![]() ![]() Thus, with the input of primary pulley rotation data, axial force on the pulleys, and resistive torque submitted to the system, we are able to find the position and speed of each discretized nodes and consequently determine the rotation and torque on th e secondary pulley. Integrating equation 2 twice in time, we can find the position values of the nodes. With the sum of forces in each node and dividing this by the mass, we have the dynamic equation of motion. Figure 1 shows belt discretization and the forces considered acting on the nodes, and equation 1 the sum of forces in each node. For the first model, we discretized the belt in node s joined by springs and dampers, then, the node posi tion on the time is determined solving the force balance in each node. I n order t o model the global CVT behavior, we divided the work in to two different models, one for the belt and the other for the drive mechanism. We will try in the following topics to briefly present the methodology and results we obtained in the current phase of the development of our project, as well as future implementations and projects. Then, we scaled back to focus just on kinematic and dynamic analysis. The original goal was to design our own CVT with this model, but after seeing the complexity and the various possibilities of the algorithm, we realized that to succeed we needed a full understanding of the model variables. Thereby the CVT design and setup process become cheaper and less exhaustive. C onsequently, it would be possible to change the inputs of mass and inertia of the flyweights, spring stiffness and analyze the outputs of secondary pulley rotation and torque. Therefore, in the last year, we decided to carry out an ambitious project as our graduation thesis, to develop an algorithm in MATLAB to predict the dynamic behavior of CVT, from the modeling of control mechanisms and belt. However, over the years, this setup was carried out mostly on an experimental basis, which required a lot of testing time and financial resources. In this context, we from the Piratas do Cerrado Baja SAE team at the University of Brasília, have dedicated ourselves for several seasons in adjusting sets of flyweights and springs to define the best set up to provide better longitudinal performance for our car. The use of CVT is quite common in Baja SAE teams, because of its ability to replace the use of the conventional clutch and the possibility of being adjusted for different applications. The continuously variable transmission (CVT) is an automotive automatic transmission concept capable of varying the transmission ratio continuously, with the possibility of keeping the engine rotation constant, and thus its torque and power delivery. They will ex plain how they have utilized MATLAB for predicting the dynamic behavior of a contin u ously variable transmission (CVT). Todays guest bloggers are Alex Silva da Purificação and Lucas Martins Ricardi from the Un i versity o f Brasília – Piratas do Cerrado Baja SAE Team. ![]()
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