An aerothermodynamic design optimization framework for hypersonic vehicles

In the aviation field great interest is growing in passengers transportation at hypersonic speed. This requires, however, careful study of the enabling technologies necessary for the optimal design of hypersonic vehicles. In this framework, the present work reports on a highly integrated design environment that has been developed in order to provide an optimization loop for vehicle aerothermodynamic design. It includes modules for geometrical parametrization, automated data transfer between tools, automated execution of computational analysis codes, and design optimization methods. This optimization environment is exploited for the aerodynamic design of an unmanned hypersonic cruiser flying at M∞=8 and 30 km altitude. The original contribution of this work is mainly found in the capability of the developed optimization environment of working simultaneously on shape and topology of the aircraft. The results reported and discussed highlight interesting design capabilities, and promise extension to more challenging and realistic integrated aerothermodynamic design problems

Aerodynamic and Acoustic Design Optimization of a Multiple Propeller Combination for Distributed Electrical Propulsion

This manuscript illustrates an optimization procedure carried out on a large-scale wind tunnel model conceived to investigate the aerodynamic and acoustic performance of a Distributed Electric Propulsion (DEP) system installed on the wings of a regional aircraft in high lift conditions. The aim of the optimization process is to obtain the best possible improvements in terms of noise and aerodynamic performance by modifying the propellers' layout of the Wing + DEP wind tunnel model. A multi-objective, multi-point design approach is adopted based on evolutionary computing. The research work is carried out in the framework of the VENUS EU-funded project GA-886019

Krueger High-Lift System Design Optimization

This work describes the cooperative/competitive design process that led to the definition of the Krueger flap to be used in the numerical and experimental tests of the European project UHURA. The project requirements are particularly challenging because it is necessary to develop a device with good aerodynamic high-lift characteristics, but it is necessary to consider many constraints of structural and kinematic nature. Indeed, the kinematics for its deployment is quite complex and imposes hard constraints on the Krueger shape, and the structural charctacteristics must allow it to withstand considerable structural stresses in the deployment phase, which is studied in the wind tunnel

High-lift devices topology optimisation using structured-chromosome genetic algorithm

This paper addresses the problem of including the choice of the High-Lift Devices (HLDs) configuration as a decision variable of an automatic optimisation tool. This task requires the coupling of an estimation routine and an optimisation algorithm. For the former, SU2 flow solver has been used. The Structured-Chromosome Genetic Algorithm (SCGA) optimiser has been employed to search for the optimal HLD. SCGA can overcome the limitations dictated by standard fixed-size continuous optimisation algorithms. Indeed, using hierarchical formulations, it can manage configurational decisions that are conventionally the responsibility of expert designers. The search algorithm bases its strategy on revised genetic operators conceived for handling hierarchical search spaces. The presented research not only shows the practicability of delegating to a specialised optimisation algorithm the complete HLD design but is intended to be a proof of concept for the whole field of multidisciplinary design optimisation. Indeed, the aerospace sector as a whole would benefit by reducing human intervention from the decision process

Computational methods in engineering design and optimization : editorial article

Across all fields of engineering sciences, many design problems are now tackled using computational techniques that aim at optimizing system performance. The evolution of complex systems has progressed along with the development of computational methods that can treat more and more complex design and simulation problems. All engineering areas, from power generation and distribution, to structural mechanics and materials, from optimal control to aerodynamics, have benefited from the development of increasingly sophisticated computational techniques. Today, the improvement in computer performance allows numerical simulation to replace a big portion of experimental tests, and numerical optimisation to handle complex, multidisciplinary design problems

Airfoil Optimization Using Far-Field Analysis of the Drag Force

Far-field analysis for drag prediction and decomposition is here explored and applied to aerodynamic shape design problems. The work aims at illustrating how the far-field approach may allow substantial reductions of the computational effort required by a shape design task. The advantage of this method, in comparison with classical near-field methods, is illustrated both in the classical field of deterministic design optimization and in the robust shape design context. In particular, the approach is shown by optimizing the wave drag of a NACA 0012 airfoil in transonic flow conditions at zero angle of attack. Both the deterministic and the robust stochastic approach have been used, and the obtained results are here reported

Bayesian Adaptive Selection Under Prior Ignorance

Bayesian variable selection is one of the popular topics in modern day statistics. It is an important tool for high dimensional statistics, where the number of model parameters is greater than the number of observations. Several Bayesian models have been proposed for variable selection. However, a convincing robust Bayesian approach is yet to be investigated. Here in this work, we investigate sensitivity analysis over a simplex of probability measures. We sample from this simplex to get an inclusion probability of each variable. The sensitivity analysis gives us a set of posteriors instead of a single posterior. This set of posteriors gives us a behaviour of the model parameters with respect to different prior elicitations resulting in robust inferential conclusions