Experimental fluids · ME 347 · Cal Poly
Axial Fan Performance Test
Measured a fan across 11 operating points, calculated flow and efficiency, and checked a lower-speed prediction against one physical test.
Question
How do pressure rise and efficiency change with flow, and can fan laws predict another speed?
My work
Reduced Pitot-static and power readings, calculated 80% performance, and scaled the results to 30% speed.
Finding
Gross efficiency reached 36% in the measured set. The single 30% pressure check fell below nearby predictions.
Performance data
Pressure rise versus flow rate
The left plot shows the 11 measured 80% operating points. The right plot shows fan-law predictions at 30% and the one measured check. Each panel uses its own scale so the low-speed result remains readable.
80% speed · measured
11 damper settings
30% speed · prediction and check
Hollow circles: predicted · gold diamond: measured
Plotted from the measured and scaled values in my ME 347 individual report. The markers show data points; no fitted curve is implied.
What I did
From instrument readings to fan performance
Set operating points
Changed duct damper settings across the 80% speed test range.
Measure response
Used Pitot-static readings in both ducts, fan pressure rise, and input-power data.
Calculate performance
Calculated flow rate, power added to the air, and gross efficiency at each point.
Check the scaling law
Predicted 30% pressure and flow, then compared them with one test point at that speed.
Results
What the data showed
- 798–3298 cfm across the measured 80% flow range.
- 0.42–1.25 in H₂O pressure rise across that range.
- 36% gross efficiency at the highest reported efficiency point, 2966 cfm.
Limit of the comparison
A lower measured pressure
At 30% speed, the measured point was 1071 cfm and 0.050 in H₂O. The nearest tabulated prediction was 1112 cfm and 0.084 in H₂O. The flows differ slightly, but the pressure gap is substantial. One check point without quantified measurement uncertainty cannot validate the full lower-speed curve.
A repeat test across several 30% operating points would help determine whether this gap persists.