A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses

In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc

Multidiscipinary Optimization For Gas Turbines Design

State-of-the-art aeronautic Low Pressure gas Turbines (LPTs) are already characterized by high quality standards, thus they offer very narrow margins of improvement. Typical design process starts with a Concept Design (CD) phase, defined using mean-line 1D and other low-order tools, and evolves through a Preliminary Design (PD) phase, which allows the geometric definition in details. In this framework, multidisciplinary optimization is the only way to properly handle the complicated peculiarities of the design. The authors present different strategies and algorithms that have been implemented exploiting the PD phase as a real-like design benchmark to illustrate results. The purpose of this work is to describe the optimization techniques, their settings and how to implement them effectively in a multidisciplinary environment. Starting from a basic gradient method and a semi-random second order method, the authors have introduced an Artificial Bee Colony-like optimizer, a multi-objective Genetic Diversity Evolutionary Algorithm [1] and a multi-objective response surface approach based on Artificial Neural Network, parallelizing and customizing them for the gas turbine study. Moreover, speedup and improvement arrangements are embedded in different hybrid strategies with the aim at finding the best solutions for different kind of problems that arise in this field.Comment: 12 pages, 6 figures. Presented at the XXII Italian Association of Aeronautics and Astronautics Conference (2013

Innovation, automation and optimization in the aero design for aeronautical turbines

In the framework of civil air transportation, the design of turbomachinery components for modern aeronautical engines involves many tough technical issues. In fact, the increasing request for performance, durability, reliability and safety, reducing at the same the production times and design costs, emissions and fuel consumption, is pushing the traditional Turbofan architecture towards its limit. Levering on past experience and legacy is not sufficient for the next generation of engines, therefore the study and introduction of innovative features and technologies is essential. Nevertheless, those innovations must be proven to be effective, initially by a numerical point of view, then experimentally and on-the-field, facing harsh engineering challenges. This Thesis activity, in collaboration with Avio Aero company, presents an innovative vision for the aerodynamic Design System (DS) of one of the most stressed components: the Low Pressure Turbine (LPT) module. The proposed DS shows highly multi-disciplinary interactions and a deep integration with the Computational Fluid Dynamic (CFD). Three key aspects characterize this vision: innovation, automation and optimization. Some innovative features are studied and implemented, especially during the first Concept Design (CD) phase at pitch-line level. In particular, a deep revision of the classic correlations for aerodynamic losses is addressed by a novel conception. This study led to two contributions in international conferences (EASN [Ampellio et al., 2012] and ASME [Ampellio et al., 2013b]). Basically, the formulation of such loss models and the relative coefficients are rearranged and tuned following a CFD-based approach, in order to better fit the fully integrated vision belonging to the proposed DS. The CFD integration is in fact merged at any level, from the concept to details, resulting in a successful strategy to move very quickly the LPT assessment from 1D to 3D. The reliability of the innovative DS as a whole is guaranteed by the development of a dedicated all-inclusive, user-friendly, error-proof, and, above all, highly automated platform, able to efficiently drive the design towards the specific pursued goals with simplicity. Besides, the optimization of turbine modules is a fundamental but difficult step, since it takes into account a huge number of variables, constraints, objectives and parameters. Different optimization methods are suited to the different problems faced during the aero design of a turbomachinery. As a consequence, many algorithms have to be embedded into the DS with cross-functional attitudes, in order to provide the best compromise solution in any situation. In general, the optimization environment for aeronautical turbines is complex and strongly based on time-consuming CFD runs, especially when it comes to Preliminary and Detailed Design (PD, DD). Then, efficient strategies specifically dedicated to expensive simulation-based problems must be adopted, with the aim at minimizing the number of analyses required to solve the problem. A discussion about applied and simulation-based numerical optimization was exposed in a Seminar held at Politecnico di Torino [Ampellio, 2013] and a large part of this dissertation will be focused on the same topic. In this scenario, an original and fast algorithm able to effectively cope with costly problems involving time-consuming analyses is developed. It is a metaheuristic based on swarm intelligence and hybridized with interpolation strategies, i.e., the Artificial super-Bee enhanced Colony (AsBeC) algorithm. Evidences for its numerical validation are discussed and it is compared on different analytical benchmarks and international competitions with other cutting-edge modern algorithms. A first version for the algorithm (ABC+) was applied to aeronautical LPTs for Single-Objective (SO) problems in an international conference proceeding (AIDAA [Ampellio et al., 2013a]), while another interesting Multi-Objective (MO) application is now published in an international Journal (Advances in Aircraft and Spacecraft Science [Ampellio et al., 2016]). Furthermore, the numerical validation of the most recent AsBeC version has been recently published at the prestigious Swarm Intelligence Journal (Springer [Ampellio and Vassio, 2016b]) and the algorithm is also a participant to the CEC 2016 contemporary worldwide competition [Ampellio and Vassio, 2016a]. In the end, some real-world applications for the new DS are reported. The major advantages of the multi-disciplinary fully integrated CFD Design System are highlighted through a notable literature test case and two of the most interesting projects of the last years for aeronautic turbomachinery: advanced high-performance LPT for large Turbofan engines and high-speed LPT for open rotors. Besides, the proposed DS is being recently used by Avio Aero even on cooled High Pressure Turbines (HPT) for the next generation of turboprops. Despite the integrated design vision debated in this Thesis was born and confined to the aero design of aeronautical turbines, the basic principles behind it find a potential application to any engineering discipline and also to many different fields. The hope is to encourage a path towards inclusiveness and multi-disciplinarity during any kind of design, promoting also the extensive use of artificial intelligence in terms of automation and optimization

