Question regarding cross-linear interpolation

In summary, the conversation revolves around finding the suction line pressure for a given temperature (72°F) and outdoor temperature (92°F). The method suggested is cross linear interpolation, which involves interpolating the x coefficient and constant separately. A reference was provided but it was found to be confusing.
  • #1
mattskie
10
0
This is not a homework problem.

I am given the equation of two lines at different temperature gradients for (suction line temp,suction line pressure) => (x,y)
At outdoor temp 95*F; y=.41667x+54.1667
At outdoor temp 85*F; y=.4231x+50.3846

I need to find suction line pressure for 72*F temp (72,y) at 92*F outdoor temp

I believe the method is cross linear interpolation, how do i do this??

Methodology needed, i can handle the calculations. This was the closest I've found to a method, but the notation is confusing and I can't get it to work: http://www.mathpages.com/home/kmath323/kmath323.htm

please and thank you
 
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  • #2
I believe this means interpolating the x coefficient and the constant separately to get the equation for 92°F.

I did not check the reference.
 
Last edited:

What is cross-linear interpolation?

Cross-linear interpolation is a mathematical method used to estimate values between two known data points. It involves using a straight line to approximate the relationship between the two points, and then using that line to calculate values for points between them.

How is cross-linear interpolation different from other interpolation methods?

Cross-linear interpolation differs from other methods, such as linear or polynomial interpolation, in that it uses two sets of data points that are not necessarily aligned on the same axis. This makes it useful for estimating values in situations where the data is not evenly spaced or aligned.

What are some practical applications of cross-linear interpolation?

Cross-linear interpolation is commonly used in fields such as engineering, finance, and computer graphics to estimate values between known data points. It can also be used to smooth out data sets and create more accurate predictions.

What are the limitations of cross-linear interpolation?

One limitation of cross-linear interpolation is that it assumes a linear relationship between data points, which may not always be the case. It is also sensitive to outliers, which can skew the estimated values. Additionally, it is not suitable for extrapolating values beyond the known data points.

Are there any alternatives to cross-linear interpolation?

Yes, there are several alternative interpolation methods, such as cubic spline, kriging, and inverse distance weighting. Each method has its own strengths and limitations, and the best choice will depend on the specific data and application. It is important to carefully consider the data and the desired outcome before selecting an interpolation method.

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