Search

Background: Standardized methods for testing Viral Filtration Efficiency (VFE) of tissues and devices are lacking and few studies are available on aerosolizing, sampling and assessing infectivity of SARS-CoV-2 in controlled laboratory settings. NanoAg-coated endonasal filters appear a promising aid for lowering viable virus inhalation in both adult and younger populations (e.g., adolescents). Objective: to provide an adequate method for testing SARS-CoV-2 bioaerosol VFE of bio-gel Ag nanoparticles endonasal filters, by a model system, assessing residual infectivity as cytopathic effect and viral proliferation on in vitro cell cultures. Methods: A SARS-CoV-2 aerosol transmission chamber fed by a BLAM aerosol generator produces challenges (from very high viral loads (105 PFU/mL) to lower ones) for endonasal filters positioned in a Y shape sampling port connected to a Biosampler. An aerosol generator, chamber and sampler are contained in a class II cabinet in a BSL3 facility. Residual infectivity is assessed from aliquots of liquid collecting bioaerosol, sampled without and with endonasal filters. Cytopathic effect as plaque formation and viral proliferation assessed by qRT-PCR on Vero E6 cells are determined up to 7 days post inoculum. Results: Each experimental setting is replicated three times and basic statistics are calculated. Efficiency of aerosolization is determined as difference between viral load in the nebulizer and in the Biosampler at the first day of experiment. Efficiency of virus filtration is calculated as RNA viral load ratio in collected bioaerosol with and without endonasal filters at the day of the experiment. Presence of infectious virus is assessed by plaque forming unit assay and RNA viral load variations. Conclusions: A procedure and apparatus for assessing SARS-CoV-2 VFE for endonasal filters is proposed. The apparatus can be implemented for more sophisticated studies on contaminated aerosols.

Selecting the best scenario of intervention to improve the functioning of complex systems represents a troublesome task both for public and private decision makers, since it requires the deployment of appropriate analysis and evaluation tools. In the case of intermodal transport systems, complexity is given by the execution of different types of activities using various resources, and by the presence of several actors operating in the same environment with diverse goals. In this paper, an integrated approach to assess design alternatives regarding rail port operations is proposed by combining business process modeling and multi-actor multi-criteria evaluation. In fact, a railway process has been graphically represented by means of the standardized modeling language called Business Process Modeling and Notation (BPMN) at different levels of detail, i.e. taking into account not only the actual transport operations but also the necessary documentary procedures to perform the freight transfer services. In addition to the identification of possible bottlenecks, the analysis of the considered railway process has enabled the determination of its most significant features. These parameters have been subsequently used as some of the criteria according to which the performances of the examined scenarios of intervention have been evaluated, adopting the Analytic Hierarchy Process (AHP) technique. Furthermore, the appraisal has been enhanced by explicitly including the key stakeholders involved in the railway process at hand. The developed methodology has been applied to the case study of the Port of Trieste, Italy, in order to investigate possibilities for an increase in railway capacity.

The rapid growth of container movements deriving from the advent of globalization has caused a relevant pressure on ports. In this context, the port efficiency performances requested by the market have been met by an increased adoption of railway transport. Despite its environmental and economic sustainability, this intermodal transport solution is characterized by a high degree of complexity given by the execution of different activities and the involvement of several stakeholders. As such, the need of deeply analyzing logistics business processes has led to the embracement of modeling techniques, which allow practitioners to identify possible criticalities along the chain. In this regard, the selection of the appropriate modeling method and notation definitely influences the results of the process examination. In the present paper, the train departure process in the Port of Trieste, Italy, has been investigated in terms of information flows: to that end, the Business Process Modeling Notation (BPMN) standard has been used. On one hand, this task has enabled to clarify the documentary procedure occurring in diverse operational scenarios and, on the other hand, it has allowed also to recognize the process bottlenecks which hinder an increase in the railway traffic volume. Furthermore, a quantitative analysis concerning the terminal and gateway capacity, before and after the implementation of a technological intervention, has demonstrated the possible growth of train flows by modifying the process only at the organizational level. These accomplishments have proved to port managers the usefulness of applying a theoretical graphical representation to a practical transport process.

