Hierarchical Competitive Net Architecture
Development of hypersonic aircraft requires a high degree of system integration. Design tools are needed to provide rapid, accurate calculations of complex fluid flow patterns. This project demonstrates that neural networks can be successfully applied to calculation of fluid flow distribution and heat transfer in a six leg heat exchanger panel, typical of the type envisioned for use in hypersonic aircraft.
We used training data generated from fluid flow and heat transfer equations in explicit form to train a neural net to solve the associated inverse dynamics problem. We developed an improved competitive net architecture. Finally, we successfully implemented a hierarchical neural network scheme to link multiple heat exchanger panels together. This method is direct, fast, and accurate.
KeywordsHide Layer Mass Flow Rate Heat Load Heat Transfer Equation Hypersonic Vehicle
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