In this paper, a multidisciplinary design optimization algorithm, the Normal Boundary Intersection (NBI) method, is applied to the design of some devices of a sailing yacht. The full Pareto front is identified for two different design problems, and the optimal configurations are compared with standard devices. The great efficiency of the optimization algorithm is demonstrated by the wideness and density of the identified Pareto front.

A mixed approach with meta-modelling techniques and machine-learning algorithms is here applied to the minimization of the lap time of a Formula 1 race car. The fine tuning of the front wing is performed in order to optimize the car for each specific racetrack. This task is performed by a simplified model, which is trained by some high-fidelity fluid dynamic simulations and then extended to the complete design space. The resulting tool is reliable, fast and easy to use. The accuracy of the resulting speed profiles of the chosen car in comparison with available measurements is indicating the overall reliability of the procedure.

Numerical optimization of complex systems benefits from the technological development of computing platforms in the last twenty years. Unfortunately, this is still not enough, and a large computational time is still necessary when mathematical models that include richer (and therefore more realistic) physical models are adopted. In this paper we show how the combination of optimization and Artificial Intelligence (AI), in particular the Machine Learning algorithms, can help in strongly reducing the overall computational times, making possible the use of complex simulation systems within the optimization cycle. Original approaches are also proposed.

The appearance of AC72 foiling catamarans in the scenario of sailing yacht competitions in 2013 raised attention to this ship design concept, although not brand new in the yacht design history. The drastic drag reduction connected with the elevation of the ship hull outside the water is obtained by the use of a foil, or a system of foils, acting as the wings of a plane, providing a lift force balancing the weight of the ship. Since this lift is proportional (non-linearly) to the ship hull speed, the take-off speed of the hull cannot be low. As a result, since we are travelling in water at high speeds, the occurrence of the phenomenon of cavitation cannot be completely avoided, and the performance of the ship undergoes deterioration. Shaping of the foil profile must consider this peculiar situation, so the design tools commonly adopted for the aero-hydrodynamic hull design optimization are no longer adequate. In this paper, we are considering the optimization of the 2D profile of a foil in three different physical conditions: single fluid, two fluids and two fluids with cavitation. The first is typical of aeronautic wing design, the second of the appendages of a displacement ship, and the third of a foiling ship. Results give evidence of the different requirements for the three different conditions.

Usually, clinicians assess the correct hemodynamic behavior and fetal wellbeing during the gestational age thanks to their professional expertise, with the support of some indices defined for Doppler fetal waveforms. Although this approach has demonstrated to be satisfactory in the most of the cases, it can be largely improved with the aid of more advanced techniques, i.e. numerical analysis and simulation. Another key aspect limiting the analysis is that clinicians rely on a limited number of Doppler waveforms observed during the clinical examination. Moreover, the use of simple velocimetric indicators for deriving possible malfunctions of the fetal cardiovascular system can be misleading, being the fetal assessment based on a mere statistical analysis (comparison with physiological ranges), without any deep physiopathological interpretations of the observed hemodynamic changes. The use of a lumped mathematical model, properly describing the entire fetal cardiovascular system, would be absolutely helpful in this context: by targeting physiological model parameters on the clinical reliefs, we could gain deep insights of the full system. The calibration of model parameters may also help in formulating patient-specific early diagnosis of fetal pathologies. In the present work, we develop a robust parameter estimation algorithm based on two different optimization methods using synthetic data. In particular, we deal with the inverse problem of recognizing the most significant parameters of a lumped fetal circulation model by using time tracings of fetal blood flows and pressures obtained by the model. This represents a first methodological work for the assessment of the accuracy in the identification of model parameters of an algorithm based on closed-loop mathematical model of fetal circulation and opens the way to the application of the algorithm to clinical data.

Targeted drug delivery systems represent a promising strategy to treat localised disease with minimum impact on the surrounding tissue. In particular, polymeric nanocontainers have attracted major interest because of their structural and morphological advantages and the variety of polymers that can be used, allowing the synthesis of materials capable of responding to the biochemical alterations of the environment. While experimental methodologies can provide much insight, the generation of experimental data across a wide parameter space is usually prohibitively time consuming and/or expensive. To better understand the influence of varying design parameters on the release profile and drug kinetics involved, appropriately-designed mathematical models are of great benefit. Here, we developed a continuum-scale mathematical model to describe drug transport within, and release from, a hollow nanocontainer consisting of a core and a pH-responsive polymeric shell. Our two-layer mathematical model accounts for drug dissolution and diffusion and includes a mechanism to account for trapping of drug molecules within the shell. We conduct a sensitivity analysis to assess the effect of varying the model parameters on the overall behaviour of the system. To demonstrate the usefulness of our model, we focus on the particular case of cancer treatment and calibrate the model against release profile data for two anti-cancer therapeutical agents. We show that the model is capable of capturing the experimentally observed pH-dependent release.

