Asia Ali Akbar, A. Awan, Sohail Nadeem, N. A. Ahammad, Nauman Raza, M. Oreijah, Kamel Guedri, S. Allahyani
Brownian motions and Thermophoresis are primary sources of nanoparticle diffusion in nanofluids, having substantial implications for the thermo-physical characteristics of nanofluids. With such a high need, the two-dimensional, laminar MHD quadratic convective stream of Carreau-Yasuda nano liquid across the stretchy sheet has been reported. The flow is caused by surface stretching. The principal purpose of this extensive study is to enhance thermal transmission. The effects of variable thermal conductivity and heat source are considered as well. The governing boundary layer equations are transmuted using similarity parameters into a series of nonlinear ODEs. The bvp4c algorithm is adopted to fix the translated system numerically. The effects of prominent similarity variables over the temperature, velocity, and concentration field are graphically visualized and verified via tables. It explored that fluid’s speed diminishes for the more significant inputs of the magnetic coefficient, Brownian motion coefficient, and Prandtl number. The thermal efficiency is improved for larger values of thermophoretic constant, varying thermal conductance, and heat-generating parameters. The concentration field has proved to be a decreasing function of nanofluid constants.
{"title":"Heat Transfer Analysis of Carreau-Yasuda Nanofluid Flow with Variable Thermal Conductivity and Quadratic Convection","authors":"Asia Ali Akbar, A. Awan, Sohail Nadeem, N. A. Ahammad, Nauman Raza, M. Oreijah, Kamel Guedri, S. Allahyani","doi":"10.1093/jcde/qwae009","DOIUrl":"https://doi.org/10.1093/jcde/qwae009","url":null,"abstract":"\u0000 Brownian motions and Thermophoresis are primary sources of nanoparticle diffusion in nanofluids, having substantial implications for the thermo-physical characteristics of nanofluids. With such a high need, the two-dimensional, laminar MHD quadratic convective stream of Carreau-Yasuda nano liquid across the stretchy sheet has been reported. The flow is caused by surface stretching. The principal purpose of this extensive study is to enhance thermal transmission. The effects of variable thermal conductivity and heat source are considered as well. The governing boundary layer equations are transmuted using similarity parameters into a series of nonlinear ODEs. The bvp4c algorithm is adopted to fix the translated system numerically. The effects of prominent similarity variables over the temperature, velocity, and concentration field are graphically visualized and verified via tables. It explored that fluid’s speed diminishes for the more significant inputs of the magnetic coefficient, Brownian motion coefficient, and Prandtl number. The thermal efficiency is improved for larger values of thermophoretic constant, varying thermal conductance, and heat-generating parameters. The concentration field has proved to be a decreasing function of nanofluid constants.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139602773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The advancement in materials and lubrication has significantly improved the load-carrying capability of polymer gears, making them ideal for replacing metal gears in power transmission. However, this conversion is not as simple as substituting steel with polymer; it requires a thorough redesign of the structural parameters specific to polymer gears. To enable the metal-to-polymer conversion of gear in power transmission, a model for optimizing polymer gear systems was developed. An investigation of the accessory transmission system of a low-thrust turbofan aeroengine was conducted. A comprehensive performance index for the accessory transmission was developed using combined weighting coefficients to achieve the optimization goals including total mass, transmission efficiency, maximum transmission error and so on. The polymer gear system developed using the proposed optimization model demonstrated a 70.4% reduction in total mass compared to the metal gear system, as well as a transmission error decrease of over 29% when compared to polymer gear systems with standard tooth profiles. The contribution analysis results demonstrated that optimizing the tooth width, pressure angle, and addendum height of polymer gears can significantly enhance the load-carrying capacity of the polymer gear system while maximizing tooth profile flexibility.
