A novel concept for integration of flame-assisted fuel cells (FFC) with a gas turbine is analyzed in this paper. Six different fuels (CH4, C3H8, JP-4, JP-5, JP-10(L), and H2) are investigated for the analytical model of the FFC integrated gas turbine hybrid system. As equivalence ratio increases, the efficiency of the hybrid system increases initially then decreases because the decreasing flow rate of air begins to outweigh the increasing hydrogen concentration. This occurs at an equivalence ratio of 2 for CH4. The thermodynamic cycle is analyzed using a temperature entropy diagram and a pressure volume diagram. These thermodynamic diagrams show as equivalence ratio increases, the power generated by the turbine in the hybrid setup decreases. Thermodynamic analysis was performed to verify that energy is conserved and the total chemical energy going into the system was equal to the heat rejected by the system plus the power generated by the system. Of the six fuels, the hybrid system performs best with H2 as the fuel. The electrical efficiency with H2 is predicted to be 27%, CH4 is 24%, C3H8 is 22%, JP-4 is 21%, JP-5 is 20%, and JP-10(L) is 20%. When H2 fuel is used, the overall integrated system is predicted to be 24.5% more efficient than the standard gas turbine system. The integrated system is predicted to be 23.0% more efficient with CH4, 21.9% more efficient with C3H8, 22.7% more efficient with JP-4, 21.3% more efficient with JP-5, and 20.8% more efficient with JP-10(L). The sensitivity of the model is investigated using various fuel utilizations. When CH4 fuel is used, the integrated system is predicted to be 22.7% more efficient with a fuel utilization efficiency of 90% compared to that of 30%.
To achieve this goal, a model of a swarm performing a collective transport task in a bounded domain featuring convex obstacles was simulated in MATLAB/ Simulink®. The closed-loop dynamic equations of this model were linearized about an equilibrium state with angular acceleration and linear acceleration set to zero. The simulation was run over 30 times to confirm system ability to successfully transport the payload to a goal point without colliding with obstacles and determine ideal operating conditions by testing various orientations of objects in the bounded domain. An additional purely MATLAB simulation was run to identify local minima of the Hessian of the navigation-like potential function. By calculating this Hessian periodically throughout the system’s progress and determining the signs of its eigenvalues, a system could check whether it is trapped in a local minimum, and potentially dislodge itself through implementation of a stochastic term in the robot controllers. The eigenvalues of the Hessian calculated in this research suggested the model local minima were degenerate, indicating an error in the mathematical model for this system, which likely incurred during linearization of this highly nonlinear system.
This paper analyzes responses to deviated Trolley Problem scenarios [5] in a simulated driving environment and still images from MIT’s moral machine website [8] to better understand how humans respond to various crashes. Also included is participants driving habits and personal values, however the bulk of that analysis is not included here. The results of the simulation prove that for the most part in driving scenarios, people would rather sacrifice themselves over people outside of the vehicle. The moral machine scenarios prove that self-sacrifice changes as the trend to harm one’s own vehicle was not so strong when passengers were introduced. Further defending this idea is the importance placed on Family Security over any other value.
Suggestions for implementing ethics into autonomous vehicle crashes stem from the results of this experiment but are dependent on more research and greater sample sizes. Once enough data is collected and analyzed, a moral baseline for human’s moral domain may be agreed upon, quantified, and turned into hard rules governing how self-driving cars should act in different scenarios. With these hard rules as boundary conditions, artificial intelligence should provide training and incremental learning for scenarios which cannot be determined by the rules. Finally, the neural networks which make decisions in artificial intelligence must move from their current “black box” state to something more traceable. This will allow researchers to understand why an autonomous vehicle made a certain decision and allow tweaks as needed.
The work presents the nonlinear equations of motion of a quadcopter. This includes the translational and rotational equations of motion, as well as an analysis of the nonlinear actuator dynamics. The work then analyzes the static properties of a quadcopter in forward flight equilibrium and shows how static properties change as physical properties of the vehicle are varied. Next, the dynamics of forward flight are linearized, and a dynamic analysis is provided.
After dynamic analysis, the work shows detailed hierarchical control system design trade studies, which includes attitude and translational inner-outer loop control. Among other designs, the following are presented: PD control, proportional control, pole-placement control. Each of these control architectures are employed for the inner loops and outer loops. The work also analyzes linear versus nonlinear simulation performance of a quadcopter, specifically for a step x-axis reference command. It is found that the nonlinear dynamics of the actuator cause significant discrepancy between linear and nonlinear simulation.
Finally, this thesis establishes directions for future graduate research. This includes hardware design, as well as moving toward design of a highly-maneuverable thrust-vectoring quadrotor which will be the focus of the proposed graduate PhD research. In summary, this thesis provides the beginning of a cohesive framework to model, analyze, control, and design quadcopters. It also lays the groundwork for graduate research and beyond.