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International Financial Reporting Standards (IFRS), used by more than 27,000 listed companies in about 100 countries around the world, are considered “investor-oriented” accounting standards. Instead, most national accounting standards in Europe could be defined as “creditor-oriented” standards, primarily focused on the financial information needs of creditors and, to a certain extent, tax authorities. In this sense, national accounting standards can be seen as typical of “weak equity” accounting systems, while IFRS exhibit all the characteristics of "strong equity" accounting systems. Even if EU Regulation No. 1606/2002 allows Member States to extend the use of IFRS beyond the scope of the Regulation itself, in most EU countries the close link between financial reporting and taxation has often represented an obstacle to full IFRS adoption by private companies, or even for the preparation of the individual financial statements of listed companies. Previous studies have investigated the accounting choices of private companies, explaining how they are mostly related to company size, auditor reputation, opportunities for growth, and the presence of a parent company reporting under IFRS. One of the factors often cited in several studies is the correlation between financial leverage and voluntary adoption of IFRS. Our aim is to determine whether the choice of adopting IFRS has an impact on private companies’ credit scoring. We compare a sample of about 1,000 Italian private companies that adopted IFRS voluntarily, representing almost the entirety of voluntary adopters in Italy, with a control sample of non-adopters to correlate their credit scoring, provided by a rating agency, with their choice to adopt IFRS and with other financial and governance variables. We believe that our research investigates the voluntary adoption of IFRS by private companies from a new perspective, allowing us to shed some light on the connections between financial reporting practices in creditor-oriented financial systems and access to credit financing.

The importance of assessing the financial distress risk of a company is a topic that has been of central value in many different economic fields and since a long time. Until the twenty-first century, most of the studies were concentrated primarily on using mathematical and statistical methods to assess the health of businesses. Many of these studies employed either accounting-based ratios or cash flow-based ratios; even if there is not a unique conclusion, the use of cash flows seems to improve the predictive capacity of the models significantly. Especially in the last twenty-five years, methods derived from different fields started to be applied in forecasting corporate failures, such as artificial neural networks, genetic algorithms, and fuzzy logic.The objective of this study was to test the goodness of the discriminatory power of ratios based only on cash flows using a model that employs genetic algorithms and fuzzy logic. Five countries (Germany, Spain, France, Great Britain, Italy) and five Nace macro sectors (Agriculture, Industry, Services, Construction, Commerce and Food) have been considered in the analysis for a total of around 719-thousand companies. The model has proven to be well-performing on most of the countries and sectors that have been tested. The results obtained are almost all adequate; in particular, in Germany and Spain, results have been particularly good.The main weaknesses of this work are the limited availability of financial data in some countries and the time delay from the reporting of financial statement to the availability of the data through web services. It means that a large-scale risk assessment requires – being useful for the public and the private sectors – greater and faster disclosure of information at European level, and standardization of financial information transparency among countries.

In order to perform the accurate tuning of a machine and improve its performance to the requested tasks, the knowledge of the reciprocal influence among the system's parameters is of paramount importance to achieve the sought result with minimum effort and time. Numerical simulations are an invaluable tool to carry out the system optimization, but modeling limitations restrict the capabilities of this approach. On the other side, real tests and measurements are lengthy, expensive, and not always feasible. This is the reason why a mixed approach is presented in this work. The combination, through recursive cokriging, of low-fidelity, yet extensive, numerical model results, together with a limited number of highly accurate experimental measurements, allows to understand the dynamics of the machine in an extended and accurate way. The results of a controllable experiment are presented and the advantages and drawbacks of the proposed approach are also discussed.

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.

Purpose – This paper aims to deal with an efficient strategy for robust optimization when a large number of uncertainties are taken into account. Design/methodology/approach – ANOVA analysis is used in order to perform a variance-based decomposition and to reduce stochastic dimension based on an appropriate criterion. A massive use of metamodels allows reconstructing response surfaces for sensitivity indexes in the design variables plan. To validate the proposed approach, a simplified configuration, an inverse problem on a 1D nozzle flow, is solved and the performances compared to an exact Monte Carlo reference solution. Then, the same approach is applied to the robust optimization of a turbine cascade for thermodynamically complex flows. Findings – First, when the stochastic dimension is reduced, the error on the variance between the reduced and the complete problem was found to be roughly estimated by the quantity (1−T¯ TSI )×100, where T¯ TSI is the summation of TSI concerning the variables respecting the TSI criterion. Second, the proposed strategy allowed obtaining a converged Pareto front with a strong reduction of computational cost by preserving the same accuracy. Originality/value – Several articles exist in literature concerning robust optimization but very few dealing with a global approach for solving optimization problem affected by a large number of uncertainties. Here, a practical and efficient approach is proposed that could be applied also to realistic problems in engineering field.