{"title":"Conceptual design and optimization of polymer gear system for low-thrust turbofan aeroengine accessory transmission","authors":"Zehua Lu, Chang Liu, Changjun Liao, Jiazan Zhu, Huaiju Liu, Yiming Chen","doi":"10.1093/jcde/qwae008","DOIUrl":"https://doi.org/10.1093/jcde/qwae008","url":null,"abstract":"\u0000 The advancement in materials and lubrication has significantly improved the load-carrying capability of polymer gears, making them ideal for replacing metal gears in power transmission. However, this conversion is not as simple as substituting steel with polymer; it requires a thorough redesign of the structural parameters specific to polymer gears. To enable the metal-to-polymer conversion of gear in power transmission, a model for optimizing polymer gear systems was developed. An investigation of the accessory transmission system of a low-thrust turbofan aeroengine was conducted. A comprehensive performance index for the accessory transmission was developed using combined weighting coefficients to achieve the optimization goals including total mass, transmission efficiency, maximum transmission error and so on. The polymer gear system developed using the proposed optimization model demonstrated a 70.4% reduction in total mass compared to the metal gear system, as well as a transmission error decrease of over 29% when compared to polymer gear systems with standard tooth profiles. The contribution analysis results demonstrated that optimizing the tooth width, pressure angle, and addendum height of polymer gears can significantly enhance the load-carrying capacity of the polymer gear system while maximizing tooth profile flexibility.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139609007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the realm of medical diagnosis, recent strides in Deep Neural Network-guided Magnetic Resonance Imaging (MRI) restoration have shown promise. Nevertheless, persistent drawbacks overshadow these advancements. Challenges persist in balancing acquisition speed and image quality, while existing methods primarily focus on singular tasks like MRI reconstruction or super-resolution, neglecting the interplay between these tasks. To tackle these challenges, this paper introduces the Mutual Co-Attention Network (MCAN) specifically designed to concurrently address both MRI reconstruction and super-resolution tasks. Comprising multiple Mutual Cooperation Attention Blocks (MCABs) in succession, MCAN is tailored to maintain consistency between local physiological details and global anatomical structures. The intricately crafted MCAB includes a feature extraction block, a local attention block, and a global attention block. Additionally, to ensure data fidelity without compromising acquired data, we propose the Channel-wise Data Consistency (CDC) block. Thorough experimentation on the IXI and fastMRI dataset showcases MCAN’s superiority over existing state-of-the-art methods. Both quantitative metrics and visual quality assessments validate the enhanced performance of MCAN in MRI restoration. The findings underscore MCAN’s potential in significantly advancing therapeutic applications. By mitigating the trade-off between acquisition speed and image quality while simultaneously addressing both MRI reconstruction and super-resolution tasks, MCAN emerges as a promising solution in the domain of MR image restoration.
{"title":"Joint MR Image Reconstruction and Super-Resolution via Mutual Co-Attention Network","authors":"Jiacheng Chen, Fei Wu, Wanliang Wang","doi":"10.1093/jcde/qwae006","DOIUrl":"https://doi.org/10.1093/jcde/qwae006","url":null,"abstract":"\u0000 In the realm of medical diagnosis, recent strides in Deep Neural Network-guided Magnetic Resonance Imaging (MRI) restoration have shown promise. Nevertheless, persistent drawbacks overshadow these advancements. Challenges persist in balancing acquisition speed and image quality, while existing methods primarily focus on singular tasks like MRI reconstruction or super-resolution, neglecting the interplay between these tasks. To tackle these challenges, this paper introduces the Mutual Co-Attention Network (MCAN) specifically designed to concurrently address both MRI reconstruction and super-resolution tasks. Comprising multiple Mutual Cooperation Attention Blocks (MCABs) in succession, MCAN is tailored to maintain consistency between local physiological details and global anatomical structures. The intricately crafted MCAB includes a feature extraction block, a local attention block, and a global attention block. Additionally, to ensure data fidelity without compromising acquired data, we propose the Channel-wise Data Consistency (CDC) block. Thorough experimentation on the IXI and fastMRI dataset showcases MCAN’s superiority over existing state-of-the-art methods. Both quantitative metrics and visual quality assessments validate the enhanced performance of MCAN in MRI restoration. The findings underscore MCAN’s potential in significantly advancing therapeutic applications. By mitigating the trade-off between acquisition speed and image quality while simultaneously addressing both MRI reconstruction and super-resolution tasks, MCAN emerges as a promising solution in the domain of MR image restoration.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139612879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shuming Zhang, Zhidong Guan, Hao Jiang, Tao Ning, Xiaodong Wang, Pingan Tan
3D reconstruction is a significant research topic in the field of Computer-Aided Design (CAD), which is used to recover editable CAD models from original shapes, including point clouds, voxels, meshes, and boundary representations (B-rep). Recently, there has been considerable research interest in deep model generation due to the increasing potential of deep learning methods. To address the challenges of 3D reconstruction and generation, we propose Brep2Seq, a novel deep neural network designed to transform the B-rep model into a sequence of editable parametrized feature-based modeling operations comprising principal primitives and detailed features. Brep2Seq employs an encoder-decoder architecture based on the Transformer, leveraging geometry and topological information within B-rep models to extract the feature representation of the original 3D shape. Due to its hierarchical network architecture and training strategy, Brep2Seq achieved improved model reconstruction and controllable model generation by distinguishing between the primary shape and detailed features of CAD models. To train Brep2Seq, a large-scale dataset comprising one million CAD designs is established through an automatic geometry synthesis method. Extensive experiments on both DeepCAD and Fusion 360 datasets demonstrate the effectiveness of Brep2Seq, and show its applicability to simple mechanical components in real-world scenarios. We further apply Brep2Seq to various downstream applications, including point cloud reconstruction, model interpolation, shape constraint generation and CAD feature recognition.