Purpose A recursive scheme for the ALIENOR method is proposed as a remedy for the difficulties induced by the method. A progressive focusing on the most promising region, in combination with a variation of the density of the alpha-dense curve, is proposed. Design/methodology/approach ALIENOR method is aimed at reducing the space dimensions of an optimization problem by spanning it by using a single alpha-dense curve: the curvilinear abscissa along the curve becomes the only design parameter for any design space. As a counterpart, the transformation of the objective function in the projected space is much more difficult to tackle. Findings A fine tuning of the procedure has been performed in order to identity the correct balance between the different elements of the procedure. The proposed approach has been tested by using a set of algebraic functions with up to 1,024 design variables, demonstrating the ability of the method in solving large scale optimization problem. Also an industrial application is presented. Originality/value In the knowledge of the author there is not a similar paper in the current literature.

The present work is motivated by the development of a mathematical model mimicking the mechanisms observed in lab-on-chip experiments, made to reproduce on microfluidic chips the in vivo reality. Here we consider the Cancer-on-Chip experiment where tumor cells are treated with chemotherapy drug and secrete chemical signals in the environment attracting multiple immune cell species. The in silico model here proposed goes towards the construction of a "digital twin" of the experimental immune cells in the chip environment to better understand the complex mechanisms of immunosurveillance. To this aim, we develop a tumor-immune microfluidic hybrid PDE-ODE model to describe the concentration of chemicals in the Cancer-on-Chip environment and immune cells migration. The development of a trustable simulation algorithm, able to reproduce the immunocompetent dynamics observed in the chip, requires an efficient tool for the calibration of the model parameters. In this respect, the present paper represents a first methodological work to test the feasibility and the soundness of the calibration technique here proposed, based on a multidimensional spline interpolation technique for the time-varying velocity field surfaces obtained from cell trajectories.

The present paper represents a first methodological work for the construction of a robust and accurate algorithm for the solution of an inverse problem given by the identification of the parameters of a lumped mathematical model of fetal circulation introduced by G. Pennati et al. (1997). The underlying estimation techniques here applied are two global search meth- ods, respectively a Parameter Space Investigation (PSI) and the Ensemble Kalman Filter (EnKF), with a refinement performed with a local search method, i.e. Levenberg- Marquardt method (LM). The results here presented show the soundness of our methodology and opens the possibility to apply these techniques for the parameter identification of waveforms obtained from Doppler clinical measurements in the next future. Our final goal is to build a non-invasive simulation tool for the description of the circulation of fetuses in the context of a patient-specific model in order to help clinicians in early diagnosis of pathologies like cardiac distress or growth retardation.

In this survey we consider mathematical models and methods recently developed to control crowd dynamics, with particular emphasis on egressing pedestrians. We focus on two control strategies: the first one consists in using special agents, called leaders, to steer the crowd towards the desired direction. Leaders can be either hidden in the crowd or recognizable as such. This strategy heavily relies on the power of the social influence (herding effect), namely the natural tendency of people to follow group mates in situations of emergency or doubt. The second one consists in modify the surrounding environment by adding in the walking area multiple obstacles optimally placed and shaped. The aim of the obstacles is to naturally force people to behave as desired. Both control strategies discussed in this paper aim at reducing as much as possible the intervention on the crowd. Ideally the natural behavior of people is kept, and people do not even realize they are being led by an external intelligence. Mathematical models are discussed at different scales of observation, showing how macroscopic (fluid-dynamic) models can be derived by mesoscopic (kinetic) models which, in turn, can be derived by microscopic (agent-based) models.

Background and Objective: The paper focuses on the numerical strategies to optimize a plasmid DNA delivery protocol, which combines hyaluronidase and electroporation. Methods: A well-defined continuum mechanics model of muscle porosity and advanced numerical optimization strategies have been used, to propose a substantial improvement of a pre-existing experimental protocol of DNA transfer in mice. Our work suggests that a computational model might help in the definition of innovative therapeutic procedures, thanks to the fine tuning of all the involved experimental steps. This approach is particularly interesting in optimizing complex and costly protocols, to make in vivo DNA therapeutic protocols more effective. Results: Our preliminary work suggests that computational model might help in the definition of innovative therapeutic protocol, thanks to the fine tuning of all the involved operations. Conclusions: This approach is particularly interesting in optimizing complex and costly protocols for which the number of degrees of freedom prevents a experimental test of the possible configuration.

In the wide scenario of the optimization techniques, a large number of algorithms are inspired by natural processes, in many different ways. One of the latest is the Imperialist Competitive Algorithm (ICA) Atashpaz-Gargari and Lucas (2007), judged by their authors as very efficient and competitive with other popular optimization algorithms. However, its diffusion is still limited, so that it has not yet been adequately studied. In this paper, we have investigated the convergence properties of the ICA algorithm, observing the effect of the various coefficients and their role in the global convergence. Some modifications, including the coupling with a local search method, have been listed/suggested and then tested on a suite of standard algebraic test functions, verifying the improvements on the speed of convergence of the original algorithm. An application to naval design has been also included, in order to check the ability to solve realistic problems.

