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# Computational Thinking and Modeling Laminar Air Drag with Code

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## Abstract

This presentation for CAST 2022 in Dallas discusses how computational thinking practices can impact physics learning when students write computer programming code to model air drag. The model produces data that students use to compare the results of a direct video measurement activity about air drag. The datasets are then analyzed using a Google Notebook (Jupyter Notebook) running python to compare laminar versus turbulent flows. Here is a live version of the slides: https://docs.google.com/presentation/d/e/2PACX-1vQrRzuUaj9V_jTBj-IJ8WTJ3ck1Ye3HIo3tjFzLen-od37qX_uNSbFsTUvpDc838rgR27VNuI2kZL1Q/pub?start=false&loop=false&delayms=3000
Computational Thinking and
Modeling Laminar Air Drag with
Code
James Newland - Bellaire HS - University of Houston College of Education
https://jimmynewland.com/
Computational Thinking (CT) In Science Classes
Taxonomy of CT in Math and Science from Weintrop, et al., (2016).
CT is deﬁned for
this project as
using data and
modeling practices
to explore a
physical system or
phenomenon using
computer
programming.
With Euler-Cromer modeling, variable accumulation allows for state changes. Usually time is
iterated and there is simple visualization (Cromer, 1981). Note, p5js calls the draw function
automatically.
Accelerate the blob STEMcoding example
Modeling Laminar Flow with STEMcoding - live version - code version
Model developed using Newtons 2nd
law from AP Physics C: Mechanics
Curriculum
Needs to happen late in AP Physics C:
Mechanics course
practicing derivation problems ﬁrst
Euler-Cromer: Updating State Variables (Position, Velocity, and Acceleration)
Model Single Box with Drag
Model 6 Boxes of Varying Mass
Finding Terminal Velocity from Model
The code generates actual data
from the model for the students
to use for further analysis and
visualization. (Javascript console
in the browser)
Finding Terminal Velocity from Model
Fluid Flow
around a Cube
from Schröder
et al., 2020 Data Reduction Google Colab Notebook
Laminar Flow Test
Air drag can be
modeled as laminar
or turbulent
Laminar
Turbulent
Falling Coffee Filters activity from Pivot Interactives
STEMcoding activity
happens on one day.
Pivot Interactives activity
happens on another day.
Then on a third day,
students can use a
(Jupyter Notebook) to
compare their model and
their real-world drag
analysis.
Google Colab (Jupyter Notebook) Analysis Python Code
References & Code
Cromer, A. (1981). Stable solutions using the Euler approximation. American Journal of
Physics, 49(5), 455–459. https://doi.org/10.1119/1.12478
Newland, J. (2020). jimmynewland/colabnotebooks: Google Colab Notebooks.
https://doi.org/10.5281/ZENODO.4318058
Orban, C. M., & Teeling-Smith, R. M. (2020). Computational Thinking in Introductory
Physics. The Physics Teacher, 58(4), 247–251. https://doi.org/10.1119/1.5145470
Schröder, A., Willert, C., Schanz, D., Geisler, R., Jahn, T., Gallas, Q., & Leclaire, B. (2020). The
ﬂow around a surface mounted cube: a characterization by time-resolved PIV, 3D
Shake-The-Box and LBM simulation. Experiments in Fluids, 61(9), 1–22.
https://doi.org/10.1007/s00348-020-03014-5
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016).
Deﬁning Computational Thinking for Mathematics and Science Classrooms. Journal of
Science Education and Technology, 25(1), 127–147.
https://doi.org/10.1007/s10956-015-9581-5
Wijaya, P. A., Fauzi, U., & Latief, F. D. E. (2019). A simple determination of air drag using
video tracker and modiﬁable projectile launcher. Physics Education, 54(5).
https://doi.org/10.1088/1361-6552/ab26eb
Ye, Q., & masso, evelyn. (2021, March 18). p5js Home. https://p5js.org/
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include “computational thinking” as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics curricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.
Article
The air drag constant for different nose shapes of projectiles is determined with the help of a simple method combining a projectile launcher, video tracking technique and spreadsheet computer code for simulating the motion. A projectile launcher with low-cost components is developed to study air drag in projectile motion. The launcher is based on air pressure and is easy to modify for different projectile types. The projectile motion is tracked using a video camera connected to a computer. The trajectory of the motion is then simulated with the help of the spreadsheet, and the air drag constant is adjusted to fit the observed data. The minimum error is used as the criteria for the best fitted air drag constant. Three types of projectiles, i.e.: conical, round, and cylindrical shape, are used in this experiment. The obtained air drag constants are in the range of 0.025–0.134. The conical shape has the lowest air drag constant as predicted.
Stable solutions using the Euler approximation
• A Cromer
• J Newland
Cromer, A. (1981). Stable solutions using the Euler approximation. American Journal of Physics, 49(5), 455-459. https://doi.org/10.1119/1.12478 • Newland, J. (2020). jimmynewland/colabnotebooks: Google Colab Notebooks. https://doi.org/10.5281/ZENODO.4318058