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

The paper investigates the sensitivity of numerically computed flow fields to uncertainties in thermodynamic models for complex organic fluids. Precisely, the focus is on the propagation of uncertainties introduced by some popular thermodynamic models to the numerical results of a computational fluid dynamics solver for flows of molecularly complex gases close to saturation conditions (dense gas flows). A tensorial-expanded chaos collocation method is used to perform both a priori and a posteriori tests on the output data generated by thermodynamic models for dense gases with uncertain input parameters. A priori tests check the sensitivity of each equation of state to uncertain input data via some reference thermodynamic outputs, such as the saturation curve and the critical isotherm. A posteriori tests investigate how the uncertainties propagate to the computed field properties and aerodynamic coefficients for a flow around an airfoil placed into a transonic dense gas stream.

A tensorial-expanded chaos collocation method is developed to take into account uncertainties on thermodynamic properties of complex organic substances. Precisely, we analyse the effect of uncertainties introduced by several thermodynamic models on the numerical results provided by a computational fluid dynamics solver for flows of molecularly complex gases close to saturation condition (dense gas flows). The tensorial-expanded chaos collocation method is used to perform both a priori and a posteriori tests on the output data generated by three popular thermodynamic models for dense gases with uncertain input parameters. A priori tests check the sensitivity of each equation of state to uncertain input data via some reference thermodynamic outputs, such as the saturation curve and the critical isotherm. A posteriori tests investigate how uncertainties propagate to the computed field properties and aerodynamic coefficients for a flow around an airfoil placed into a transonic dense gas stream.

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.

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 atmospheric combustor model with optical access for confined, non-premixed swirl-stabilized flames was developed in order to investigate the combustion behaviour of gas turbine flames fired with low-caloric syngases. The applied measuring techniques are Particle Image Velocimetry (PIV) for cold aerodynamic analysis, IR thermometry in open flame conditions, thermocouple traverses and global emissions analyzer in confined flame conditions. Two different fuels were chosen: propane and a synthetic mixture of CH4 , CO, CO2 , H2 having a composition typical of a gas from wood pyrolysis. Thermal powers between ∼5 kW and ∼20 kW were obtained with two different air flow rates and equivalence ratio varied in the range φ = 0.2–1.0. The experimental results constitute a database for the validation of numerical combustion models. Preliminary numerical analysis was carried out with STAR-CD software package.Copyright © 2009 by ASME

We consider the application of Fictitious Domain approach combined with least squares spectral elements for the numerical solution of fluid dynamic incompressible equations. Fictitious Domain methods allow problems formulated on a complicated shaped domain ? to be solved on a simpler domain ? containing ? . Least Squares Spectral Element Method has been used to develop the discrete model, as this scheme combines the generality of finite element methods with the accuracy of spectral methods. Moreover the least squares methods have theoretical and computational advantages in the algorithmic design and implementation. This paper presents the formulation and validation of the Fictitious Domain Least Squares Spectral Element approach for the steady incompressible Navier-Stokes equations. The convergence of the approximated solution is verified solving two-dimensional benchmark problems, demonstrating the predictive capability of the proposed formulation.

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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 paper describes the Robust Design Optimization (RDO) of an Electric Pump for Dishwashers. The aim of this work is to apply RDO techniques to the design of a large scale product and, in particular, the optimization target is to improve the efficiency and lower the cost of the whole system, as well as to fulfil requirements on the overall dimensions and head. Once an optimized geometry which meets all the requirements is obtained it will become a standard pump listed in the company catalogue. The authors gave a particular attention to the numerical model validations and to the theoretical study of the hydraulic and electromechanical coupling. The parametric model representing the main geometrical features of the water pump was created using the commercial CAD tool, Catia v5 and it had some simplifications, compared with the model used for the production water pump but it was considered to be accurate enough to provide a good analysis of the hydraulic performance of the pump. A numerical model of the hydraulic part of the water pump was created using the commercial mesh generator, ICEM CFD and CFX5 was used as Computational Fluid Dynamics (CFD) solver. A Genetic Algorithm (GA) was used for the Robust Design Optimization of the electric water pump and the best configuration was selected using a Multi Criteria Decision Making (MCDM) tool over Pareto frontier designs. In spite of the complexity of the operation necessary for the optimization of the electric motor and hydraulic pump assembly, the optimized electric water pump is more efficient than optimizing just the electric motor, and coupling it with an existing pump. The optimized electric water pump will be validated by experimental tests in the near future.

