Two-Dimensional-Based Hybrid Shape Optimisation of a 5-Element Formula 1 Race Car Front Wing under FIA Regulations

Front wings are a key element in the aerodynamic performance of Formula 1 race cars. Thus, their optimisation makes an important contribution to the performance of cars in races. However, their design is constrained by regulation, which makes it more difficult to find good designs. The present work develops a hybrid shape optimisation approach to obtain an optimal five-element airfoil front wing under the FIA regulations and 17 design parameters. A first baseline design is obtained by parametric optimisation, on which the adjoint method is applied for shape optimisation via Mesh Morphing with Radial Basis Functions. The optimal front wing candidate obtained outperforms the parametric baseline up to a 25% at certain local positions. This shows that the proposed and tested hybrid approach can be a very efficient alternative. Although a direct 3D optimisation approach could be developed, the computational costs would be dramatically increased (possibly unaffordable for such a complex five-element front wing realistic shape with 17 design parameters and regulatory constraints). Thus, the present approach is of strong interest if the computational budget is low and/or a fast new front wing design is desired, which is a frequent scenario in Formula 1 race car design.The authors want to acknowledge the financial support from the Ramón y Cajal 2021 Excellence Research Grant action from the Spanish Ministry of Science and Innovation (FSE/AGENCIA ESTATAL DE INVESTIGACIÓN), the UMA18-FEDERJA-184 grant, and the Andalusian Research, Development and Innovation Plan (PAIDI—Junta de Andalucia) fundings. Partial funding for open access charge: Universidad de Málag

On the estimation of three-dimensional porosity of insect-proof screens

The two-dimensional estimation is the approach to porosity par excellence in the literature of insect-proof screens for their geometric characterisation and estimation of their aerodynamic parameters. However, this is not an accurate estimation, since the geometry of insect-proof screens consists of interlaced threads that create a three-dimensional woven structure, leading to different thicknesses and overlapping of threads. This paper suggests a mathematical approach to reconstruct computationally the 3D structure of the screens and to estimate the volumetric porosity, relying solely on easily measurable quantities such as diameter of threads, spacing of threads and thickness. The results on the application to 20 + 6 insect-proof screens in this work evidence that the suggested approach outperforms the standard two-dimensional modelling. These results also support experimental observations in the relationship between porosity and pressure drop not explainable by the two-dimensional approach. To increase the reliability on the analysis of porosity, the propagation of experimental uncertainty has been also included in the comparison between brand new and old&washed insect-proof screens. A software (Poro3D v1.0) using the methodology developed in this work can be downloaded as supplementary material to this manuscript to instantly obtain both 3D and 2D porosities, as well as the reconstruction of 3D geometries

Semi-Analytical Calculation of Pore-Related Parameters of Wire/Woven Screens

Wire/woven screens have a wide range of applications, from being used as simple mechanical screening device to nanoscreen wicking with nanofluids. The vast number of applications makes important to study these screens with high accuracy, to reduce errors in characterisation and performance predictions. Previous works to date focused either on the study of these screens as a two-dimensional surface (e.g. in ventilation openings as insect-proof screens) or as three-dimensional structures under important assumptions (symmetric mesh, thickness of two times their diameter, linear evolution of the pore area along the thickness). These incomplete modellings introduce errors in applications such as the estimation of permeability of the porous media (two-dimensional porosity is identical for two meshes with the same projected area of pore but different thickness) or computational simulations of ventilation in buildings/greenhouses, where these parameters are imposed as boundary condition. The present investigation shows a method to calculate three-dimensional pore related structural properties semi-analytically for the first time and for any plain square mesh. We found that when sweeping the mesh with a plane parallel to it there are up to six different zones or stretches which can be integrated by a piece-wise approach (here named Discretisation Method). Results demonstrated high accuracy in the calculation of three-dimensional porosity and constriction factor (a parameter that is calculated by integration over the pore volume). Due to the mathematical complexity in the method, a software (AeroScreen v1.0) is available to obtain pore-related structural parameters from diameters, separations and thickness of the screen

Probabilistic Combination of Non-Linear Eigenprojections For Ensemble Classification

