Challenge
Longboard racing speeds average 65km/h and can be as high as 80 to 95km/h on steep courses. The optimal design for the deck of a downhill racing longboard is a balance of weight and stiffness, and depends upon the characteristics of the individual rider. This simulation study compares the impact of material selection and board construction on board performance, and recommends optimal construction for a variety of rider weights.
This work was performed as part of the 2015 University of Waterloo Engineering Analysis and Design Symposium, in which students took engineering design problems from concept to simulation and validation in under two weeks.
Solution
Dimensions of two actual longboard decks were used to create geometries for two different designs. Four supports on the bottom of the deck modelled the fixed connection between the hangers and the deck. To represent the feet of the rider, two force boundary conditions were applied with 60% of the force on the front and 40% on the back.
Maple and bamboo were used as the material for the respective geometries, with material properties assumed to be isotropic. The bamboo outer plies were 1.4mm thick and the inner plies were 2.4mm thick. The maple plies were all 2mm thick. For each deck, 15 simulations were run. The weight of the rider was varied to include 100, 125 and 150lbs. The number of plies was varied to include 3, 4, 5, 6 and 7 plies.
Results
The simulations found that:
- As weight applied increases, displacement increases.
- As number of plies increases, thickness and mass of board increase and displacement decreases.
- Maple board had more deflection than the bamboo board.
- The ideal number of plies that optimise the flex–weight were dependent on the weight of the rider. In general, the optimal number were five maple plies and six bamboo plies.
To validate the results, the height of the deck was measured from the ground before and after applying weights. The difference between the two measurements is the maximum deflection of the board. This validation testing confirmed that the simulation provides an accurate model of the displacement of the maple longboard. The simulation of the bamboo longboard captured the correct trends, but absolute results were less accurate, likely due to the uncertainties in the material properties (axial tensile Young’s Modulus of bamboo can vary by a factor of 5).
Conclusion
This study highlights how ANSYS AIM can be used to make design decisions of engineering interest with a minimum amount of time and training. The use of ANSYS AIM at University of Waterloo has also taught students such essential concepts as design geometry, complex elements, boundary conditions and loads in a practical, hands-on manner, and was able to reinforce the importance of understanding and validating inputs and numerical results.