In Unit 5 Activity 2 we were asked to predict and then find the brightness and flow rate of a closed circuit with a three cell battery pack, a long bulb, a capacitor parallel to the long bulb, and a round bulb. I originally predicted that the round bulb would be brighter when the circuit was first closed and when the circuit reached a steady state, and that the wire between the round bulb and the negative side of the battery would have a higher flow rate than the other wires. However, in the steady state the long bulb was actually brighter, and all the wires had the same flow rate.
The new data allowed me to understand how different resistances in bulbs affects the brightness of bulbs in the circuit. Before this activity I knew that round bulbs had lower resistance because they have shorter and wider filaments, and long bulbs have higher resistance since their filaments are much longer and thinner. Because of this, 2 round bulbs in a closed circuit would be brighter than 2 long bulbs in a separate but identical circuit. I based my predictions on this concept, which is why | presumed the round bulb would be brighter.
The actual results revealed that the round bulb was brighter for a brief instant of time before the long bulb lit up and eventually the long bulb was bright while the round bulb was not visibly lit. I also assumed that the flow rate in the wire coming off the round bulb would be higher because the bulb had a lower resistance so charge would charge could pass through at a faster rate. Since the compass deflected 5° counterclockwise at each point measured in the circuit, the activity proved was that the flow rate was the same throughout the circuit no matter its position.
This introduced me to the important concept of flow rate being the same in all bulbs in a series circuit, which has been important as we have moved onto more complex circuits and as we have started measuring the flow rate. After the activity was complete, we went through and made a step-by-step color coding guide for the pressure difference at different stages in the lighting process. The pressure difference coding mirrored the bulbs lighting, as the round bulb started off lit and with a large pressure difference around it, before the bulb began to dim and the pressure difference decreased.
The long bulb started off not visibly lit and with a low pressure difference, but both the brightness and the pressure difference grew until the long bulb was the only one visibly lit. This taught me that in a circuit with bulbs of different resistances, the higher resistance one requires more pressure difference to drive the same amount of flow rate. Since the long bulb required such a high pressure difference to drive the charge, there was a much lower pressure difference around the round bulb, which is why it wasn’t visibly lit.
I learned that in order for a steady flow rate to exist in a circuit the bulb with the higher resistance must also have a higher pressure difference around it. Understanding this principle has first helped me understand basic circuits better, as the pressure difference coding revealed why long bulbs in a basic circuit are not as bright as long bulbs in a separate basic circuit. The principle has also been key as we moved into more complex circuits with multiple different bulbs, and even with parallel circuits. Unit 2 Activity 3 dealt with the similarities between a battery pack and a Genecon.
Bulb brightness and compass deflection were measured for battery packs with different numbers of cells, as were the Genecon turns needed to replicate those results. However, the data collected about the battery packs was instrumental in understanding the more complex topic of Ohm’s Law. The brightness and compass deflection for one cell, two cell, and three cell battery pack were recorded during the activity. The clear trend of the data was that the more cells the battery pack had, the brighter the bulbs would be and the greater the deflection.
The data showed the relationship between the number of battery cells, or the power source, and the brightness of the bulbs, as well as the flow rates relationship with both the brightness of the bulbs and the power source. Since an increase in one of these factors also increased the other two, each one of these relationships is direct. Unit 6 Activity 3 was an introduction to Ohm’s Law, and the activity in which we learned AV = IR. In the formula, AV is the voltage difference of the battery, l is the current of the circuit, and R is the resistance.
The data from the activity revealed that voltage difference has a direct relationship with both the current, as an increase in voltage difference results in an increase in the flow rate. The activity from Unit 2 helped me understand Ohm’s Law later in the course by establishing the factors that can influence the brightness of bulbs. The compass deflection indicated the current of the circuit, with large deflections being higher flow rates. The greater the deflection, the brighter the bulb, which connects to bulbs being brighter in circuits that have higher currents.
The increase of the compass deflection as the number of battery cells increased relates to the direct relationship between voltage difference and current. Multiple battery cells have more voltage than just one, so the current would be higher. Activity 3’s data provided most of the reasoning for Ohm’s Law, just without the names or numbers. The concepts behind the direct relationship of battery cells and compass deflection are the same concepts that support the direct relationship of voltage difference and current included in Ohm’s Law.
When we began gather data using Ohm’s Laws, the trend of current increasing as voltage increases made sense because Unit 2 Activity 3 taught me that flow rate increases when the number of battery cells increases. The Unit 2 Activity 2 CER discussed the origin of mobile charge in charging and discharging circuits, and my claim was that mobile charge originates from every conductive surface in a circuit. In that activity, we created a charging circuit with a 3 cell battery pack, 2 round bulbs, and a capacitor, and then placed a compass under four different areas of the circuit and recorded whether or not there was a deflection.
