[2026-Sep] ChatGPT 6 AGI Pro: - Re-examining Super Teflon for Aircraft and Nonstick Pan Surfaces

We get the worlds first AGI (Advanced General Intelligence) inventing day and night using resistive prompting where it thinks up an idea, then makes three attempts to discredit its own work. If it passes it write up a detailed paper on the subject, and the benefactor in all this is you!

Of Interest it rejected Grok 4's previous research, claiming it failed the 3-part validity tests:

During it's research it produced the following python research and simulation:

bash -lc mkdir -p /mnt/data/aerorib_work/figures /mnt/data/aerorib_work/output && cat > /mnt/data/aerorib_work/make_figures.py <<'PY'
import math
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle, Polygon, FancyArrowPatch

out='/mnt/data/aerorib_work/figures'

# ISA cruise assumptions
h=10668.0
T0=288.15; L=0.0065; g=9.80665; R=287.05287; p0=101325.0
T=T0-L*h
p=p0*(T/T0)**(g/(R*L))
rho=p/(R*T)
mu0=1.716e-5; Tref=273.15; S=110.4
mu=mu0*(T/Tref)**1.5*(Tref+S)/(T+S)
a=math.sqrt(1.4*R*T); M=0.78; U=M*a; nu=mu/rho

def cf_turb(Re, M=0.78):
    return 0.455/(np.log10(Re)**2.58)/(1+0.144*M**2)**0.65

# 1: riblet spacing vs streamwise distance
x=np.linspace(1,40,160)
Re=U*x/nu
cf=cf_turb(Re,M)
utau=U*np.sqrt(cf/2)
for sp, label in [(12,'s+ = 12'),(15,'s+ = 15'),(18,'s+ = 18')]:
    s=sp*nu/utau*1e6
    plt.plot(x,s,label=label)
plt.xlabel('Representative streamwise development length x (m)')
plt.ylabel('Physical riblet spacing s (micrometers)')
plt.title('Cruise-scale riblet spacing from wall-unit similarity\nISA 35,000 ft, Mach 0.78, smooth turbulent flat-plate estimate')
plt.grid(True,alpha=0.25); plt.legend(); plt.tight_layout()
plt.savefig(out+'/riblet_spacing_vs_x.png',dpi=220); plt.close()

# 2: aircraft total drag reduction vs coverage
coverage=np.linspace(0,1,101)
f_sf=0.45; local=0.07
for eta,label in [(0.70,'70% realization'),(0.85,'85% realization'),(1.0,'idealized realization')]:
    total=100*f_sf*coverage*local*eta
    plt.plot(100*coverage,total,label=label)
plt.xlabel('Fraction of skin-friction-producing area effectively treated (%)')
plt.ylabel('Estimated total-aircraft drag reduction (%)')
plt.title('First-order aircraft-level drag benefit of riblets\nAssumes skin friction = 45% of total drag, local riblet benefit = 7%')
plt.grid(True,alpha=0.25); plt.legend(); plt.tight_layout()
plt.savefig(out+'/drag_reduction_coverage.png',dpi=220); plt.close()

# 3: block fuel savings sensitivity from source paper annual gallons
baseline=2.6e6
fuel_reduction=np.linspace(0,3,61)
price=2.40
savings=baseline*(fuel_reduction/100)*price/1000
plt.plot(fuel_reduction,savings)
plt.xlabel('Block-fuel reduction (%)')
plt.ylabel('Annual fuel-cost savings (thousand USD)')
plt.title('Annual savings sensitivity for a 2.6 million gal/year reference aircraft\nIllustrative fuel price: $2.40/gal')
plt.grid(True,alpha=0.25); plt.tight_layout()
plt.savefig(out+'/fuel_savings_sensitivity.png',dpi=220); plt.close()