Innovation, automation and optimization in the aero design for aeronautical turbines

In the framework of civil air transportation, the design of turbomachinery components for modern aeronautical engines involves many tough technical issues. In fact, the increasing request for performance, durability, reliability and safety, reducing at the same the production times and design costs, emissions and fuel consumption, is pushing the traditional Turbofan architecture towards its limit. Levering on past experience and legacy is not sufficient for the next generation of engines, therefore the study and introduction of innovative features and technologies is essential. Nevertheless, those innovations must be proven to be effective, initially by a numerical point of view, then experimentally and on-the-field, facing harsh engineering challenges. This Thesis activity, in collaboration with Avio Aero company, presents an innovative vision for the aerodynamic Design System (DS) of one of the most stressed components: the Low Pressure Turbine (LPT) module. The proposed DS shows highly multi-disciplinary interactions and a deep integration with the Computational Fluid Dynamic (CFD). Three key aspects characterize this vision: innovation, automation and optimization. Some innovative features are studied and implemented, especially during the first Concept Design (CD) phase at pitch-line level. In particular, a deep revision of the classic correlations for aerodynamic losses is addressed by a novel conception. This study led to two contributions in international conferences (EASN [Ampellio et al., 2012] and ASME [Ampellio et al., 2013b]). Basically, the formulation of such loss models and the relative coefficients are rearranged and tuned following a CFD-based approach, in order to better fit the fully integrated vision belonging to the proposed DS. The CFD integration is in fact merged at any level, from the concept to details, resulting in a successful strategy to move very quickly the LPT assessment from 1D to 3D. The reliability of the innovative DS as a whole is guaranteed by the development of a dedicated all-inclusive, user-friendly, error-proof, and, above all, highly automated platform, able to efficiently drive the design towards the specific pursued goals with simplicity. Besides, the optimization of turbine modules is a fundamental but difficult step, since it takes into account a huge number of variables, constraints, objectives and parameters. Different optimization methods are suited to the different problems faced during the aero design of a turbomachinery. As a consequence, many algorithms have to be embedded into the DS with cross-functional attitudes, in order to provide the best compromise solution in any situation. In general, the optimization environment for aeronautical turbines is complex and strongly based on time-consuming CFD runs, especially when it comes to Preliminary and Detailed Design (PD, DD). Then, efficient strategies specifically dedicated to expensive simulation-based problems must be adopted, with the aim at minimizing the number of analyses required to solve the problem. A discussion about applied and simulation-based numerical optimization was exposed in a Seminar held at Politecnico di Torino [Ampellio, 2013] and a large part of this dissertation will be focused on the same topic. In this scenario, an original and fast algorithm able to effectively cope with costly problems involving time-consuming analyses is developed. It is a metaheuristic based on swarm intelligence and hybridized with interpolation strategies, i.e., the Artificial super-Bee enhanced Colony (AsBeC) algorithm. Evidences for its numerical validation are discussed and it is compared on different analytical benchmarks and international competitions with other cutting-edge modern algorithms. A first version for the algorithm (ABC+) was applied to aeronautical LPTs for Single-Objective (SO) problems in an international conference proceeding (AIDAA [Ampellio et al., 2013a]), while another interesting Multi-Objective (MO) application is now published in an international Journal (Advances in Aircraft and Spacecraft Science [Ampellio et al., 2016]). Furthermore, the numerical validation of the most recent AsBeC version has been recently published at the prestigious Swarm Intelligence Journal (Springer [Ampellio and Vassio, 2016b]) and the algorithm is also a participant to the CEC 2016 contemporary worldwide competition [Ampellio and Vassio, 2016a]. In the end, some real-world applications for the new DS are reported. The major advantages of the multi-disciplinary fully integrated CFD Design System are highlighted through a notable literature test case and two of the most interesting projects of the last years for aeronautic turbomachinery: advanced high-performance LPT for large Turbofan engines and high-speed LPT for open rotors. Besides, the proposed DS is being recently used by Avio Aero even on cooled High Pressure Turbines (HPT) for the next generation of turboprops. Despite the integrated design vision debated in this Thesis was born and confined to the aero design of aeronautical turbines, the basic principles behind it find a potential application to any engineering discipline and also to many different fields. The hope is to encourage a path towards inclusiveness and multi-disciplinarity during any kind of design, promoting also the extensive use of artificial intelligence in terms of automation and optimization

A hybrid ABC for expensive optimizations: CEC 2016 competition benchmark

An evolution of the Artificial Bee Colony (ABC) optimization algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented for leading to the best improvement with a low number of analyses. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of ABC structure and original hybridizations with interpolation strategies. The aforementioned techniques are tested on the expensive benchmark of the Special Session on RealParameter Single Objective Optimization at CEC 2016. In this specific case, the hybridization with a quadratic trust region approach assumes a major importance. Moreover, the AsBeC results are compared to the algorithms tested on the same benchmark at CEC 2015, showing remarkable competitiveness and robustness