find the optimal values of eight engine parameters in order to minimize fuel usage, aircraft cost and engine annulus area over a given mission. The engine cycle selected for study was the mixed stream, low bypass turbofan. SQP and GA optimization algorithms were integrated with on-design and off- design engine cycle analysis and mission analysis computer codes created by the authors to obtain the optimized conceptual engine design for an imaginary short range interceptor and the Global Strike Aircraft U.S. Air Force concept. The process used a nonspecific approach that can be applied to a wide variety of missions and aircraft. All the codes were written in Matlab, and so operate under the same programming architecture and can be easily upgraded or modified

Parametric design and multi-objective optimisation of containerships

The introduction of new regulations by the International Maritime Organisation, the fluctuation of fuel price levels, along with the continuous endeavour of the shipping industry for economic growth and profits has led the shipbuilding industry to explore new and cost-efficient designs for various types of merchant ships. In this respect, proper use of modern computer-aided design/computer-aided engineering systems (CAD/CAE) extends the design space, while generating competitive designs in short lead time. The present paper deals with the parametric design and optimisation of containerships. The developed methodology, which is based on the CAESES/Friendship-Framework software system, is demonstrated by the conceptual design and multi-objective optimisation of a midsized, 6,500 TEU containership. The methodology includes a complete parametric model of the ship’s external and internal geometry and the development and coding of all models necessary for the determination of the design constraints and the design efficiency indicators, which are used for the evaluation of parametrically generated designs. Such indicators defining the objective functions of a multi-objective optimisation problem are herein the energy efficiency design index, the required freight rate, the ship’s zero ballast container box capacity and the ratio of the above to below deck number of containers. The set-up multi-objective optimisation problem is solved by use of the genetic algorithms

Parametric design and multiobjective optimization of containerships

The introduction of the energy efficiency design index (EEDI) and ballast water treatment regulations by the International Maritime Organization, the fluctuation of fuel price levels, along with the continuous endeavor of the shipping industry for economic growth and profits has led the shipbuilding industry to explore new and cost-efficient designs for various types of merchant ships. In this respect, proper use of modern computer-aided design/computer-aided engineering systems (CAD/CAE) extends the design space, while generating competitive designs with innovative features in short lead time. The present article deals with the parametric design and optimization of containerships. The developed methodology, which is based on the CAESES/Friendship-Framework software system, is demonstrated by the conceptual design and multiobjective optimization of a midsized, 6500-TEU containership. The methodology includes a complete parametric model of the ship’s external and internal geometry and the development and coding of all models necessary for the determination of the design constraints and the design efficiency indicators, which are used for the evaluation of parametrically generated designs. Such indicators defining the objective functions of a multiobjective optimization problem are herein the EEDI, the required freight rate, the ship’s zero ballast container box capacity, and the ratio of the above to below deck number of containers.The set-up multiobjective optimization problem is solved by use of the genetic algorithms, and clear Pareto fronts are generated. Identified optimal design proves very competitive compared with the standard containership designs in the market

A Novel Design Approach to X-Band Minkowski Reflectarray Antennas using the Full-Wave EM Simulation-based Complete Neural Model with a Hybrid GA-NM Algorithm

In this work, a novel multi-objective design optimization procedure is presented for the Minkowski Reflectarray RAs using a complete 3-D CST Microwave Studio MWS-based Multilayer Perceptron Neural Network MLP NN model including the substrate constant εr with a hybrid Genetic GA and Nelder-Mead NM algorithm. The MLP NN model provides an accurate and fast model and establishes the reflection phase of a unit Minkowski RA element as a continuous function within the input domain including the substrate 1 ≤ εr ≤ 6; 0.5mm ≤ h ≤ 3mm in the frequency between 8GHz ≤ f ≤ 12GHz. This design procedure enables a designer to obtain not only the most optimum Minkowski RA design all throughout the X- band, at the same time the optimum Minkowski RAs on the selected substrates. Moreover a design of a fully optimized X-band 15×15 Minkowski RA antenna is given as a worked example with together the tolerance analysis and its performance is also compared with those of the optimized RAs on the selected traditional substrates. Finally it may be concluded that the presented robust and systematic multi-objective design procedure is conveniently applied to the Microstrip Reflectarray RAs constructed from the advanced patches