# 4: Layer stack schematic
fig,ax=plt.subplots(figsize=(8,5.5))
ax.set_xlim(0,10); ax.set_ylim(0,10); ax.axis('off')
layers=[
    (1.0,1.0,8.0,1.0,'Release liner / installation side'),
    (1.0,2.0,8.0,1.0,'Aerospace acrylic PSA, 25-50 um'),
    (1.0,3.0,8.0,1.2,'Microreplicated carrier + riblet relief, 50-100 um total'),
    (1.0,4.2,8.0,0.7,'Si/O-containing DLC transition (DLN), 0.15-0.35 um'),
    (1.0,4.9,8.0,0.9,'Load-bearing a-C:H DLC, 0.4-0.9 um'),
    (1.0,5.8,8.0,0.6,'Low-F a-C:H:F cap, 0.2-0.5 um'),
]
for x0,y0,w,hg,label in layers:
    ax.add_patch(Rectangle((x0,y0),w,hg,fill=False,linewidth=1.5))
    ax.text(5,y0+hg/2,label,ha='center',va='center',fontsize=10)
# riblet triangles on top
for xc in np.linspace(1.4,8.6,12):
    ax.add_patch(Polygon([[xc-0.25,6.4],[xc,7.5],[xc+0.25,6.4]],closed=True,fill=False,linewidth=1.2))
ax.text(5,8.2,'Flow-aligned riblets: zone-scaled spacing s approx. 60-85 um, height h approx. 0.4-0.55 s',ha='center',fontsize=11)
ax.annotate('External airflow',xy=(8.5,8.9),xytext=(2.0,8.9),arrowprops=dict(arrowstyle='->',lw=1.5),ha='center')
ax.set_title('AeroRib-FDLC-X proposed removable surface stack',fontsize=14,pad=10)
plt.tight_layout(); plt.savefig(out+'/layer_stack.png',dpi=220); plt.close()

# 5: Riblet geometry and wall units
fig,ax=plt.subplots(figsize=(8,4.8)); ax.axis('off'); ax.set_xlim(0,10); ax.set_ylim(0,6)
basey=1.4
ax.plot([0.8,9.2],[basey,basey],lw=1.5)
centers=[2.0,4.0,6.0,8.0]
for c in centers:
    ax.add_patch(Polygon([[c-0.8,basey],[c,4.0],[c+0.8,basey]],closed=True,fill=False,linewidth=1.5))
# s dimension
ax.annotate('',xy=(2.0,0.7),xytext=(4.0,0.7),arrowprops=dict(arrowstyle='<->',lw=1.2))
ax.text(3.0,0.35,'spacing s',ha='center')
# h dimension
ax.annotate('',xy=(1.25,basey),xytext=(1.25,4.0),arrowprops=dict(arrowstyle='<->',lw=1.2))
ax.text(0.75,2.7,'height h',rotation=90,va='center')
ax.text(5.0,5.1,'s+ = s u_tau / nu    ;    h+ = h u_tau / nu',ha='center',fontsize=12)
ax.text(5.0,4.65,'u_tau = U sqrt(Cf/2)     and     Cf = 2 tau_w / (rho U^2)',ha='center',fontsize=11)
ax.set_title('Riblet geometry is scaled to the viscous wall units, not to solid-solid CoF',fontsize=13)
plt.tight_layout(); plt.savefig(out+'/riblet_geometry.png',dpi=220); plt.close()

# 6: qualitative falsification funnel
fig,ax=plt.subplots(figsize=(8.5,5.8)); ax.axis('off'); ax.set_xlim(0,10); ax.set_ylim(0,10)
steps=[
('Candidate improvement',8.8,7.8),
('Attack 1: governing physics',7.5,6.4),
('Attack 2: materials/durability',6.2,5.0),
('Attack 3: manufacturability / off-design',4.9,3.6),
('Retain only if all three survive',3.6,2.2),
('Integrated AeroRib-FDLC-X concept',2.3,0.8),
]
for text,y,w in steps:
    x0=(10-w)/2
    ax.add_patch(Rectangle((x0,y),w,0.8,fill=False,linewidth=1.4))
    ax.text(5,y+0.4,text,ha='center',va='center',fontsize=10.5)
for i in range(len(steps)-1):
    ax.annotate('',xy=(5,steps[i+1][1]+0.8),xytext=(5,steps[i][1]),arrowprops=dict(arrowstyle='->',lw=1.2))
ax.set_title('Three-attack falsification gate used in this redesign',fontsize=14)
plt.tight_layout(); plt.savefig(out+'/falsification_funnel.png',dpi=220); plt.close()

# 7: A surrogate performance envelope for s+ (clearly labeled design surrogate)
sp=np.linspace(0,35,300)
# smooth, asymmetric-ish Gaussian around 15, peak 7%, negative outside ~28
benefit=7*np.exp(-((sp-15)/7.5)**2) - 0.018*(sp-15)**2
benefit=np.clip(benefit,-4,7)
plt.plot(sp,benefit)
plt.axhline(0,linewidth=0.8)
plt.axvspan(10,20,alpha=0.12)
plt.xlabel('Riblet spacing in wall units, s+')
plt.ylabel('Local skin-friction change (%)')
plt.title('Design surrogate for riblet sizing sensitivity\nNot experimental data; used only for tolerance/off-design screening')
plt.grid(True,alpha=0.25); plt.tight_layout()
plt.savefig(out+'/riblet_design_surrogate.png',dpi=220); plt.close()

print('Generated figures in',out)
PY
python /mnt/data/aerorib_work/make_figures.py
ls -1 /mnt/data/aerorib_work/figures