Refrigeration_HW_Spring23

docx

School

Georgia Institute Of Technology *

*We aren’t endorsed by this school

Course

4056

Subject

Mechanical Engineering

Date

Dec 6, 2023

Type

docx

Pages

9

Uploaded by Amogh_Mellacheruvu

Report
Homework for Refrigeration Lab Create MATLAB code to analyze the data provided in the “Refrigeration_HW_Spring23.xlsx” file and answer the questions below 1. Use the averaged steady-state data at 0 psi to calculate the sensor offsets for the following variables and report them in Table 1 below. The 0 psi case corresponds to ambient temperature and pressure along with 0 flow. Remember that the provided pressure data is gauge pressure. Hint: Make sure to convert units and convert your gauge pressure into absolute pressure. Offsets are calculated as 0 psi case minus ambient measurements. Table 1 : Calculated offset values for each sensor Sensor P 3 [N/m 2 ] T 4 hot [K] T 4 cold [K] T load [K] T 5 [K] ´ m cold [kg/s] ´ m hot [kg/s] Offset Values 171770 -27.0508 -27.5571 -28.0996 -26.4549 -0.0054 -0.0056 2. Used the averaged steady state data and each pressure along with the sensor offsets from Part 1 to report the corrected steady state values for each sensor in Table 2 below. Please report P3 as absolute pressure. Hint: Subtract offsets from data found after unit conversions. Table 2 : Calculated values for absolute pressure and temperature, and mass flow rates adjusted for sensor offsets Nominal PSI P 3 [N/m 2 ] T 4 hot [K] T 4 cold [K] T load [K] T 5 [K] ´ m cold [kg/s] ´ m hot [kg/s] 0 99000 49.9593 49.0481 48.0716 50.4360 0.0084 0.0087 20 6.9575e6 1044.8 1044.2 1043.8 1045.1 0.0110 0.0112 40 6.4842e6 975.9 975.4 975.0 976.3 0.0110 0.0112 60 4.2131e6 646.6 646.2 645.7 647.0 0.0110 0.0112 80 3.0596e6 479.3 478.8 478.4 479.8 0.0109 0.0111
Homework for Refrigeration Lab 3. Please paste your MATLAB plots for T 4 cold (y-axis) vs P 3 (x-axis) and T 4 hot (y-axis) vs P 3 (x-axis) in absolute units (K and N/m 2 for temperatures and pressures, respectively). Be sure to include the regression line and all pertinent annotations for each plot. (NOTE: for the homework, you can use MATLAB “eps” (2.2204e-16) for the uncertainty inputs of the “york_regression.m “and “plot_ellipse_data.m” functions)
Homework for Refrigeration Lab 0 1 2 3 4 5 6 7 P3 [N/m2] 10 6 0 200 400 600 800 1000 1200 T4 Cold [K] T4 Cold vs. P3
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
  • Access to all documents
  • Unlimited textbook solutions
  • 24/7 expert homework help
Homework for Refrigeration Lab 0 1 2 3 4 5 6 7 P3 [N/m2] 10 6 0 200 400 600 800 1000 1200 T4 Hot [K] T4 Hot vs. P3 4. Based on the plots in Question 3, please comment on the quality of the fits. Does a linear fit match either/both sets of data, or should non-linear fit functions be used?
Homework for Refrigeration Lab a. This data fits the york regression extremely closely, and within the bounds. The linear regression is accurate for these models. 5. Please paste your MATLAB plots for ´ m cold (y-axis) vs P 3 (x-axis) and ´ m hot (y-axis) vs P 3 (x-axis) in absolute units (kg/s and N/m 2 for mass flow rates and pressures, respectively). Be sure to include the regression line and all pertinent annotations for each plot. (NOTE: for the homework, you can use MATLAB “eps” (2.2204e-16) for the uncertainty inputs of the “york_regression.m “and “plot_ellipse_data.m” functions)
Homework for Refrigeration Lab 0 1 2 3 4 5 6 7 P3 [N/m2] 10 6 0.0085 0.009 0.0095 0.01 0.0105 0.011 0.0115 mdot Hot [kg/s] mdot Hot vs. P3
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
  • Access to all documents
  • Unlimited textbook solutions
  • 24/7 expert homework help
Homework for Refrigeration Lab 0 1 2 3 4 5 6 7 P3 [N/m2] 10 6 8 8.5 9 9.5 10 10.5 11 11.5 mdot Cold [kg/s] 10 -3 mdot Cold vs. P3 6. Based on the plots in Question 5, please comment on the quality of the fits. Does a linear fit match either/both sets of data, or should non-linear fit functions be used?
Homework for Refrigeration Lab a. For me, The data does not represent a linear fit, as P3 at 20 Psi is the largest pressure value of the dataset, and will prevent the data from being linear. A NON-LINEAR fit must be used. 7. For each test case (each Nominal PSI), please calculate the Insulation Thermal Resistance ( R ins ) between the load and the ambient air, and paste these values into Table 3, below. NOTE: You would need A ins and L ins measurements to calculate experimental k ins , but you can calculate R ins from the given data. Table 3 : Calculated Insulation Thermal Resistance values for nominal test pressures Nominal PSI R ins [K ⋅s/J] 20 -73.4727 40 -71.3237 60 -38.6530 80 -17.6845 8. Is there a trend in the R ins data? Compare this with the theoretical R ins . Justify your answer. Hint: Refer to the R ins formula in the equation sheet. I calculated R ins via the equation found in the slides using T amb T load ´ m cold ∙C p ( T 5 T 4 c ) . I used this approach because of the lack of data with the area and depth of the insulation provided. These values produced a significant deviation from what I was expecting, as the theoretical
Homework for Refrigeration Lab value for R ins is 0.033. However, from the data collected, the insulation becomes closer to the theoretical as the pressure increases. As the pressure increases, the thermal resistance of the insulation also increases.
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
  • Access to all documents
  • Unlimited textbook solutions
  • 24/7 expert homework help