{"title":"Brep2Seq: A dataset and hierarchical deep learning network for reconstruction and generation of computer-aided design models","authors":"Shuming Zhang, Zhidong Guan, Hao Jiang, Tao Ning, Xiaodong Wang, Pingan Tan","doi":"10.1093/jcde/qwae005","DOIUrl":"https://doi.org/10.1093/jcde/qwae005","url":null,"abstract":"\u0000 3D reconstruction is a significant research topic in the field of Computer-Aided Design (CAD), which is used to recover editable CAD models from original shapes, including point clouds, voxels, meshes, and boundary representations (B-rep). Recently, there has been considerable research interest in deep model generation due to the increasing potential of deep learning methods. To address the challenges of 3D reconstruction and generation, we propose Brep2Seq, a novel deep neural network designed to transform the B-rep model into a sequence of editable parametrized feature-based modeling operations comprising principal primitives and detailed features. Brep2Seq employs an encoder-decoder architecture based on the Transformer, leveraging geometry and topological information within B-rep models to extract the feature representation of the original 3D shape. Due to its hierarchical network architecture and training strategy, Brep2Seq achieved improved model reconstruction and controllable model generation by distinguishing between the primary shape and detailed features of CAD models. To train Brep2Seq, a large-scale dataset comprising one million CAD designs is established through an automatic geometry synthesis method. Extensive experiments on both DeepCAD and Fusion 360 datasets demonstrate the effectiveness of Brep2Seq, and show its applicability to simple mechanical components in real-world scenarios. We further apply Brep2Seq to various downstream applications, including point cloud reconstruction, model interpolation, shape constraint generation and CAD feature recognition.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139612618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tiantong Lv, Zipeng Chen, Dengfeng Wang, Xuejing Du
Combined application of steel, aluminum and CFRP is the main direction of future lightweight body development. However, the anisotropy and additional lamination design variables of CFRP parts poses significant challenges for the development of multi-material bodies. This study establishes a parametric design method for the variable-thickness lamination scheme based on non-uniform rational B-splines (NURBS), it can be coupled with existing parametric design methods for structural shapes to formulate a complete parametric design and modelling of CFRP components. On this basis, a homogenized intermediate material property is derived from classic laminate theory by introducing lamination assumptions, it enables a stepwise multi-material body optimization method to solve the challenge that components’ material design variables switching between CFRP and alloy will introduce/eliminate lamination design variables iteratively, posing a great optimization convergence difficulty. The proposed parametric modeling method for CFRP components was validated by experimental tests of a fabricated roof beam, and the proposed optimization method was applied to a vehicle body, achieving 15.9%, 23.9%, 18.6%, 12.2% increase in bending and tortional stiffness and modal frequencies; 20.2%, 9.3%, 12.7% reduction of weight and peak acceleration in frontal and side collisions. This study enables the forward design of multi-material bodies compatible with CFRP parts.
