I want to develop a model for heat transfer between two concentric cylinders in contact, one being heated and the second one passively cooled, but I am not really sure, what way would be the best to do it.
My plan is to use two two heat capacitors with heat capacity according to each material and a heat conductor between them. But I'm not really sure, if putting heat conductor in-between the cylinders is the right thing to do as the two cylinders are touching and there is nothing separating them. How to then calculate what should be the thermal conductance then.
Thanks for all of your suggestions.
I tried to make a model of heat transfer between two concentric cylinders in contact, but don't know, what is the best way, to do it, with thermal conductor or without.
It depends on your boundary conditions. It sounds like you have a combination of heat flux (inner face) - thermal conductance (cylinder 1) - thermal conductance (cylinder 2) - heat transfer coefficient.
You should be able to find the thermal conductance (or equivalently resistance) of a hollow cylinder in a heat transfer textbook, or compute the formula with the help of one, which you can then put in the thermal conductance components' parameters.
For simple boundary conditions, this will probably even be an analytic solution.
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During the triangulation of a cell phone, we need to find out distance of cell phone from tower using signal strength on that phone. Is there any equation to calculate distance between tower and phone by putting signal strength in that equation? If yes, then what is that equation.
Please help
Theoretically, you should refer to some EM textbooks or Wiki (https://en.wikipedia.org/wiki/Signal_strength_in_telecommunications). It depends on your frequency band, GSM/3G/4G/5G/etc. It also depends on ground building (settlement) type, ground surface with lots of tall concrete buildings tends to block signal much more aggressively than a rural area with a grass plain.
Practically, you should do some physical measurement yourself because how your signal strength is computed (is it in log scale, linear scale, SNR, etc) does affect many things. Take note of near field effect, that is, when your cellphone is very very close to the station, the signal strength variation behaviour can be very different.
No, it is not so simple. At the very list you need to know the transmission power of the base station.
I want to simulate a quadcopter flight (x,y,z,roll,pitch,yaw).
I need a simplest option to simulate/visualization - maybe 'X' / cross sign.
like this video: in 2:00 : https://www.youtube.com/watch?v=dvNzxVqqgnw
given (x,y,z,roll,pitch,yaw) , how can I simulate/visualization using the simplest way ?
I know how to plot 3d point with 'X' sign (using plot3)- but this thing doesn't control angles (roll,pitch,yaw) - is there a similar function that takes (x,y,z,roll,pitch,yaw) ??
Thanks
I am going to assume that you have the flight data ((x,y,z,roll,pitch,yaw) and simply want to display it.
Plot3 plots a single 3d point, which in itself cannot have pitch, roll, or yaw. It sounds like you will need to design a shape that can represent your quadcopter (a triangle of 3 points would do - anything with more than 1 dimension will. A square would probably be easiest), and then write a function that can compute the 3d coords of each point, given the coords of one of one of the points, and the pitch roll yaw data. Then use the plot3 to plot each point individually (and maybe add lines between them or something)
I realize this may not be helpful, but your question is extremely broad.
This paper describes nicely the geometry of a stereo image system. I am trying to figure out, if the cameras tilted towards each other with a certain angle, how the calculation would change? I looked around but couldn't find any reference to tilted camera systems.
Unfortunately, the calculation changes significantly. The rectified case (where both cameras are well-aligned to each other) has the advantage that you can calculate the disparity and the depth is proportional to the disparity. This is not the case in the general case.
When you introduce tilts, you end up with something called epipolar geometry. Here is a paper about this I just googled. In order to calculate the depth from a pixel-pair you need the fundamental matrix or the essential matrix. Both are not easy to obtain from the image pair. If, however, you have the geometric relation of both cameras (translation and rotation), calculating these matrices is a lot easier.
There are several ways to calculate the depth of a pixel-pair. One way is to use the fundamental matrix to rectify both images (although rectifying is not easy either, or even unique) and run a simple disparity check.
I am new to matlab and my goal seems a little challenging for me. Hopefully I can get some directions from you guys.
Basically I have a 2D indoor office floor plan where i want to map my RF propagation model onto the map. I have seen questions in this forum on heat generation onto a map, but unfortunately it brings me no where.
i.e. Heat map generator of a floor plan image
A little explanation on what i wish to achieve. Firstly, on my map (with grid points) I should be able to plot some RF access points.
