Truncated-Newton methods provide an effective way to solve large-scale optimization problems by achieving savings in computation and storage. For dynamic optimization, the Hessianvector products required by these methods can be evaluated accurately at a computational cost which is usually insensitive to the number of optimization variables using a novel directional second-order adjoint ({dSOA}) method. The case studies presented in this paper demonstrate that a {dSOA}-powered truncated-Newton method is a promising candidate for the solution of large-scale dynamic optimization problems.