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Grant
Agency: European Commission | Branch: H2020 | Program: MSCA-ITN-ETN | Phase: MSCA-ITN-2016 | Award Amount: 3.88M | Year: 2017

In an expanding world with limited resources and increasing uncertainty, optimisation and uncertainty quantification become a necessity. Optimisation can turn a problem into a solution, thus the main focus of this ETN is to explore and develop new approaches to treat uncertainty in complex engineering systems and use novel optimisation techniques to efficiently deal with large scale problems with many objectives and uncertain quantities. It is generally recognised, in fact, that neglecting the impact of uncertainty on the design of any system or process can lead to unreliable design solutions. Common approaches that make use of safety margins to account for uncertainty in design and manufacturing are not adequate to fully capture the growing complexity of engineering systems and provide reliable and optimal solutions. Aerospace engineering is here taken as a paradigmatic area of research and development that is concerned with complex systems, or system of systems, in which optimality and reliability are of paramount importance. UTOPIAE will train the future generation of engineers and mathematicians who will be able to tackle the complexity of aerospace systems and provide greener, more affordable and safer transportation solutions.


Grant
Agency: European Commission | Branch: FP7 | Program: CP | Phase: FoF-ICT-2011.7.3 | Award Amount: 5.59M | Year: 2011

Product improvements, manufacturing processes, product operations and maintenance services require software integrating solutions being able to capture and process information from various actors and different operational phases with the objective of enhancing efficiency and improving sustainability performance from a life-cycle perspective. Static, isolated systems generally in use cannot react fast and flexibly enough, as there are no appropriate services for capturing knowledge and putting it to practical use in relevant time.\nEPES (Eco Process Engineering System) will provide service oriented ICT solutions to generate services, which improve the performance of highly customized industrial processes, products and services (PPS) during their life, in cases in which no standard, off-the-shelf solutions can be applied. In many sectors, PPS improvements require an efficient combination and reconfiguration of software services to meet varying requirements along the product/process life cycle and effectively take into account different ecological constraints (eco-constraints), such as reduction of energy and materials consumption in processes. An important factor influencing this improvement process is the dynamic, flexible, and customizable nature of the software services that back up the life cycle.\nEPES will develop novel ICT-tools, supported by an application methodology, to be combined and embedded in the decision-making processes in companies from different sectors, starting with the three companies from wind farm maintenance, cable production and aeronautics, which represent typical examples of companies in need for such highly reconfigurable services to continuously optimize their product performance and service delivery.\nThe innovation brought by the project is a holistic, modular and extendable system that allows the easy reconfiguration of services in order to react flexibly to relevant insights and to meet varying requirements along the PPS life cycle. The EPES system will include features for collaborative work, a Virtual Factory knowledge repository, service configuration, and a simulation and decision-making platform, with the ultimate objective of facilitating the use, reuse, storage, analysis and sharing of knowledge within the Virtual Factory. EPES responds to the companies need for moving from static isolated systems to a more flexible and loosely coupled system applicable along the whole PPS cycle. Thus, instead of having desktop applications and engineering islands of analysis, EPES aims to integrate existing tools and provide them as configurable services for non-ICT experts.\nThe main advantages of EPES include increased efficiency in generating and reconfiguring services for PPS by at least 20% and an increased flexibility of such services, especially in the cases where eco constraints play a key role. This leads to a number of potential business benefits, measured by Key Performance Indicators, specific for various applications, such as reduction of costs along PPS and of the ecological impact of improved PPS\nKeywords: Eco-constraint, Sustainability Intelligence, Configurable life-cycle service, Virtual Factory, Collaborative work, Process Product Service (PPS)


Grant
Agency: European Commission | Branch: FP7 | Program: CP-FP | Phase: AAT.2013.4-2.;AAT.2013.1-1. | Award Amount: 5.83M | Year: 2013

