MDO applications to conventional and novel turboprop aircraft within agile European project

In this paper, multidisciplinary design optimization within the AGILE European project is applied to two turboprop aircraft. The first one is a conventional configuration characterized by wing mounted engines, while the second one is an innovative configuration with rear engines installation on the horizontal tail tip with an innovative power plant architecture. Both configurations are suited for 90 passengers, a design range of 1200 nautical miles and a cruise Mach number equal to 0.56. The methodologies used to analyze both configurations include aerodynamic performance in clean, landing and takeoff configurations, mission performance, weight and balance, stability and control, emissions, in terms of Global Warming Potential parameter, and Direct Operating Cost estimation. The latest two will be considered as objective functions for the optimization loop. Aim of this paper is to compare both configurations highlighting benefits and limits. Particular attention has been posed on the innovative approach used to analyze the use cases. The whole design process is made up ofdifferent tools belonging to a specific partner. Each partner is specialized in a specific discipline. The design process has been setup to be completely automated so that, partners, distributed worldwide are able to communicate and exchange results through remote connection. In this way each discipline has been assigned to the suited specialist

Game theory and evolutionary algorithms applied to MDO in the AGILE European project

In this paper, an optimization technique in aircraft design field, based on game theory and evolutionary algorithms to define the key variables for Multi-Disciplinary aircraft Optimization (MDO) into AGILE (Aircraft 3rd Generation MDO for Innovative Collaboration of Heterogeneous Teams of Experts) European project, is presented. This work represents one of the contributions given by UniNa (University of Naples “Federico II”) research group within the AGILE project, which is coordinated by the DLR and funded by EU through the project HORIZON 2020 that aims to create an evolution of MDO, promoting a novel approach based on collaborative remote design and knowledge dissemination among various teams of experts. Since the aircraft design field is very complex in terms of number of involved variables and the dimension of the space of variation, it is not feasible to perform an optimization process on all the design parameters; this leads to the need to reduce the number of the parameters to the most significant ones. A multi-objective optimization approach allows many different variables, which could be a constraint or an objective function for the specific investigation; thus, setting the constraints and objectives to reach, it is possible to perform an optimization process and control which parameters significantly affect the final result. Within AGILE project, UniNa research group aims to perform wing optimization processes in a preliminary design stage, coupling Nash game theory (N) with typical genetic evolutionary algorithm (GA), reducing computational time and allowing a more realistic association among objective functions and variables, to identify the main ones that significantly affect final result and that consequently must be considered by the partners of the AGILE consortium to perform MDO in the final part of project, applying the proposed optimization technique to novel aircraft configuration

Streamlining Cross-Organizational Aircraft Development: Results from the AGILE Project

The research and innovation AGILE project developed the next generation of aircraft Multidisciplinary Design and Optimization processes, which target significant reductions in aircraft development costs and time to market, leading to more cost-effective and greener aircraft solutions. The high level objective is the reduction of the lead time of 40% with respect to the current state-of-the-art. 19 industry, research and academia partners from Europe, Canada and Russia developed solutions to cope with the challenges of collaborative design and optimization of complex products. In order to accelerate the deployment of large-scale, collaborative multidisciplinary design and optimization (MDO), a novel methodology, the so-called AGILE Paradigm, has been developed. Furthermore, the AGILE project has developed and released a set of open technologies enabling the implementation of the AGILE Paradigm approach. The collection of all the technologies constitutes AGILE Framework, which has been deployed for the design and the optimization of multiple aircraft configurations. This paper focuses on the application of the AGILE Paradigm on seven novel aircraft configurations, proving the achievement of the project’s objectives

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

3rd generation of collaborative MDO for more efficient aircraft preliminary design. The turboprop case within the AGILE project

One of the most important aspect within the aircraft design discipline is to be able to perform rapid and reliable analyses on diferent aircraft configurations. In this scenario has arisen the AGILE (Aircraft 3rd Generation MDO for Innovative Collaboration of Heterogeneous Teams of Experts) project. This one was a successful European project, part of the HORIZON 2020 programme, which gave a relevant contribution to the state of the art of Multidisciplinary Design Analysis and Optimization (MDAO) approach in aircraft design. The project aimed to create an evolution of MDO, promoting a novel approach based on collaborative remote design and knowledge dissemination among various and heterogeneous teams of experts. To achieve this goal, great importance was attached to massive collaboration among experts, to the development of technologies to allow remote and distributed design approach between partners worldwide dislocated and to the development of tool and/or new optimization methodologies. The collaboration aspect was strongly faced by the author by setting up a whole aircraft design and optimization toolchain in terms of data exchanging, tools integration, workflow implementation and execution. Furthermore, thanks to the aircraft design tools and a new optimization technique which couples Nash game theory and genetich algorithm developed by the author, well-assessed AGILE technologies and setup of the whole framework, a complete MDAO task was performed on diferent disruptive aircraft configurations, with a main focus of the candidate on two innovative regional turboprop architectures. This work can be divided in two main parts: the first is a more descriptive one, to best clarify the global scenario in which the research was conducted, and the second moves the focus on the major author's contributions to the AGILE project

