Proceedings - Rochester Institute of Technologyedge.rit.edu/edge/P14414/public/FinalDocuments/P14414... · Web viewThis quantity is used in the calculation of Drag Force. Dynamic

Multidisciplinary Senior Design ConferenceKate Gleason College of Engineering

Rochester Institute of TechnologyRochester, New York 14623

Project Number: P14414

WIND RESISTANCE TEST STAND

Raymond ZhengMechanical Engineering

Lori LiebmanMechanical Engineering

Gregory HydeIndustrial Engineering

Joseph RojanoMechanical Engineering

Katie BentleyMechanical Engineering

Sarah BrownellFaculty Advisor

Brian ThornISE Faculty Advisor

Abstract

Arborloos are ecological outhouses that help both provide a means of sanitation for rural communities where conventional water treatment plants are not present and reuse human compost for the creation of fertile soil. Arborloo designs that are currently used in Haiti are vulnerable to damage from hurricane winds and flying debris. This project focused on creating a test set up that will allow for safe testing of scale model arborloo designs to determine hurricane resistance. The scale model test stand provided information about the aerodynamic performance of an Arborloo during a Class I hurricane, and allowed for safe testing of various arborloo sizes, shapes, and materials. This design incorporated the use of the RIT wind tunnel to test arborloo models at incremental wind speeds. Sensors within the test stand provided information about surface pressure and forces felt by the arborloo model. This information was collected and processed using a data acquisition system and a software program output useful data to the user. The long term goal of this project is to create a set-up for testing, devise a test procedure, and provide recommendations for an optimal arborloo shape and design based on testing results. The drag coefficients derived experimentally were found to be in the range of 1.7 to 2.0. However, these results are in disagreement with findings from computational fluid analysis and literature. Via Computational Fluid Dynamics (CFD), the resulting coefficient of drag was close to 1.0. It was concluded that these discrepancies are due to unaccounted wind tunnel blockage effects. It is well known among RIT faculty that the RIT wind tunnel has innate defects. These problem areas kept the team from having data in agreement with what was expected. Before these issues are resolved, it is recommended that future teams avoid investing too many resources in the wind testing of arborloos using the RIT wind tunnel.

Copyright © 2014 Rochester Institute of Technology

Proceedings of the Multi-Disciplinary Senior Design Conference Page 2

NomenclatureA – Characteristic Area

The characteristic area of an object is a parameter used to normalize the aerodynamic effects on said object. For example, although both a table-tennis ball and a racquetball are spheres, a characteristic area is chosen (most often a cross sectional area exposed to airflow) for direct comparison between the two shapes.

CD – Drag CoefficientThe drag coefficient is a dimensionless parameter that characterizes an objects resistance to fluid flow and allows for direct comparison of aerodynamic effects on different shapes. Some examples of drag coefficients are the Empire State Building (1.3-1.5) and the Eiffel Tower (1.8-2.0). [1] The Eiffel tower suffers from more drag overall which means that, for a given airflow condition, the Eiffel tower will be subjected to a larger drag force than the Empire State Building.

FD – Drag ForceThe dynamic force caused by friction that resists opposing flow [1]. Drag is what pushes aft on an airplane while flying and what causes freefalling objects to reach a terminal velocity. In regards to this project, the drag force is what causes the arborloo to be pushed back and get uprooted from its location.

Q∞ - Dynamic PressureThe dynamic pressure is the kinetic energy per unit volume of a fluid [1]. This quantity is used in the calculation of Drag Force. Dynamic pressure is the characterization of the aerodynamic effects as a function of wind speed and air density.

Re – Reynolds NumberDimensionless parameter that characterizes flow conditions. It is defined by the ratio of inertial forces to viscous forces[1].

There is one equation that relates all of these quantities and the major theoretical background used throughout the length of this project:

CD=FD

12ρV 2 A

=FDQ∞ A

Equation (1) Drag Equation

BackgroundAn arborloo is an ecological outhouse most commonly used in the third world due to the lack of plumbing

infrastructure. However in areas susceptible to natural disasters like Haiti, the arborloo is easily demolished. Because the arborloo is a rather large investment for the people of Haiti, making sure the investment is not lost during the more-than-common hurricanes that devastate Haiti is a field that needed to be investigated. The aspect of the arborloo design that was of most importance to the team was its performance in high winds applications. Structural tests are necessary to optimize design and learn about how the design performs under simulated hurricane-like conditions. Although the structure may seem sturdy at low speeds there will be a point of failure that needs to be anticipated. The team’s goal is to provide a test that yields data that can be used to discern when a particular structure may fail. On average the annual Haitian income is $350 [2], so purchasing an arborloo would be a large investment that would be at least two months income. To protect this investment, future teams need to analyze test data that can help them come to a conclusion as to when to disassemble the arborloo in the event of a hurricane.

