A major challenge encountered in the development of systems exposed to weather stressors, such as autonomous vehicles (AVs) and unmanned aerial vehicles (UAVs), is to ensure their proper functioning under adverse rain or snow conditions. Since the sensing of the surroundings by these vehicles relies on optical sensors such as lidars and cameras, it is essential to ensure the robustness of these systems from the early stages of the project. In this respect, experiments in climatic wind tunnels provide a solution for simulating the operating conditions in which the autonomous vehicles will be confronted. This work proposes a method based on field measurements and unsupervised machine learning to faithfully reproduce in controlled environments real weather conditions captured during wintertime in Ontario, Canada. The purpose of this paper is not to investigate correlations between observed weather conditions and the characteristics of the precipitation encountered, but rather to establish a consistent method based on outdoor disdrometer data to identify critical parameters to be simulated in climatic wind tunnels. To achieve this goal, weather data such as temperature, relative humidity, and droplet size distribution (DSD) were recorded at GM's McLaughlin Advanced Technology Track (MATT) using an FD70 disdrometer and WXT530 weather transmitter, both manufactured by Vaisala, installed on a car provided by the Automotive Center of Excellent (ACE) team of Ontario Tech University. The implementation of the proposed method allowed the identification of precipitation clusters characterized by parameters of a theoretical model for particle size distributions fitted to the collected data.
The Amazon has a great potential of natural resources. There is a wide diversity of plant specimens and many of them can be extracted and processed such as jute, sisal and mauve fibers. It is known that usualy small communities from Amazon build their boats using a rudimentary and ecologically incorrect method, which comes from brazilian natives. This method consists in cut of trees with large trunks, which are burned and scraped until they get the desired shape. This process is not viable due to the long time spent and also it is environmentally unacceptable nowadays. Considering these facts, it was created a project at UFPA aiming to evaluate the building viability of small boats adapted to the Amazon region. These boats have been made with composite material, polymeric resin reinforced with natural fiber fabric. Hence, this paper brings the first stage of this project, treating on the build of a small boat with reduced dimensions. This prototype was experimentally studied, using strain gages, and evaluated numerically by finite element method through the software ANSYS®. This stage was very important to optimize the parameters used in the finite element model, which was employed to analyse the model with real dimensions. As a result, the numerical models showed a good compatibility with real tests. Additionally, the designed boat demonstrated to be able to hold the imposed loads.
The straightening process is the main cause of residual stresses in the manufacture of rails. It is a non-trivial process with cyclic plastic loads, solid-solid contact and complex geometry, which computational simulation is often complex and timeconsuming. In this work, a new methodology was developed by means of a quasi-static modeling instead explicit dynamic. This methodology was proved to be effective and fast. Sixteen cases were simulated, and a C-shaped pattern for longitudinal stress, as seen through the literature, was obtained in the most of them, even with large variations between the main parameters: the yield strength, the tangent modulus and the initial curvature of the rail. The longitudinal normal residual stresses were higher than the transversals ones, as expected. The results obtained by simulations were the basis for the use of a Gaussian process regressor to predict the residual stresses from any initial parameters. This tool confirmed that the parameters that more affect the final state of residual stresses are, in this order, yield strength, tangent modulus and curvature. This is relevant information, since the hardest data to obtain in practice are the initial curvature of rail. Both simulation methodology and the statistical Gaussian process tool could be useful to perform life fatigue analysis in rails, since this needs the initial state of residual stresses to be more reliable.
A great deal of current scientific and technological advances in aeronautics concerns innovative wing design in order to increase aerodynamic performance, it is normal to seek better efficiency and refinement for critical structures. Inspired from the nature, deformations and vibrations are applied to aircraft wings. Thanks to smart-materials that deform a structure, an electroactive morphing wing prototype at reduced scale has been realized within the Smart Morphing and Sensing project. Force measurements show that electroactive morphing increase lift up to 2% with wake thickness reduction of around 10%. High-speed timeresolved particle image velocimetry reveals important effects on flow dynamics as well as on time average. Based on these results, this paper proposes the experimental study of the influence of a Shape Memory Alloy (SMA) actuator on the dynamics of a reduced scale A320 wing by means of time-resolved PIV. The velocity fields obtained are analyzed using Proper Orthogonal Decomposition and reconstruction of the dynamic system is performed to identify coherent structures present at the flow. . .
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