NLP seems to be doing pretty well

julia/src/inner_loop/propagator.jl

@@ -56,7 +56,20 @@ function prop(ΔVs::Matrix{T},
         for k in 1:7 push!(states[k], interpolated_state[k]) end
       end
     end
-    state = prop_one(ΔVs[i,:], state, craft, time/n, primary)
+    try
+      state = prop_one(ΔVs[i,:], state, craft, time/n, primary)
+    catch e
+      if isa(e,PropOne_Error)
+        for val in e.ΔV_unit
+          # If this isn't true, then we just let it slide
+          if abs(val) > 1.0001
+            rethrow()
+          end
+        end
+      else
+        rethrow()
+      end
+    end
     for j in 1:7 push!(states[j], state[j]) end
     state[7] >= craft.dry_mass || throw(Mass_Error(state[7]))
   end
@@ -68,4 +81,4 @@ end
 """
 Convenience function for propagating a state with no thrust
 """
-prop(x::Vector{Float64}, t::Float64, p::Body=Sun) = prop(zeros(1000,3), [x;1e10], no_thrust, t, p)[1]
+prop(x::Vector{Float64}, t::Float64, p::Body=Sun) = prop(zeros(1000,3), [x;1.], no_thrust, t, p)[1]