{"title":"Parametric design and modeling method of CFRP laminated components applicable for multi-material vehicle body development","authors":"Tiantong Lv, Zipeng Chen, Dengfeng Wang, Xuejing Du","doi":"10.1093/jcde/qwae007","DOIUrl":"https://doi.org/10.1093/jcde/qwae007","url":null,"abstract":"\u0000 Combined application of steel, aluminum and CFRP is the main direction of future lightweight body development. However, the anisotropy and additional lamination design variables of CFRP parts poses significant challenges for the development of multi-material bodies. This study establishes a parametric design method for the variable-thickness lamination scheme based on non-uniform rational B-splines (NURBS), it can be coupled with existing parametric design methods for structural shapes to formulate a complete parametric design and modelling of CFRP components. On this basis, a homogenized intermediate material property is derived from classic laminate theory by introducing lamination assumptions, it enables a stepwise multi-material body optimization method to solve the challenge that components’ material design variables switching between CFRP and alloy will introduce/eliminate lamination design variables iteratively, posing a great optimization convergence difficulty. The proposed parametric modeling method for CFRP components was validated by experimental tests of a fabricated roof beam, and the proposed optimization method was applied to a vehicle body, achieving 15.9%, 23.9%, 18.6%, 12.2% increase in bending and tortional stiffness and modal frequencies; 20.2%, 9.3%, 12.7% reduction of weight and peak acceleration in frontal and side collisions. This study enables the forward design of multi-material bodies compatible with CFRP parts.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139525911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shuang Wang, Heming Jia, A. Hussien, L. Abualigah, Guanjun Lin, Hongwei Wei, Zhenheng Lin, K. G. Dhal
In this study, an improved version of Aquila Optimizer (AO) known as EHAOMPA has been developed by using the Marine Predators Algorithm (MPA). MPA is a recent and well-behaved optimizer with a unique memory saving and FADs mechanism. At the same time, it suffers from various defects such as inadequate global search, sluggish convergence, and stagnation of local optima. However, AO has contented robust global exploration capability, fast convergence speed, and high search efficiency. Thus, the proposed EHAOMPA aims to complement the shortcomings of AO and MPA while bringing new features. Specifically, the representative-based hunting technique is incorporated into the exploration stage to enhance population diversity. At the same time, random opposition-based learning (ROBL) is introduced into the exploitation stage to prevent the optimizer from sticking to local optima. This study tests the performance of EHAOMPA's on twenty-three standard mathematical benchmark functions, 29 complex test functions from the CEC2017 test suite, six constrained industrial engineering design problems, and a CNN-hyperparameter optimization for COVID-19 CT-image detection problem. EHAOMPA is compared with four existing optimization algorithm types, achieving the best performance on both numerical and practical issues. Compared to other methods, the test function results demonstrate that EHAOMPA exhibits a more potent global search capability, a higher convergence rate, increased accuracy, and an improved ability to avoid local optima. The excellent experimental results in practical problems indicate that the developed EHAOMPA has great potential in solving real-world optimization problems. The combination of multiple strategies can effectively improve the performance of the algorithm. The source code of the EHAOMPA is publicly available at https://github.com/WangShuang92/EHAOMPA.
{"title":"Boosting Aquila Optimizer by Marine Predators Algorithm for Combinatorial Optimization","authors":"Shuang Wang, Heming Jia, A. Hussien, L. Abualigah, Guanjun Lin, Hongwei Wei, Zhenheng Lin, K. G. Dhal","doi":"10.1093/jcde/qwae004","DOIUrl":"https://doi.org/10.1093/jcde/qwae004","url":null,"abstract":"\u0000 In this study, an improved version of Aquila Optimizer (AO) known as EHAOMPA has been developed by using the Marine Predators Algorithm (MPA). MPA is a recent and well-behaved optimizer with a unique memory saving and FADs mechanism. At the same time, it suffers from various defects such as inadequate global search, sluggish convergence, and stagnation of local optima. However, AO has contented robust global exploration capability, fast convergence speed, and high search efficiency. Thus, the proposed EHAOMPA aims to complement the shortcomings of AO and MPA while bringing new features. Specifically, the representative-based hunting technique is incorporated into the exploration stage to enhance population diversity. At the same time, random opposition-based learning (ROBL) is introduced into the exploitation stage to prevent the optimizer from sticking to local optima. This study tests the performance of EHAOMPA's on twenty-three standard mathematical benchmark functions, 29 complex test functions from the CEC2017 test suite, six constrained industrial engineering design problems, and a CNN-hyperparameter optimization for COVID-19 CT-image detection problem. EHAOMPA is compared with four existing optimization algorithm types, achieving the best performance on both numerical and practical issues. Compared to other methods, the test function results demonstrate that EHAOMPA exhibits a more potent global search capability, a higher convergence rate, increased accuracy, and an improved ability to avoid local optima. The excellent experimental results in practical problems indicate that the developed EHAOMPA has great potential in solving real-world optimization problems. The combination of multiple strategies can effectively improve the performance of the algorithm. The source code of the EHAOMPA is publicly available at https://github.com/WangShuang92/EHAOMPA.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139526759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fatma A Hashim, Reham R. Mostafa, Ruba Abu Khurma, R. Qaddoura, P. A. Castillo