This paper presents the multi-objective optimisation of the MH114 high-lift airfoil. We seek the set of Pareto optimal solutions that maximise the airfoil lift and minimise the drag. The lift and drag forces are considered uncertain due to geometrical uncertainties. The uncer- tainty quantification of the probabilistic aerodynamic force values re- quires a large number of samples. However, the prediction of the aero- dynamic forces is expensive due to the numerical solution of the Navier- Stokes equations. Therefore, a multi-fidelity surrogate assisted approach is employed to combine expensive RANS simulations with cheap poten- tial flow calculations. The multi-fidelity surrogate-based approach allows us to economically optimise the aerodynamic design of the airfoil under uncertainty.

Response Surface Methods (RSMs) are statistical and numerical models that approximate the relationship between multiple input variables and an output variable. This chapter introduces the methodology and its importance for engineer- ing design optimisation. The basic steps to build RSMs and validate the model accuracy are explained. An overview of three classical methods (Least Squares, Radial Basis Functions, and Kriging) is provided. A simple wing structure design optimisation problem is used to illustrate the different phases of the response surface methodology and its application to design optimisation. This example also includes the case of noisy data.

The relevant pressure exerted on ports following the advent of globalization and containerization has contributed to transform the role of such intermodal transport facilities, increasing their complexity with reference to both performed activities and involved actors. The potential of solid hinterland connections in enhancing port competitiveness has sustained the larger embracement of the railway mode, which has proved to be a financially and environmentally sustainable solution, especially on long hauls. However, the limited residual capacity currently characterizing several railway network and nodes may threaten the accommodation of additional future traffic volumes and, thus, it requires the adoption of a coordinated strategy to more efficiently manage existing railway infrastructures, other than the realization of some new ones. In this regard, an integrated methodology has been developed to specifically model, simulate and optimize port railway processes, as part of a more complex system, imposing the maximization of capacity with the concurrent minimization of used shunting locomotives, to varying of infrastructural and operational conditions. This combination of techniques has been applied to the case study of the Port of Trieste, Italy, showing the greater influence of tracks availability over changes in the operational conditions, in terms of a shorter duration of specific administrative tasks and the employment of more shunting locomotives.

Trains move along the railway infrastructure according to specific timetables. The timetables are based on the running time calculation and they are usually calculated without considering explicitly energy consumption. Since green transportation is becoming more and more important from environmental perspectives, energy consumption minimization could be considered also in timetable calculation. In particular, the Energy-Efficient Train Timetabling Problem (EETTP) consists in the energy-efficient timetable calculation considering the trade-off between energy efficiency and running times. In this work, a solution approach to solve a multi-objective EETTP is described in which the two objectives are the minimization of both energy consumption and the total travel time. The approach finds the schedules to guarantee that the train speed profiles minimize the objectives. It is based on modeFRONTIER and OpenTrack that are integrated by using the OpenTrack Application Programming Interface in a modeFRONTIER workflow. In particular, the optimization is made by modeFRONTIER, while the calculation of the train speed profiles, energy consumption and total travel time is made by OpenTrack. The approach is used with Multi-objective Genetic Algorithm-II and the Non-dominating Sorting Genetic-II, which are two genetic algorithms available in modeFRONTIER. The solution approach is tested on a case study that represents a real situation of metro line in Turkey. For both algorithms, a Pareto Front of solution which are a good trade-off between the objectives are reported. The results show significant reduction of both energy consumption and total travel time with respect to the existing timetable.