Secondly, on every access points I wish to map my RF propagation model. The tricky part is, propagation is not isotropic and have many regularities that are caused by the walls on the floor plan made from different materials. My model is based on distance and received signal strength between the access points. It is very much like the image below.
Is this feasible on matlab? If yes, what are some steps or key terms i should be looking out for?
I have an accelerometer and magnetometer each producing raw X, Y and Z readouts. From this I need to determine the magnetic heading of an object.
I'm not that great at trig, but I've put together a formula that does respond pretty well to the rotation of the device, but also responds to movement that one would not think is relevant, such as angling the device in such a way that has no impact on the direction it is pointed. Such as laying it flat and "rolling" the device.
I think the formula I have for calculating the magnetic heading is fine, but I think my pitch and roll radians for input are wrong.
So I guess the core of my question (unless someone actually has a formula around that does this), is how do you calculate angles, in radians, using an accelerometer for pitch and roll.
Then secondly, any info on the heading formula itself would be great.
Depending on the accuracy your application requires, you may need to solve several problems:
Are the accelerometer axes calibrated? I've seen MEMs accelerometers that had axes that were not mutually perpendicular, and had significantly different response characteristics for each axis (typically X and Y would match, and Z would differ). You will need to synthesize ideal XYZ axes from whatever physical reading your device provides. (Google 'accelerometer calibration'.)
Are the magnetometer axes calibrated? Similar problem as above, except much harder to check: It is very difficult to generate uniform calibrated magnetic fields. If you use the ambient geomagnetic field, you will need to carefully control the local magnetism of your work environment and your tools. (Google 'magnetometer calibration'.)
After the accelerometer and magnetometer have been individually calibrated, their axes need to be calibrated relative to each other. Since both of these devices are typically soldered to a PCB, there is almost guaranteed to be significant misalignment. In many cases, the board layout and device parameters may not even permit the XYZ axes to correspond with each other! This may be the hardest part to do from a lab perspective, so I'd recommend you do a direct comparison using other hardware that has both kinds of sensors and is already calibrated (such as an iPhone or Android phone - but verify the device before use). Normally, this is accomplished by adjusting the prior two calibration matrices until they generate vectors that are correctly aligned relative to each other.
Only after you are generating mutually calibrated magnetic and accelerometer vectors can you apply the solutions suggested by the other respondents.
I've only described the static solution, where both the magnetometer and accelerometer are motionless relative to the local gravitational and magnetic fields. If you need to generate responses in real-time while the system is rapidly moving, you will need to account for the time behavior of each sensor. There are two basic ways to do this: 1) Apply time-domain filters to each sensor so that their outputs share a common time domain (generally adding some delay). 2) Use predictive modeling to modify the sensor outputs in real-time (less delay, but more noise).
I've seen Kalman filters used for such applications, but applying them in a vector domain may require using quaternions instead of Euler matrices. Quaternions are easier to use computationally (fewer operations needed compared to matrices), but I found them to be much more difficult to comprehend and get right.
Or, you may choose a completely different path, and use statistics and data fitting to do all the above work in one giant step. Consider the problem as follows: Given 6 input values (XYZ each from uncalibrated magnetometer and accelerometer) and a reference to the device (assuming it is hand-held, and there is an arrow painted on the case), output a single angle representing the magnetic bearing toward which the arrow on the case is pointing, and the elevation of the arrow relative to the gravity vector (tilt of the case).
Using a calibrated reference device (as mentioned above), pair it with the device to be calibrated, and take a several hundred data points, with the device at different orientations. Then use a powerful math package such a Matlab, MathCAD, R or SciPy to setup and solve the nonlinear equations to create the transformation matrices.
I would point to Euler Angles and Roll Pich Yaw.
You're not thinking in enough dimensions. This would be the answer in only 2 dimensions, and it works great if you can find a way to ensure "Z" always aligns with gravity.
int heading=180-atan2(mag_datX, mag_datY)/0.0174532925; // 0/359=N, 90=E, 180=S, 270=W
(if you're reading directly form the device - beware that it probably returns X, Z, Y - not X, Y, Z !)
However - this is not a 2D compass problem - imagine you take the needle out of the compass, balance it so that gravity plays no part in keeping it "level", and you'll find that "north" will point a bit up or down - depending where on earth you are (or, if at the poles, directly up or down!).
So you need to try and compute the THREE DIMENSIONAL vector from all 3 values - which is a matrix operation.