The Agile method applied to aircraft design at University of Naples

At University of Naples Federico II (UniNa) the AGILE paradigm has been assumed as guideline to develop new methodologies, tools and software applied to the aircraft design. A heterogeneous team work cooperates to find and develop more reliable methods, updating the older, implements them into state-of-the-art framework and software language, and integrates these new procedures into a cluster of partners in order to perform MDO on innovative aircraft configurations, such as in the AGILE European project context. Methodologies have been well tested and validated for several aircraft configurations and they have been applied into the Design Challenge Level 0 (DC-L0) and Design Challenge Level 1 (DC-L1) of the AGILE project. Results have been useful to set-up the 1st stage of the 3rd MDO framework creation, which is the main goal of the AGILE European project

Application of game theory and evolutionary algorithm to the regional turboprop aircraft wing optimization

Nash equilibrium and evolutionary algorithm are used to optimize a wing of a regional turboprop aircraft, with the aim to compare different optimization strategies in the aircraft design field. Since the aircraft design field is very complex in terms of number of involved variables and space of analysis, it is not possible to perform an optimization process accounting for all possible parameters. This leads to the need to reduce the number of the variables to the most significant ones. A multi-objective optimization approach is here performed, paying attention to the variables which mainly influence the objective functions. Results of Nash-Genetic algorithm are compared against those of both a typical Pareto front and a scalarization, showing that the proposed approach locates almost all solutions on the Pareto front, while the scalarization results are confined only in a zone of this front. The optimization elapsed time for a single optimization point is less than 32% of an entire Pareto front, but the designer must initially choose the players’ cards assignment

Bayesian learning of multiple essential graphs

L’apprendimento strutturale di modelli grafici e` un approccio consolidato all’identificazione di dipendenze complesse in reti biologiche. Presentiamo qui una metodologia bayesiana per l’apprendimento di reti orientate da dati osservazionali quando si osservino sottogruppi distinti di una popolazione.Structural learning of graphical models is a well-established approach to the identification of complex dependencies in biological networks. We here present a Bayesian methodology for learning directed networks from observational data when distinct subgroups of a population are observed

Multiple arrows in the Bayesian quiver: Bayesian learning of partially directed structures from heterogeneous data.

Motivated by the identification of complex dependencies in biological networks, we present a Bayesian method for structural learning of graphical models that exhibits two distinctive features: i) it does not assume a priori an ordering of the variables, but it learns arrows when possible and lines otherwise; ii) it assumes that the observations form subgroups having different but similar structures

Development of a tomato-based food for special medical purpose as therapy adjuvant for patients with HCV infection

OBJECTIVE: The present study aimed to develop a food for special medical purposes (FSMP) and to assess its efficacy as adjuvant therapy in patients with chronic hepatitis C virus (HCV). DESIGN: Open randomized clinical trials with a tomato-based FSMP used as adjuvant treatment to the pharmacological therapy with pegilated interferon and ribavirin. SUBJECTS: Eight healthy volunteers and 39 HCV patients. INTERVENTIONS: For the bioavailability study, healthy subjects consumed 100 g/die FSMP for a week and their serum carotenoid profile at baseline, after the week of administration and 7 days later was determined. The same quantity of FSMP for 6 months by 20 of the 39 HCV patients was consumed in the clinical trial. Serum transaminase, haemoglobin (Hb) and hydroperoxide concentrations during the therapy were monitored in all patients. RESULTS: FSMP consumption caused a fourfold increase of lycopene serum concentration in healthy subjects. A significant increase of carotenoids after 1 month of consumption also in patients with HCV was recorded. Transaminase and Hb serum levels, as well as therapeutic response, were not influenced by FSMP. The decrease in serum hydroperoxides was independent from FSMP consumption in long-term responder patients, whereas nonresponder (NR) patients of FSMP group showed higher reductions than NR patients of Control group. CONCLUSIONS: The FSMP was effective in improving carotenoid status in healthy subjects. In HCV patients, it did not influence the therapeutic response, but it prevented carotenoid serum depletion and it was effective in improving the oxidative status during antiviral therapy in NR patient