In this work, a design optimisation strategy is presented for expensive and uncertain single- and multi-objective problems. Computationally expensive design fitness evaluations prohibit the application of standard optimisation techniques and the direct calculation of risk measures. Therefore, a surrogate-assisted optimisation framework is presented. The computational budget limits the number of high-fidelity simulations which makes impossible to accurately approximate the landscape. This motivates the use of cheap low-fidelity simulations to obtain more information about the unexplored locations of the design space. The information stemming from numerical experiments of various fidelities can be fused together with multi-fidelity Gaussian process regression to build an accurate surrogate model despite the low number of high-fidelity simulations. We propose a new strategy for automatically selecting the fidelity level of the surrogate model update. The proposed method is extended to multi-objective applications. Although, Gaussian processes can inherently model uncertain processes, here the deterministic input and uncertain parameters are treated separately and only the design space is modelled with a Gaussian process. The probabilistic space is modelled with a polynomial chaos expansion to allow also uncertainties of non-Gaussian type. The combination of the above techniques allows us to efficiently carry out a (multi-objective) design optimisation under uncertainty which otherwise would be impractical.

Uncertainty-based optimisation techniques provide optimal airfoil de- signs that are less vulnerable to the presence of uncertainty in the operational conditions (i.e., Mach number, angle-of-attack, etc.) at which an airfoil is func- tioning. These uncertainty-based techniques typically require numerous function evaluations to accurately calculate the statistical measure of the quantity of inter- est. To render the computational burden down, the design optimisation of the air- foil is performed by a multi-fidelity surrogate-based technique. The high-fidelity aerodynamic performance is calculated with a compressible RANS solver using a fine grid. At the low-fidelity level a coarser grid is used. To obtain accurate drag predictions despite the lower grid resolution the so-called far-field drag approxi- mation is employed.

In the design of modern manufacturing products, design optimization is a key activity to improve product performance and reduce costs and time to market. In the automatic search of the design space, design optimization makes extensive use of virtual prototype simulations. Nowadays, engineering products draw together many components; each part is described by different physics and the performance assessment covers the whole range of engineering analysis, e.g. fluid-dynamics, structural, thermal, electro-magnetic, etc. Companies providing these different components may be a notable number and the simulation of physics dimensions may be addressed by each single player of the production chain counting on disparate levels of expertise and computing resources. This paper shows how an inter-organizational design optimization workflow has been set up using ESTECO technology for simulation process and data management to assess and optimize the performance of a wind turbine. Different organizations have been involved in the project: a company expert in wind power unit and expert in CAD (Computer Aided Design) and CFD (Computational Fluid-Dynamics), and ESTECO providing the SPDM (Simulation Process and Data Management) system. In the case presented in this paper, the partners successfully cope with a complex design scenario and effectively collaborate sharing different kinds of information and resources. The SPDM environment, thanks to the web interface and the collaborative environment for simulation process and data management, speeds up the communications among the partners, enforces standardization and the formalization of simulation processes, promotes knowledge reuse, and facilitates the sharing of the needed resources ranging from licensed software to computing power. Each partner of the simulation workflow brings his expertise for a single design analysis. The CAD expert generates the geometry of the blades providing a parametric model and the CAD solution used to update it. The CFD expert sets up the aerodynamic simulation model, which uses the geometry model to evaluate its aerodynamic performance, and makes it available to partners in a shared repository. Finally the wind power unit expert uses the data coming from the CFD simulation to evaluate, using an internally developed code, the power efficiency of the system in the complete range of conditions. While the CAD solver and the internal code can easily run on local resources, the CFD simulation may require a considerable amount of computational resources and software licenses. ESTECO technology allows organizations and teams to take full advantage of current on-demand cloud computing services such as Amazon EC2 or other public, private and hybrid cloud preserving the protection of the exchanged data through the use of virtual private networks and secure communications protocols. Once the design optimization workflow has been set up, the CAD expert submits, through the web interface, a parametric geometry that is optimized to meet the design requirements. Finally, users interested in the results of the optimizations can easily access and select the best designs.

