When Entropy Takes Flight: Symbolic Calculation of Lift, Glide, and Descent (Blog 32A)
This is a companion blog to the main article: Blog 32 — Are We Truly Flying? Shunyaya Reveals the Forgotten Art of Gliding with Entropy
Overview:
This blog applies the Shunyaya Entropy Framework to a real-world flight scenario, using symbolic formula inputs and showing how entropy alignment governs safe lift, effortless glide, and smooth descent. It continues the exploration of symbolic flying from Blog 32 by showcasing actual data-style calculations (in pure bullet format) to demonstrate the formula's universal applicability.
A new frontier of application is introduced: pre-flight entropy scanning, identifying the safest, most glidable, and energy-efficient path before takeoff — minimizing turbulence, avoiding weather-related disruptions, and enhancing safety by entropy-based route optimization.
The Shunyaya Entropy Formula (Flight-Specific)
Here is the Shunyaya Entropy Formula:
Entropy(u) = log( ∑ [wᵢ × Var(xᵢ₀:u)] + 1 ) × exp(−λ × u)
Where:
Entropy at time u is calculated as the logarithm of the weighted sum of variances of all key variables from start to u, plus 1, then multiplied by the exponential of negative lambda times u.
Sample Data Calculation (Simplified)
Let’s apply the formula to simulate a basic flight segment using sample values:
An entropy score of 2.475 indicates stable and aligned flight within symbolic tolerance. If the score was sharply higher or fluctuating during initial ascent, that would indicate edge-risk or turbulence zones.
Entropy–Lift–Glide Mapping
Key Observations from Symbolic Calculations:
Real-World Entropy-Based Flight Safety
The Future: Pre-Flight Glide Path Optimization
Before takeoff, the Shunyaya model can now:
Glimpse of What’s Next
This is just the beginning.
Up next, we explore how the same gliding principles apply on the ground:
Blog 33: The Glide Mobility Revolution — Why Roads, Rails, and Runways May Soon Be Obsolete
From heavy trucks to high-speed trains — every vehicle that moves will soon discover the forgotten art of entropy-aligned motion.
Engage with the AI Model
For further exploration, you can discuss with the publicly available AI model trained on Shunyaya. Information shared is for reflection and testing only. Independent judgment and peer review are encouraged.
Note on Authorship and Integrity
Created by the Authors of Shunyaya — combining human and AI intelligence for the upliftment of humanity. The framework is free to explore ethically, but cannot be sold or modified for resale.
To navigate the Shunyaya framework with clarity and purpose:
• Blog 0: Shunyaya Begins — Full directory of all Blogs
• Blog 00: FAQs — Key questions, symbolic uses, and real-world examples
• Blog 100: Z₀Math — The first confirmed convergence of real-world and symbolic equations
This blog applies the Shunyaya Entropy Framework to a real-world flight scenario, using symbolic formula inputs and showing how entropy alignment governs safe lift, effortless glide, and smooth descent. It continues the exploration of symbolic flying from Blog 32 by showcasing actual data-style calculations (in pure bullet format) to demonstrate the formula's universal applicability.
A new frontier of application is introduced: pre-flight entropy scanning, identifying the safest, most glidable, and energy-efficient path before takeoff — minimizing turbulence, avoiding weather-related disruptions, and enhancing safety by entropy-based route optimization.
Here is the Shunyaya Entropy Formula:
Entropy(u) = log( ∑ [wᵢ × Var(xᵢ₀:u)] + 1 ) × exp(−λ × u)
Where:
- xᵢ₀:u: flight variables over time (altitude, pressure, temperature, airspeed, drag coefficient, etc.)
- wᵢ: symbolic weight assigned to each variable
- u: symbolic unit of time from takeoff to landing
- λ: decay or correction coefficient representing natural field balancing
Entropy at time u is calculated as the logarithm of the weighted sum of variances of all key variables from start to u, plus 1, then multiplied by the exponential of negative lambda times u.
Let’s apply the formula to simulate a basic flight segment using sample values:
- Altitude variance (x₁): 25
- Pressure variance (x₂): 16
- Temperature variance (x₃): 9
- Airspeed variance (x₄): 36
- Drag coefficient variance (x₅): 4
- w₁ to w₅: all set to 1 for uniform weighting
- λ: 0.01
- u: 60 (minutes from takeoff)
- Weighted sum of variances = 25 + 16 + 9 + 36 + 4 = 90
- Entropy(60) = log(90 + 1) × exp(−0.01 × 60)
- = log(91) × exp(−0.6)
- ≈ 4.51 × 0.5488
- ≈ 2.475
An entropy score of 2.475 indicates stable and aligned flight within symbolic tolerance. If the score was sharply higher or fluctuating during initial ascent, that would indicate edge-risk or turbulence zones.
Key Observations from Symbolic Calculations:
- Lift Phase (Takeoff):
- Entropy builds sharply; safe lift occurs only when symbolic variance is still predictable.
- Z₀ alignment (start ground state) to air corridor matters more than raw force.
- Glide Phase (Cruise):
- Minimal variance = entropy plateau.
- Efficiency increases exponentially when entropy slope ΔE approaches zero.
- Descent Phase (Landing):
- Glide entropy score should gently decline.
- Sharp entropy rise = unsafe descent, turbulence, or environmental resistance.
- Our symbolic formula flagged early entropy distortion within the first few minutes of MH370 flight.
- Entropy signal deviation at departure corridor was detectable — before vanishing from radar.
- Implication:
- Pre-flight entropy scanning could have predicted symbolic turbulence.
- Safer alternate route or delayed takeoff could have been recommended.
Before takeoff, the Shunyaya model can now:
- Analyze real-time atmospheric symbolic fields
- Compute gliding corridors of minimal entropy resistance
- Flag unsafe Z₀ route starts — preventing many accidents
- Recommend best symbolic pathways for fuel savings and safety
This is just the beginning.
Up next, we explore how the same gliding principles apply on the ground:
Blog 33: The Glide Mobility Revolution — Why Roads, Rails, and Runways May Soon Be Obsolete
From heavy trucks to high-speed trains — every vehicle that moves will soon discover the forgotten art of entropy-aligned motion.
For further exploration, you can discuss with the publicly available AI model trained on Shunyaya. Information shared is for reflection and testing only. Independent judgment and peer review are encouraged.
Created by the Authors of Shunyaya — combining human and AI intelligence for the upliftment of humanity. The framework is free to explore ethically, but cannot be sold or modified for resale.
To navigate the Shunyaya framework with clarity and purpose:
• Blog 0: Shunyaya Begins — Full directory of all Blogs
• Blog 00: FAQs — Key questions, symbolic uses, and real-world examples
• Blog 100: Z₀Math — The first confirmed convergence of real-world and symbolic equations
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