Computational models comprised of systems of ordinary differential equations (ODEs) are used in a variety of domains to represent real world systems. These systems, which are often derived through subject matter expertise, require validation using data from operational use of the represented system. Given the high dimensional, non-linear setting, proper instrumentation of systems for validation testing poses unique and unanswered design challenges. Despite the importance of validating ODE computational models, little work has been performed to compare design construction measures with respect to their ability to recover model parameters. We discuss existing design measures from a statistical perspective and explore their efficacy using a simulation study of a Kirchoff-Love thin plate. Borrowing ideas from classical design theory and high dimensional sampling theory, we propose two algorithmic improvements to current ODE non-linear optimal design. We also present a simulation-based method to determine the number of sensors needed for validation testing of ODE computational models using operational data.