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Month: March 2018

Kinematics for Senior Physics

Rick Debenedetti shares his experience with using a class set of Smart Carts to explore kinematics.  His presentation includes tips and demonstrations using SPARKvue software to introduce displacement, velocity and acceleration to a grade 11 physics class.

Learn how wireless technology allows students to explore authentic learning experiences within a limited time frame. Using wireless sensors means teachers can focus on the students rather than the equipment, and students are more likely to enjoy and learn from the activities, as they feel natural and are spontaneous.

This session demonstrates kinematics for senior physics.

The PASCO Smart Cart is the ultimate tool for studying kinematics, dynamics and more. It features built-in sensors that measure force, position, velocity, and 6 degrees of freedom in acceleration. Measurements can be made both on or off a dynamics track and transmit the data wirelessly over Bluetooth®.

Measuring Headwinds and GPS Speed with PASCO’s New Wirelsss Weather Sensor

PASCO’s new wireless weather & environmental sensor with GPS provides for some obvious investigations such as monitoring changes in weather.  However, as this sensor measures 17 different parameters, there are almost countless ways that measurements can be used individually or in combination to explore the world.

Two of the weather sensor’s 17 measurements relate to speed – the wind speed and movement speed of the sensor itself (as provided by the GPS sensor).  Recognizing the similarity of these two measurements I was curious if the weather sensor’s GPS could be used to assess the accuracy of the weather sensor’s wind speed measurement.

GPS speed has proven to be very accurate, especially in open spaces, where there are no trees or buildings blocking satellite signals.  Therefore, using the GPS to evaluate the accuracy of the wind speed sensor is a reasonable test.

Without over thinking the experimental test, I decided to go for a quick run across our parking lot holding the sensor up in the air like a torch carrier in the Olympics (okay maybe I’m over-romanticizing) and see how the headwind I generate from my sprint correlates to the GPS Speed measurement.

Being in less than optimum shape, after a long winter hiatus from anything resembling exercise, I kept my run to about 100 M (50 M in both directions).  Looking at the satellite image below that depicts my run (each dot is a separate measurement), you’ll see that there were cars in my way requiring several strenuous leaps.

Notwithstanding the strange looks I received during my run, the test proved quite successful.  The graph below shows wind speed in green and GPS Speed in blue.  During the first half of the run the two speeds correlate very closely.  On my return however there is a significant difference which I suspect was caused by a trailing wind gust that would have the effect of reducing the headwind.

In conclusion it appears that the Weather Sensor measures wind speed fairly accurately. However, in this test the wind speed sensor is measuring headwind which is a combination of traveling speed and actual wind.  Therefore more rigorous testing would be required to make a fair assessment, with external sources of wind eliminated or at least accounted for (can you think of ways how this might be done?).

In the classroom I suspect the weather sensor will be used in many interesting ways that has little to do with weather.  In the months to follow I hope to share some more of my playful discoveries with this sensor.

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