Benchmarking of an Algebraic Tensor-Based HOSVD and a Feed Forward Neural Network for Conceptual Design of Pulse Detonation Engine Nozzles

Conceptual design is a critical phase in the development of propulsion plants. Its aim is to explore multidimensional design spaces to provide guidelines for the detailed design phase. This exploration is necessarily broad in scope and shallow in fidelity. Lately, developments in computing hardware have allowed the use of simplified computational fluid dynamics (CFD) models for conceptual design purposes, thereby increasing significantly the amount of data to be processed and generalised. In this context, the present work benchmarks two specific surrogate-model implementations for the conceptual design of a rocket-type pulse detonation engine: a tensor-based method based on high-order singular value decomposition (HOSVD) and a fully connected feed-forward neural network (NN). Two complementary comparisons were considered: HOSVD versus NN, using the same factorial databases to assess the effect of the surrogate method; and factorial versus low-discrepancy sampling, using the same NN architecture to assess the effect of the database distribution. The benchmark, which involved five input architecture parameters and five output operation parameters, was performed for both the direct analysis problem (outputs obtained from inputs) and the inverse design problem (inputs obtained from outputs). Three different situations were considered in the comparison—dimensionally balanced, overdetermined, and underdetermined—to simulate conditions that typically arise in these design phases. Three databases of different sizes were generated for the comparison. The results provide a case-specific assessment of the performance of the two implementations under the conditions considered and may provide useful guidance on the application of these data-analysis approaches in conceptual design.