Metamodel-based design optimization in industrial turbomachinery

Fans and Blowers community is experiencing, during those years, an incredible push in rethinking design approaches and strategies. The change in regulations on minimum efficiency grades and market requirements on even more customized products demand a changing in the way design in fan technology is perceived. In this context, even if synthetic approaches for fan design and analysis are still valuable tools, they need to be flanked by metamodels in order to overcome the limitations and criticism introduced by empirical relationships developed in the past for specific applications. In addition, by replacing computation-intensive functions with approximate surrogate models, it is possible to adopt advanced and nested optimization methods, such as those based on Evolutionary Algorithms, drastically improving the overall optimization computational time. Surrogate-based Optimizations based on Evolutionary Algorithm should become common practice in design optimization because of their capability of find optima in the design space, thanks to their intrinsic balance between exploitation and exploration. This work proposes methods for interweave elements of metamodeling techniques and multi-objective optimization problems with the synthetic approaches classically developed by the turbomachinery community. The entire Thesis can be ideally divided into two parts; the first gives a brief survey on the classical fan design and analysis approaches and reports two synthetic in-house codes for axial fan performance prediction. The second part present the state-of-the-art in metamodeling and optimization techniques, underlining the role of metamodeling in supporting design optimization and focusing in the more reliable and accurate framework for multi-objective optimization in fans engineering design

The Interplay of Perceived Risks and Benefits in Deciding to Become Vaccinated against COVID-19 While Pregnant or Breastfeeding: A Cross-Sectional Study in Italy

The present study examined the role of the perception of risks and benefits for the mother and her babies in deciding about the COVID-19 vaccination. In this cross-sectional study, five hypotheses were tested using data from a convenience sample of Italian pregnant and/or breastfeeding women (N = 1104, July–September 2021). A logistic regression model estimated the influence of the predictors on the reported behavior, and a beta regression model was used to evaluate which factors influenced the willingness to become vaccinated among unvaccinated women. The COVID-19 vaccination overall risks/benefits tradeoff was highly predictive of both behavior and intention. Ceteris paribus, an increase in the perception of risks for the baby weighed more against vaccination than a similar increase in the perception of risks for the mother. Additionally, pregnant women resulted in being less likely (or willing) to be vaccinated in their status than breastfeeding women, but they were equally accepting of vaccination if they were not pregnant. COVID-19 risk perception predicted intention to become vaccinated, but not behavior. In conclusion, the overall risks/benefits tradeoff is key in predicting vaccination behavior and intention, but the concerns for the baby weigh more than those for the mother in the decision, shedding light on this previously neglected aspect

The interplay of perceived risks and benefits in deciding to become vaccinated against COVID-19 while pregnant or breastfeeding: A cross-sectional study in Italy.

The present study examined the role of the perception of risks and benefits for the mother and her babies in deciding about the COVID-19 vaccination. In this cross-sectional study, five hypotheses were tested using data from a convenience sample of Italian pregnant and/or breastfeeding women (N = 1104, July–September 2021). A logistic regression model estimated the influence of the predictors on the reported behavior, and a beta regression model was used to evaluate which factors influenced the willingness to become vaccinated among unvaccinated women. The COVID-19 vaccination overall risks/benefits tradeoff was highly predictive of both behavior and intention. Ceteris paribus, an increase in the perception of risks for the baby weighed more against vaccination than a similar increase in the perception of risks for the mother. Additionally, pregnant women resulted in being less likely (or willing) to be vaccinated in their status than breastfeeding women, but they were equally accepting of vaccination if they were not pregnant. COVID-19 risk perception predicted intention to become vaccinated, but not behavior. In conclusion, the overall risks/benefits tradeoff is key in predicting vaccination behavior and intention, but the concerns for the baby weigh more than those for the mother in the decision, shedding light on this previously neglected aspect

Clinical characteristics of a large cohort of patients with narcolepsy candidate for pitolisant: a cross-sectional study from the Italian PASS Wakix® Cohort

Introduction Narcolepsy is a chronic and rare hypersomnia of central origin characterized by excessive daytime sleepiness and a complex array of symptoms as well as by several medical comorbidities. With growing pharmacological options, polytherapy may increase the possibility of a patient-centered management of narcolepsy symptoms. The aims of our study are to describe a large cohort of Italian patients with narcolepsy who were candidates for pitolisant treatment and to compare patients' subgroups based on current drug prescription (drug-naive patients in whom pitolisant was the first-choice treatment, switching to pitolisant from other monotherapy treatments, and adding on in polytherapy). Methods We conducted a cross-sectional survey based on Italian data from the inclusion visits of the Post Authorization Safety Study of pitolisant, a 5-year observational, multicenter, international study. Results One hundred ninety-one patients were enrolled (76.4% with narcolepsy type 1 and 23.6% with narcolepsy type 2). Most patients (63.4%) presented at least one comorbidity, mainly cardiovascular and psychiatric. Pitolisant was prescribed as an add-on treatment in 120/191 patients (62.8%), as switch from other therapies in 42/191 (22.0%), and as a first-line treatment in 29/191 (15.2%). Drug-naive patients presented more severe sleepiness, lower functional status, and a higher incidence of depressive symptoms. Conclusion Our study presents the picture of a large cohort of Italian patients with narcolepsy who were prescribed with pitolisant, suggesting that polytherapy is highly frequent to tailor a patient-centered approach