The objectives of the optimisation management that considers both assembly and production Hybrid Flow-Shops lines are several: optimal numbers of machines, shifts, product priority and time-period scheduling. The approach uses a combination of DES software with an MDO (Multidisciplinary Design Optimization) software.. The DES model is optimized following the process: 1) A Multi-objective Genetic Algorithm cycle generates a set of optimal solutions; 2) A Clustering cycle groups the solutions into different sets; 3) The selection of the preferred solution via post-processing; 4) A mono-objective optimisation of each cluster; and 5) creation of weekly scheduling with optimal results. Finally, application and results are described and discussed.

This paper describes the application of an Optimization-under-Uncertainty (OuU) ap- proach for the probabilistic design of engine components in an industrial setting. After a short discussion of recent OuU applications to turbomachinery components we will describe the model used for this study as well as its design and noise parameterization. This is followed by a description of the OuU method employed and the results obtained. Finally we will draw conclu- sions based on our experience and identify opportunities for improvement towards industrially viable OuU approaches.

Ducted propellers are promising candidates for electrical aircrafts where the propulsion efficiency must be highly optimised. The aerodynamic performance and efficiency of ducted propellers can be obtained through models and experiments of various fidelity, ranging from cheap analytic formulas to expensive Computational Fluid Dynamics simulations. The ducted propeller design problem also involves several uncertainties arising from environmental conditions or manufacturing imperfections. To alleviate the computational burden of the uncertainty-based design optimisation of ducted propellers, we propose a new approach that combines multi-fidelity information fusion and dimensionality reduction by using different surrogate models for the design and probabilistic spaces. The method is validated on a simplified propeller model.

This paper introduces the concept of Resilience Engineering in the context of space systems design and a model of Global System Reliability and Robustness that accounts for epistemic uncertainty and imprecision. In particular, Dempster-Shafer Theory of evidence is used to model uncertainty in both system and environmental parameters. A resilience model is developed to account for the transition from functional to degraded states, and back, during the operational life and the dependency of these transitions on system level design choices and uncertainties. The resilience model is embedded in a network representation of a complex space system. This network representation, called Evidence Network Model (ENM), allows for a fast quantification of the global robustness and reliability of system. A computational optimisation algorithm is then proposed to derive design solutions that provide an optimal compromise between resilience and performance. The result is a set of design solutions that maximise the probability of a system to recover functionalities in the case of a complete or partial failure and at the same time maximises the belief in the desired target value of the performance index.

Sailing sports are experiencing a period of radical innovations. Traditional displacing monohulls have always been the reference for yachting in the past, but the continuous quest for performances has generated many variations and many convergences with surprising results. On the aerodynamic side conventional soft sails have been sometime replaced by rigid wings, but the biggest innovation has been the use of traction kites as a propulsion device. On the hydrodynamic side we have seen the rapid growth of hydrofoils, an old concept that is having a rebirth. Probably the most remarkable expression of innovation and integration between concepts having different roots is the hydrofoil kite-board, point of encounter of the traditional wave surfing, the traction kite innovation and the hydrofoil technology. Hydrofoil kite-board can reach speeds up to 3 times the wind speed, are at least one order of magnitude cheaper than any boat with comparable performances, easy to manage and races are spectacular. Hydrofoil are object of investigation trying to further improve performances as well as to increase the stability and the “sailability". In the following we will present a concept that extends the range of efficiency of the foil through a completely passive morphing of the wing.

A combined experimental–computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasirandom number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as selforganizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process.

An automatic computational optimization of a U-shaped diffuser with a 180 deg turn is performed with the objective of reducing the flow distortion at the outflow. Both laminar and turbulent flow conditions are examined using both uniform velocity and fully developed inflow conditions. The design variables are the semi-major and semi-minor axes of the diffuser cross section at the midpoint of the 180 deg turn. The optimal designs are compared with the baseline design which used a circular cross-section throughout. The optimal designs show uniform improvement (reduction) in distortion at the exit by reshaping of the cross-section at the midpoint of the 180 deg turn. The principal characteristic of all optimal designs is an over-expansion of the flow (i.e., the cross sectional area at the midpoint of the 180 deg turn is greater than the crosssectional area at the exit).

