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Introduction to Volume 12 Issue 1
Steven I. Gordonpp. 1–1
A brief introduction to this issue of the Journal of Computational Science Education from the editor.
Volume 12 Issue 1 — January 2021
Contents
The Computer Science Education Collaborative: Promoting Computer Science Teacher Education Programs for Preservice and In-service Teachers
Regina Toolin, Lisa Dion, and Robert Ericksonpp. 2–7
https://doi.org/10.22369/issn.2153-4136/12/1/1BibTeX
@article{jocse-12-1-1,
author={Regina Toolin and Lisa Dion and Robert Erickson},
title={The Computer Science Education Collaborative: Promoting Computer Science Teacher Education Programs for Preservice and In-service Teachers},
journal={The Journal of Computational Science Education},
year=2021,
month=jan,
volume=12,
issue=1,
pages={2--7},
doi={https://doi.org/10.22369/issn.2153-4136/12/1/1}
}
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This article reports on the efforts of the Computer Science Education Collaborative during the period between 2018–2020 to develop and implement a new computer science licensure program for preservice teachers seeking a license to teach computer science in grades 7–12 in Vermont. We present a brief review of the literature related to computer science teacher education and describe the process of developing the computer science education minor and major concentration at the University of Vermont. As a form of reflection, we discuss the program development process and lessons learned by the collaborative that might be informative to other institutes of higher education involved in CS teacher education program design and implementation. Finally, we describe next steps for developing in-service licensure programs for teachers seeking computer science professional development or licensure in grades 7–12.
Laboratory Glassware Identification: Supervised Machine Learning Example for Science Students
Arun K. Sharmapp. 8–15
https://doi.org/10.22369/issn.2153-4136/12/1/2BibTeX
@article{jocse-12-1-2,
author={Arun K. Sharma},
title={Laboratory Glassware Identification: Supervised Machine Learning Example for Science Students},
journal={The Journal of Computational Science Education},
year=2021,
month=jan,
volume=12,
issue=1,
pages={8--15},
doi={https://doi.org/10.22369/issn.2153-4136/12/1/2}
}
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This paper provides a supervised machine learning example to identify laboratory glassware. This project was implemented in an Introduction to Scientific Computing course for first-year students at our institution. The goal of the exercise was to present a typical machine learning task in the context of a chemistry laboratory to engage students with computing and its applications to scientific projects. This is an end-to-end data science experience with students creating the dataset, training a neural network, and analyzing the performance of the trained network. The students collected pictures of various glassware in a chemistry laboratory. Four pre-trained neural networks, Inception-V1, Inception-V3, ResNet-50, and ResNet-101 were trained to distinguish between the objects in the pictures. The Wolfram Language was used to carry out the training of neural networks and testing the performance of the classifier. The students received hands-on training in the Wolfram Language and an elementary introduction to image classification tasks in the machine learning domain. Students enjoyed the introduction to machine learning applications and the hands-on experience of building and testing an image classifier to identify laboratory equipment.
