Exploring a Variable-Inertia Computational Model (NKTg) with Go

Getting Help
1

Hi everyone,

I’d like to share a simple computational model I’ve been exploring in Go, based on what I call a variable-inertia state function (NKTg).
The goal here is practical computation and reproducibility, not claiming any new physical theory.

This model evaluates how position, velocity, and mass (including small mass variation) combine into a single numerical quantity that might be useful in simulations, control logic, or state analysis.

I implemented a minimal example in Go to demonstrate how to compute this invariant in a reproducible way.

Model Overview (Practical)

We define:

  • p = m * v — linear momentum

  • NKTg1 = x * p — interaction of position and momentum

  • NKTg2 = dm_dt * p — interaction of mass variation and momentum

  • NKTg = sqrt(NKTg1² + NKTg2²) — combined numerical magnitude

This is a numerical formulation that you can compute step by step. It’s intentionally simple so we can test, compare, and extend as needed.

Go Code Example

Here’s a minimal Go program illustrating the concept:

package main

import (
	"fmt"
	"math"
)

// computeNKTg evaluates basic NKTg quantities
func computeNKTg(x, v, m, dmDt float64) (p, N1, N2, Ntotal float64) {
	// linear momentum
	p = m * v

	// core components
	N1 = x * p
	N2 = dmDt * p

	// combined magnitude
	Ntotal = math.Sqrt(N1*N1 + N2*N2)
	return
}

func main() {
	// example parameters (arbitrary for demonstration)
	x := 4.49839644e9  // displacement (km)
	v := 5.43          // velocity (km/s)
	m := 1.0243e26     // mass (kg)
	dmDt := -2e-5      // small mass variation (kg/s)

	p, N1, N2, Ntotal := computeNKTg(x, v, m, dmDt)

	fmt.Println("Results of NKTg evaluation:")
	fmt.Printf("  p (momentum) = %.5e\n", p)
	fmt.Printf("  NKTg1 = %.5e\n", N1)
	fmt.Printf("  NKTg2 = %.5e\n", N2)
	fmt.Printf("  Combined NKTg = %.5e\n", Ntotal)
}

How to Run

  1. Save as nktg.go

  2. Run with:

    go run nktg.go

Why This Can Be Useful

  • Numerical Stability: The formulation avoids derivatives or symbolic operations — everything is computed directly.

  • Reproducibility: With fixed inputs, results are deterministic in Go (no external dependencies).

  • Extensibility: You can integrate this into:

    • simulation code,

    • dynamic systems modeling,

    • real-time data processing.

The model doesn’t replace classical physics — it’s a computational invariant you can use in code.

Discussion Points (Optional)

If you’re interested, I’d love feedback on:

  • benchmarking different Go implementations

  • extending this to vector state (3D) instead of scalars

  • comparing with standard state invariants like momentum or energy

  • using concurrency to compute sequences of states

Thanks for reading!
Happy to discuss any ideas or improvements.

2

Can you disclose how much of this is ai written

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