Algorithmic differentiation of hyperbolic flow problems

Highlights: • Algorithmic differentiation (AD) of a code for computational fluid dynamics (CFD). • AD customization to compute first-order sensitivities to inviscid flows with shocks. • Implementation of a theoretical calculus based on the sensitivities of the shocks. • Successful application of the calculus to the two-dimensional Euler equations. We are interested in the development of an algorithmic differentiation framework for computing approximations to tangent vectors to scalar and systems of hyperbolic partial differential equations. The main difficulty of such a numerical method is the presence of shock waves that are resolved by proposing a numerical discretization of the calculus introduced in Bressan and Marson (1995) [5]. Numerical results are presented for the one-dimensional Burgers equation and the Euler equations. Using the essential routines of a state-of-the-art code for computational fluid dynamics (CFD) as a starting point, three modifications are required to apply the introduced calculus. First, the CFD code is modified to solve an additional equation for the shock location. Second, we customize the computation of the corresponding tangent to the shock location. Finally, the modified method is enhanced by algorithmic differentiation. Applying the introduced calculus to problems of the Burgers equation and the Euler equations, it is found that correct sensitivities can be computed, whereas the application of black-box algorithmic differentiation fails.