Sea Horse Optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of sea horses, SHO integrates the logarithmic helical equation and Levy flight, effectively incorporating both random movements with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more comprehensive exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named mSHO. The enhancement primarily focuses on bolstering SHO's exploitation capabilities by replacing its original method with an innovative local search strategy encompassing three distinct steps: a neighborhood-based local search, a global non-neighbor-based search, and a method involving circumnavigation of the existing search region. These techniques improve mSHO algorithm's search capabilities, allowing it to navigate the search space and converge toward optimal solutions efficiently. To evaluate the efficacy of the mSHO algorithm, comprehensive assessments are conducted across both the CEC2020 benchmark functions and nine distinct engineering problems. A meticulous comparison is drawn against nine metaheuristic algorithms to validate the achieved outcomes. Statistical tests, including Wilcoxon's rank-sum and Friedman's tests, are aptly applied to discern noteworthy differences among the compared algorithms. Empirical findings consistently underscore the exceptional performance of mSHO across diverse benchmark functions, reinforcing its prowess in solving complex optimization problems. Furthermore, the robustness of mSHO endures even as the dimensions of optimization challenges expand, signifying its unwavering efficacy in navigating complex search spaces. The comprehensive results distinctly establish the supremacy and efficiency of the mSHO method as an exemplary tool for tackling an array of optimization quandaries. The results show that the proposed mSHO algorithm has a total rank of 1 for CEC’2020 test functions. In contrast, the mSHO achieved the best value for the engineering problems, recording a value of 0.012665, 2993.634, 0.01266, 1.724967, 263.8915, 0.032255, 58507.14, 1.339956, and 0.23524 for the pressure vessel design, speed reducer design, tension/compression spring, welded beam design, three-bar truss engineering design, industrial refrigeration system, multi-Product batch plant, cantilever beam problem, multiple disc clutch brake problems, respectively. Source codes of mSHO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/135882-improved-sea-horse-algorithm.
{"title":"A new approach for solving global optimization and engineering problems based on modified Sea Horse Optimizer","authors":"Fatma A Hashim, Reham R. Mostafa, Ruba Abu Khurma, R. Qaddoura, P. A. Castillo","doi":"10.1093/jcde/qwae001","DOIUrl":"https://doi.org/10.1093/jcde/qwae001","url":null,"abstract":"\u0000 Sea Horse Optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of sea horses, SHO integrates the logarithmic helical equation and Levy flight, effectively incorporating both random movements with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more comprehensive exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named mSHO. The enhancement primarily focuses on bolstering SHO's exploitation capabilities by replacing its original method with an innovative local search strategy encompassing three distinct steps: a neighborhood-based local search, a global non-neighbor-based search, and a method involving circumnavigation of the existing search region. These techniques improve mSHO algorithm's search capabilities, allowing it to navigate the search space and converge toward optimal solutions efficiently. To evaluate the efficacy of the mSHO algorithm, comprehensive assessments are conducted across both the CEC2020 benchmark functions and nine distinct engineering problems. A meticulous comparison is drawn against nine metaheuristic algorithms to validate the achieved outcomes. Statistical tests, including Wilcoxon's rank-sum and Friedman's tests, are aptly applied to discern noteworthy differences among the compared algorithms. Empirical findings consistently underscore the exceptional performance of mSHO across diverse benchmark functions, reinforcing its prowess in solving complex optimization problems. Furthermore, the robustness of mSHO endures even as the dimensions of optimization challenges expand, signifying its unwavering efficacy in navigating complex search spaces. The comprehensive results distinctly establish the supremacy and efficiency of the mSHO method as an exemplary tool for tackling an array of optimization quandaries. The results show that the proposed mSHO algorithm has a total rank of 1 for CEC’2020 test functions. In contrast, the mSHO achieved the best value for the engineering problems, recording a value of 0.012665, 2993.634, 0.01266, 1.724967, 263.8915, 0.032255, 58507.14, 1.339956, and 0.23524 for the pressure vessel design, speed reducer design, tension/compression spring, welded beam design, three-bar truss engineering design, industrial refrigeration system, multi-Product batch plant, cantilever beam problem, multiple disc clutch brake problems, respectively. Source codes of mSHO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/135882-improved-sea-horse-algorithm.