One-dimensional motion with constant acceleration
Experiment objectives:
⦁ Achieve a better understanding of how to solve position, velocity and acceleration problems in one-dimensional motion with constant acceleration
⦁ Learn how to use data curve fitting to extract information from experiment data
⦁ Cultivate the habit of keeping all experimental data in a well-organized manner
⦁ Cultivate the habit of verifying data constancy in experimental work
Experiment introduction:
One-dimensional motion with constant acceleration serves as the foundation for us to understand more complicated motion. Free fall is a good example of such motion in
our daily life.
If the acceleration is constant, then the final position f of an object can be determined as
f 2, (1)
provided that the initial position i, the initial velocity i and the time duration of motion Δ are known.
Another commonly seen equation of such motion is
f2 −i2 = 2 ⋅ ⋅ Δ, (2)
where Δ = f −i is the distance of travel between the initial and final positions.
In this experiment, we will study one-dimensional motion with constant acceleration by rolling a racquet ball down an inclined track, and use two different methods to
determine its acceleration.
The first method makes use of Equation (2), its experiment setup is shown in Figure 2 (a). In this method, two smart gates will be placed on the track to measure the
velocities of the ball at the moments when it passes through them respectively.
The smart gate is a C shaped device as seen in Figure 1. There are two infrared emitters on one side and two receivers on the opposing side. When an object moves
through the gate and blocks the infrared signals one after another, the smart gate measures the time difference and calculates the speed through the gate.
Figure 1: Manufacturer’s schematic of a smart gate.
Data curve fitting method
The second method involves a technique widely used in scientific and engineering analysis – data curve fitting. In this method, we will replace the smart gates with a
motion sensor at the top of the track, which traces the position of the racquet ball as it rolls down. The experimental setup is shown in Figure 2 (b). As shown in
Equation (1), the position f of the ball is a quadratic function of time duration Δ. Figure 3 shows a typical position data graph from the motion sensor. If we fit
the data curve with a quadratic function, it will extract the numerical values of all relevant quantities behind the data. For example, the curve fit in Figure 3
reveals a function of time 0.185 ⋅ t2 + 0.0281 ⋅ + 0.0593. After comparing corresponding Δ terms of the function to Equation (1), we obtain that the acceleration is
= 2×0.185 = 0.370m/s2. The instruction of how to enable data curve fit in Capstone is included in the Capstone experiment file.
(a) (b)
Figure 2: Experiment setups of using two smart gates (a) and using a motion sensor (b) to determine the acceleration of a rolling ball
Please note that a data fit function does not automatically provide reliable results. One has to pay close attention to how well the fit function matches the data
points. The RMSE value in the data fit reflects the quality of the fit function. RMSE stands for root mean square error, which measures how close or far off all data
points are to the fit function. The smaller the RMSE value is, the better the fit function is. Ideally, one would like to see all data points fall onto the fit
function curve; in this case, the RMSE value is equal to 0. However, for real experiment data, data points always fluctuate around the fit function curve. For the data
in this experiment, a RMSE value smaller than 0.002 can be viewed as excellent, a value smaller than 0.005 acceptable. Anything higher demands a rerun.
Figure 3: Data fitting to extract valuable information. The data shows the position of a racquet ball rolling down a track away from a motion sensor, along with the
data curve fit function for the highlighted data. The positions before motion starts, during motion, and after the motion sensor losses tracking are all shown.
Experiment data consistency
Due to the nature of experimental work, repeated measurements of the same quantity usually do not generate the same reading. Therefore, it is critical to examine data
consistency in experimental work. Two common ways to check data consistency are
⦁ Compare the difference of the largest and smallest values for repeated measurements obtained using the same method;
⦁ Compare the difference of the respective average values obtained using two different methods.
In both methods, smaller difference indicates greater data consistency.
Exploration:
During the Exploration, roll a racquet ball down a tilted track on the lab table, and
⦁ Conduct repeated measurements of the racquet ball acceleration using the double-smart gate method
⦁ Conduct repeated measurements of the racquet ball acceleration using the data curve fitting method
⦁ Compare the data consistency using the methods outlined above
Exploration grade: 20 points
Please draft one or two sentences along with your measurement data and/or calculation results to answer each of the following questions. Some of the questions may
appear in the post-lab quiz. Your instructor will randomly check your answers.