Data-driven clustering of combined Functional Motor Disorders based on the Italian registry

Functional Motor Disorders (FMDs) represent nosological entities with no clear phenotypic characterization, especially in patients with multiple (combined FMDs) motor manifestations. A data-driven approach using cluster analysis of clinical data has been proposed as an analytic method to obtain non-hierarchical unbiased classifications. The study aimed to identify clinical subtypes of combined FMDs using a data-driven approach to overcome possible limits related to "a priori" classifications and clinical overlapping

Modelling of axial fan and anti-stall ring on a virtual test rig for air performance evaluation

Market requests for higher performance fans and legal requirements to meet minimum efficiency grades drive industrial fan designers to study or re-think stall control solutions. In tunnel and metro fans this means to study how anti-stall rings work and to provide strategy to improve their efficiency. Here we present a numerical study validated against available experiments on the fitting of an anti-stall ring on an axial fan for tunnel and metro operations. The study synthetize the effects of the anti-stall ring with an actuator disk, strongly reducing the computational load required and validating a new methodology. Such approach can be implemented to easily derive fan performance with different geometries of the anti-stall ring plenum as well as its fins

On surrogate-based optimization of truly reversible blade profiles for axial fans.

Open literature offers a wide canvas of techniques for surrogate-based multi-objective optimization. The large majority of works focus on methodological and theoretical aspects and are applied to simple mathematical functions. The present work aims at defining and assessing surrogate-based techniques used in complex optimization problems pertinent to the aerodynamics of reversible aerofoils. Specifically, it addresses the following questions: how meta-model techniques affect the results of the multi-objective optimization problem, and how these meta-models should be exploited in an optimization test-bed. The multi-objective optimization problem (MOOP) is solved using genetic optimization based on non-dominated sorting genetic algorithm (NSGA)-II. The paper explores the possibility to reduce the computational cost of multi-objective evolutionary algorithms (MOEA) using two different surrogate models (SM): a least square method (LSM), and an artificial neural network (ANN). SMs were tested in two optimization approaches with different levels of computational effort. In the end, the paper provides a critical analysis of the results obtained with the methodologies under scrutiny and the impact of SMs on MOEA. The results demonstrate how surrogate model incorporation into MOEAs influences the effectiveness of the optimization process itself, and establish a methodology for aerodynamic optimization tasks in the fan industry

Monitoring sleep in the age of smartphones: a validation procedure of accelerometric devices

Introduction and aims: the gold standard for sleep staging is the in-laboratory polysomnography (PSG), followed by manual scoring. A wide range of limitations of this approach has been reported, ranging from high costs to low compliance. The market of the so-called “quantified self” lists an increasing number of tracking devices, which also offer the opportunity to measure sleep parameters. Still, the validation of these devices is limited. Methods: in 20 young healthy subjects, we recorded 24 hrs of portable EEG data combined with by a low-cost commercially available accelerometric recordings (Fitbit Ultra). A validation procedure based on a multi-layer perceptron artificial neural network (ANN) has been employed in order to optimize actigraphy-based versus EEG-based vigilance state scoring. Results: The ANN approach extracted an algorithm leading to high accuracy (0.939+-0.03), sensitivity (0.936+-0.07) and specificity (0.944+-0.03) in the estimation of 5-minute sleep epochs, for the comparison of EEG-based and actigraphy-based scoring. The training phase reached saturation after 4 subjects. The estimation of standard sleep parameters (TST, WASO, Sleep Onset) showed no statistical difference between the automatic ANN-actigraphy-based scoring and the standard EEG-based one. Conclusion: The high concordance between ANN-actigraphy-based scoring and the standard manual EEG-based one, as well as the estimation of sleep parameters, makes low-cost actigraphy a viable strategy for collecting objective sleep-wake data. Finally, we propose a validation procedure that could be employed for testing future devices as well as existing ones, requiring relative long (24 hrs) simultaneous portable EEG and actigraphic recordings, in a relative small sample (n=4)

Derivative design of axial fan range: from academia to industry

The work presented in this paper concerns a useful method for axial fans preliminary design based on the “Derivative Design” concept. The emphasis is, on one side, on education and, on the other, on the practical help that such method can provide in the early preliminary design process. A complete data set of an axial fan measured with ISO 5801 standards is the start point for the investigation and the prediction of the multiple possible performance that different fan configurations can provide, in terms of dimensionless duty coefficients. In particular, configurations with different number of blades, and hence of solidity, are studied. The typical options of derivative design are explored and relations for performance prediction are presented. A detailed description of the derivative design methodology is followed by tests and validation. The tools employed are a fully three dimensional code, the Advanceded Actuator Disk Mode (AADM), and two other in-house codes, the Meanline Axisymmetric Calculation (MAC) and Axisymmetric Laboratory (AXLAB). Results of the derivative design method are reported, showing a good accuracy against the AADM data. The MAC and AXLAB ensure still acceptable results when increasing the solidity of the machine. On the contrary, a decrease of solidity leads to higher relative errors in the prediction of the load coefficient. In conclusion, an exploration of the possible fields of operation of a blade profile can be carried out by a correct prediction of the stage diffusion factor