The work presents an approach for the optimized design of small gas turbine combustors, which integrates a 0-D code, CFD analyses and an advanced game theory multi-objective optimization algorithm. The output of the 0-D code is a baseline design of the combustor, given the required fuel characteristics, the basic geometry (tubular or annular) and the combustion concept (i.e. lean premixed primary zone or diffusive processes). For the optimization of the baseline design a simplified parametric CAD/mesher model is then defined and submitted to a CFD code. Free parameters of the optimization process are position and size of the liner hole arrays, their total area and the shape of the exit duct, while different objectives are the minimization of NOx emissions, pressure losses and combustor exit Pattern Factor. A 3D simulation of the optimized geometry completes the design procedure. As a first demonstrative example, the integrated design process was applied to a tubular combustion chamber with a lean premixed primary zone for a recuperative methane-fuelled small gas turbine of the 100 kW class. Both overall geometry and computed performance resulted to be comparable with the ones of a newly designed state of the art combustor prototype, assumed as test-case for the described integrated approach. Significant topics of the research are the demonstration of the usefulness of advanced optimization techniques in combustor design and the preliminary validation of the combustion-emission model, a 2-step implementation of the EDM one, completed with the thermal NOx formation mechanism.

In this work we present a synergic integration of the Functional Mock-Up Interface (FMI) and Business Process Model and Notation (BPMN) standards aimed at managing coupled system simulations. The expressiveness of the BPMN diagrams enabled us to define the relationship between the involved systems and guarantees a one-to-one correspondence with an XML file which is the starting point for the automation and the Functional Mock-Up Unit (FMU) orchestration. For that purpose we describe a typical (although non-standard) master algorithm governing the time-dependent simulation of a coupled system. The dependency diagram and the execution algorithm rely on a very limited set of BPMN extension elements since the standard already offers a range of basic elements which facilitate the implementation of a specific execution environment for FMI co-simulation. This study explores the theoretical issues behind the FMI-BPMN integration and the practical implementation problems. The final result is the complete BPMN diagram for the master algorithm, fully interfaced with the FMI functions of the FMU execution blocks.

A multi-dimensional random number generation algorithm was used to distribute chemical concentrations of each of the alloying elements in the candidate alloys as uniformly as possible while maintaining the prescribed bounds on the minimum and maximum allowable values for the concentration of each of the alloying elements. The generated candidate alloy compositions were then examined for phase equilibria and associated magnetic properties using a thermodynamic database in the desired temperature range. These initial candidate alloys were manufactured, synthesized and tested for desired properties. Then, the experimentally obtained values of the properties were fitted with a multi-dimensional response surface. The desired properties were treated as objectives and were extremized simultaneously by utilizing a multi-objective optimization algorithm that optimized the concentrations of each of the alloying elements. This task was also performed by another conceptually different response surface and optimization algorithm for double-checking the results. A few of the best predicted Pareto optimal alloy compositions were then manufactured, synthesized and tested to evaluate their macroscopic properties. Several of these Pareto optimized alloys outperformed most of the candidate alloys on most of the objectives. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of the alloys. A sensitivity analysis of each of the alloying elements was also performed to determine which of the alloying elements contributes the least to the desired macroscopic properties of the alloy. These elements can then be replaced with other candidate alloying elements such as not-so-rare earth elements.

