Peter E. Johnson

A Control Systems Laboratory for Undergraduate Mechanical Engineering Education

This article describes seven laboratory experiments that have been developed for the automatic controls course at Valparaiso University. It also presents the results of a self-assessment survey taken by the students after they had done these laboratory experiments. Automatic controls recently became a required course for all undergraduate mechanical engineering students. When taught as an elective, it was noticed that many students tended to struggle with this class. Most students perceive this class to be a collection of different mathematical tools without any application or use in their future careers. To alleviate this situation and assist students in visualizing control systems in practical situations, a half-credit elective control laboratory, which consists of five experiments and two laboratory projects, has been developed. These experiments will help students to understand the application of this topic and to learn to develop appropriate mathematical models and control routines in closed-loop systems with computers in the loop.

Enhancing the Undergraduate Educational Experience: Development of a Micro-Gas Turbine Laboratory

A Capstone C30 MicroTurbine has been installed, instrumented, and utilized in a junior-level laboratory course at Valparaiso University. The C30 MicroTurbine experiment enables Valparaiso University to educate students interested in power generation and turbine technology. The first goal of this experiment is for students to explore a gas turbine generator and witness the discrepancies between idealized models and real thermodynamic systems. Secondly, students measure and analyze data to determine where losses occur in a real gas turbine. The third educational goal is for students to recognize the true costs associated with natural gas use, i.e. the hidden costs of transporting the gas to the consumer. Overall, the gas turbine experiment has garnered positive feedback from students. The twenty-six students who performed the lab in Spring 2014 rated the quality and usefulness of the gas turbine experiment as 4.28 and 4.19, respectively, on a 1–5 Likert scale, where 1 is low and 5 is high.

A Novel Engineering Tool for Creative Design of Fluid Systems

Computational fluid dynamics is not often used early in the conceptual design stage of product development due to the lengthy computation times involved with solving complex computational fluid dynamics models. At this early stage, design options are being explored and significant changes are common, and therefore updated solutions must be found quickly to make these models effective. Because of this, computational fluid dynamics models are often reduced to analysis tools used later in the process and are used for refinement rather than for creative engineering design. This paper presents a novel method to create computational fluid dynamics models that can be used earlier in the engineering design process. The key aspects of analysis models used in the initial, creative phase of design are the ability to make changes and re-analyze the altered model quickly. Typically, computational fluid dynamics analysts choose to re-analyze the entire altered model to maintain the same level of accuracy. This can take a significant amount of time because the entire domain must be recalculated. Much of this time is devoted to fine-tuning the model, i.e., improving the accuracy of details of the domain that are sometimes non-essential to the bulk characteristics of the flowfield. However, in the early stage of the design process, decisions are often made based on the large-scale behavior of the fluid flow; fine details are often inconsequential. We have taken advantage of this premise to decrease the turnaround time required to re-analyze a computational fluid dynamics model using the Adaptive Modeling by Evolving Blocks Algorithm. The Adaptive Modeling by Evolving Blocks Algorithm is a genetic programming-based optimization program that segregates a flowfield and places minimal cost solvers in regions with simple flow dynamics while placing full-scale computational fluid dynamics solvers in the more complex regions to preserve accuracy. The program evolves the combined segregation scheme and solver placement until a reliably accurate, faster multi-solver model is found. Substantial reductions in solution times have been found in some cases. The results show an improvement in the speed of the multi-solver when compared with a single-model solution with no significant loss of accuracy.