Resistance reduction of a military ship by variable-accuracy metamodel-based multidisciplinary robust design optimization

A method for simulation-based multidisciplinary robust design optimization (MRDO) affected by uncertainty is presented, based on variable-accuracy metamodelling. The approach encompasses a variable level of refinement of the design of experiments (DoE) used for the metamodel training, a variable accuracy for the uncertainty quantification (UQ), and a variable level of coupling between disciplines for the multidisciplinary analysis (MDA). The results of the present method are compared with a standard MRDO, used as a benchmark and solved by fully coupled MDA and fully accurate UQ, without metamodels. The hull-form optimization of the DTMB 5415 subject to stochastic speed is presented. A two-way steady coupled system is considered, based on hydrodynamics and rigid-body equation of motion. The objective function is the expected value of the total resistance, and the design variables pertain to the modification of the hull form. The effectiveness and the efficiency of the present method are evaluated in terms of optimal design performances and number of simulations required to achieve the optimal design

Resistance reduction of a military ship by variable-accuracy metamodel-based multidisciplinary robust design optimization

A method for simulation-based multidisciplinary robust design optimization (MRDO) affected by uncertainty is presented, based on variable-accuracy metamodelling. The approach encompasses a variable level of refinement of the design of experiments (DoE) used for the metamodel training, a variable accuracy for the uncertainty quantification (UQ), and a variable level of coupling between disciplines for the multidisciplinary analysis (MDA). The results of the present method are compared with a standard MRDO, used as a benchmark and solved by fully coupled MDA and fully accurate UQ, without metamodels. The hull-form optimization of the DTMB 5415 subject to stochastic speed is presented. A two-way steady coupled system is considered, based on hydrodynamics and rigid-body equation of motion. The objective function is the expected value of the total resistance, and the design variables pertain to the modification of the hull form. The effectiveness and the efficiency of the present method are evaluated in terms of optimal design performances and number of simulations required to achieve the optimal design

Dense conjugate initialization for deterministic PSO in applications: ORTHOinit+

This paper describes a class of novel initializations in Deterministic Particle Swarm Optimization (DPSO) for approximately solving costly unconstrained global optimization problems. The initializations are based on choosing specific dense initial positions and velocities for particles. These choices tend to induce in some sense orthogonality of particles’ trajectories, in the early iterations, in order to better explore the search space. Our proposal is inspired by both a theoretical analysis on a reformulation of PSO iteration, and by possible limits of the proposals reported in Campana et al. (2010); Campana et al. (2013). We explicitly show that, in comparison with other initializations from the literature, our initializations tend to scatter PSO particles, at least in the first iterations. The latter goal is obtained by imposing that the initial choice of particles’ position/velocity satisfies specific conjugacy conditions, with respect to a matrix depending on the parameters of PSO. In particular, by an appropriate condition on particles’ velocities, our initializations also resemble and partially extend a general paradigm in the literature of exact methods for derivative-free optimization. Moreover, we propose dense initializations for DPSO, so that the final approximate global solution obtained is possibly not too sparse, which might cause troubles in some applications. Numerical results, on both Portfolio Selection and Computational Fluid Dynamics problems, validate our theory and prove the effectiveness of our proposal, which applies also in case different neighborhood topologies are adopted in DPSO

On the use of Synchronous and Asynchronous Single-objective Deterministic Particle Swarm Optimization in Ship Design Problems

A guideline for an effective and efficient use of a deterministic variant of the Particle Swarm Optimization (PSO) algorithm is presented and discussed, assuming limited computational resources. PSO was introduced in Kennedy and Eberhart (1995) and successfully applied in many fields of engineering optimization for its ease of use. Its performance depends on three main characteristics: the number of swarm particles used, their initialization in terms of initial location and speed, and the set of coefficients defining the behavior of the swarm. Original PSO makes use of random coefficients to sustain the variety of the swarm dynamics, and requires extensive numerical campaigns to achieve statistically convergent results. Such an approach can be too expensive in industrial applications, especially when CFD simulations are used, and for this reason, efficient deterministic approaches have been developed (Campana et al. 2009). Additionally, the availability of parallel architectures has offered the opportunity to develop and compare synchronous and asynchronous implementation of PSO. The objective of present work is the identification of the most promising implementation for deterministic PSO. A parametric analysis is conducted using 60 analytical test functions and three different performance criteria, varying the number of particles, the initialization of the swarm, and the set of coefficients. The most promising PSO setup is applied to a ship design optimization problem, namely the high-speed Delft catamaran advancing in calm water at fixed speed, using a potential-flow code