⦁ How many measurements do you repeat for each method? (It has been noticed that for many beginners, “repeated measurements” means “3 and only 3 measurements”;
it is unclear where the magic number 3 comes from; but in experiment work, the number of repeated measurements should be limited by resources, like time, not by an
arbitrary number.)
⦁ What is the typical of the RMSE value of your data fit functions?
⦁ What are the two average acceleration values obtained? Are they close?
⦁ How consistent are your data?
⦁ For the smart gate method, you need to use the scale on the track to record the position of the gates. The smallest scale on the track is millimeter. When you
record the position numbers, how many decimal place should you keep?
Please also present the following to your instructor for a grade:
⦁ All measured data
⦁ Relevant calculation
Please note that points will NOT be marked down if any of the above is wrong; however, points will be deducted based on the following guideline.
-10 -5 No deduction
More than half of the materials are missing, illegible and/or poorly organized; results cannot be understood. Some but less than half of the materials are missing,
illegible and/or poorly organized; efforts have to be made to understand the results. Everything is legible and well organized; instructors can easily understand the
results.
Exploration notes:
The motion sensor functions by emitting sound signals from this port, and detecting the reflected
Connect the smart gate to its cable, then to your computer through the USB Link, as shown to the left.
Application:
In the Application part, your instructor has exactly the same smart gate setup as you do. Your instructor will let a racquet ball roll down a track from a
predetermined position. The two smart gates will generate a data set which includes the positions, 1 and 2, of the gates and the speed values of the ball, 1 and 2,
recorded by them (the initial speed of ball is zero.)
Your instructor will assign you a random speed value = ______, and ask you to make use of the data and determine the position on the track where the racquet ball
attains the speed value.
Inform your instructor once you have confidently calculated the position. Your instructor will place a smart gate at the position, and measure the speed of the ball.
Your Application grade will be determined by how close the measured speed compare to the assigned value.
Application Grade: 20 points
Please present your measurement data, relevant calculation and the determined mass to your instructor for a grade, which will be determined based on the following
guideline:
Measured speed is within ____ of the assigned value. ≤ ±5.0% ≤ ±6.5% ≤ ±8.0% ≤ ±9.5% ≤ ±11% > ±11%
Points 20 17 14 11 8 5
Additionally, points will be deducted based on the guideline below.
-10 -5 No deduction
More than half of the materials are missing, illegible and/or poorly organized; results cannot be understood. Some but less than half of the materials are missing,
illegible and/or poorly organized; efforts have to be made to understand the results. Everything is legible and well organized; instructors can easily understand the
results.
⦁ Lab 2 Report Rubric – data figure
Figures (also called data plots) are very frequently used in technical documents. In this experiment, we collected a few sets of friction force vs time data. Usually,
not all data points need to be shown; instead, we just need to present the portion of the data which is relevant and meaningful. Please recall your experiments, and
think about what part of the data was useful to help you answer questions; what part was not used.
It is important to make sure that all figures are self-contained, which means that without reading contexts, just by looking at the figure, your readers can understand
all relevant information you want to present to them; in other words, all plot axis, all data curves, etc. must be addressed. Meanwhile, irrelevant information does
not show up in it.
A good figure example is given on the next page.
Requirements:
In this report, please use Microsoft Excel to create one figure of two different position vs time data curves based on the following rubric. Please watch the video at
this linkif you need help exporting data from Capstone to Excel.
Item 0 points 2 points
Figure size The figure is too small or too large. The printed figure should be about 6 inches wide and 4 inches high.
Axes label Both axes labels are missing. Axes labels clearly shows what physical quantities they represent respectively.
Axes values and units Axes values and units are missing. Axes values are shown, and units of the values are included.
Text font size Text font is either too small to read or excessively too large. Text font size is readable, and does not occupy too much figure space.
Data curves (4pts) The two data curves are not distinguished. The two data curves are distinguished using different curve styles; for example, different
colors; or one is drawn as solid line, the other dashed line; or one is thin line, the other thick line.
Data curve legends (4pts) Data curve legends are missing. Legends are used to label which curve is for which run.
Data curve placement (4pts) A significant portion of the figure area is left unused. The meaningful part of the data curves should occupy the majority
(75~95%) of the figure area.
Don’t forget to attach a copy of this rubric to your lab report, otherwise 5 points will be marked down.
Page 1
Good example:
Page 2
Data
