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The aim of the present study is to investigate the relationship between personality, trait affectivity, and severity of delusions in patients with delusional disorder (DD). Thirty-two outpatients affected by DD were administered the Structured Interview for DSM-IV-TR Personality Disorders (SIDP-IV), the Pathological Narcissism Inventory (PNI), the Positive and Negative Affect Schedule (PANAS), and the Psychotic Symptom Rating Scale (PSYRATS). We analyzed the prevalence of personality disorder in our sample of patients with DD and studied the correlations between the severity of delusions and the different affective variables. Finally, we obtained a multivariate explanatory model of the severity of the delusions. The severity of delusions was directly associated with “grandiose fantasy” item of narcissistic personality and inversely related with the feelings of shame, fear, and guilt. In the multivariate model, the feeling of shame was the only independent variable capable of accounting for the severity of delusions that, in DD patients, would lie on an affective core of shame.

Identification and quantification of functional groups in activated carbons by means of a multi-technique approach.

The adsorption of H2 and CO over Pt/Al2O3 was studied in gas and in liquid phase by FT-IR and ATR-IR spectroscopies under otherwise similar conditions. The solvent competes with hydrogen and CO for terrace and kink metal sites.

Autonomous surface vehicles (ASV) allow the investigation of coastal areas, ports and harbors as well as harsh and dangerous environments such as the arctic regions. Despite receiving increasing attention, the hydrodynamic analysis of ASV performance subject to variable operational parameters is little investigated. In this context, this paper presents a multi-fidelity (MF) hydrodynamic analysis of an ASV, namely the Shallow Water Autonomous Multipurpose Platform (SWAMP), at surveying speed in calm water and subject to variable payload and location of the center of mass, accounting for the variety of equipment that the vehicle can carry. The analysis is conducted in deep water, which is the condition mostly encountered by the ASV during surveys of coastal and harbors areas. Quantities of interest are the resistance, the vehicle attitude, and the wave generated in the region between the catamaran hulls. These are assessed using a Reynolds Averaged Navier Stokes Equation (RANSE) code and a linear potential flow (PF) solver. The objective is to accurately assess the quantities of interest, along with identifying the limitation of PF analysis in the current context. Finally, a multi-fidelity Gaussian Process (MF-GP) model is obtained combining RANSE and PF solutions. The latter also include variable grid refinement and coupling between hydrodynamic loads and rigid body equations of motion. The surrogate model is iteratively refined using an active learning approach. Numerical results show that the MF-GP is effective in producing response surfaces of the SWAMP performance with a limited computational cost. It is highlighted how the SWAMP performance is significantly affected not only by the payload, but also by the location of the center of mass. The latter can be therefore properly calibrated to minimize the resistance and allow for longer-range operations.

Ottimizzazione geometria del propulsore

Analisi di configurazione propulsiva propedeutica alla progettazione e Definizione geometria ottimizzata propulsore pump-jet

The paper presents a study on four adaptive sampling methods of a multi-fidelity (MF) metamodel, based on stochastic radial basis functions (RBF), for global design optimisation based on expensive ...

Despite the increased computational resources, the simulation-based design optimization (SBDO) procedure can be very expensive from a computational viewpoint, especially if high-fidelity solvers are required. Multi-fidelity metamodels have been successfully applied to reduce the computational cost of the SBDO process. In this context, the paper presents the performance assessment of an adaptive multi-fidelity metamodel based on a Gaussian process regression (MF-GPR) for noisy data. The MF-GPR is developed to: (i) manage an arbitrary number of fidelity levels, (ii) deal with objective function evaluations affected by noise, and (iii) improve its fitting accuracy by adaptive sampling. Multi-fidelity is achieved by bridging a low-fidelity metamodel with metamodels of the error between successive fidelity levels. The MF-GPR handles the numerical noise through regression. The adaptive sampling method is based on the maximum prediction uncertainty and includes rules to automatically select the fidelity to sample. The MF-GPR performance are assessed on a set of five analytical benchmark problems affected by noisy objective function evaluations. Since the noise introduces randomness in the evaluation of the objective function, a statistical analysis approach is adopted to assess the performance and the robustness of the MF-GPR. The paper discusses the efficiency and effectiveness of the MF-GPR in globally approximating the objective function and identifying the global minimum. One, two, and three fidelity levels are used. The results of the statistical analysis show that the use of three fidelity levels achieves a more accurate global representation of the noise-free objective function compared to the use of one or two fidelities., 12 pages, 6 figures, to be published in conference proceedings of AIAA AVIATION 2021 FORUM