DECISION MAKING BASED ON COMMUNITY NOISE ANNOYANCE IN THE MULTI-OBJECTIVE OPTIMIZA- TION OF A COMMERCIAL AIRCRAFT

The present paper deals with an innovative decision making approach for the selection of air- craft design concepts and operational procedures aimed at the reduction of the community noise annoyance. Specifically, the least annoying solutions pertaining to the Pareto front re- sulting from a multidisciplinary, multi-objective design and procedure optimization process, are selected on the basis of psychoacoustic requirements. The need of such an approach stems from the fact that the environmental requirements imposed to the commercial aviation are be- coming stricter and stricter, as the trend of the traffic increase is rapidly growing following the market demand. The fulfillment of these requirements often leads to design decisions that can be conflicting, thus forcing the designer to look for trade-off solutions since the early con- ceptual phase of the design process. The multi-objective optimization is the methodological approach most suited to cope with this kind of problems. Using this approach, the Pareto optimality is achieved as the set of non-dominated solutions (the Pareto front), i.e., the solu- tions such that none of the objectives can be improved without deteriorate at least one of the others. The criterion used to select one of the non-dominated solutions along the Pareto front represents a key point. Within the EC-funded project COSMA, the outcomes of an extensive campaign of psychometric tests aimed at the identification of the most annoying characteristics of the aircraft noise are used to rank the points on the Pareto fronts with respect to the level of annoyance produced on the residential community. The approach is highly innovative, being the first time that psychoacoustic considerations are directly used in the design and procedural optimization process. The results obtained for different classes of aircraft in several operating conditions are presented

Resistance reduction of a military ship by variable-accuracy metamodel-based multidisciplinary robust design optimization

A method for simulation-based multidisciplinary robust design optimization (MRDO) affected by uncertainty is presented, based on variable-accuracy metamodelling. The approach encompasses a variable level of refinement of the design of experiments (DoE) used for the metamodel training, a variable accuracy for the uncertainty quantification (UQ), and a variable level of coupling between disciplines for the multidisciplinary analysis (MDA). The results of the present method are compared with a standard MRDO, used as a benchmark and solved by fully coupled MDA and fully accurate UQ, without metamodels. The hull-form optimization of the DTMB 5415 subject to stochastic speed is presented. A two-way steady coupled system is considered, based on hydrodynamics and rigid-body equation of motion. The objective function is the expected value of the total resistance, and the design variables pertain to the modification of the hull form. The effectiveness and the efficiency of the present method are evaluated in terms of optimal design performances and number of simulations required to achieve the optimal design

Multi-objective, multi-disciplinary optimization of take-off and landing procedures to minimize the environmental impact of commercial aircraft: the noise vs fuel consumption trade-off within the EC project COSMA

The approach exploited within the the EC funded project COSMA (Community Oriented Solu- tions to Minimize Aircraft Annoyance, FP7) for multi-objective optimization aimed at optimal take-off and landing trajectories (minimizing noise emissions and fuel burn) is presented. The methodology has been recently extended to include aircraft design variables, performing a si- multaneous design/procedure optimization. As noise indicator, the area bounded by the SEL 60dBA contour is chosen, whereas the fuel burn refers to the specified procedure. A binary coded multi-objective genetic algorithm (MOGA) is applied to find the front of non-dominated solutions (Pareto front) and an optimal compromise solution is selected. The trajectory is par- titioned into segments, and the 3D spatial coordinates of each segment extreme, along with the aircraft speed and high-lift devices settings are taken as optimization variables. Aircraft performance and noise emissions are evaluated through the multidisciplinary simulation, de- sign and optimization tool FRIDA (Framework for Innovative Design in Aeronautics). The approach is applied to three classes of aircraft, from mid to long range and the relevant results for mid range aircraft are presented herein

Efficient simulation-based design optimization for fluid-structure interaction problems affected by uncertainty

A strategy is presented for efficient simulation-based Multidisciplinary Robust Design Optimization (MRDO) of fluid-structure interaction problems affected by uncertainty. The focus is on a racing-sailboat fin, subject to stochastic operating conditions. The elastic deformation of the fin induced by hydrodynamic loads cannot be neglected while evaluating the hydrodynamic performances, thus a fully coupled hydroelastic problem is considered, including fluid mechanics (CFD) and structural analysis (FEM). The multidisciplinary analysis (MDA) identifies the multidisciplinary equilibrium by numerical iterations. The stochastic operating scenario is identified by speed and yaw angle, which are defined by means of a probability density function. The distributions of the relevant output parameters are evaluated using uncertainty quantification methods (UQ), requiring a large number of multidisciplinary analyses (MDA). Solving the MRDO problem represents a challenge from the algorithmic and computational viewpoints, since requires coupling together: (1) a minimization algorithm, (2) a UQ tool and (3) simulation-based MDAs. The objective of the present work is the development and validation of an efficient strategy for MRDO. Specifically, the objective function is the expected value of the fin efficiency - lift to drag ratio (CL/CD) - over the stochastic operating conditions, whereas the design variables pertain to the fin geometry, which is modified using a Free-Form Deformation (FFD) technique; the MDA is solved iteratively with a variable level of coupling between the disciplines involved; the UQ is carried out using the Monte Carlo method, based on metamodels; design optimization is solved using subsequent Design of Experiments (DoE), metamodels, and Particle Swarm Optimizations (PSO). During the optimization process, a DoE refinement is performed on smaller design windows with an increasing density of numerical experiments, level of UQ accuracy and MDA coupling. The method is validated versus a standard MRDO, solved by fully coupled MDAs without metamodels. Effectiveness and efficiency of the method are evaluated in terms of optimal design performances and number of simulations required