This paper describes the use of integrated multidisciplinary analysis and optimization of a race car model on a predefined circuit. The objective is the definition of the most efficient geometric configuration that can guarantee the lowest lap time. In order to carry out this study it has been necessary to interface the design optimization software modeFRONTIER with the following softwares: CATIA v5, a three dimensional CAD software, used for the definition of the parametric geometry; A.D.A.M.S./Motorsport, a multi-body dynamic simulation software; IcemCFD, a mesh generator, for the automatic generation of the CFD grid; CFX, a Navier-Stokes code, for the fluid-dynamic forces prediction. The process integration gives the possibility to compute, for each geometrical configuration, a set of aerodynamic coefficients that are then used in the multiboby simulation for the computation of the lap time. Finally an automatic optimization procedure is started and the lap-time minimized. The whole process is executed on a Linux cluster running CFD simulations in parallel.

The flight performance of a ramjet-powered missile is improved through the use of an automated optimization loop relying on computational-fluid-dynamics tools. A generic supersonic airbreathing missile is first described,and its performance is assessed for a representative mission using Reynolds-averaged Navier-Stokes computations for aerodynamics prediction and theoretical engine performance models. The loop links together an optimization algorithm with an aerodynamic software computing the aerodynamic balance of the missile. Several optimizations are performed using different global algorithms such as simplex, evolutionary strategies, or genetic algorithms. The first ones are mono-objective: for each point of the mission (acceleration, cruise, and maneuver), the best inlet shape has to be found. Then multiobjective optimizations are performed in order to find the pareto front, that is, the best set of shapes satisfying the whole mission.

This paper reports the optimization process of a MW-size, medium voltage, squirrel cage induction motor for industrial applications. Starting from an existing construction, the aim of the optimization is to maximize the induction machine power density, while fulfilling high demanding, specific international Oil&Gas industry regulations. For every considered design, the motor performance is estimated by means of an algorithm based on T-type equivalent circuit. The circuit parameters are determined with a combination of magnetostatic and time harmonic Finite Elements Analysis (FEA), together with analytical formulas. This approach allows the evaluation of the precise parameters dependency on punctual current and frequency values, and allows good performance prediction ability. The multi-objective optimization process input are geometrical variables related to the motor electromagnetic configuration having direct impact on the output performance, is based on Genetic Algorithms (GA), and has been developed with the commercial software ModeFRONTIER®.

This paper describes the combination of several optimization technologies that can be used to tackle challenging design problems. The approach, that uses a multi-objective genetic algorithm, a neural network, and a gradient-based optimizer, is first outlined with the help of a computationally inexpensive mathematical test function. Then the methodology is applied to the design of a sailing yacht fin keel, coupling the optimization codes to 3D Navier–Stokes simulations. To perform the multi-objective optimization task a parallel computer is employed.

Numerical flow analysis in turbomachinery is an indispensable tool for water turbines design. Following the design of axial runner, the energetic and cavitation characteristics can be predicted usi...

This article proposes an effective methodology for the fluid-dynamic design optimization of the sliding spool of a hydraulic proportional directional valve: the goal is the minimization of the flow force at a prescribed flow rate, so as to reduce the required opening force while keeping the operation features unchanged. A full three-dimensional model of the flow field within the valve is employed to accurately predict the flow force acting on the spool. A theoretical analysis, based on both the axial momentum equation and flow simulations, is conducted to define the design parameters, which need to be properly selected in order to reduce the flow force without significantly affecting the flow rate. A genetic algorithm, coupled with a computational fluid dynamics flow solver, is employed to minimize the flow force acting on the valve spool at the maximum opening. A comparison with a typical single-objective optimization algorithm is performed to evaluate performance and effectiveness of the employed genetic algorithm. The optimized spool develops a maximum flow force which is smaller than that produced by the commercially available valve, mainly due to some major modifications occurring in the discharge section. Reducing the flow force and thus the electromagnetic force exerted by the solenoid actuators allows the operational range of direct (single-stage) driven valves to be enlarged.