International audience; The performance of surrogate-based optimization is highly affected by how the surrogate training set is defined. This is especially true for multi-fidelity surrogate models, where different training sets exist for each fidelity. Adaptive sampling methods have been developed to improve the fitting capabilities of surrogate models, avoiding to define an a priori design of experiments, adding training points only where necessary or most useful (i.e., providing the highest knowledge gain) to the optimization process. Nevertheless, the efficiency of the adaptive sampling is highly affected by its initialization. The paper presents and discusses a novel initialization strategy with a limited training set for adaptive sampling. The proposed strategy aims to reduce the computational cost of evaluating the initial training set. Furthermore, it allows the surrogate model to adapt more freely to the data. In this work, the proposed approach is applied to single-and multi-fidelity stochastic radial basis functions for an analytical test problem and the shape optimization of a NACA hydrofoil. Numerical results show that the results of the surrogate-based optimization are improved, thanks to a more effective and efficient domain space exploration and a significant reduction of high-fidelity evaluations.

Infrared spectroscopy is typically not used to establish the oxidation state of metal-based catalysts. In this work, we show that the baseline of spectra collected in diffuse reflectance mode of a series of Pd/Al2O3 samples of increasing Pd content varies significantly and reversibly under alternate pulses of CO or H2 and O2. Moreover, these baseline changes are proportional to the Pd content in Pd/Al2O3 samples exhibiting comparable Pd particle size. Similar measurements by X-ray absorption spectroscopy on a different 2 wt.% Pd/Al2O3 confirm that the baseline changes reflect the reversible reduction-oxidation of Pd. Hence, we demonstrate that changes in oxidation state of metal-based catalysts can be determined using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and that this behavior is part of the spectral changes that are returned by experiments under operando conditions.

The paper presents a multi-fidelity coordinate-search derivative-free algorithm for nonsmooth constrained optimization (MF-CS-DFN), in the context of simulation-based design optimization (SBDO). The objective of the work is the development of an optimization algorithm able to improve the convergence speed of the SBDO process. The proposed algorithm is of a line-search type and can handle objective function evaluations performed with variable accuracy. The algorithm automatically selects the accuracy of the objective function evaluation based an internal steplength parameter. The MF-CS-DFN algorithm starts the optimization with low accuracy and low-cost evaluations of the objective function, then the accuracy (and evaluation cost) is increased. The method is coupled with a potential flow solver whose accuracy is determined by the computational grid size. No surrogate models are used in the current study. The algorithm is applied to the hull-form optimization of a destroyer-type vessel in calm water using 14 hull-shape parameters as design variables. The optimization aims at the total resistance reduction. Seven refinements of the computational grid are used by the multi-fidelity optimizations. Four setups of the MF-CS-DFN algorithm are tested and compared with an optimization performed only on the finest grid. The results show that three of the tested setups achieve better performance than the high-fidelity optimization, converging to a lower resistance value with a reduced computational cost.

Palladium-based catalysts are exploited on an industrial scale for the selective hydrogenation of hydrocarbons. The formation of palladium carbide and hydride phases under reaction conditions changes the catalytic properties of the material, which points to the importance of operando characterization for determining the relation between the relative fractions of the two phases and the catalyst performance. We present a combined time-resolved characterization by X-ray absorption spectroscopy (in both near-edge and extended regions) and X-ray diffraction of a working palladium-based catalyst during the hydrogenation of ethylene in a wide range of partial pressures of ethylene and hydrogen. Synergistic coupling of multiple techniques allowed us to follow the structural evolution of the palladium lattice as well as the transitions between the metallic, hydride and carbide phases of palladium. The nanometric dimensions of the particles resulted in the considerable contribution of both surface and bulk carbides to the X-ray absorption spectra. During the reaction, palladium carbide is formed, which does not lead to a loss of activity. Unusual contraction of the unit cell parameter of the palladium lattice in the spent catalyst was observed upon increasing hydrogen partial pressure.

Il presente report rendiconta l'attività di ricerca condotta nella Linea di Attività 1.13 del progetto di ricerca "PIANO TRIENNALE DI REALIZZAZIONE 2019-2021 DELLA RICERCA DI SISTEMA ELETTRICO NAZIONALE Tema 1.8 Energia Elettrica dal mare". La presente Linea di Azione consta di tre attività di sviluppo parallele la cui integrazione è stata messa a punto nel corso della durata della LA1.13 per essere applicata nelle successive fasi del progetto. Nello specifico gli obiettivi della LA1.13 sono: 1. Messa a punto del solutore high fidelity FUNAERO per lo studio dell'aerodinamica non stazionaria del rotore mediante confronto su test case disponibili in letteratura utilizzando anche un solutore CFD in-house (Xnavis). 2. Sviluppo, implementazione e verifica di diverse strategie di accoppiamento di FUNAERO con i modelli per lo studio della dinamica della piattaforma sviluppati in LA1.1 ed LA1.2. Validazione mediante dati di letteratura disponibili. 3. Definizione preliminare del modello di sintesi multi-fedeltà e delle relative mappe del comportamento aero/idrodinamico del sistema nonché dell'incertezza associata alla loro predizione attraverso il modello di sintesi.

