Moteur à quatre temps | EPFL Graph Search

vignette|Moteur à quatre temps. vignette|Le châssis de la première automobile avec moteur à quatre temps (1890), moteur inventé par le Français Fernand Forest mais breveté en 1862 par Beau De Rochas, lors d'une diffusion privée. Un moteur à quatre temps est un Moteur à explosion basé sur le cycle de Beau de Rochas décrit en 1862. Il a été développé par Nikolaus Otto en 1867, perfectionné par Gottlieb Daimler et Wilhelm Maybach en 1887, ainsi que par l'ingénieur français Fernand Forest en 1890, suivi par Rudolf Diesel en 1893. Il est toujours le principal type de motorisation des véhicules automobiles terrestres au début du . Évolutions Tout au long du et en ce début de , les chercheurs et ingénieurs ont sans cesse tenté d'optimiser le rendement du moteur à quatre temps et son adaptation à des usages divers. Mais les considérations industrielles et commerciales ont souvent freiné l'apparition des innovations des chercheurs. Motivés entre autres par les enjeux écologiques mo

Aeroelastic characterisation of a bio-inspired flapping membrane wing

Alexander Gehrke, Karen Ann J Mulleners, Jules Ange Philippe Richeux, Esra Uksul

Natural fliers like bats exploit the complex fluid-structure interaction between their flexible membrane wings and the air with great ease. Yet, replicating and scaling the balance between the structural and fluid-dynamical parameters of unsteady membrane wings for engineering applications remains challenging. In this study, we introduce a novel bio-inspired membrane wing design and systematically investigate the fluid-structure interactions of flapping membrane wings. The membrane wing can passively camber, and its leading and trailing edges rotate with respect to the stroke plane. We find optimal combinations of the membrane properties and flapping kinematics that out-perform their rigid counterparts both in terms of increased stroke-average lift and efficiency, but the improvements are not persistent over the entire input parameter space. The lift and efficiency optima occur at different angles of attack and effective membrane stiffnesses which we characterise with the aeroelastic number. At optimal aeroelastic numbers, the membrane has a moderate camber between 15% and 20% and its leading and trailing edges align favourably with the flow. Higher camber at lower aeroelastic numbers leads to reduced aerodynamic performance due to negative angles of attack at the leading edge and an over-rotation of the trailing edge. Most of the performance gain of the membrane wings with respect to rigid wings is achieved in the second half of the stroke when the wing is decelerating. The stroke-maximum camber is reached around mid-stroke but is sustained during most of the remainder of the stroke which leads to an increase in lift and a reduction in power. Our results show that combining the effect of variable stiffness and angle of attack variation can significantly enhance the aerodynamic performance of membrane wings and has the potential to improve the control capabilities of micro air vehicles.

IOP Publishing Ltd2022

Phenomenology and scaling of optimal flapping wing kinematics

Alexander Gehrke, Karen Ann J Mulleners

Biological flapping wing fliers operate efficiently and robustly in a wide range of flight conditions and are a great source of inspiration to engineers. The unsteady aerodynamics of flapping wing flight are dominated by large-scale vortical structures that augment the aerodynamic performance but are sensitive to minor changes in the wing actuation. We experimentally optimise the pitch angle kinematics of a flapping wing system in hover to maximise the stroke average lift and hovering efficiency with the help of an evolutionary algorithm and in situ force and torque measurements at the wing root. Additional flow field measurements are conducted to link the vortical flow structures to the aerodynamic performance for the Pareto-optimal kinematics. The optimised pitch angle profiles yielding maximum stroke-average lift coefficients have trapezoidal shapes and high average angles of attack. These kinematics create strong leading-edge vortices early in the cycle which enhance the force production on the wing. The most efficient pitch angle kinematics resemble sinusoidal evolutions and have lower average angles of attack. The leading-edge vortex grows slower and stays close-bound to the wing throughout the majority of the stroke-cycle. This requires less aerodynamic power and increases the hovering efficiency by 93% but sacrifices 43% of the maximum lift in the process. In all cases, a leading-edge vortex is fed by vorticity through the leading edge shear layer which makes the shear layer velocity a good indicator for the growth of the vortex and its impact on the aerodynamic forces. We estimate the shear layer velocity at the leading edge solely from the input kinematics and use it to scale the average and the time-resolved evolution of the circulation and the aerodynamic forces. The experimental data agree well with the shear layer velocity prediction, making it a promising metric to quantify and predict the aerodynamic performance of the flapping wing hovering motion.

IOP PUBLISHING LTD2021

Swimming phase-based performance evaluation using a single IMU in front crawl

Kamiar Aminian, Farzin Dadashi, Vincent Gremeaux, Mahdi Hamidi Rad

The purpose of this study was to assess the potential of using a sacrum-worn inertial measurement unit (IMU) for performance evaluation in each swimming phase (wall push-off, glide, stroke preparation, and swimming) of national-level swimmers in front crawl technique. Nineteen swimmers were asked to wear a sacrum IMU and swim four one-way 25-m trials in front crawl, attached to a tethered speedometer and filmed by cameras in the whole lap for validation. Based on the literature, several goal metrics were defined over speedometer data, each one representing the performance of the swimmer either in one phase (maximum velocity of wall push-off phase) or several phases (time of 15 meters for wall push-off, glide, stroke preparation phases). Following a macro-micro approach, the IMU parameters of each swimming phase were used to predict the goal metrics. The selected IMU parameters were in line with the characteristics of movement within each phase and can estimate the corresponding goal metric with an R2 over 0.8 and relative RMSE lower than 10%.