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139388909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The optimal ship hull form in contemporary design practice primarily consists of three parts: hull form modification, performance prediction, and optimization. Hull form modification is a crucial step to affect optimization efficiency because the baseline hull form is varied to search for performance improvements. The conventional hull form modification methods mainly rely on human decisions and intervention. As a direct expression of the 3-D hull form, the lines are not appropriate for machine learning techniques. This is because they do not explicitly express a meaningful performance metric despite their relatively large data dimension. To solve this problem and develop a novel machine-based hull form design technique, an autoencoder, which is a dimensional reduction technique based on an artificial neural network, was created in this study. Specifically, a convolutional autoencoder was designed; firstly, a convolutional neural network (CNN) preprocessor was used to effectively train the offsets, which are the half-width coordinate values on the hull surface, to extract feature maps. Secondly, the stacked encoder compressed the feature maps into an optimal lower-dimensional-latent vector. Finally, a transposed convolution layer restored the dimension of the lines. In this study, 21 250 hull forms belonging to three different ship types of containership, LNG carrier, and tanker, were used as training data. To describe the hull form in more detail, each was divided into several zones, which were then input into the CNN preprocessor separately. After the training, a low-dimensional manifold consisting of the components of the latent vector was derived to represent the distinctive hull form features of the three ship types considered. The autoencoder technique was then combined with another novel approach of the surrogate model to form an objective function neural network. Further combination with the deterministic particle swarm optimization (DPSO) method led to a successful hull form optimization example. In summary, the present convolutional autoencoder has demonstrated its significance within the machine learning-based design process for ship hull forms.
{"title":"A Study on Ship Hull Form Transformation Using Convolutional Autoencoder","authors":"Jeongbeom Seo, Dayeon Kim, Inwon Lee","doi":"10.1093/jcde/qwad111","DOIUrl":"https://doi.org/10.1093/jcde/qwad111","url":null,"abstract":"\u0000 The optimal ship hull form in contemporary design practice primarily consists of three parts: hull form modification, performance prediction, and optimization. Hull form modification is a crucial step to affect optimization efficiency because the baseline hull form is varied to search for performance improvements. The conventional hull form modification methods mainly rely on human decisions and intervention. As a direct expression of the 3-D hull form, the lines are not appropriate for machine learning techniques. This is because they do not explicitly express a meaningful performance metric despite their relatively large data dimension. To solve this problem and develop a novel machine-based hull form design technique, an autoencoder, which is a dimensional reduction technique based on an artificial neural network, was created in this study. Specifically, a convolutional autoencoder was designed; firstly, a convolutional neural network (CNN) preprocessor was used to effectively train the offsets, which are the half-width coordinate values on the hull surface, to extract feature maps. Secondly, the stacked encoder compressed the feature maps into an optimal lower-dimensional-latent vector. Finally, a transposed convolution layer restored the dimension of the lines. In this study, 21 250 hull forms belonging to three different ship types of containership, LNG carrier, and tanker, were used as training data. To describe the hull form in more detail, each was divided into several zones, which were then input into the CNN preprocessor separately. After the training, a low-dimensional manifold consisting of the components of the latent vector was derived to represent the distinctive hull form features of the three ship types considered. The autoencoder technique was then combined with another novel approach of the surrogate model to form an objective function neural network. Further combination with the deterministic particle swarm optimization (DPSO) method led to a successful hull form optimization example. In summary, the present convolutional autoencoder has demonstrated its significance within the machine learning-based design process for ship hull forms.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139388176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ruyi Dong, Yanan Liu, Siwen Wang, A. Heidari, Mingjing Wang, Yi Chen, Shuihua Wang, Huiling Chen, Yu-dong Zhang
The Kernel Search Optimizer (KSO) is a recent metaheuristic optimization algorithm that has been proposed in recent years. The KSO is based on kernel theory, eliminating the need for hyper-parameter adjustments, and demonstrating excellent global search capabilities. However, the original KSO exhibits insufficient accuracy in local search, and there is a high probability that it may fail to achieve local optimization in complex tasks. Therefore, this paper proposes a Multi-Strategy Enhanced Kernel Search Optimizer (MSKSO) to enhance the local search ability of the KSO. The MSKSO combines several control strategies, including chaotic initialization, chaotic local search mechanisms, the High-Altitude Walk Strategy (HWS), and the Levy Flight (LF), to effectively balance exploration and exploitation. The MSKSO is compared with ten well-known algorithms on fifty benchmark test functions to validate its performance, including single-peak, multi-peak, separable variable, and non-separable variable functions. Additionally, the MSKSO is applied to two real engineering economic emission dispatch (EED) problems in power systems. Experimental results demonstrate that the performance of the MSKSO nearly optimizes that of other well-known algorithms and achieves favorable results on the EED problem. These case studies verify that the MSKSO outperforms other algorithms and can serve as an effective optimization tool.