International audience; The paper presents some recent trends in multi-fidelity digital modelling for marine engineering applications. Digital modelling is achieved by machine learning methods, namely multi-fidelity surrogate models, trained by computational fluid dynamics (CFD). Adaptative approaches are discussed for radial basis functions and Gaussian process models. Simulation-based design optimisation problems are presented to discuss the use and effects of different adaptivity concepts: (1) adaptive refinement of the computational-domain discretization in CFD; (2) adaptive sampling of the design/operational space; (3) adaptive selection of the fidelity used for the surrogate model training in a multi-fidelity environment; (4) adaptivity of the models to noise. Model adaptation allows for the efficient training of machine learning models, reducing the computational cost associated to building the training sets and improving the overall accuracy of the digital representation.

This paper presents a comparison of two methods for the forward uncertainty quantification (UQ) of complex industrial problems. Specifically, the performance of Multi-Index Stochastic Collocation (MISC) and adaptive multi-fidelity Stochastic Radial Basis Functions (SRBF) surrogates is assessed for the UQ of a roll-on/roll-off passengers ferry advancing in calm water and subject to two operational uncertainties, namely the ship speed and draught. The estimation of expected value, standard deviation, and probability density function of the (model-scale) resistance is presented and discussed; the required simulations are obtained by the in-house unsteady multi-grid Reynolds Averaged Navier-Stokes (RANS) solver $\chi$navis. Both MISC and SRBF use as multi-fidelity levels the evaluations on the different grid levels intrinsically employed by the RANS solver for multi-grid acceleration; four grid levels are used here, obtained as isotropic coarsening of the initial finest mesh. The results suggest that MISC could be preferred when only limited data sets are available. For larger data sets both MISC and SRBF represent a valid option, with a slight preference for SRBF, due to its robustness to noise.

International audience; An adaptive-fidelity approach to metamodeling from noisy data is presented for design-space exploration and design optimization. Computational fluid dynamics (CFD) simulations with different numerical accuracy (spatial discretization) provides metamodel training sets affected by unavoidable numerical noise. The-fidelity approximation is built by an additive correction of a low-fidelity metamodel with metamodels of differences (errors) between higher-fidelity levels whose hierarchy needs to be provided. The approach encompasses two core metamodeling techniques, namely: i) stochastic radial-basis functions (SRBF) and ii) Gaussian process (GP). The adaptivity stems from the sequential training procedure and the auto-tuning capabilities of the metamodels. The method is demonstrated for an analytical test problem and a CFD-based optimization of a NACA airfoil, where the fidelity levels are defined by an adaptive grid refinement technique of a Reynolds-averaged Navier-Stokes (RANS) solver. The paper discusses: i) the effect of using more than two fidelity levels; ii) the use of least squares regression as opposed to exact interpolation; iii) the comparison between SRBF and GP; and iv) the use of two sampling approaches for GP. Results show that in presence of noise, the use of more than two fidelity levels improves the model accuracy with a significant reduction of the number of high-fidelity evaluations. Both least squares SRBF and GP provide promising results in dealing with noisy data.

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts.

In this work we followed the formation of palladium nanoparticles, starting from palladium (II) acetate precursor, inside a poly(4-vinylpyridine-co-divinylbenzene) polymer in presence of different reducing agents. The formation and growth of palladium nanoparticles in presence of H-2 was followed as a function of temperature by simultaneous XANES-SAXS techniques, coupled with DRIFT spectroscopy in operando conditions. It was found that the pyridyl functional groups in the polymer plays a fundamental role in the stabilization of the palladium (II) acetate precursor, as well as in the stabilization of the palladium nanoparticles. The effect of a thermal treatment in alcohol (ethanol and 2-propanol) was preliminarily investigated by means of DRIFT spectroscopy in operando conditions. We found that alcohols act as reducing agents for Pd(OAc)(2). The obtained palladium nanoparticles were preliminarily characterized by means of IR spectroscopy using CO as probe molecule. (C) 2016 Elsevier B.V. All rights reserved.

