adding tordoidal simulator and drawing

optimizer.py

@@ -57,6 +57,7 @@ class TransformerOptimizer:
         Vp_step: float = 0.5,
         B_max_T: float = 0.3,
         Vs_max: float = float('inf'),
+        Is_max: float = float('inf'),
         core_loss_W: float = 0.0,
         power_tolerance_percent: float = 2.0,
         fallback_max_power: bool = True,
@@ -71,6 +72,7 @@ class TransformerOptimizer:
         - Vp_min, Vp_max, Vp_step: Input voltage search range
         - B_max_T: Maximum allowed flux density (Tesla)
         - Vs_max: Maximum allowed secondary voltage (V)
+        - Is_max: Maximum allowed secondary current (A)
         - core_loss_W: Core loss at this operating point
         - power_tolerance_percent: Acceptable power delivery error (%)
         - fallback_max_power: If True and target cannot be met, find max power with best efficiency
@@ -115,6 +117,7 @@ class TransformerOptimizer:
                 Vp_step=Vp_step,
                 B_max_T=B_max_T,
                 Vs_max=Vs_max,
+                Is_max=Is_max,
                 core_loss_W=core_loss_W,
                 power_tolerance_percent=power_tolerance_percent,
                 fallback_max_power=fallback_max_power,
@@ -151,6 +154,7 @@ class TransformerOptimizer:
         Vp_step: float,
         B_max_T: float,
         Vs_max: float,
+        Is_max: float,
         core_loss_W: float,
         power_tolerance_percent: float,
         fallback_max_power: bool,
@@ -195,6 +199,9 @@ class TransformerOptimizer:
             if sim["Vs_rms"] > Vs_max:
                 continue
 
+            if sim["Is_rms"] > Is_max:
+                continue
+
             power_error = abs(sim["P_out_W"] - target_power_W) / target_power_W * 100
 
             result = OptimizationResult(