Transport Phenomena in High-speed Wall-bounded Flows Subject to Concave Surface Curvature
Guillermo Araya and Ernie Riverapp. 16–23
https://doi.org/10.22369/issn.2153-4136/12/1/3BibTeX
@article{jocse-12-1-3,
author={Guillermo Araya and Ernie Rivera},
title={Transport Phenomena in High-speed Wall-bounded Flows Subject to Concave Surface Curvature},
journal={The Journal of Computational Science Education},
year=2021,
month=jan,
volume=12,
issue=1,
pages={16--23},
doi={https://doi.org/10.22369/issn.2153-4136/12/1/3}
}
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Turbulent boundary layers that evolve along the flow direction are ubiquitous. Moreover, accounting for the effects of wall-curvature driven pressure gradient and flow compressibility adds significant complexity to the problem. Consequently, hypersonic spatially-developing turbulent boundary layers (SDTBL) over curved walls are of crucial importance in aerospace applications, such as unmanned high-speed vehicles, scramjets, and advanced space aircraft. More importantly, hypersonic capabilities would provide faster responsiveness and longer range coverage to U.S. Air Force systems. Thus, the acquired understanding of the physics behind high speed boundary layers over curved wall-bounded flows can lead to the development of more efficient control techniques for the fluid flow (e.g., wave drag reduction) and aerodynamic heating on hypersonic vehicle design. In this investigation, a series of numerical experiments is performed to evaluate the effects of strong concave curvature and supersonic/hypersonic speeds (Mach numbers of 2.86 and 5, respectively) on the thermal transport phenomena that take place inside the boundary layer. The flow solver to be used is based on a RANS approach. Two different turbulence models are compared: the SST (Shear Stress Transport) model by Menter and the standard k-ω model by Wilcox. Furthermore, numerical results are validated by means of experimental data from the literature (Donovan et al., J. Fluid Mech., 259, 1-24, 1994) for the moderate concave curvature case and a Mach number of 2.86. The present study allows us to initially obtain a first insight of the flow physics for a forthcoming better design of 3D meshes and computational boxes, as part of a more ambitious project that involves Direct Numerical Simulation (DNS) of curved wall-bounded flows in the supersonic/hypersonic regime. The uniqueness of this RANS analysis in concave curved walls can be summarized as follows: (i) study of the compressibility effects on the time-averaged velocity and temperature, (ii) analysis of the influence of different inflow boundary conditions.
Performance Evaluation of Monte Carlo Based Ray Tracer
Ayobami Ephraim Adewalepp. 24–31
https://doi.org/10.22369/issn.2153-4136/12/1/4BibTeX
@article{jocse-12-1-4,
author={Ayobami Ephraim Adewale},
title={Performance Evaluation of Monte Carlo Based Ray Tracer},
journal={The Journal of Computational Science Education},
year=2021,
month=jan,
volume=12,
issue=1,
pages={24--31},
doi={https://doi.org/10.22369/issn.2153-4136/12/1/4}
}
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The main objective of computer graphics is to effectively depict an image in a virtual scene in its realistic form within a reasonable amount of time. This paper discusses two different ray tracing techniques and the performance evaluation of the serial and parallel implementation of ray tracing, which in its serial form is known to be computationally intensive and costly for previous computers. The parallel implementation was achieved using OpenMP with C++, and the maximum speedup was ten times that of the serial implementation. The experiment in this paper can be used to teach high-performance computing students the benefits of multi-threading in computationally intensive algorithms and the benefits of parallel programming.
Training Neural Networks to Accurately Determine Energies of Structures Outside of the Training Set Using Agglomerative Clustering
Carlos A. Barragan and Michael N. Grovespp. 32–38
https://doi.org/10.22369/issn.2153-4136/12/1/5BibTeX
@article{jocse-12-1-5,
author={Carlos A. Barragan and Michael N. Groves},
title={Training Neural Networks to Accurately Determine Energies of Structures Outside of the Training Set Using Agglomerative Clustering},
journal={The Journal of Computational Science Education},
year=2021,
month=jan,
volume=12,
issue=1,
pages={32--38},
doi={https://doi.org/10.22369/issn.2153-4136/12/1/5}
}
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Machine learning has accounted for solving a cascade of data in an efficient and timely manner including as an alternative molecular calculator to replace more expensive ab initio techniques. Neural networks (NN) are the most predictive for new cases that are similar to examples in their training sets; however, it is sometimes necessary for the NN to accurately evaluate structures not in its training set. In this project, we quantify how clustering a training set into groups with similar geometric motifs can be used to train a NN so that it can accurately determine the energies of structures not in the training set. This was accomplished by generating over 800 C8H7N structures, relaxing them using DFTB+, and grouping them using agglomerative clustering. Some of these groups were assigned to the training group and used to train a NN using the pre-existing Atomistic Machine-learning Package (AMP). The remaining groups were evaluated using the trained NN and compared to the DFTB+ energy. These two energies were plotted and fitted to a straight line where higher R2 values correspond to the NN more accurately predicting the energies of structures not in its training set. This process was repeated systematically with a different number of nodes and hidden layers. It was found that for limited NN architectures, the NN did a poor job predicting structures outside of its training set. This was improved by adding hidden layers and nodes as well as increasing the size of the training set.
