Optimization of Gusset Geometry for Racecar Frame Design
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This research optimizes sheet metal gusset geometry to support a suspension pickup point in a Formula SAE racecar. The sheet metal gusset design incorporates an external radial cut-out and an internal triangular cut-out, each of which can be adjusted in size to optimize the stiffness to mass ratio. A finite element analysis was set up using a heavy braking load case, which applied 3900 N to the suspension point being supported by the gusset. A parametric optimization (finite element analysis) was run in SolidWorks to gather mass and stiffness data for each of the 143 designs under the prescribed load case. The parametric optimization was run on both a simulated front hoop and a test fixture, which showed a similar trend in their results. Experimental testing was performed on three designs. The gusset profiles were waterjet and TIG welded to the test fixture tube frames. The results of the test agreed with the simulation results with a discrepancy of less than 10% in all cases. The results of the parametric optimization were then analyzed using the Karush–Kuhn–Tucker (KKT) optimization method in MATLAB and an optimal design set was found. Any design in this set has the minimum mass for its given stiffness. This process can be used to determine the design with the maximum stiffness/mass ratio for any given stiffness requirement and is accurate to within 10% of the simulated value. The optimization method is not limited to specific gusset geometry and can be used to reduce the mass of any gusset with known loading conditions.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".