Virtual prototyping (VP) is a key technology for environmental friendly and cost effective design in the aircraft industry. However, the underlying analysis and simulation tools (for loads, stresses, emissions, noise), are currently applied with a unique set of input data and model variables, although realistic operating conditions are a superposition of numerous uncertainties under which the industrial products operate (uncertainties on operational conditions, on geometries resulting from manufacturing tolerances, numerical error sources and uncertain physical model parameters). Major new developments in this new scientific area of Uncertainty Management and Quantification (UM and UQ) and Robust Design methods (RDM) are needed to bridge the gap towards industrial readiness, as the treatment of uncertainties enables a rigorous management of performance engagements and associated risks. This is the main objective of the UMRIDA project, which has the following action lines: Address major challenges in UQ and RDM to develop and apply new methods able to handle large numbers of simultaneous uncertainties, generalized geometrical uncertainties in design and analysis within a turn-around time acceptable for industrial readiness in VP systems. To respond to the validation requirements of UQ and RDM, a new generation of database, formed by industrial challenges (provided by the industrial partners), and more basic test cases, with prescribed uncertainties, is proposed. The methods developed will be assessed quantitatively towards the industrial objectives on this database, during the project and at two open workshops. The gained experience will be assembled in a Best Practice Guide on UQ and RDM. It is anticipated that the UMRIDA project will have a major impact on most of the EU objectives for air transport, by enabling design methods to take into account uncertainty based risk analysis.


Grant
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: NMBP-23-2016 | Award Amount: 3.90M | Year: 2017

The mission of COMPOSELECTOR is to develop a Business Decision Support System (BDSS), which integrates materials modelling, business tools and databases into a single workflow to support the complex decision process involved in the selection and design of polymer-matrix composites (PMCs). This will be achieved by means of an open integration platform which enables interoperability and information management of materials models and data and connects a rich materials modelling layer with industry standard business process models. In order to satisfy the need for effectively designing and producing increasingly sophisticated materials, components and systems with advanced performance on a competitive time scale there is a particular need in industry for chemistry/physics-based materials models and modelling workflows which capture the performance of materials, accounting for material internal microstructure and effects of processing, provide accuracy/validation of predicted data, and relevant management of uncertainty and assemble knowledge ready for decision makers to act upon. COMPOSELECTOR will address these needs by integration of (discrete and continuum) materials models and process models as well as structured and unstructured data into a standards-based, open integration framework, implementing uncertainty management and multi-criteria optimisation in order to provide actionable choices, and building tailored knowledge apps to support decision makers. The human interface of COMPOSELECTOR will be supported by Visual Analytics capable of integrating qualitative, quantitative and cognitive aspects for a user-friendly management of the vast quantity of available data. The COMPOSELECTOR BDSS will be applied to and validated by end users targeting accurate, reliable, efficient and cost effective decision-making and management of polymer matrix composite (PMC) materials in the transport and aerospace value chains.


Rigoni E.,Esteco Srl | Turco A.,Esteco Srl
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

Metamodels can speed up the optimization process. Previously evaluated designs can be used as a training set for building surrogate models. Subsequently an inexpensive virtual optimization can be performed. Candidate solutions found in this way need to be validated (evaluated by means of the real solver). This process can be iterated in an automatic way: this is the reason of the fast optimization algorithms. At each iteration the newly evaluated designs enrich the training database, permitting more and more accurate metamodels to be build in an adaptive way. In this paper a novel scheme for fast optimizers is introduced: the virtual optimization - representing an exploitation process - is accompanied by a virtual run of a suited space-filler algorithm - for exploration purposes - increasing the robustness of the fast optimizer. © 2010 Springer-Verlag.


Turco A.,ESTECO Srl
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

AFSQP is a Sequential Quadratic Programming algorithm which obtains global convergence through an adaptive filter technique. This adaptivity is the major innovation in this work. The resulting algorithm can deal with constraints involving different length scales without requiring their normalization. The effort related to gradients computation is compensated by achieving superlinear local convergence rate (under some hypothesis on the problem, the algorithm can reach quadratic rates). Second order derivatives are approximated with classical BFGS formula and need not to be computed. We describe the theoretical background of the algorithm as well as its implementation details. A comparison between AFSQP and four different SQP implementations is performed considering several small and medium scale problems selected within Hoch and Schittkowski suite. We focus attention on the number of point evaluations required. © 2010 Springer-Verlag.