Il presente report rendiconta l'attività di ricerca condotta nella Linea di Attività 1.12 del progetto di ricerca "PIANO TRIENNALE DI REALIZZAZIONE 2019-2021 DELLA RICERCA DI SISTEMA ELETTRICO NAZIONALE Tema 1.8 Energia Elettrica dal mare". La presente Linea di Azione consta di due attività di sviluppo parallele che dovranno essere poi integrate nelle successive fasi del progetto. Nello specifico gli obiettivi della LA1.12 sono: 1. La messa a punto di un solutore high fidelity per lo studio dell'aerodinamica non stazionaria del rotore al fine di interfacciarlo, nelle successive LA, con il solutore della dinamica della piattaforma galleggiante (Sezione 1); 2. La definizione delle specifiche del modello di sintesi multifedeltà e l'analisi di risultati preliminari effettuati su funzioni test (Sezione 2). Sezione 1: Aerodinamica di rotore La predizione delle prestazioni della turbina e dei carichi trasmessi dal rotore alla piattaforma è basata su un solutore agli elementi di contorno (Boundary Integral Equation Method, BIEM) per l'aerodinamica di sistemi rotanti in flusso potenziale sviluppato presso il CNR-INM. Tale solutore aerodinamico, FUNAERO (Free-wake UNsteady AEROdynamics), descrive i carichi non stazionari agenti sulle pale di un aerogeneratore in condizioni operative e la dinamica delle strutture vorticose a valle del rotore tenendo conto della variazione di profilo e direzione del vento, e di un moto imposto dovuto, ad esempio, alla dinamica della piattaforma galleggiante. Il presente documento riporta la descrizione degli sviluppi teorico/numerici relativi al solutore e la sua validazione mediante confronto con dati sperimentali ottenuti in galleria del vento sul rotore eolico in scala dell'esperimento Mexico. Le condizioni operative considerate per la validazione sono relative sia ad un vento assiale sia con inclinazione (yaw) rispetto all'asse di rotazione della turbina. Infine vengono presentate due applicazioni relative ad un caso di shear estremo per una turbina onshore ed uno di moto imposto al rotore (turbina offshore galleggiante). In quest'ultimo caso è stato considerato il moto corrispondente ad un'oscillazione di pitch della piattaforma poiché, in base alla letteratura sull'argomento, risulta essere quello più rilevante ai fini del comportamento dinamico della macchina. Sezione 2: Metamodelli adattivi multifedeltà E' stato sviluppato un modello di sintesi basato su metamodelli adattivi multifedeltà per l'assimilazione/fusione dei risultati da solutori a fedeltà variabile. Il modello è in grado di: (a) aumentare la densità dei casi oggetto di validazione riducendo il costo computazionale (attraverso griglie multiple e campionamento adattivo), (b) fornire un modello alternativo per l'aerodinamica (accurato e a basso costo computazionale) per l'accoppiamento con altri modelli sviluppati nel progetto ove di interesse, (c) costituire una base per ulteriori raffinamenti ed indagini sulle prestazioni di piattaforma/impianto. Il modello si basa su funzioni a base radiale di tipo stocastico che forniscono, assieme alla predizione, anche l'incertezza ad essa associata. Essa è alla base delle metodologie di campionamento adattivo, tanto nel contesto della fedeltà singola che multipla. Lo sviluppo e la fase di valutazione dei risultati su funzioni test sono state condotte assumendo le caratteristiche dimensionali del problema oggetto di studio.

The report presents a multi-fidelity metamodel to combine data produced by CFD computer simulations with different accuracy, such as potential ow and Reynolds averaged Navier-Stokes equations solvers. The multi-fidelity (MF) metamodel is based on stochastic radial basis functions and is built as the sum of a low-fidelity-trained metamodel and an error metamodel, based on the difference between high- and low-fidelity simulations. The MF metamodel is adaptively refined using dynamic sampling criteria, based on the prediction uncertainty in combination with the objective optimum and the computational cost of high- and low-fidelity evaluations. Four adaptive sampling methods are demonstrated by four analytical benchmark problems. The performance of the adaptive sampling methods is assessed via objective function convergence. The presented results are also published in Serani et al. (2019).