A thermophotovoltaic (TPV) system is able to convert directly thermal energy, generated by a high temperature heat source, into electricity through thermophotovoltaic cells. Although the energy flux has three steps, designing a TPV system with high efficiency is a challenging task. This particular device has been studied for house heating applications in order to reach better performances and higher efficiency values, compared to traditional boilers. The main issue is to achieve high and uniform temperature values on the emitter surface. In the first step of this project a novel swirl gas burner is being developed and optimized in order to fulfill these objectives. Experimental tests have been performed on a first prototype considering different values of input power, thus fuel flow rate and air mass flow rate, changing some geometrical characteristics of the burner. Collected results have then be used to create response surface functions, to be used in a multi-objective optimization considering efficiency, maximum and mean temperature of the emitter

In this paper a novel approach to the optimization of large windfarms is presented. The wind turbine distribution at a given site is optimized in order to extract the maximum energy for the minimum installation costs. The optimization is made by associating a windfarm simulation model based on wake superposition with a genetic search code. The purpose of the paper is to prove the feasibility of the method by analyzing the results obtained in some simple applications. As a test case, a square site subdivided into 100 square cells as possible turbine locations has been taken, and the optimization is applied to the number and position of the turbines for three wind cases: single direction, constant intensity with variable direction, and variable intensity with variable direction.

In this paper the Tensorial-expanded Chaos Collocation method has been used to solve Fluid Dynamic problems with geometric uncertainties. The main advantage of the Tensorial-expanded Chaos Collocation method is its non-intrusive formulation, so existing deterministic solvers can be used. A Least-Squares Spectral Element Method has been employed for the analysis of the deterministic differential problems obtained by Tensorial-expanded Chaos Collocation. This algorithm exploits a Fictitious Domain approach, so it is particularly suitable to solve differential problems defined on stochastic domains. The great capabilities of the Tensorial-expanded Chaos Collocation method combined to the Fictitious Domain-Least-Squares Spectral Element Method are demonstrated by numerical experiments.

The status of the development of insertion devices for the 1.5–2‐GeV synchrotron radiation source ELETTRA is described. Recent results achieved with a prototype pure permanent magnet undulator and the construction and performance of a prototype hybrid multipole wiggler are presented. The parameters and design of the first undulator that will be constructed for ELETTRA, U12.5, are also presented, and some details are given about the magnetic measurement system.

In this paper the Tensorial-expanded Chaos Collocation method has been applied to fluid dynamic problems with geometric tolerances and aleatoric boundary conditions. The main advantage of the Tensorial-expanded Chaos Collocation method is its non-intrusive formulation, so existing deterministic solvers can be used. A Least-Squares Spectral Element method has been employed for the analysis of deterministic differential problems obtained by Tensorial-expanded Chaos Collocation. This algorithm exploits a Fictitious Domain approach, so it is particularly suitable to solve differential problems defined on stochastic domains. The great capabilities of the Tensorial-expanded Chaos Collocation method combined to Fictitious Domain-Least-Squares Spectral Element method are demonstrated by numerical experiments.

An analysis is presented of a complex set of data on the strength of steels as a function of chemical composition, heat treatment, and test temperature. The steels represent a special class designed to resist deformation at elevated temperatures (750–950 K) over time periods in excess of 30 years, whilst serving in hostile environments. The aim was to compare two methods, a neural network based on a Bayesian formulation, and genetic programming in which the data are formulated in an evolutionary procedure. It is found that in the present context, the neural network is able more readily to capture greater complexity in the data whereas a genetic program seems to require greater intervention to achieve an accurate representation.

When designing a commercial product, engineers have to meet several requirements which boil down to finding the better performances and the higher reliability as possible. Another significant factor that determines product quality is its sensitivity to external or uncontrollable variations. This methodology of design is generally called Robust Design [1]. This paper shows an application of Robust Design methodology to a multi-disciplinary optimization of an engine crankshaft by considering uncertainties in terms of manufacturing errors over the shaft dimensions as well as dynamic loads variability. The application is run using ANSYS Workbench solver and modeFRONTIER [2], through a direct interface between the two codes that has been recently developed. A full Robust Design analysis is applied in order to check the stability of the best candidate solutions according to uncertainties in terms of both manufacturing errors and forcing loads The results obtained are very encouraging, and the procedure described can be applied, in principle, to even more complex problems.

no available

A conceptual design study of the mechanical structure for the APPLE II undulators of the FERMI@Elettra project has been carried out using FEM structural analysis and multiobjective optimization software. In this paper the predicted undulator performance is presented taking into account the mechanical deformations due to the variable magnetic forces. The resultant magnetic field and optical phase errors are shown to be negligibly small.

