The Next Generation of Computational Science and Engineering

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Computational Tools and Facilities for the Next-Generation Analysis and Design Environment

This document contains presentations from the joint UVA/NASA Workshop on Computational Tools and Facilities for the Next-Generation Analysis and Design Environment held at the Virginia Consortium of Engineering and Science Universities in Hampton, Virginia on September 17-18, 1996. The presentations focused on the computational tools and facilities for analysis and design of engineering systems, including, real-time simulations, immersive systems, collaborative engineering environment, Web-based tools and interactive media for technical training. Workshop attendees represented NASA, commercial software developers, the aerospace industry, government labs, and academia. The workshop objectives were to assess the level of maturity of a number of computational tools and facilities and their potential for application to the next-generation integrated design environment

Elementary Teacher Adaptations to Engineering Curricula to Leverage Student and Community Resources

This paper addresses an important consideration for promoting equitable engineering instruction: understanding how teachers contextualize curricular materials to draw upon student and community resources. We present a descriptive case study of two 5th grade teachers who co-designed a Next Generation Science Standards (NGSS)-aligned curricular unit that integrated science, engineering, and computational modeling. The five-week project challenged students to redesign their school grounds to reduce water runoff and increase accessibility for students with disabilities. The teachers implemented the project with one Grade 5 class with a large proportion of students having individualized learning plans and cultural backgrounds minoritized in science, technology, engineering, and mathematics fields. Data sources include classroom videos, teacher interviews, and student artifacts. Findings demonstrate how teachers made helpful, important adaptations to contextualize the curriculum unit and draw upon students’ community-based resources. This case highlights the role of the teacher in enacting engineering materials that privilege student and community resources in elementary classrooms. Findings also underscore the importance of teacher customizations to promote equitable, NGSS-based engineering instruction in elementary classrooms

Multiple Modes In Science Instruction: Diversifying Opportunities For Students To Learn

To become scientifically literate, students need to interpret science concepts using numbers, text, and visuals. Scientists use multiple modes to communicate their ideas to each other and the public, including images, text, mathematical notations, symbols, diagrams, charts, and graphs. Several of the science and engineering practices in the Next Generation Science Standards incorporate multiple modes of representing information: developing and using models; analyzing and interpreting data; using mathematical and computational thinking; and obtaining, evaluating, and communicating data. Here, Wilson and Bradbury discuss the benefits of multiple modes in science instruction and how teachers can incorporate them in their teaching

Training future engineers: Integrating Computational Thinking and effective learning methodologies into education

This article examines the effectiveness and interest generated among primary and secondary education students through activities aimed at developing Computational Thinking skills, in the context of the coronavirus disease 2019 pandemic. The shift to online or hybrid learning models posed a significant challenge for educators, particularly those lacking digital skills. The study sought to answer several research questions, including the impact of online versus in-person teaching on preuniversity students and gender differences in Computer Science perception, and Computational Thinking skills performance. The study employed a four-phase methodology, consisting of pre- and posttraining measurements of Computer Science perception and Computational Thinking skills development through specific activities delivered in-person or online. The results indicate that in-person training is more effective for developing Computational Thinking skills, particularly at the secondary education level. Furthermore, there is a need to focus on maintaining girls' interest in Computer Science during primary school, as interest levels tend to decline significantly in secondary school. These findings have significant implications for Engineering Education in the context of digital transformation and the increasing importance of Computational Thinking skills in various fields of engineering. This study highlights the importance of developing Computational Thinking skills among preuniversity students and the need for effective training methods to achieve this goal and underscore the significance of investing in Engineering Education to prepare the next generation of engineers for the rapidly changing digital landscape

