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The resilience of transport systems, facing natural or man-made disruptions, has been widely discussed in literature in terms of recovery capabilities concerning infrastructures, suggesting solutions to provide users an acceptable level of service along the interrupted network. However, in the context of the Covid-19 outbreak, the disruption has stressed the resilience of transport systems not on the supply side but rather at organizational level for transport service providers. Indeed, the sudden and drastic decrease in users due to the restrictions imposed by governments to limit the pandemic spread has implicated severe economic consequences in the running of transport companies. In this paper, attention has been focused on the public transport sector to analyse the effects of different initiatives, which companies could undertake in response to the demand shock caused by the Covid-19 emergency. Notably, an optimization procedure has been developed with the aim of determining feasible Pareto-front solutions, which correspond to trade-off conditions for the concurrent maximization of the company profit and the minimization of outsourcing services. The time span necessary to implement the examined recovery measures has been considered together with the limitation to appropriate threshold values for the main cost and income items influencing the company operations management. The proposed approach has been applied to the case study of an Italian public transport company to appraise different post-Covid-19 resilience strategies.

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.

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.

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.

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 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.

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.

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.

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

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 purpose of this work is to optimize the stator shape of an axial compressor, in order to maximize the global efficiency of the machine, fixing the rotor shape. We have used a 3D parametric mesh and the CFX-Tascflow code for the flow simulation. To find out the most important variables in this problem, we have run a preliminary series of designs, whose results have been analyzed by a statistic tool. This analysis has helped us to choose the most appropriate variables and their ranges in order to implement the optimization algorithm more efficiently and rapidly. For the simulation of the fluid flow through the machine, we have used a cluster of 12 processors.