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Major Components
Major Components
Nokalon 364 Trawl Floats
Standard Nokalon 364 trawl floats were used. Floats are stated to displace 1 liter, they have a diameter of 12.5 cm. Holes are both sides of the float. These allow floats to be connected in-line.
60-Pound Test Fishing Line
60-pound test fishing line was used in the retrieval system to allow the team to retrieve the configuration after each descent. Such heavy-duty line was necessary to eliminate the risk of losing gear, especially for the high-velocity hardware-only drop.
1/4-20 Threaded Nylon Rod
Two threaded nylon rods were used to rigidly fasten the spheres together after it was observed that unwanted oscillation of the trailing spheres was creating extra drag.
1/4-20 Threaded Rod
A stock 50.48 cm (2 ft) long, 1/4-20 threaded rod is the the backbone of the experimental setup. All non-float and retrieval system components connect to and must be compatible with this.
Depth Sensor
Star Oddi, DST Centi-TD, temperature and depth sensors are use to determine velocity. These are same sensors NIWC uses in experiments.
Dual Sensor Holder
2 sensors are mounted for each drop. The threaded rod is imperial Unified Thread Standard, while the sensors are metric. The dual sensor holder is designed to accommodate both.
Impact Buffer 1: 5/16-18 Screw
Impact Buffer 2: Rubber Bumper
Either a 5/16-18 screw or a rubber bumper is used as impact protection in case the system makes a hard landing. Normally the screw is implemented for ease of use, but the bumper will be attached in especially sensitive scenarios.
Summary of Key Results
Summary of Key Results
1-Sphere Results
Before accounting for the drag introduced by the hardware, the drag coefficients were higher than the accepted value of 0.47. Out of these two datasets, the data collected from Canyonview was determined to be the most reliable due to the amount of data collected there in comparison to the limited amount at TRANSDEC.
After accounting for the drag contributed by the hardware, the drag coefficient of just the spheres was determined using two methods. The averaging method is calculating the drag coefficient for all the trials and averaging them. This method was determined to be the most appropriate for this particular configuration, and the justification is in detail in the report. The peak velocity method uses the data with the highest terminal velocity to calculate the drag coefficient.
2-Sphere Results
To study the effect of separation distance and the addition of spheres, the drag coefficients given different separation between the floats were tabulated below in comparison to CFD predictions. The plot and empirical formula for the coefficient values at different distances are shown on the home page. Data collected at Canyonview with the splinted configuration was the most reliable data collected, therefore the drag coefficients are the most accurate. Once again, justification for this decision is in the report.
2-5 Ocm Separation Sphere Results
Due to time constraints, all configurations with three or more inline spheres were studied with 0cm separation between floats. This was studied to further investigate the effect of additional spheres. All the tabulated data below is determined by averaging the five trials with the highest terminal velocity for each configuration.
[Hoerner, Sighard F. Fluid-Dynamic Drag. Hoerner Fluid Dynamics, 1965 ]
Notice the dip in the drag coefficient at three spheres in the table above. This phenomenon has been studied before on blunt cylindrical objects. The plot can be seen on the left where the fineness ratio is defined as the axial length of the object divided by the cross-sectional diameter. In the case of the study on inline spheres, 2 inline spheres would have a fineness ratio of 2 and 3 spheres would have a ratio of 3, and so on. The top curve is most similar to the team's data with respect to the drag coefficient values, but the bottom curve captures the rise in drag coefficient after a fineness ratio of about 3-4. Regardless, this dip in drag coefficient is well documented and is not an anomaly in the team's data.
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