Methodology In order to get a full understanding of what was going to be needed in the project, the team had to evaluate what

the customer needed and translate that to engineering specifications. The team was required to create a system that could be used to provide the required testing environment and have a way to measure the forces on the arborloo. The



team first brainstormed ideas on how to match Reynolds Number (Re) through density and fluid speed. Some of the ideas included using a tow tank to drag the arborloo through water to simulate the fluid flow. Other ideas included dragging an arborloo behind a boat or on top of a team member’s car so the correct speeds could be obtained to simulate a hurricane. While these ideas were feasible in their design, they were outside of the budget and time constraints for the project. Through the use of Pugh charts and systems level analysis, the team decided to use the RIT wind tunnel as the most feasible and cost effective way to perform the tests.

Figure 1: Velocity Profile and Pressure Distribution of full size arborloo exposed to 95 mph winds(Left)Velocity Profile and Pressure Distribution of scale arborloo exposed to 95 mph wind(Right)

The team’s next step was using analytical techniques to confirm that using a wind tunnel would be an effective way to mimic Type I hurricanes. The chosen software was COMSOL Multiphysics®. Figure 1 shows a vertical slice of the velocity profile around the two arborloos being subjected to 95 mph winds, the one on the left is the full size model and the one on the right is the scale model. As can be seen the profiles are identical. The pressure distribution on the right in Figure 1 is also identical. Based on identical fluid flow and pressure distribution patterns, the team concluded scaling laws could be applied and the test could be performed to provide valuable data. By measuring the forces and pressure distribution on a scale model arborloo and knowing the properties of air and the wind speed the coefficient of drag can be determined using the Drag Equation. That coefficient of drag can then be used to calculate force felt on a full size arborloo, using the same equation. Figure 2 shows that for a given Re above 1000, a constant CD exists which allows for scaling laws to apply.



Figure 2: Characteristics of Drag Coefficients as a function of different Reynolds Numbers on different shapes

With the method for testing now selected, the team designed the test. Research was conducted to fully understand how to use the wind tunnel effectively. Blockage effects and boundary layer effects were known to be contributing sources of error for testing [3].

Once the team had a better understanding of how to safely use and operate the wind tunnel, the team could move forward with the design and implementation of the project. Many factors needed to be considered when coming up with the best way to get the desired results. Factors such as arborloo size, mounting location, mounting type, anemometer type, and software packages used were just a few that the team needed to consider. These factors will be described in much more detail in the Final Design section. The following is a high level hierarchy of the system and the various subsystems the team chose to focus on to accomplish a final design and how each of the subsystems interacts with one another.

Figure 3: High Level Hierarchy of System Design

Results and discussion

Final DesignThe team’s final design [Figure 7] for the test stand utilizes the existing equipment of the KGCOE wind tunnel. The team fabricated a metal flange that bolted onto the circular hole in the middle of the base plate. This flange rests in the base plate of the wind tunnel and has a hole through which the load cell shaft is placed. The load cell shaft is used to support the load cell and the arborloo model. This shaft can be adjusted up and down according to the size of the model to be tested. A collar with a set screw is welded to the bottom of the flange [Figure 6] that is used to lock the load cell shaft in place.

The arborloo model used for validating this design is a simple, predictable shape to allow the team to check the accuracy of the system. Subsequent models can be any shape, as long as they contain a center shelf with a hole pattern that will attach to the load cell, as shown in the CAD drawings. The center shelf is located near the center of the arborloo model with four holes for the load cell bolts and two dowel pins to ensure the correct alignment of the



load cell for accurate data collection. The model arborloo has an array of evenly spaced pressure taps arranged on the front face [Figure 5]. These pressure taps are connected to the Clippard EMC-12 pressure scanner which reads the pressure from each tap and outputs the data to the computer shown in Figure 8. The JR3 load cell and shaft are mounted between the model arborloo and the base of the wind tunnel as shown in Figure 6. The load cell gathers data about the forces and moments in the x, y and z directions and outputs to the computer in a separate file. The data collected from these two methods are processed using MatLab code, and are compared to ensure the accuracy of the results. The baseline model is a flat plate arborloo that simulates the “worst possible condition” to test as a flat plate provides the highest coefficient of drag. This model is a simple closed box arborloo that has a center shelf to mount the load cell and shaft. Future arborloo test shapes should be an improvement over this shape.