Exploring Computational Chemistry on Emerging Architectures

Emerging architectures, such as next generation microprocessors, graphics processing units, and Intel MIC cards, are being used with increased popularity in high performance computing. Each of these architectures has advantages over previous generations of architectures including performance, programmability, and power efficiency. With the ever-increasing performance of these architectures, scientific computing applications are able to attack larger, more complicated problems. However, since applications perform differently on each of the architectures, it is difficult to determine the best tool for the job. This dissertation makes the following contributions to computer engineering and computational science. First, this work implements the computational chemistry variational path integral application, QSATS, on various architectures, ranging from microprocessors to GPUs to Intel MICs. Second, this work explores the use of analytical performance modeling to predict the runtime and scalability of the application on the architectures. This allows for a comparison of the architectures when determining which to use for a set of program input parameters. The models presented in this dissertation are accurate within 6%. This work combines novel approaches to this algorithm and exploration of the various architectural features to develop the application to perform at its peak. In addition, this expands the understanding of computational science applications and their implementation on emerging architectures while providing insight into the performance, scalability, and programmer productivity

The First Year of an Undergraduate Service Learning Partnership to Enhance Engineering Education and Elementary Pre-Service Teacher Education

This IUSE project was designed to address three major challenges faced by undergraduate engineering students (UES) and pre-service teachers (PSTs): 1) retention for UESs after the first year, and continued engagement when they reach more difficult concepts, 2) to prepare PSTs to teach engineering, which is a requirement in the Next Generation Science Standards as well as many state level standards of learning, and 3) to prepare both groups of students to communicate and collaborate in a multi-disciplinary context, which is a necessary skill in their future places of work. This project was implemented in three pairs of classes: 1) an introductory mechanical engineering class, fulfilling a general education requirement for information literacy and a foundations class in education, 2) fluid mechanics in mechanical engineering technology and a science methods class in education, and 3) mechanical engineering courses requiring programming (e.g., computational methods and robotics) with an educational technology class. All collaborations taught elementary level students (4th or 5th grade). For collaborations 1 and 2, the elementary students came to campus for a field trip where they toured engineering labs and participated in a one-hour lesson taught by both the UESs and PSTs. In collaboration 3, the UESs and PSTs worked with the upper-elementary students in their school during an afterschool club. In collaborations 1 and 2, students were assigned to teams and worked remotely on some parts of the project. A collaboration tool, built in Google Sites and Google Drive, was used to facilitate the project completion. The collaboration tool includes a team repository for all the project documents and templates. Students in collaboration 3 worked together directly during class time on smaller assignments. In all three collaborations lesson plans were implemented using the BSCS 5E instructional model, which was aligned to the engineering design process. Instruments were developed to assess knowledge in collaborations 1 (engineering design process) and 3 (computational thinking), while in collaboration 2, knowledge was assessed with questions from the fundamentals of engineering exam and a science content assessment. Comprehensive Assessment of Team Member Effectiveness (CATME) was also used in all 3 collaborations to assess teamwork across the collaborations. Finally, each student wrote a reflection on their experiences, which was used to qualitatively assess the project impact. The results from the first full semester of implementation have led us to improvements in the implementation and instrument refinement for year 2

Organic Materials in Silico:From force field development to predicting dielectric properties

Organic electronics have a wide range of applications, from utilizing the solar energy for electricity generation to devices that seamlessly integrate with biological surfaces. The virtually unlimited chemical space of organic molecules, while offering the possibility of ideal molecules for each of these applications, also makes it more challenging to find them. A common approach to navigating through this vast chemical space towards better performing devices is identifying design rules by correlating changes in molecular or morphological structure to the improvement of specific properties. Functionalizing organic molecules with polar side chains is one such design rule that has become a ubiquitous strategy in the search for the next generation organic materials. This thesis elucidates, by advancing and applying computational methods, what happens at the molecular level by the inclusion of polar side chains and provides a deeper understanding of the interplay between molecular structure and dielectric and electronic properties, with the aim of guiding the field towards engineering better performing devices. A strong emphasis is given to both the accurate computation of the dielectric constant and the understanding of the relevant dielectric contributions for organic electronics. Additionally, the computational method introduced in this thesis, which is readily applicable in various materials science and biophysics studies, allows approaching quantum mechanical accuracy using computationally much more feasible molecular dynamics simulations. Overall, the findings of this thesis contribute to the goal of materials design based on computational approaches by improving existing models as well as the understanding of several property-structure relationships