The quadratic-drag AI video yields a fitted drag coefficient (CD ≈ 3.18) within the literature range reported for skydivers¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? Fitting the skydiver trajectory gave p2 = 1.2247 m⁻¹, from which b ≈ 97.98 kg/m and CD ≈ 3.18 were recovered, falling within the reported range CD ≈ 1.5–3.5. → Laura B. Alvarado-Cruz 2026

Evidence¶

Laura B. Alvarado-Cruz 2026¶

Laura B. Alvarado-Cruz, Josep Ll. Suñer, Pedro Yuste, Juan C. Castro-Palacio, Juan A. Monsoriu, and Francisco M. Muñoz-Pérez. (2026). From Prompts to Physical Laws: A Generative AI Workflow for Engineering Physics Education. https://arxiv.org/abs/2609.19400 doi:10.48550/arxiv.2609.19400

q2 · i? · design · r2

Parameter-interpretation analysis of Model 3 (quadratic drag) in the Results section. The logarithmic x(t) fit (p1 = 2.4318 m/s, p2 = 1.2247 m⁻¹, R² = 0.9953) yielded CD ≈ 3.18, which "falls within the rangeC D≈1.5–3.5 reported in the literature for skydivers".

"This falls within the rangeC D≈1.5–3.5 reported in the literature for skydivers [35, 36], supporting the physical plausibility of the AI-generated trajectory."

Discussion¶

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