The theoretical background of the PoPe and iPoPe verification scheme is presented. Verification is performed using the simulation output of production runs. The computing overhead is estimated to be at most 10%. PoPe or iPoPe calculations can be done offline provided the necessary data is stored, for example additional time slices, or online where iPoPe is more effective. The computing overhead is mostly that of storing the necessary data. The numerical error is determined and split into a part proportional to the operators, which are combined to form the equations to be solved, thus modifying their control parameters, completed by a residual error orthogonal to these operators. The accuracy of the numerical solution is determined by this modification of the control parameters. The PoPe and iPoPe methods are illustrated in this paper with simulations of a simple mechanical system with chaotic trajectories evolving into a strange attractor with sensitivity to initial conditions. We show that the accuracy depends on the particular simulation both because the properties of the numerical solution depend on the values of the control parameter, and because the target accuracy will depend on the problem that is addressed. One shows that for a case close to bifurcations between different states, the accuracy is determined by the level of detail of the bifurcation phenomena one aims at describing. A unique verification index, the PoPe index, is proposed to characterise the accuracy, and consequently the verification, of each production run. The PoPe output allows one to step beyond verification and analyse for example the numerical scheme efficiency. For the chosen example at fixed PoPe index, therefore at fixed numerical error, one finds that the higher order integration scheme, comparing order 4 to order 2 Runge-Kutta time stepping, reduces the computation cost by a factor 4.