The present research propose optimization methods that are robust in the sense that they produce solutions insensitive to changes in the input parameters: these optimization methods are known as Robust Design Techniques. The need for Robust Design method appears in many contests: during the preliminary design process, the exact value of some input parameters is not known, consequently the aim is try to look for a solution as less dependent on the unknown input parameters as possible. The concept of robust optimization is demonstrated by using a 2-D airfoil shape optimization problem. It has been observed [2] that minimizing drag at a single design point has unintended consequences at nearby off-design points. Hicks and Vanderplaats demonstrate that a direct optimization approach that minimizes drag at one mach number (e.g., M=0,75) actually increases drag at nearby Mach number (e.g., M=0,70). To avoid this consequence the airfoil drag minimization problem could be faced by means of an inverse optimization approach but the final result could be almost the same of the single point design [7]. The conclusion is that the latter techniques produce solutions that perform well in correspondence of the design point but have poor off-design characteristics

In this paper we illustrate a new method for design of wings based on the optimization of the performance stability. This technique is an alternative approach to the traditional one which considers just a design point and tends to “over-optimize”, producing solutions that perform well at the design point but have poor off-design characteristics. This method has been implemented for wing design in transonic field, searching for solutions that are stable in terms of performance with the fluctuations of Mach number and angle of attack. The high fidelity simulation has been performed with a Navier-Stokes code and a multi objective genetic algorithm has been used to optimize; in fact it has been proved that the design of airfoils, where the performance (lift and drag) and the stability are both objectives, is implemented more efficiently using a multi objective approach. In the optimization phase in order to reduce the number of needed simulations a response surface based on statistics (DACE) will be used.

This paper describes an application of Robust Design methodology in the transonic airfoil design. It has been observed that, minimizing the drag at a single design point (Mach number and angle of attack fixed), it is possible to find solutions characterized by poor offdesign performances (over-optimizing problem). For this reasons, the stability of the performances inside the range of operative conditions is an important objective in the design. Once the operative conditions are defined (range of Mach number and angle of attack), a Multi Objective approach is ne eded; in particular, two are the objectives to be optimized: the mean performances inside the range of operative conditions (optimise mean value of the aerodynamic coefficients) and the stability of the solution (minimize variance of the coefficients). In this Multi Objective optimization problem, we have applied a competitive Game Strategy, based on Nash equilibrium, combined with a particular mono-objective algorithm, the Simplex. The players are in charge of different objectives, corresponding to the two objectives, that have to be optimized by the Simplex algorithm. Since the variables space is split between the two players, each player influences the choices of the other one in the course of the optimisation, until an equilibrium point, corresponding to the best compromise between the objectives, is found. About the optimization test case, the range of operative conditions is Mach=0.73±0.0 5 and angle of attack 2°±0.5, and the original RAE2822 airfoil is parameterized. To reduce the high number of CFD analysis based on Navier -Stokes equations, a statistic extrapolation method, based on an adaptation of DACE, is used to define the required response surfaces. According to our results, the methodology seems to be a promising approach which offers a new possibility to the designer, in particular when a good compromise of performance and stability is required, with cheap computational resources.

In this article we describe a new method for the aerodynamic optimisation and inverse design problem resolution. This method is based on the coupling of a classical optimiser with a neural-network. A Navier–Stokes flow solver is used for an accurate computation of the objective function. At first the neural-network, which has been trained by an initial small database, is used to obtain, by the interpolation of the design sensitivities, a new design point, which is then computed by the Navier–Stokes solver in order to update the neural-network training database for further iterative step. Since the neural-network provides the optimiser with the derivatives, the objective function has to be evaluated only once at every step. By this method, the computational effort is significantly reduced with respect to the classical optimisation methods based on the design sensitivities, that are computed directly by the flow solver. The method proposed has been positively tested on the inverse design of a three-dimensio...

α Angle of attack Ψ set of orthogonal polynomials with respect to the probability density function of the input parameters ξ n-dimensional random variable δij Kronecker delta x d-dimensional space variable μ Mean φ stochastic process φi deterministic part of the Generalized Polynomial Chaos series dependent on (x, t) Ψi stochastic part of the Generalized Polynomial Chaos series dependent on (ξ(θ)) σ Standard Deviation θ random event Cd Drag coefficient Cl Lift coefficient Cm Momentum coefficient d space dimensionality E Expected value f source term L differential operator Lp Legendre polynomial of order p M Mach number N number of total terms of the Generalized Polynomial Chaos series n uncertainties dimensionality p order of the expansion t time variable V ar Variance

A Tensorial-expanded Chaos Collocation method is used to quantify the effect of uncertainties on thermophysical properties of compl ex organic substances on the computed flow properties. Specifically, we investigate the e ffect of uncertainties introduced by several thermodynamic models on the numerical results provided by a computational fluid dynamics code for flows of molecularly complex gases close to saturation condition (dense gas flows). Most thermodynamic models of current use actually require data about thermophysical properties of the fluid, such as cri tical-point properties, which are typically affected by large uncertainties. Moreover, more acc urate thermodynamic models typically require a larger number of input parameters, thus i ntroducing new sources of uncertainty. The Tensorial-Expanded Chaos Collocation method investigated in this paper is used to perform both a priori and a posteriori tests on the output data generated by three popula r thermodynamic models for dense gases with uncertain thermophysical input parameters. A priori tests check the sensitivity of each equation of st ate to uncertain input data via some reference thermodynamic outputs, such as the saturation curve and the critical isotherm. A posteriori tests investigate uncertainties introduced in the pressure field around an airfoil placed into a transonic dense gas stream.