FIGURE 4: Final Design CAD

FIGURE 5 (left): Final Design CAD Arborloo Model

FIGURE 6 (right): Final Design CAD Underside



FIGURE 7 (left): Final Design Arborloo Set-up

FIGURE 8 (right): Pressure Test Set-up

Testing Results

Figure 9: Expected Results from TestingAs can be seen in Figure 9, the range of forces expected was between 0 to 113.7N (~25.6 lbf) – at a max wind tunnel speed of 100 mph and a conservative drag coefficient estimate of 2.0. Based on research and preliminary studies the above curves are a range of the expected test results the team was looking for when it comes to a CD value. With each curve is a corresponding force that shows what forces would be expected based on the resulting C D. Based off CFD analysis, the team expected to find a CD closer to 1.

TABLE 1: CFD Results



X Location on Scale Arborloo (in)

Y lo

catio

n on

sca

le A

rbor

loo

(in)

Experimental Pressure Distribution

-3 -2 -1 0 1 2 30

2

4

6

8

10

12

Pre

ssur

e (P

a)

1350

1400

1450

1500

1550

1600

1650

1700

1750

1800

1850

Figure 10: Experimental Pressure Distribution (left), CFD Pressure Distribution (right)

As can be seen in the above Figure 10, the pressure distribution measured experimentally is not exactly what was expected from CFD analysis. The main contributor to the difference in distribution is the number of taps we were able to place on the arborloo. Using only ten different taps allowed for a finite testing area which could contribute to the differnce betweeen expected and actual. The software used to create the filled contour map for the experimetnal pressure distribution was MATLAB®. The number of contours used was 20.

Figure 11: Comparison of Load Cell and Pressure Scanner Experimental Data

The results for the model arborloo can be seen in Figure 11. The Load Cell testing provided results with a CD of approximately 2. The Pressure Scanner testing provided results that closely match a CD of approximately 1.7. The distribution is largely concentrated in the center of the arborloo and slowly dies off at the edges (the averaging of the pressure did not give weight to specific readings). This causes a disparity in finding the drag force through pressure integration.



CONCLUSIONS AND RECOMMENDATIONS

One flaw with our testing method is that the theory that we are using is only valid for some geometries, this limits design ideas for future teams. It is clear from the collected data and knowledge of hurricane conditions, that the best way to ensure that the structure would not be destroyed is if the structure can be easily disassembled and stored away. The force exerted on an arborloo by 95 mph winds is at the very best 415 lbf. (C D = 1) and at worst 830 lbf. (CD = 2). This is comparable to two male gorillas (330 lbf.) sitting on the face of the arborloo.

A large portion of time spent was planning for duplicating the effects of a hurricane. A big issue that we came across was that the velocity that we needed to obtain to match Reynolds number was incredibly high. Upon consulting a professor, we learned that our initial ideas for testing the arborloo’s ability to withstand a hurricane could be simplified. This happened at the very end of the design process, and we had to quickly determine what we would be doing moving forward. If we had consulted this professor earlier, we could have gotten a much earlier start on the design of our wind tunnel test stand, rather than focusing on designs that could match the Reynolds Number.

It is not uncommon with the RIT wind tunnel to experience errors of 100%. This issue arises with the results due to the fact that the test does not compensate for blockage effects. These blockage effects are due to the constrained flow within the wind tunnel. This condition can alter the test results. To get results that correspond to actual hurricane effects, future teams should use CFD modeling and computation to determine the blockage effects and derive a correction factor. An empirical correction factor should also be derived for the wind tunnel in order to account for these effects. A benchmarking document will be provided to help future teams understand the blockage effects, and what needs to be done to make the corrections. Formulating these correction factors is very arduous and time-consuming. It is recommended that future teams avoid these problems all together as the final results may not be worth the work required to estimate these errors. Rather than completing the project experimentally, it should be satisfactory to use well established literature. The reader is directed to Sahini’s thesis [3] page 82 which further explains a CFD method to find such correction factors.

References [1] Benson, Tom. "Index of Aerodynamics Slides." Index of Aerodynamics Slides. FirstGov, 22 Feb. 2013. Web. 7

May 2014. <https://www.grc.nasa.gov/www/k-12/airplane/short.html>.[2] "Some Basic Information On Haiti." . Haiti Outreach, 1 Jan. 2009. Web. 7 May 2014.

<http://haitioutreach.org/wp-content/uploads/2009/06/Haiti-Info-History.pdf>.[3] Deepak Sahini (2004). Wind Tunnel Blockage Corrections: A Computational Study. Unpublished master's

thesis. Texas Tech University Lubbock, TX[4] Fox, Robert W., and Alan T. McDonald. Introduction to Fluid Mechanics. 8th ed. Hoboken, N.J: John Wiley,

2012. Print.

ACKNOWLEDGMENTS Special thanks to all of the faculty that helped the team along the way. Especially Sarah Brownell

(Guide/Customer), Brain Thorn (Guide/Customer), Professor John Wellin, Pedro Cruz Dilone, and Kevin Gebo for the time and effort they put in to support the success of the project. Without their invaluable input the project would not have been completed.