{"title":"Multi-strategy enhanced kernel search optimization and its application in economic emission dispatch problems","authors":"Ruyi Dong, Yanan Liu, Siwen Wang, A. Heidari, Mingjing Wang, Yi Chen, Shuihua Wang, Huiling Chen, Yu-dong Zhang","doi":"10.1093/jcde/qwad110","DOIUrl":"https://doi.org/10.1093/jcde/qwad110","url":null,"abstract":"The Kernel Search Optimizer (KSO) is a recent metaheuristic optimization algorithm that has been proposed in recent years. The KSO is based on kernel theory, eliminating the need for hyper-parameter adjustments, and demonstrating excellent global search capabilities. However, the original KSO exhibits insufficient accuracy in local search, and there is a high probability that it may fail to achieve local optimization in complex tasks. Therefore, this paper proposes a Multi-Strategy Enhanced Kernel Search Optimizer (MSKSO) to enhance the local search ability of the KSO. The MSKSO combines several control strategies, including chaotic initialization, chaotic local search mechanisms, the High-Altitude Walk Strategy (HWS), and the Levy Flight (LF), to effectively balance exploration and exploitation. The MSKSO is compared with ten well-known algorithms on fifty benchmark test functions to validate its performance, including single-peak, multi-peak, separable variable, and non-separable variable functions. Additionally, the MSKSO is applied to two real engineering economic emission dispatch (EED) problems in power systems. Experimental results demonstrate that the performance of the MSKSO nearly optimizes that of other well-known algorithms and achieves favorable results on the EED problem. These case studies verify that the MSKSO outperforms other algorithms and can serve as an effective optimization tool.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139174429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinwon Lee, Changmo Yeo, Sang-Uk Cheon, Jun Hwan Park, D. Mun
In recent years, there have been many studies using artificial intelligence to recognize machining features in 3D models in the CAD/CAM field. Most of these studies converted the original CAD data into images, point clouds, or voxels for recognition. This led to information loss during the conversion process, resulting in decreased recognition accuracy. In this paper, we propose a graph-based network called BRepGAT to segment faces in an original B-rep model containing machining features. We define descriptors that represent information about the faces and edges of the B-rep model from the perspective of feature recognition. These descriptors are extracted from the B-rep model and transformed into homogeneous graph data, which is then passed to graph networks. BRepGAT recognize machining features on a face-by-face based on the graph data input. Our experimental results using the MFCAD18++ dataset showed that BRepGAT achieved state-of-the-art recognition accuracy (99.1%). Furthermore, BRepGAT showed relatively robust performance on other datasets besides MFCAD18++.
{"title":"BRepGAT: Graph neural network to segment machining feature faces in a B-rep model","authors":"Jinwon Lee, Changmo Yeo, Sang-Uk Cheon, Jun Hwan Park, D. Mun","doi":"10.1093/jcde/qwad106","DOIUrl":"https://doi.org/10.1093/jcde/qwad106","url":null,"abstract":"In recent years, there have been many studies using artificial intelligence to recognize machining features in 3D models in the CAD/CAM field. Most of these studies converted the original CAD data into images, point clouds, or voxels for recognition. This led to information loss during the conversion process, resulting in decreased recognition accuracy. In this paper, we propose a graph-based network called BRepGAT to segment faces in an original B-rep model containing machining features. We define descriptors that represent information about the faces and edges of the B-rep model from the perspective of feature recognition. These descriptors are extracted from the B-rep model and transformed into homogeneous graph data, which is then passed to graph networks. BRepGAT recognize machining features on a face-by-face based on the graph data input. Our experimental results using the MFCAD18++ dataset showed that BRepGAT achieved state-of-the-art recognition accuracy (99.1%). Furthermore, BRepGAT showed relatively robust performance on other datasets besides MFCAD18++.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139216195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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