Finite elements versus experimental for a CFRP structure

The work presented herein is part of a project focused on the optimization of a carbon fibre reinforced epoxy matrix (CFRP) structure. The implemented process resorts to finite elements modelling in order to evaluate the performance of the analysed structure. To validate the finite elements model at the basis of the optimization process a scale model prototype of the composite structure was built and tested under similar loading conditions. The experimental results determined were then compared with those obtained from simulations of a built numerical model depicting the experimental set up and taken into account the mechanical and geometrical properties of the composite part, the used accessories, the interface between parts and production constraints.Portuguese Foundation for Science and Technology under project UID/CTM/50025/2013 and scholarship SFRH / BD / 51106 / 201

Forecasting COVID-19 with Importance-Sampling and Path-Integrals

Background Forecasting nonlinear stochastic systems most often is quite difficult without giving in to temptations to simply simplify models for the sake of permitting simple computations Objective Here two basic algorithms Adaptive Simulated Annealing ASA and path-integral codes PATHINT PATHTREE and their quantum generalizations qPATHINT qPATHTREE are suggested as being useful to fit COVID-19 data and to help predict spread or control of this pandemic Multiple variables are considered e g potentially including ethnicity population density obesity deprivation pollution race environmental temperature Method ASA and PATHINT PATHTREE have been demonstrated as being effective to forecast properties in three disparate disciplines in neuroscience financial markets and combat analysis Results Not only can selected systems in these three disciplines be aptly modeled but results of detailed calculations have led to new results and insights not previously obtaine

Monitorización inteligente en tiempo real del acabado superficial de micro-piezas basado en el modelado híbrido incremental

Este trabajo propone la aplicación de una estrategia de modelado híbrido incremental (HIM) para la estimación en tiempo real de la rugosidad superficial en procesos de micromecanizado. Esta estrategia comprende fundamentalmente dos pasos. En primer lugar, se obtiene un modelo híbrido incremental representativo del proceso de micromecanizado. El resultado final de este modelo es una función de dos entradas (avance por diente cuadrático y vibración media cuadrática (rms) en el eje Z) y una salida (rugosidad superficial). En segundo lugar, se evalúa el modelo híbrido incremental en tiempo real para obtener la rugosidad superficial. El modelo se corrobora experimentalmente mediante su integración en un sistema embebido de monitorización en tiempo real del acabo superficial. La evaluación del prototipo demuestra una tasa de éxito en la estimación de la rugosidad superficial del 83%. Estos resultados son la base para el desarrollo de sistemas embebidos en la monitorización del acabo superficial de micro-piezas en tiempo real y el posterior desarrollo de una herramienta a nivel industria

Data-Driven Simulator: Redesign of Chickpea Harvester Reels

Conventional redesign methodologies applied on the grain harvester headers for the mechanical harvesting of chickpeas cause its progress to not be as rapid and technological. This paper presents a hybrid modeling-optimization methodology to design harvester reels for efficient chickpea harvesting. The five fabricated headers were tested in both real and virtual modeling environments to optimize the operational parameters of the reel for minimum losses. Harvesting losses data gathered from chickpea fields over ten years of trials were fed into a fuzzy logic model, which in turn was merged with simulated annealing to develop a simulator. To this end, simulated annealing was used to produce combinations of reel diameter and number of bats, to be fed into the fuzzy model until achieving a minimum harvesting loss. The proposed model predicts the reel structure measured in-field evaluation, which fits well with the previously established mathematical model. A significant improvement in harvesting performance, 71% pod harvesting, validates the benefits of the proposed fuzzy-simulated annealing approach to optimize the design of grain harvester headers

Hybrid Classical-Quantum Computing: Applications to Statistical Mechanics of Neocortical Interactions

Several commerical quantum computers are now available that offer Hybrid Classical-Quantum computing Application is made to a classical-quantum model of human neocortex Statistical Mechanics of Neocortical Interactions SMNI which has had its applications published in many papers since 1981 However this project only uses Classical super- computers Since 2015 PATHINT has been used as a numerical algorithm for folding path-integrals Applications in several systems in several disciplines has generalized been from 1 dimension to N dimensions and from classical to quantum systems qPATHINT Papers have applied qPATHINT to neocortical interactions and financial options The classical space described by SMNI applies nonlinear nonequilibrium multivariate statistical mechanics to synaptic neuronal interactions while the quantum space described by qPATHINT applies synaptic contributions from Ca2 waves generated by astrocytes at tripartite neuron-astrocyte-neuron site

Large-Scale Modelling of the Environmentally-Driven Population Dynamics of Temperate Aedes albopictus (Skuse)

The Asian tiger mosquito, Aedes albopictus, is a highly invasive vector species. It is a proven vector of dengue and chikungunya viruses, with the potential to host a further 24 arboviruses. It has recently expanded its geographical range, threatening many countries in the Middle East, Mediterranean, Europe and North America. Here, we investigate the theoretical limitations of its range expansion by developing an environmentally-driven mathematical model of its population dynamics. We focus on the temperate strain of Ae. albopictus and compile a comprehensive literature-based database of physiological parameters. As a novel approach, we link its population dynamics to globally-available environmental datasets by performing inference on all parameters. We adopt a Bayesian approach using experimental data as prior knowledge and the surveillance dataset of Emilia-Romagna, Italy, as evidence. The model accounts for temperature, precipitation, human population density and photoperiod as the main environmental drivers, and, in addition, incorporates the mechanism of diapause and a simple breeding site model. The model demonstrates high predictive skill over the reference region and beyond, confirming most of the current reports of vector presence in Europe. One of the main hypotheses derived from the model is the survival of Ae. albopictus populations through harsh winter conditions. The model, constrained by the environmental datasets, requires that either diapausing eggs or adult vectors have increased cold resistance. The model also suggests that temperature and photoperiod control diapause initiation and termination differentially. We demonstrate that it is possible to account for unobserved properties and constraints, such as differences between laboratory and field conditions, to derive reliable inferences on the environmental dependence of Ae. albopictus populations

Helicopter Flight Procedures for Community Noise Reduction

A computationally efficient, semiempirical noise model suitable for maneuvering flight noise prediction is used to evaluate the community noise impact of practical variations on several helicopter flight procedures typical of normal operations. Turns, "quick-stops," approaches, climbs, and combinations of these maneuvers are assessed. Relatively small variations in flight procedures are shown to cause significant changes to Sound Exposure Levels over a wide area. Guidelines are developed for helicopter pilots intended to provide effective strategies for reducing the negative effects of helicopter noise on the community. Finally, direct optimization of flight trajectories is conducted to identify low noise optimal flight procedures and quantify the magnitude of community noise reductions that can be obtained through tailored helicopter flight procedures. Physically realizable optimal turns and approaches are identified that achieve global noise reductions of as much as 10 dBA Sound Exposure Level