Textbooks have traditionally served as the primary educational resources in classes for decades. However, with the transition to online learning prompted by the onset of the COVID-19 pandemic, there has been a significant shift towards online learning materials. As the pandemic subsides, students, particularly those in engineering disciplines, have persisted in utilizing these alternative resources, prompting questions about their effectiveness and identifying the most suitable options. This study aims to uncover the underlying reasons for the decline in textbook usage and to identify the most effective resources for student learning. The study involved approximately 170 students enrolled in a Low Speed Aerodynamics course at Arizona State University (ASU). These students were invited to participate in a series of surveys after we introduced new changes to the course such as recitations, holistic grading, and an online interactive textbook. Around 40 students voluntarily responded to the surveys. Additionally, interviews were conducted with four professors to gather insights into why students may not be using textbooks, and to gather their opinions on recitations, the Connect software, and holistic grading, if they have incorporated these into their own courses. The survey findings revealed that although traditional textbooks offer detailed explanations to aid in grasping concepts, students often prefer alternative resources such as supplementary materials, recitations, and office hours for applying their knowledge to homework or tests. Holistic grading then provides meaningful feedback on the concepts they need to revisit after attempting to apply their understanding during tests. From our sur- vey, it is evident that reaching a definitive solution regarding textbook selection and identifying optimal resources remains challenging. Nevertheless, students ex- pressed a preference for interactions among peers and with professors, indicating that changes incorporating these elements were more favorably received. Further exploration into the continued implementation of holistic grading and recitations could provide insights into the enduring impact of the findings from this study over time.

The following analysis was conducted at the Arizona State University open loop wind tunnel. Two 1/24-th scale NASCAR models were placed in a wind tunnel test section and were adjusted to study drafting that commonly occurs at superspeedway racetracks. The purpose of the experiment was to determine how drafting affects a leading and trailing car through changes in distance. A wind tunnel model was developed consisting of two 2019 NASCAR Chevy Camaro race car models, two bar-style load cells, and a programmed Arduino UNO. Two trials were run at each drafting distance, 0, 0.5, 1, 1.5, and 2 car lengths apart. Each trial was run at a wind tunnel velocity of 78 mph (35 m/s) and force data was collected to represent the drag effects at each drafting location. Based on previously published experimentation, this analysis provided important data that related drafting effects in scale model race cars to full-scale vehicles. The experiment showed that scale model testing can be accurately completed when the wind tunnel Reynolds number is of the same magnitude as a full-scale NASCAR. However, the wind tunnel data collected was proven to be fully laminar flow and did not compare to the flow characteristics of typically turbulent flow seen in superspeedway races. Overall, the analytical drag analysis of drafting NASCAR models proved that wind tunnel testing is only accurate when many parameters are met and should only be used as a method of validation to full-scale testing.

The objectives of this project are to design a statically determinant load cell mechanism for a prototype tow tank ultimately culminating in the testing of the aerodynamic performance of a Formula One racing car model. This paper also serves as a proof of concept for force data collection for a full-sized tow tank being developed by Isabella All [8]. The project includes the design and construction of the load cell mechanism which utilizes a load cell to measure the force in a specific member of the mechanism which is then used to determine the semi-lift and drag forces for a given test model. For this specific project, a model of the front-end of an F1 racing car was used for data collection and analysis. It was found that for a short period of time within each test run, constant force data was able to be collected from the load cell which could then be transformed into semi-lift and drag force data. Ultimately, the drag coefficient acting on the model was found to be in the range of 0.9 to 1.3 which somewhat falls in line with the estimated values of 0.7 to 1.0 [1] for F1 racing vehicles. Although the final data collected may not be entirely accurate due to errors discussed in the paper, the ideas presented in this project can be fully realized with some minor changes and adjustments.