The procedure for the discharging circuit was similar, except there was no battery pack in that circuit and deflection was only measured at three places. I used the evidence of the the immediate compass deflection, the instantaneous and simultaneous lighting of the bulbs, and the movement of charge in a circuit without a battery pack to create a clear argument for mobile charge originating on all conductive surfaces in a circuit.
My first piece of evidence was the movement of the compass; no matter where the compass was in a circuit, it would deflect immediately when the closed loop was formed. The fact that the compass lit immediately meant that charge had to be in the wires as well as the battery pack and capacitor. If only the battery pack in the charging circuit or only the capacitor in the discharging circuit contained mobile charge, a compass father away from the battery pack or capacitor would take longer to move than one directly after the battery pack/capacitor.
Instead, the each compass immediately moved which means charge has to be in more than just power sources, hence charge originating on all conductive surfaces, like the wires. The bulbs lighting simultaneously and instantaneously also proves mobile charge is located on all conductive surfaces for similar reasons. The bulbs lighting up instantly means that each one was near the charge when the closed loop was formed, and since they are different distances away from the power source, the charge had to be located in other places than just the battery pack or capacitor.
The bulbs lighting simultaneously is proof of my claim because if charge only came from a power source, it would take the second bulb longer to light, so both of them lighting at the same time proves there is charge on all the conductive surfaces. Lastly, the fact that the bulbs lit and the compass moved in the discharging circuit proves that the battery pack is not the only source of charge. Since the discharging circuits contained a capacitor but not a battery, it proves that capacitors can also be power sources, meaning that circuits are not limited to getting their charge from the battery back.
Also, the bulbs in the discharging circuit lit up instantaneously and simultaneously, which supports the notion of charge existing in more than one location in the circuit, as does the deflection of the compass when the closed loop was formed. All of these reasonings add up to prove that charge originates in circuits in places other than batteries, and must be located on all conductive surfaces or else such results would not be present. The modeling of motion can take various forms, from visual ones like motion maps and graphs, to mathematical equations, to words.
Each form has its own pros and cons, but there is one type of representation that provides the most information and is therefore the best form to model motion in. Graphical representations work best for modeling motion because it provides information about the starting and stopping locations of the object and the speed of the object during different time periods, all of which is presented in an easy to interpret way. Graphs of motion provide an accurate way of determining where the object is located in comparison to the origin during its path.
The graph of the motion of robots shows the location of the robots in comparison to the origin on the y-axis, so to find the location of the robot at a certain point in time, which is on the x-axis, you just have to know the y-value for that x-value. The motion map also shows this, however the motion map uses dots to represent seconds while the x-axis of the graph represents time, so those reading the motion map would have to count the dots to find a certain time instead of just reading the time intervals on the graph.
Verbal descriptions of motion can describe the trend of the path, but often could find the distance from the origin at an exact second because the descriptions do not go over each second of the path. The yintercept of mathematical equations reveal where the object starts in relation to the origin, however in order to find the location of the object at a certain point in time one would either have to solve the equation for each second or graph it, which essentially negates its use. Graphs are also a good representation of the speed of an object, especially those which change speed.
The speeds of the robots on the graph was calculated by finding the slope of the line, but a general idea of which robot was the fastest or slowest is also obvious just be looking at the graph. Motion maps give a loose estimate on the speed, as close dots mean a slow speed and ones farther away mean higher speeds, but finding exact speeds can be difficult for those without experience with motion maps, whereas graphs are much more common and intuitive because they specifically label the time and distance.
A verbal description can reveal changes in speeds and pauses but if the speaker is just describing what they saw, the exact values can’t be calculated so another form of representation, like an equation, would be required. Similarly, a basic equation can only cover one portion of a graph, which would not work for an object that started or changed speeds. Even if a piecewise function was created, one could only imagine the path of the object and would have to graph it to gain a visual representation. Perhaps the best argument for graphical representation is that it presents the most information in a format that most people would understand.
Although a verbal description can contain the same information, the speaker must first find that information using other forms of representation, and even then two people listening to the same speaker may visualize two very different graphs. Not only does a graph give the exact speeds of the object at different times, it also provides a visual representation of the path so there is no room for interpretation. Mathematical equations don’t provide enough information and only provide visual representation when turned into a graph, and motion maps are not as well known as graphs, so fewer people can understand them.