Science and Engineering Summer Apprentice Program

Science and Engineering Apprentice Program

The Science and Engineering Apprenticeship program puts research in engineering, computer science, physics, and math into the hands of students during the summer between high school and college. Students selected for the program often have their first experience working in a laboratory research-and-development environment and learn more about careers in their chosen fields of study.

Each apprentice completes a summer-long project and prepares a technical report and presentation outlining their work. The presentations are judged, with winners receiving special recognition and small cash prizes; the technical reports are combined into one larger publication and distributed to project sponsors, U.S. government officials, university administrators, and counselors at area high schools. In addition to their projects, the apprentices enjoy a tour of Lake Travis Test Station, attend technical seminars presented by ARL:UT’s research and support staff, and network during student socials that provide an opportunity for the students to discuss research projects with their peers.

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Science and Engineering Apprenticeships

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About the S&E

ARL:UT’s Science and Engineering Apprenticeship Program has been in effect since 1982 for students with an interest in electrical, mechanical and aerospace engineering, physics, mathematics, and computer science. Over 576 students have taken part. The program is competitive. U.S. citizenship is required, and preference is given to students planning to attend the University of Texas at Austin. Many participants return to ARL:UT in student and research positions and stay on to contribute for several years.

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  • Graduating high school senior entering a 4-year college or university in the upcoming fall semester
  • Available to work throughout the summer appointment period (Unpaid time off is available only for attendance of freshman college orientation).
  • Must have applied and been admitted to The University of Texas at Austin
  • U.S. Citizen
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Download the Application. Directions are included in the application (16 pages). Return it to ARL:UT by U.S. mail or email by the date specified.

Apprentice flyer Download the Flyer. High school educators, please print and distribute this flyer to interested graduating seniors in your science and advanced math classes.

2021 Student Projects

Below are abstracts from three student projects.

Student Presentations

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Dynamic Mechanical Testing Additive Manufactured Polymers

Additive manufacturing (AM) allows users to produce one-off components with both low fabrication waste and complex geometries, making AM useful for prototyping and final production. Fused deposition modeling (FDM) is especially common because of its relative accessibility, low cost, and simplicity. The elastic properties of AM components are of particular interest in the design of acoustic systems; however, many materials remain under-characterized in the elastic regime. In this project, elastic and acoustic properties of off-the-shelf AM materials were measured experimentally. The base materials, polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS), were extruded using an FDM 3D printer into coupons of appropriate size for each measurement technique. This project focused on composites and on materials with variable porosity; the materials evaluated included wood, copper, and steel particle filled PLA filaments and a temperature-activated foaming PLA. A dynamic mechanical analyzer (DMA) assessed the time and temperature dependence of viscoelastic moduli of the materials, and time-temperature superposition was employed to estimate the sample response up to gigahertz frequencies. Environmental degradation of the materials was measured by comparing the mechanical properties of samples soaked in both salt and fresh water to the original un-soaked samples. Additionally, ultrasonic analysis was used to calculate the sound speed and loss the materials. All tests were repeated to measure variance, and all data were compiled into a database.
St. Stephens Episcopal High School, Austin, Texas, Advanced Technology Laboratory

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Data Visualization and Adversarial Attacks for Marine-Life-Classifying Neural Network

In recent years, machine learning advancements have facilitated huge steps forward in image classification. However, recent work has shown that image classification models are susceptible to adversarial attacks, nearly-imperceptible alterations to the images that significantly degrade performance. In this project, we apply state-of-the-art image classification algorithms to a new problem, the identification of marine life calls, and we demonstrate that adversarial attacks are just as damaging to our models. We used a collection of pre-classified audio samples of north atlantic right whale calls to develop a neural network that can isolate whale calls from ocean noise. The audio samples were converted to spectrogram images and upscaled to fit two common neural networks: AlexNet and ResNet. These networks were trained to detect the presence/absence of a whale call in 93% of samples. However, after visualizing a sample of the incorrectly-labeled data, we found that about half of the incorrectly-classified samples contained unmarked calls from other whale species or a right whale call missed by researchers, and our actual detection accuracy is likely significantly higher. Once satisfied with our classification accuracies, we generated adversarial images to attack our model. Two classes of attacks are visualized in this paper, white-box attacks in which we had unrestricted access to the all parts of our neural network, and black-box attacks that only accessed the network’s inputs and outputs, similar to a real-world attack. Our models could be applied to detect and differentiate marine life calls from potential adversaries such as submarines in monitored waters. In the future, we could expand our model to classify multiple types of marine life and consider a broader array of adversarial attacks.
Georgetown High School, Austin, Texas, Signal and Information Sciences Laboratory

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Evaluating Lava Flows and Lunar Rilles with Lidar Products

Volcanic activity on the surface of the Moon has shaped the general topography by producing a network of lava tubes and channels that collapsed, creating the observable sinuous rilles. Measurements such as channel width and depth can be used to accurately predict the original magma properties such as viscosity that created these features. With these predictions, we can understand the eruption behaviors and changes in the propagation parameters. The goal of this work was to define the width and depth measurements through the filtration and analysis of lidar data on the lunar surface. This data was collected by the laser altimeter on the Lunar Reconnaissance Orbiter (LRO). 19 potential sinuous rilles in the Schrodinger Basin near the southern pole were analyzed using a multistep approach. First, the lidar data at the flow locations were filtered based on the orientation between the flow propagation direction and the lidar track. The extents of observable channels were defined within each lidar dataset and used to estimate the depth and width parameters. The quantity and accuracy of these measurements proved to be sufficient to calculate the physical extents of the flows. Results from this work can be utilized in advanced modeling techniques to evaluate the original magma properties that produced the emplaced flow channels. Additionally, this automated script can be applied to other lunar rille sites to evaluate regional trends. Quantitative analysis of these lunar rilles provides critical information depicting the volcanic history of the area.
Westlake High School, Austin, Texas, Signal and Information Sciences Laboratory

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About the Banner

Science and Engineering Apprentices are each assigned their own summer research project. This student is testing new underwater acoustic materials using near-field acoustic holography.

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