Particle Swarm Optimization is an evolutionary optimization algorithm, largely studied during the years: analysis of convergence, determination of the optimal coefficients, hybridization of the original algorithm and also the determination of the best relationship structure between the swarm elements (topology) have been investigated largely. Unfortunately, all these studies have been produced separately, and the same coefficients, derived for the original topology of the algorithm, have been always applied. The intent of this paper is to identify the best set of coefficients for different topological structures. A large suite of objective functions are considered and the best compromise coefficients are identified for each topology. Results are finally compared on the base of a practical ship design application.

In this paper, we deal with a group variable in size of pedestrians moving in a unknown confined environment and searching for an exit. Pedestrian dynamics are simulated by means of a recently introduced microscopic (agent-based) model, characterized by an exploration phase and an egress phase. First, we study the model to reveal the role of its main parameters and its qualitative properties. Second, we tackle a robust optimization problem by means of the Particle Swarm Optimization method, aiming at reducing the time-to-target by adding in the walking area multiple obstacles optimally placed and shaped. Robustness is sought against the number of people in the group, which is an uncertain quantity described by a random variable with given probability density distribution.

In this paper we are concerned with the simulation of crowds in built environments, where obstacles play a role in the dynamics and in the interactions among pedestrians. First of all, we review the state-of-the-art of the techniques for handling obstacles in numerical simulations. Then, we introduce a new modeling technique which guarantees both impermeability and opacity of the obstacles, and does not require ad hoc runtime interventions to avoid collisions. Most important, we solve a complex optimization problem by means of the Particle Swarm Optimization method in order to exploit the so-called Braess's paradox. More precisely, we reduce the evacuation time from a room by adding in the walking area multiple obstacles optimally placed and shaped.

This paper presents a comparative study between a large number of different existing sequential quadrature schemes suitable for Robust Design Optimization (RDO), with the inclusion of two partly original approaches. Efficiency of the different integration strategies is evaluated in terms of accuracy and computational effort: main goal of this paper is the identification of an integration strategy able to provide the integral value with a prescribed accuracy using a limited number of function samples. Identification of the different qualities of the various integration schemes is obtained utilizing both algebraic and practical test cases. Differences in the computational effort needed by the different schemes is evidenced, and the implications on their application to practical RDO problems is highlighted.

Numerical optimisation of a ship hull requires, like every shape design optimisation problem, the definition of a parametric expression of the object to be deformed. In this phase, some decisions are taken regarding the shape variability and the portion of the hull to be modified: the parameterisation of the hull is problem-dependent, with implications from the performances to be optimised (objective functions), and the right choice is not easy. In this paper, a parameterisation tool able to automatically select the optimal parameters selection and configuration, detecting together the most convenient portions of the hull to be modified and its optimal shape, is presented: the final solution is directly influenced by the characteristics of the specific optimisation problem. The total number of design parameters represents the only free choice about the parameterisation, while the areas on which the deformation is implemented, together with all the other parameters, are automatically selected without any further action by the designer.

Robust Design Optimization (RDO) represents a really interesting opportunity when the specifications of the design are careful and accurate: the possibility to optimize an industrial object for the real usage situation, improving the overall performances while reducing the risk of occurrence of off-design con- ditions, strictly depends on the availability of the information about the probability of occurrence of the various operative conditions during the lifetime of the design. Those data are typically not available prior than the production of a prototype. However, once the design has been produced and is operative, navigation data can be collected and utilized for the modification (refitting) of the current design, possibly in an early stage of its lifetime, in order to adapt the design to the real operative conditions at a time when the lifetime is still long enough to allow the payback of the cost of the modification by the obtained savings. In the present paper, five sister ships have been observed for a time period of two months, recording their operative data. Statistical distribution of speed and displacement are derived. An optimization framework is then applied, and some modifications of a small portion of the hull are proposed in order to increase significantly the performances of the hull, decreasing the operative cost of the ship. Dedicated numerical techniques are adopted in order to reduce the time required for the re-design activities.

Application of interpolation/approximation techniques (metamodels, for brevity) is commonly adopted in numerical optimization, typically to reduce the overall execution time of the optimization process. A limited number of trial solution are computed, cov- ering the design variable space: those trial points are then used for the determination of an estimate of the objective function in any desired location of the design space. The behaviour of the prediction of the objective function in between two trial points depends on the structure of the adopted metamodel, and there is no possibility, in principle, to determine a priori if one method is preferable to another. Nevertheless, some metamodels require the adjustment of a set of tuning parameters, and this operation is critical for the prevision qualities of the metamodel. In this paper, some base parameters of the kernel of the kriging metamodel are tuned in order to improve the overall quality of the prediction.

In this paper, multi-disciplinary optimization techniques are applied to sail design. Two different mathematical models, providing the solution of the fluid-dynamic and the structural problems governing the behaviour of a complete sailplan, are coupled in a fluid-structure interaction (FSI) scheme, in order to determine the real flying shape of the sails and the forces acting on them. A numerical optimization algorithm is then applied, optimizing the structural pattern of the sailplan in order to maximize the driving force or other significant quantities.