built environment

RL Testing/Function Fitting.ipynb

@@ -1922,6 +1922,106 @@
     "print(\"✓ All tests passed! Standalone predictor matches original model perfectly.\")\n",
     "print(\"\\nThe standalone predictor is ready for integration into your simulator!\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21babeb3",
+   "metadata": {},
+   "source": [
+    "### Now, let's find the equilibrium height for our pod, given mass of 5.8 kg. \n",
+    "\n",
+    "5.8 kg * 9.81 $m/s^2$ = 56.898 N"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "id": "badbc379",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "EQUILIBRIUM GAP HEIGHT FINDER (Analytical Solution)\n",
+      "======================================================================\n",
+      "Pod mass: 5.8 kg\n",
+      "Total weight: 56.898 N\n",
+      "Target force per yoke: 28.449 N\n",
+      "Parameters: currL = 0 A, currR = 0 A, roll = 0°\n",
+      "\n",
+      "using scipy.optimize.fsolve:\n",
+      "  Gap: 16.491741 mm  →  Force: 28.449000 N\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find equilibrium gap height for 5.8 kg pod using polynomial root finding\n",
+    "import numpy as np\n",
+    "from maglev_predictor import MaglevPredictor\n",
+    "from scipy.optimize import fsolve\n",
+    "\n",
+    "# Initialize predictor\n",
+    "predictor = MaglevPredictor()\n",
+    "\n",
+    "# Target force for 5.8 kg pod (total force = weight)\n",
+    "# Since we have TWO yokes (front and back), each produces this force\n",
+    "target_force_per_yoke = 5.8 * 9.81 / 2  # 28.449 N per yoke\n",
+    "\n",
+    "print(\"=\" * 70)\n",
+    "print(\"EQUILIBRIUM GAP HEIGHT FINDER (Analytical Solution)\")\n",
+    "print(\"=\" * 70)\n",
+    "print(f\"Pod mass: 5.8 kg\")\n",
+    "print(f\"Total weight: {5.8 * 9.81:.3f} N\")\n",
+    "print(f\"Target force per yoke: {target_force_per_yoke:.3f} N\")\n",
+    "print(f\"Parameters: currL = 0 A, currR = 0 A, roll = 0°\")\n",
+    "print()\n",
+    "\n",
+    "# Method 2: Use scipy.optimize.fsolve for verification\n",
+    "def force_error(gap_height):\n",
+    "    # Handle array input from fsolve (convert to scalar)\n",
+    "    gap_height = float(np.atleast_1d(gap_height)[0])\n",
+    "    force, _ = predictor.predict(currL=0, currR=0, roll=0, gap_height=gap_height)\n",
+    "    return force - target_force_per_yoke\n",
+    "\n",
+    "# Try multiple initial guesses to find all solutions\n",
+    "initial_guesses = [8, 10, 15, 20, 25]\n",
+    "scipy_solutions = []\n",
+    "\n",
+    "print(\"using scipy.optimize.fsolve:\")\n",
+    "for guess in initial_guesses:\n",
+    "    solution = fsolve(force_error, guess)[0]\n",
+    "    if 5 <= solution <= 30:  # Valid range\n",
+    "        force, torque = predictor.predict(currL=0, currR=0, roll=0, gap_height=solution)\n",
+    "        error = abs(force - target_force_per_yoke)\n",
+    "        if error < 0.01:  # Valid solution (within 10 mN)\n",
+    "            scipy_solutions.append((solution, force))\n",
+    "\n",
+    "# Remove duplicates (solutions within 0.1 mm)\n",
+    "unique_solutions = []\n",
+    "for sol, force in scipy_solutions:\n",
+    "    is_duplicate = False\n",
+    "    for unique_sol, _ in unique_solutions:\n",
+    "        if abs(sol - unique_sol) < 0.1:\n",
+    "            is_duplicate = True\n",
+    "            break\n",
+    "    if not is_duplicate:\n",
+    "        unique_solutions.append((sol, force))\n",
+    "\n",
+    "for gap_val, force in unique_solutions:\n",
+    "    print(f\"  Gap: {gap_val:.6f} mm  →  Force: {force:.6f} N\")\n",
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ec4bb72",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {