Search

The paper shows how cost-reduction methods can be synergistically combined to enable high-fidelity hull-form optimization under stochastic conditions. Specifically, a multi-objective hull-form optimization is presented, where (a) physics-informed design-space dimensionality reduction, (b) adaptive metamodeling, (c) uncertainty quantification (UQ) methods, and (d) global multi-objective algorithm are efficiently and effectively combined to achieve high-fidelity simulation-based design optimization (SBDO) solutions. The application pertains to the multi-objective optimization for resistance and seakeeping (operational efficiency and effectiveness) of a destroyer-type vessel. Two hierarchical multi-objective SBDO problems are presented, with a level of complexity decreasing from the most general (stochastic sea state, heading, and speed) to the least general (deterministic regular wave, at fixed sea state, heading, and speed). Design-space dimensionality reduction is based on a generalized Karhunen-Loève expansion of the shape modification vector combined with low-fidelity-based physical variables. A multi-objective deterministic particle swarm optimization algorithm is applied to a stochastic radial-basis-function metamodel that provides objective predictions. UQ methods include Gaussian quadrature and metamodel-based importance sampling. Numerical simulations are based on unsteady Reynolds-averaged Navier-Stokes and potential flow solvers. The paper shows and discusses the joint effort of computational-cost reduction methods in enabling high-fidelity SBDO, providing guidelines for future research directions in this area.

Methodologies for reducing the design-space dimensionality in shape optimization have been recently developed based on unsupervised machine learning methods. These methods provide reduced dimensionality representations of the design space, capable of maintaining a certain degree of the original design variability. Nevertheless, they usually do not allow to use directly the original parameterization method, representing a limitation to their widespread application in the industrial field, where the design parameters often pertain to well-established parametric models, e.g. CAD (computer-aided design) models. This work presents how to embed the parametric-model original parameters in a reduced-dimensionality representation of the design space. The method, which takes advantage from the definition of a newly-introduced generalized feature space, is demonstrated, as a proof of concept, for the reparameterization of 2D Bezier curves and 3D free-form deformation design spaces and the consequent solution of simulation-driven design optimization problems of a subsonic airfoil and a naval destroyer in calm water, respectively., Paper submitted to Computer Methods in Applied Mechanics and Engineering

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.

The expected increase of the air traffic and the emergence of new paradigms of air transportation induced by the evolution of the market demand are going to significantly worsen the community noise scenario of our cities. An accurate and timely prediction of the noise emission is, therefore, a mandatory aspect of the design process of any innovative air transport solution. Unfortunately, aeroacoustic simulations are extremely expensive and a popular approach to circumvent the problem is the development of surrogate models equally accurate yet computationally cheaper for the evaluation of the objective functions. This work deals with a multi-fidelity approach to surrogate modeling for the identification of optimal engine installation to maximise noise shielding. The paper explores the possibility to couple reinforcement learning techniques with a trust-region-based multi fidelity approach to meta modelling. Specifically, an accelerated variant of the well known Q-learning algorithm, namely the Evolutionary Q-learning (EVQL) is used to train a group of agents, each one deployed in one of a set of trust subregions, defined around the high-accuracy samples. The subregions of confidence are updated according to the exit directions of each agent. These directions are also used to enrich the set of high-accuracy samples by evaluating the meta model uncertainties along them in the untrusted regions of the domain.

Ottimizzazione geometria del propulsore

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

Parametric Sensitivity Analysis (PSA) investigates the sensitivity of parameters, defining the design space of a shape-optimisation problem, for tackling the challenges of the curse of dimensionality or decreasing the uncertainty in design's performance. However, the analytical implementation of PSA can often be tricky, especially if the chosen method requires the evaluation of high-dimensional integrals or if the baseline simulation codes do not provide an analytical solution to design performance. Therefore, PSA needs to be implemented with sampling methods, such as Monte Carlo sampling, which is highly susceptible to slow convergence and necessitates a sufficiently large number of samples for stable results, especially for high-dimensional problems. In this work, we aim to address above the mentioned challenges associated with PSA by offloading the evaluation of parametric sensitivities from physical quantities to quantities, which are relatively inexpensive but, like physical metrics, provide important clues about the form, distribution and validity of the design. It is well known that shape's integral properties, such as geometric moments and their invariants serve as a geometric foundation for different designs' physical analyses. In this connection, we propose a geometric moment-dependent PSA approach, that harnesses the geometric variation of designs in the design space using geometric moments as a quantity of interest (QoI) to identify parametric sensitivities. These results can serve as prior estimates of parametric sensitivities with respect to physics. The selection of geometric moments in our work is motivated by the following baseline insights: - It is very likely that physics analysis requires the computation of such integral properties of the geometry such as the stiffness and mass matrix, and moments of a domain are sufficient to ensure accurate integration of a large class of integrands. - Like physics, geometric moments can also act as a compact shape signature or descriptor to a specific design falling in a specific category, which facilitates various shape processing tasks. To validate our approach and experimentally demonstrate the effectiveness of geometric moments, we used two ship hulls parameterised with 27 and 26 parameters using two different techniques based on \textit{procedural deformation} (PD) and global modification function (GMF), respectively. In this setting, we use the {\it wave-resistance coefficient (C_w)} as the physical QoI, as it plays a crucial role in ship hull design. The longitudinal distribution of the hulls' geometry has a similar impact on geometric moments as C_w. To commence, we construct the so-called shape-signature vector (MI^s), that will be used as shape descriptor and contains all the geometric moments up to s order. To align better with C_w, all moments in this vector are taken invariant with respect to translation and scaling. A Global Variance-Based Sensitivity Analyses (GVBSA) is performed for learning parametric sensitivities with respect to MI^s and C_w. Here MI^s is purely a vector quantity containing the moments of various orders while C_w is a scalar and computationally expensive one. Therefore, learning sensitivities to MI^s requires implementing a multivariate extension of GVBS, such as covariance decomposition, which provides generalised sensitivity indices of design parameters to all moments in MI^s. The results from the experiments conducted in this study show a good correlation between the sensitive parameters obtained from C_w and MI^s, specifically with the fourth-order shape-signature vector MI^4. In the case of the PD-based hull, 7 parameters sensitive to MI^4 are also among the 8 parameters sensitive to C_w. Interestingly, similar results are obtained for the GMF-based hull, where 6 out of 7 sensitive parameters to C_w are also sensitive to MI^4. Afterwards, two different design spaces are constructed for both hull models, one with sensitive parameters obtained with C_w and the other with mathcalMI^4. Shape optimisation is performed in both spaces performed with a meta-heuristic optimisation approach. Final optimisation results showed that the design generated from design space constructed with sensitive parameters of C_w and MI^4 for both types of hulls offer similar performance. These results indicate that PSA performed with moments can reasonably estimate parameters' sensitivity to the design's physics with considerably reduced computational cost.

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.

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.

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.