Geometrical deep learning for performance prediction of high-speed craft

Abbas, Asad; Rafiee, Ashkan; Haase, Max; Malcolm, Andrew

doi:10.1016/j.oceaneng.2022.111716

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…The analyzed studies published in the years 2020-2022 demonstrated the applications of the proposed concept of ML, CFDs, and big data [47,52,91]. Fig.…”

Section: Discussionmentioning

confidence: 95%

Maritime Computing Transportation, Environment, and Development: Trends of Data Visualization and Computational Methodologies

Chaichana

2023

Adv. technol. innov.

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This research aims to characterize the field of maritime computing (MC) transportation, environment, and development. It is the first report to discover how MC domain configurations support management technologies. An aspect of this research is the creation of drivers of ocean-based businesses. Systematic search and meta-analysis are employed to classify and define the MC domain. MC developments were first identified in the 1990s, representing maritime development for designing sailboats, submarines, and ship hydrodynamics. The maritime environment is simulated to predict emission reductions, coastal waste particles, renewable energy, and engineer robots to observe the ocean ecosystem. Maritime transportation focuses on optimizing ship speed, maneuvering ships, and using liquefied natural gas and submarine pipelines. Data trends with machine learning can be obtained by collecting a big data of similar computational results for implementing artificial intelligence strategies. Research findings show that modeling is an essential skill set in the 21st century.

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“…The analyzed studies published in the years 2020-2022 demonstrated the applications of the proposed concept of ML, CFDs, and big data [47,52,91]. Fig.…”

Section: Discussionmentioning

confidence: 95%

Maritime Computing Transportation, Environment, and Development: Trends of Data Visualization and Computational Methodologies

Chaichana

2023

Adv. technol. innov.

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“…In these statistical models, geometrical deformations of the meshes were computed as linear combinations of the principal components, so they cannot handle complex geometrical structures of the head and skull meshes. More advanced statistical shape modeling methods (e.g., Gaussian-based PCA [ 73 ]), geometric deep learning [ 74 ], and Generative Adversarial Networks (e.g., SP-GAN [ 75 ]) can be employed to solve these issues. In further works, we will implement the computed muscle strains as the baseline values for diagnosing facial muscle behaviors.…”

Section: Discussionmentioning

confidence: 99%

Novel Baseline Facial Muscle Database Using Statistical Shape Modeling and In Silico Trials toward Decision Support for Facial Rehabilitation

et al. 2023

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Backgrounds and Objective: Facial palsy is a complex pathophysiological condition affecting the personal and professional lives of the involved patients. Sudden muscle weakness or paralysis needs to be rehabilitated to recover a symmetric and expressive face. Computer-aided decision support systems for facial rehabilitation have been developed. However, there is a lack of facial muscle baseline data to evaluate the patient states and guide as well as optimize the rehabilitation strategy. In this present study, we aimed to develop a novel baseline facial muscle database (static and dynamic behaviors) using the coupling between statistical shape modeling and in-silico trial approaches. Methods: 10,000 virtual subjects (5000 males and 5000 females) were generated from a statistical shape modeling (SSM) head model. Skull and muscle networks were defined so that they statistically fit with the head shapes. Two standard mimics: smiling and kissing were generated. The muscle strains of the lengths in neutral and mimic positions were computed and recorded thanks to the muscle insertion and attachment points on the animated head and skull meshes. For validation, five head and skull meshes were reconstructed from the five computed tomography (CT) image sets. Skull and muscle networks were then predicted from the reconstructed head meshes. The predicted skull meshes were compared with the reconstructed skull meshes based on the mesh-to-mesh distance metrics. The predicted muscle lengths were also compared with those manually defined on the reconstructed head and skull meshes. Moreover, the computed muscle lengths and strains were compared with those in our previous studies and the literature. Results: The skull prediction’s median deviations from the CT-based models were 2.2236 mm, 2.1371 mm, and 2.1277 mm for the skull shape, skull mesh, and muscle attachment point regions, respectively. The median deviation of the muscle lengths was 4.8940 mm. The computed muscle strains were compatible with the reported values in our previous Kinect-based method and the literature. Conclusions: The development of our novel facial muscle database opens new avenues to accurately evaluate the facial muscle states of facial palsy patients. Based on the evaluated results, specific types of facial mimic rehabilitation exercises can also be selected optimally to train the target muscles. In perspective, the database of the computed muscle lengths and strains will be integrated into our available clinical decision support system for automatically detecting malfunctioning muscles and proposing patient-specific rehabilitation serious games.

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“…Data-driven approaches based on modern machine learning (ML) algorithms for water-related problems have become a topic of significant research due to both academic and practical interests [34][35][36][37][38][39][40] and have recently been employed to complement CFD models. The existing studies either used ML algorithms to improve hydrodynamic models or directly developed surrogates for the numerical models.…”

Section: Introductionmentioning

confidence: 99%

Fast Prediction of Solute Concentration Field in Rotationally Influenced Fluids Using a Parameter-Based Field Reconstruction Convolutional Neural Network

Yan

Mohammadian

Yu³

et al. 2023

Water

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Many high-performance fluid dynamic models do not consider fluids in a rotating environment and often require a significant amount of computational time. The current study proposes a novel parameter-based field reconstruction convolutional neural network (PFR-CNN) approach to model the solute concentration field in rotationally influenced fluids. A new three-dimensional (3D) numerical solver, TwoLiquidMixingCoriolisFoam, was implemented within the framework of OpenFOAM to simulate effluents subjected to the influence of rotation. Subsequently, the developed numerical solver was employed to conduct numerical experiments to generate numerical data. A PFR-CNN was designed to predict the concentration fields of neutrally buoyant effluents in rotating water bodies based on the Froude number (Fr) and Rossby number (Ro). The proposed PFR-CNN was trained and validated with a train-validation dataset. The predicted concentration fields for two additional tests demonstrated the good performance of the proposed approach, and the algorithm performed better than traditional approaches. This study offers a new 3D numerical solver, and a novel PFR-CNN approach can predict solute transport subjected to the effects of rotation in few seconds, and the PFR-CNN can significantly reduce the computational costs. The study can significantly advance the ability to model flow and solute transport processes, and the proposed CNN-based approach can potentially be employed to predict the spatial distribution of any physical variable in the lentic, ocean, and earth system.

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Geometrical deep learning for performance prediction of high-speed craft

Cited by 7 publications

References 38 publications

Maritime Computing Transportation, Environment, and Development: Trends of Data Visualization and Computational Methodologies

Maritime Computing Transportation, Environment, and Development: Trends of Data Visualization and Computational Methodologies

Novel Baseline Facial Muscle Database Using Statistical Shape Modeling and In Silico Trials toward Decision Support for Facial Rehabilitation

Fast Prediction of Solute Concentration Field in Rotationally Influenced Fluids Using a Parameter-Based Field Reconstruction Convolutional Neural Network

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