Many real-world engineering design involve simultaneous optimization of multiple objectives, with remarkable differences with the single-objective optimization. The goal of the single-objective optimization is finding the best solution, which corresponds to the maximum value of one objective function. On the contrary, in the multi-objective case, the goal is to find the best compromise solutions between the different conflicting criterions. For these reasons, new optimization approaches are needed. The Pareto approach (inside the Game Theory methodology) has been demonstrated as an efficient way to solve problems with multicriterion choice; by following this approach one could achieve not only a solution, but a set with the best compromise designs among different objectives. Since only one design among all the alternatives could be put on production, an efficient way to select the more appropriate alternative is needed. In the final Pareto set all the solutions are non dominated but only one is the best compromise according to the project manager requirements. In order to solve the choosing problem, a useful numeric tool comes to the aid: Multi Criteria Decision Making (MCDM). MCDM is both an approach and a set of techniques, with the goal of providing an overall ordering of designs, starting from the most preferred to the least preferred one. In this paper different MCDM techniques are adopted, in order to understand the capability and the differences between the different algorithms. The database is the optimal Pareto set of an aeronautic example, the multi objective design of a transonic airfoil with uncertainties.

Robust Design Optimization is the most appropriate approach to face problems characterized by uncertainties in the operating conditions, that represent a crucial point of aeronautical research activities. The Robust Design methodology illustrated in this paper is based on the multi-objective approach: applying the statistical definition of stability, the method finds, at the same time, optimised solutions for performances and stability. Game Theory is an innovative and efficient numerical methodology that can be applied to solve this kind of multi-objective optimization problems. A Competitive Game Strategy is applied in this paper by linking a mono-objective algorithm, like Downhill Simplex, with a statistical analysis methodology, based on t-Student or on the correlation matrix, that allow to find the optimal variables decomposition between the players (objectives) in the course of the optimization. An alternative to this statistical procedure is given by the innovative Self-OrganisingMaps (SOM) theory, used to find correlations between input or output variables and based on non-linear ordered regression for topology data mapping. The test case used to compare the different methodologies, after a preliminary test on mathematical functions, is the optimization of a symmetric airfoil in transonic and Eulerian flow field with uncertainties in the free stream Mach Number; once the most efficient algorithm is chosen, it is applied to the most demanding optimization of a RAE2822 airfoil in transonic and viscous flow field with uncertainties in the free stream Mach Number and in the angle of attack. In these optimization cases, an adaptive Response Surface Methodology, called DACE, has been used in order to reduce the number of computations required.

The present work presents a novel approach for the optimised design of small gas turbine combustors, that integrates a 0-D code. CFD analyses and an advanced game theory multi-objective optimization algorithm. The output of the 0-D code is a baseline design of the combustor, given the required fuel characteristics, the basic geometry (tubular or annular) and the combustion concept (i.e. lean premixed primary zone or diffusive processes). For the optimization of the baseline design a parametric CAD/mesher model is then defined and submitted to a CFD code. Free parameters of the optimization process are position and size of the liner holes arrays, their total area and the shape of the exit duct, while different objectives are the minimisation of NOx emissions, pressure losses and combustor exit Pattern Factor. As a first demonstrative example, the integrated design process was applied to a tubular combustion chamber with a lean premixed primary zone for a recuperative methane-fuelled small gas turbine of the 100 kW class.Copyright © 2006 by ASME