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Description
The slider-crank mechanism is popularly used in internal combustion engines to convert the reciprocating motion of the piston into a rotary motion. This research discusses an alternate mechanism proposed by the Wiseman Technology Inc. which involves replacing the crankshaft with a hypocycloid gear assembly. The unique hypocycloid gear arrangement allows the piston and the connecting rod to move in a straight line, creating a perfect sinusoidal motion. To analyze the performance advantages of the Wiseman mechanism, engine simulation software was used. The Wiseman engine with the hypocycloid piston motion was modeled in the software and the engine's simulated output results were compared to those with a conventional engine of the same size. The software was also used to analyze the multi-fuel capabilities of the Wiseman engine using a contra piston. The engine's performance was studied while operating on diesel, ethanol and gasoline fuel. Further, a scaling analysis on the future Wiseman engine prototypes was carried out to understand how the performance of the engine is affected by increasing the output power and cylinder displacement. It was found that the existing Wiseman engine produced about 7% less power at peak speeds compared to the slider-crank engine of the same size. It also produced lower torque and was about 6% less fuel efficient than the slider-crank engine. These results were concurrent with the dynamometer tests performed in the past. The 4 stroke diesel variant of the same Wiseman engine performed better than the 2 stroke gasoline version as well as the slider-crank engine in all aspects. The Wiseman engine using contra piston showed poor fuel efficiency while operating on E85 fuel. But it produced higher torque and about 1.4% more power than while running on gasoline. While analyzing the effects of the engine size on the Wiseman prototypes, it was found that the engines performed better in terms of power, torque, fuel efficiency and cylinder BMEP as their displacements increased. The 30 horsepower (HP) prototype, while operating on E85, produced the most optimum results in all aspects and the diesel variant of the same engine proved to be the most fuel efficient.
ContributorsRay, Priyesh (Author) / Redkar, Sangram (Thesis advisor) / Mayyas, Abdel Ra'Ouf (Committee member) / Meitz, Robert (Committee member) / Arizona State University (Publisher)
Created2014
Description
In nearly all commercially successful internal combustion engine applications, the slider crank mechanism is used to convert the reciprocating motion of the piston into rotary motion. The hypocycloid mechanism, wherein the crankshaft is replaced with a novel gearing arrangement, is a viable alternative to the slider crank mechanism. The geared hypocycloid mechanism allows for linear motion of the connecting rod and provides a method for perfect balance with any number of cylinders including single cylinder applications. A variety of hypocycloid engine designs and research efforts have been undertaken and produced successful running prototypes. Wiseman Technologies, Inc provided one of these prototypes to this research effort. This two-cycle 30cc half crank hypocycloid engine has shown promise in several performance categories including balance and efficiency. To further investigate its potential a more thorough and scientific analysis was necessary and completed in this research effort. The major objective of the research effort was to critically evaluate and optimize the Wiseman prototype for maximum performance in balance, efficiency, and power output. A nearly identical slider crank engine was used extensively to establish baseline performance data and make comparisons. Specialized equipment and methods were designed and built to collect experimental data on both engines. Simulation and mathematical models validated by experimental data collection were used to better quantify performance improvements. Modifications to the Wiseman prototype engine improved balance by 20 to 50% (depending on direction) and increased peak power output by 24%.
ContributorsConner, Thomas (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Georgeou, Trian (Committee member) / Arizona State University (Publisher)
Created2011
Description
Trajectory forecasting is used in many fields such as vehicle future trajectory prediction, stock market price prediction, human motion prediction and so on. Also, robots having the capability to reason about human behavior is an important aspect in human robot interaction. In trajectory prediction with regards to human motion prediction, implicit learning and reproduction of human behavior is the major challenge. This work tries to compare some of the recent advances taking a phenomenological approach to trajectory prediction. \par The work is expected to mainly target on generating future events or trajectories based on the previous data observed across many time intervals. In particular, this work presents and compares machine learning models to generate various human handwriting trajectories. Although the behavior of every individual is unique, it is still possible to broadly generalize and learn the underlying human behavior from the current observations to predict future human writing trajectories. This enables the machine or the robot to generate future handwriting trajectories given an initial trajectory from the individual thus helping the person to fill up the rest of the letter or curve. This work tests and compares the performance of Conditional Variational Autoencoders and Sinusoidal Representation Network models on handwriting trajectory prediction and reconstruction.
ContributorsKota, Venkata Anil (Author) / Ben Amor, Hani (Thesis advisor) / Venkateswara, Hemanth Kumar Demakethepalli (Committee member) / Redkar, Sangram (Committee member) / Arizona State University (Publisher)
Created2021
Description
Multiple robotic arms collaboration is to control multiple robotic arms to collaborate with each other to work on the same task. During the collaboration, theagent is required to avoid all possible collisions between each part of the robotic arms.
Thus, incentivizing collaboration and preventing collisions are the two principles which
are followed by the agent during the training process. Nowadays, more and more
applications, both in industry and daily lives, require at least two arms, instead of
requiring only a single arm. A dual-arm robot satisfies much more needs of different
types of tasks, such as folding clothes at home, making a hamburger in a grill or
picking and placing a product in a warehouse.
The applications done in this paper are all about object pushing. This thesis
focuses on how to train the agent to learn pushing an object away as far as possible.
Reinforcement Learning (RL), which is a type of Machine Learning (ML), is then
utilized in this paper to train the agent to generate optimal actions. Deep Deterministic
Policy Gradient (DDPG) and Hindsight Experience Replay (HER) are the two RL
methods used in this thesis.
ContributorsLin, Steve (Author) / Ben Amor, Hani (Thesis advisor) / Redkar, Sangram (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2023
Description
Reinforcement Learning(RL) algorithms have made a remarkable contribution in the eld of robotics and training human-like agents. On the other hand, Evolutionary Algorithms(EA) are not well explored and promoted to use in the robotics field. However, they have an excellent potential to perform well. In thesis work, various RL learning algorithms like Q-learning, Deep Deterministic Policy Gradient(DDPG), and Evolutionary Algorithms(EA) like Harmony Search Algorithm(HSA) are tested for a customized Penalty Kick Robot environment. The experiments are done with both discrete and continuous action space for a penalty kick agent. The main goal is to identify which algorithm suites best in which scenario. Furthermore, a goalkeeper agent is also introduced to block the ball from reaching the goal post using the multiagent learning algorithm.
ContributorsTrivedi, Maitry Ronakbhai (Author) / Amor, Heni Ben (Thesis advisor) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2021