{'fraction_non_alphanumeric': 0.021764032073310423, 'fraction_numerical': 0.0, 'mean_word_length': 5.198581560283688, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Designing high speed monohull small crafts (HSMSC) using neural networks guided CFD based optimization

A new optimal design strategy for monohull vessels is proposed. The goal of the proposed procedure is to make monohulls competitive with their multihull counterparts. The resulting designs, thus, would combine the advantages of high speed vessels with those of simpler monohull vessels. The proposed strategy proposes a new re-formulation of hull optimization problem objective and uses a new class of Artificial Neural Networks (ANNs) to achieve it. Computational Fluid Dynamics (CFD) simulation is used to provide the necessary data for ANN training. The advantage of the used ANN over classical ones is that it adopts a one-shot lagrangian-like training procedure. The proposed design strategy is proven to be effective through several CFD-validated design examples.