Lab Learning objectives PS2 2006
Lab 1. Body Temperature Measurement (Rob’s summary)
Students will learn about measurement, as applied to the body. They will:
1. Learn how to deal with the fact that repeated measurements give different
results, and how to describe this phenomenon.
2. Compare the results from more than one method of measuring.
a. What is being measured?
b. What is the relationship between sets of results?
c. Is there a “best” method?
3. Understand how confidence in the average improves as more measurements are
taken.
Lab 2. Coffee Filter Drop. (Rob’s summary)
1. Learn to use models (F=ma in various forms) to analyze motion data.
2. Learn to analyze graphs of motion to determine appropriate model.
3. Characterize the uncertainty in the measurements.
4. Analyze graphs of data to test models. (linearizing and fitting).
Lab 3. Force Plate and the vertical Jump
Understand how to apply a model (F=dp/dt) to data. Make connections between
F(t) and z(t) both qualitatively and quantitatively.
a. Calculate impulse from F(t) and compare to predicted.
b. Find acceleration from net force for a jump.
c. Integrate acceleration to get velocity and position.
d. Compare results with direct position measurement.
Lab 4. Viscous Drag.
Concepts: Pressure, Buoyancy, drag, Re.
Models: Atmospheric pressure, buoyant force, v^2 drag, v^1 drag.
Learning objectives
1. Find the slope of a graph of experimental data, with uncertainty.
2. Plot v vs r data in a way that it can be analyzed to test a model.
3. Extract information from slope and intercept.
4. Take h vs P data with barometer and use it to calculate interfloor dist.
Lab 5. Brownian Motion
Concepts: <x> over time is zero, <x^2> over time increases linearly.
Models: Random Walk
Learning Goals.
1. Understand how a time lapse movie is used to make many one-step
measurements in a random walk.
2. Understand the significance of the distribution of Δx.
3. Explain that mean of Δx is zero, but mean of Δx^2 is not.
4. Show from the class data that <x^2> is linear in time, relate to D.
5. Understand how the SD of the distribution is related to the size of a
molecule, and therefore to k.
More details below:
Lab 1. (Copied from lab handout) Body Temperature Measurement.
1. Learn to use the Logger Pro software and the LabPro interface
2. Understand the concept of distributions
3. Understand the size and nature of random measurement error, and learn how to
analyze it
4. Analyze the correlation between two measurements and identify the relationship
between them
5. Understand the distribution of body temperatures: mean, standard deviation, and
correlation with other variables
Rob’s summary:
Students will learn about measurement, as applied to the body. They will:
1. Learn how to deal with the fact that repeated measurements give different
results, and how to describe this phenomenon.
2. Compare the results from more than one method of measuring.
a. What is being measured?
b. What is the relationship between sets of results?
c. Is there a “best” method?
3. Understand how confidence in the average improves as more measurements are
taken.
Lab 2 (Copied from lab handout) Coffee Filter Drop.
1. Learn how to analyze random error in a real physical measurement
2. Measure the gravitational acceleration g
3. Investigate the force of air drag and demonstrate that lightweight objects falling
in air quickly
reach terminal velocity
4. Determine the force law for air drag as a function of velocity
5. Investigate the time required to reach (or approach) terminal velocity
6. Determine the force law for air drag as a function of radius
Rob’s summary:
1.
2.
3.
4.
Learn to use models (F=ma in various forms) to analyze motion data.
Learn to analyze graphs of motion to determine appropriate model.
Characterize the uncertainty in the measurements.
Analyze graphs of data to test models. (linearizing and fitting).
Lab 3. Force Plate and the vertical Jump
What they were asked to do:
1. Drop bag of rice on Force plate and find impulse. Analyze.
2. Stand on force plate and wave arms. Calc. CM motion.
3. Jump.
a. Qualitative analysis of F(t).
b. Calculate height of jump from kinematics.
c. Compare squat and countermotion jump.
4. Elevator: F(t) to v to z. Find m, find change in position.
5. Jump with Sonar position sensor.
Learning objectives: Applications of F=dp/dt. Make connections between F(t) and
z(t) both qualitatively and quantitatively.
a. Calculate impulse from F(t) and compare to predicted.
b. Find acceleration from net force for a jump.
c. Integrate acceleration to get velocity and position.
d. Compare results with direct position measurement.
Lab 4. Viscous Drag.
Concepts: Pressure, Buoyancy, drag, Re.
Models: Atmospheric pressure, buoyant force, v^2 drag, v^1 drag.
Procedure:
1.Movie of Teflon spheres.
2. Collect z vs. t data.
3. plot with error bars and find max and min slope.
4. Report vterminal with error.
5. Take more data for spheres of different density.
6. Use all data to determine functional form of force vs. v and find density of
fluid.
7. Measure atmospheric pressure in an elevator ride.
8. Make a Sculpey shape to drop.
Learning objectives
1. Find the slope of a graph of experimental data, with uncertainty.
2. Plot v vs r data in a way that it can be analyzed to test a model.
3. Extract information from slope and intercept.
4. Take h vs P data with barometer and use it to calculate interfloor dist.
Lab 5. Brownian Motion
Concepts: <x> over time is zero, <x^2> over time increases linearly.
Models: Random Walk
Procedure:
Make a sample
Make a time-lapse video
Do video analysis.
Create sets of delta-x from data
Fit to Gaussian, extract mean and SD.
Find value for k from Einstein-Smoluchowski.
Repeat for larger spheres.
Learning Goals.
1. Understand how a time lapse movie is used to make many one-step
measurements in a random walk.
2. Understand the significance of the distribution of Δx.
3. Explain that mean of Δx is zero, but mean of Δx^2 is not.
4. Show from the class data that <x^2> is linear in time, relate to D.
5. Understand how the SD of the distribution is related to the size of a
molecule, and therefore to k.