Nicolich M.,ESTECO SpA | Cassio G.,ESTECO SpA
CEUR Workshop Proceedings | Year: 2014

Design optimization is a key activity to improve product performance in the design of modern manufacturing products, in order to reduce costs and time to market. Design optimization makes extensive use of virtual prototype simulations in the automatic search of the design space. Nowadays, engineering products draw together many components assembled in subsystems and systems. Each component is described by different physics, and the performance assessment covers the whole range of engineering analysis - e.g. mechanical, structural, thermal, electromagnetic, etc.-, requiring multiple simulation processes. Many groups are involved in providing these different components and the simulation of physics dimensions are carried out by each single player counting on disparate levels of expertise and computing resources. This paper shows how SOMO collaborative and distributed execution framework is used to compose multiple simulation processes at component level to generate system models managing the complexity of running multidisciplinary design projects. Driving process, component and subsystem knowledge with system models, SOMO allows a larger inference space for design, the ability to continually connect at the system level, and a basis for knowledge capture. In this paper a real test case performed on the design and optimization of wind turbine is presented. The design workflow is managed by different engineering experts through a collaborative framework. Copyright © held by the authors.


Turco A.,ESTECO Srl | Kavka C.,ESTECO Srl
International Journal of Innovative Computing and Applications | Year: 2011

We present a multi-objective genetic algorithm called magnifying front genetic algorithm (MFGA) designed in order to treat complex real-world optimisation problems. A first source of complexity is the presence of different input variables classes (real, discrete and categorical). MFGA is able to treat appropriately each of them as well as any combination. Moreover, real-world applications often require a long time to evaluate objective values from input variables. We deal with this issue working on elitism (in order to tune properly the balance between explorative and exploitative capabilities of the algorithm) and introducing a parallel steady-state evolution scheme, which is able to use the available computing resources as much intensively as possible. We test the algorithm on two different scenarios: mathematical benchmarks and real-world applications. For the latter one we chose a problem arising in multi-processor system-on-chip (MPSoC) design, a field which is characterised by discrete and more often categorical variables. © 2011 Inderscience Enterprises Ltd.


Turco A.,ESTECO srl
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

We propose a metamodel approach to the approximation of functions gradients within a hybrid genetic algorithm. The underlying structure is implemented in order to support parallel execution of the code: a genetic and a SQP algorithm run in different threads and can ask designs evaluations independently, but keeping all the available resources always working. A common archive collects the results and generates the population for the GA and the starting points for the SQP runs. A particular attention is dedicated to elitism and to constraints. The hybridization is performed through a modified e - constrained method. The general philosophy of the algorithm is to concentrate on not wasting information: metamodels, archiving and elitism, steady-state parallel evolution are key elements for this scope and they will be discussed in details. A preliminary but explanatory row of tests concludes the paper highlighting the benefits of this new approach. © Springer-Verlag Berlin Heidelberg 2011.


Russo R.,Esteco Spa | Clarich A.,Esteco Spa | Carriglio M.,Esteco Spa
International Review of Mechanical Engineering | Year: 2012

This paper shows a multi-objective optimization example in the automotive area using the optimization environment software modeFRONTIER [1] integrated with ANSA as mesh morpher and ABAQUS as structural solver. In the modeFRONTIER environment, the users can easily define a workflow linking together different modules that represent the 'bricks' of the optimization problem: design variables, model and mesh files, CAE nodes, specified outputs, as well as optimization objectives and constraints [2]. In particular, several CAE tools can be easily integrated in the process through the available direct interface nodes, allowing the automatic update ofparameters and extraction of specified results, until the required optimization objectives are satisfied. © 2012 Praise Worthy PrizeS.r.l. - All right reserved.

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