The emergence of new technologies has changed the way clinicians perform diagnosis. Medical imaging play a crucial role in this process, given the amount of information that they usually provide as non-invasive techniques. Despite the high quality offered by these images and the expertise of clinicians, the diagnostic process is not a straightforward task since different pathologies can have similar signs and symptoms. For this reason, it is extremely useful to assist this process with the inclusion of an automatic tool that reduces the bias when analyzing this kind of images. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify relevant patterns while providing information about the reliability of the classification. Specifically, each image is divided into patches and features contained in each one of them are extracted by applying kernel principal component analysis (PCA). The use of base classifiers within an ensemble allows our system to identify the informative patterns regardless of their size or location. Decisions of each individual patch are then combined according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario where distinguishing between pneumonia patients and controls from chest Computed Tomography (CCT) images, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would highly increase the computational cost) evidence the applicability of our proposal in a real-world environment.Projects PGC2018-098813-B-C32 and RTI2018-098913-B100 (Spanish “Ministerio de Ciencia, Innovación y Universidades”)UMA20-FEDERJA-086, A-TIC-080-UGR18 and P20 00525 (Consejer´ıa de econom´ıa y conocimiento, Junta de Andaluc´ıa)European Regional Development Funds (ERDF)Spanish ”Ministerio de Universidades” through Margarita-Salas gran

Temporal series analysis of population cycle threshold counts as a predictor of surge in cases and hospitalizations during the SARS-CoV-2 pandemic

Tools to predict surges in cases and hospitalizations during the COVID-19 pandemic may help guide public health decisions. Low cycle threshold (CT) counts may indicate greater SARS-CoV-2 concentrations in the respiratory tract, and thereby may be used as a surrogate marker of enhanced viral transmission. Several population studies have found an association between the oscillations in the mean CT over time and the evolution of the pandemic. For the first time, we applied temporal series analysis (Granger-type causality) to validate the CT counts as an epidemiological marker of forthcoming pandemic waves using samples and analyzing cases and hospital admissions during the third pandemic wave (October 2020 to May 2021) in Madrid. A total of 22,906 SARS-CoV-2 RT-PCR-positive nasopharyngeal swabs were evaluated; the mean CT value was 27.4 (SD: 2.1) (22.2% below 20 cycles). During this period, 422,110 cases and 36,727 hospital admissions were also recorded. A temporal association was found between the CT counts and the cases of COVID-19 with a lag of 9–10 days (p ≤ 0.01) and hospital admissions by COVID-19 (p < 0.04) with a lag of 2–6 days. According to a validated method to prove associations between variables that change over time, the short-term evolution of average CT counts in the population may forecast the evolution of the COVID-19 pandemic

A Structural Parametrization of the Brain Using Hidden Markov Models Based Paths in Alzheimer's Disease

The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called Computed Aided Diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on Hidden Markov Models. The path is traced using information of intensity and spatial orientation in each node, adapting to the structural changes of the brain. Each path is itself a useful way to extract features from the MRI image, being the intensity levels at each node the most straightforward. However, a further processing consisting of a modification of the Gray Level Co-occurrence Matrix can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to the structural changes in Alzheimer's Disease, as well as providing a significant feature reduction. This methodology achieves high performance, up to 80.3\% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's Disease Neuroimaging Initiative (ADNI).TIC218, MINECO TEC2008-02113 and TEC2012-34306 projects, Consejería de Economía, Innovación, Ciencia y Empleo de la Junta de Andalucía P09-TIC-4530 and P11-TIC-71

Evaluation of Ionic Liquids as In Situ Extraction Agents during the Alcoholic Fermentation of Carob Pod Extracts

Anhydrous ethanol is a promising alternative to gasoline in fuel engines. However, since ethanol forms an azeotrope with water, high-energy-consumption separation techniques such as azeotropic distillation, extractive distillation, and molecular sieves are needed to produce anhydrous ethanol. This work discusses the potential development of an integrated process for bioethanol production using ionic liquids and Ceratonia siliqua as a carbohydrate source for further fermentation of the aqueous extracts. A four-stage counter-current system was designed to improve the sugar extraction yield to values close to 99%. The alcoholic fermentation of the extracts showed ethanol concentrations of 95 g/L using the microorganism Saccharomyces cerevisae. The production of anhydrous ethanol through extractive distillation with ethylene glycol was simulated using CHEMCAD software, with an energy consumption of 13.23 MJ/Kg of anhydrous ethanol. Finally, several ionic liquids were analyzed and are proposed as potential solvents for the recovery of bioethanol for the design of an integrated extraction-fermentation-separation process, according to their ability to extract ethanol from aqueous solutions and their biocompatibility with the microorganism used in this study.This research was funded by the Fundación Séneca, grant number 20957/PI/18 and The Ministry of Economy and Competitiveness (MINECO) grant number RTI2018-099011-B-I00