Molecular Simulations for Understanding the Stabilization of Fullerenes in Water
Kendra Noneman, Christopher Muhich, Kevin Ausman, Mike Henry, and Eric Jankowskipp. 39–48
https://doi.org/10.22369/issn.2153-4136/12/1/6BibTeX
@article{jocse-12-1-6,
author={Kendra Noneman and Christopher Muhich and Kevin Ausman and Mike Henry and Eric Jankowski},
title={Molecular Simulations for Understanding the Stabilization of Fullerenes in Water},
journal={The Journal of Computational Science Education},
year=2021,
month=jan,
volume=12,
issue=1,
pages={39--48},
doi={https://doi.org/10.22369/issn.2153-4136/12/1/6}
}
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Making materials out of buckminsterfullerene is challenging, because it requires first dispersing the molecules in a solvent, and then getting the molecules to assemble in the desired arrangements. In this computational work, we focus on the dispersion challenge: How can we conveniently solubilize buckminsterfullerene? Water is a desirable solvent because of its ubiquity and biocompatibility, but its polarity makes the dispersion of nonpolar fullerenes challenging. We perform molecular dynamics simulations of fullerenes in the presence of fullerene oxides in implicit water to elucidate the role of interactions (van der Waals and Coulombic) on the self-assembly and structure of these aqueous mixtures. Seven coarse-grained fullerene models are characterized over a range of temperatures and interaction strengths using HOOMD-Blue on high performance computing clusters. We find that dispersions of fullerenes stabilized by fullerene oxides are observable in models where the net attraction among fullerenes is about 1.5 times larger than the attractions between oxide molecules. We demonstrate that simplified models are sufficient for qualitatively modeling micellization of these fullerenes and provide an efficient starting point for investigating how structural details and phase behavior depend upon the inclusion of more detailed physics.
Performance Analysis of the Parallel CFD Code for Turbulent Mixing Simulations
Tulin Kaman, Alaina Edwards, and John McGarigalpp. 49–58
https://doi.org/10.22369/issn.2153-4136/12/1/7BibTeX
@article{jocse-12-1-7,
author={Tulin Kaman and Alaina Edwards and John McGarigal},
title={Performance Analysis of the Parallel CFD Code for Turbulent Mixing Simulations},
journal={The Journal of Computational Science Education},
year=2021,
month=jan,
volume=12,
issue=1,
pages={49--58},
doi={https://doi.org/10.22369/issn.2153-4136/12/1/7}
}
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Understanding turbulence and mixing due to the hydrodynamic instabilities plays an important role in a wide range of science and engineering applications. Numerical simulations of three dimensional turbulent mixing help us to predict the dynamics of two fluids of different densities, one over the other. The focus of this work is to optimize and improve the computational performance of the numerical simulations for the compressible turbulent mixing on Blue Waters, the petascale supercomputer at the National Center for Supercomputing Applications. In this paper, we study the effect of the programming models on time to solution. The hybrid programming model, which is a combination of parallel programming models, becomes a dominant approach. The most preferable hybrid model is the one that involves the Message Passing Interface (MPI), such as MPI + Pthreads, MPI + OpenMP, MPI + MPI-3 shared memory programming, and others with accelerator support. Among all choices, we choose the hybrid programming model that is based on MPI + OpenMP. We extend the purely MPI parallelized code with OpenMP parallelism and develop the hybrid version of the code. This new hybrid implementation of the code is set up in a way that multiple MPI processes handle the interface propagation, whereas multiple OpenMP threads handle the high order weighted essentially non-oscillatory numerical scheme.