An adaptive N-fidelity approach to metamodeling from noisy data is presented for uncertainty quantification and design-space exploration. Computational fluid dynamics (CFD) simulations with different numerical accuracy provides metamodel training sets affected by unavoidable numerical noise. The N-fidelity approximation is built by an additive correction of a low-fidelity metamodel with metamodels of differences between higher-fidelity levels whose hierarchy needs to be provided. The approach encompasses two core metamodeling techniques, namely: i) stochastic radial-basis functions and ii) Gaussian process. The adaptivity stems from the sequential training procedure and the auto-tuning capabilities of the metamodels. The method is demonstrated for two CFD-based problems.

This work is focused on the determination of the most likely border morphologies in the H-terminated graphenic domains that constitute activated carbons, focusing on four different carbons and six supported-metal catalysts. Among the available characterization techniques, Inelastic Neutron Scattering spectroscopy provides detailed vibrational spectra containing the fingerprint of the terminal C-H groups of carbon materials. From the experimental spectra, we observed clear differences among carbons having different origin, and a systematic decrease in the integrated area upon metal deposition that linearly scales with the nanoparticles dispersion. Density Functional Theory simulations are fundamental to assign the experimental bands to specific species. Thus, an extended simulation work on both regular and defective aromatic models was carried out, providing the inelastic neutron scattering fingerprint of a large number of different C-H terminations. By fitting the experimental spectra with a linear combination of the simulated spectra, it was possible to quantify the concentration of each terminal C-H geometry in the samples, and to identify the species most affected by the metal nanoparticles deposition. Specific benzene rings exposing a single C-H group appear to have a decisive role in the interaction with the metal nanoparticles and their deposition procedure.

The aim of metamodelling in CFD-based automatic shape optimization is to replace expensive CFD computations in the optimization process by evaluations of a cheap surrogate model, created from a limited training set of simulations. Multi-fidelity metamodels [1] make this process even more efficient by basing a part of the metamodel on inexpensive low-fidelity simulations and introducing a correction based on a few high-fidelity simulations. To adaptively define the training sets of the low-fidelity and correction metamodels, different sampling strategies can be used [2]. However, the CFD results on which the metamodels are based contain numerical errors. Especially if new grids are generated for each simulation, the numerical errors for two similar geometries may be different. These differences manifest themselves as numerical noise in the training sets for the metamodels. Adaptive sampling strategies which determine new training points by evaluating the uncertainty of the metamodel, may react to this noise by placing new points in noisy regions, rather than in places where the overall metamodel is unreliable. This deteriorates the metamodel quality. In this paper, we study multifidelity metamodeling approaches which eliminate a part of the numerical noise in the metamodel. For metamodels based on Dynamic Radial Basis Functions [3], this is obtained by introducing less RBF degrees of freedom than training points and computing the RBF fits using least-squares approximation. The number of DoF will be defined adaptively as the training progresses, based on a metric like leave-one-out cross-validation combined with in-the-loop optimization of the metamodel. Applications to the shape optimisation of 2D NACA four-digit airfoils and a destroyer-type vessel will be presented. The CFD simulations are based on a Reynolds-averaged Navier Stokes solver that uses adaptive grid refinement to create the meshes.

We report the series of Pd K-edge X-ray absorption spectra collected during hydrogenation of ethylene with variable ethylene/hydrogen ratio over carbon supported palladium nanoparticles. The data presented in this article includes normalized X-ray absorption spectra, k2-weighted oscillatory χ(k) functions extracted from the extended X-ray absorption fine structure (EXAFS) and k2-weighted Fourier-transformed EXAFS data, χ(R). Each spectrum is reported together with the hydrogen, ethylene and helium flow rates, adjusted during its collection. In addition, time evolution of the ratio of m/Z signals of 30 and 28 registered by online mass spectrometer is presented. The data analysis is reported in Bugaev et al., Catal. Today, 2019 [1].

In this work we investigated in detail the effects of nitric acid on the surface chemistry of two carbons, activated by steam and by phosphoric acid, meant to identify the nature and the concentration of the oxidized surface species. To this aim, the oxidized carbons were characterized by means of a large number of complementary techniques, including micro-Raman spectroscopy, N2 physisorption, Boehm titration method, 13C solid state nuclear magnetic resonance, X-ray photoelectron spectroscopy, diffuse reflectance infrared and inelastic neutron scattering spectroscopy. Carboxylic and carboxylate groups are mainly formed, the latter stabilized by the extended conjugation of the π electrons and being more abundant on small and irregular graphitic platelets. We demonstrated that the presence of oxygen-containing groups acts against the palladium dispersion and causes the appearance of an appreciable induction time in hydrogenation reactions. The carbon with more oxygenated surface species (and in particular more carboxylate groups) must be chosen in the hydrogenation of polar substrates, while it is detrimental to the hydrogenation of nonpolar substrates.