Journal of Computational Design and Engineering最新文献

{"title":"Numerical Investigation of Entropy Generation of Joule Heating in Non-Axisymmetric Flow of Hybrid Nanofluid Towards Stretching Surface","authors":"Khadija Rafique, Zafar Mahmood, Adnan, Umar Khan, Taseer Muhammad, Magda Abd El-Rahman, Sanaa A Bajri, H. A. Khalifa","doi":"10.1093/jcde/qwae029","DOIUrl":"https://doi.org/10.1093/jcde/qwae029","url":null,"abstract":"\u0000 The industrial sector has shown a growing interest in hybrid nanofluids affected by magnetohydrodynamics (MHD) owing to their wide range of applications, including photovoltaic water heaters and scraped surface heat exchangers. The main purpose of this study is to look at how entropy is created in a hybrid nanofluid of $A{l}_2{O}_3 - Cu$ mixed with ${H}_2O$ at a non-axisymmetric stagnation point flow with joule heating and viscous dissipation. By using appropriate non-similarity transformations, the PDEs governing the boundary layer region of this issue are transformed into a set of nonlinear PDEs. The BVP4c MATLAB program, which uses local non-similarity and additional truncation, may fix the problem. The velocity profiles in both directions grow when the values of ${phi }_2, M,lambda $ and A parameters increase. The temperature profile rises as the values of A and $Ec$ grow and lowers as ${phi }_2$ and M increase. The obtained numerical findings demonstrate significant impacts on both the heat transfer rate and fluid flow parameters of the hybrid nanofluid. When the concentration of nanoparticles and the magnetic parameter are heightened, there is an enhancement seen in the skin friction coefficient and decline in heat transfer rate. In addition, the entropy production profile shows an increasing tendency as a function of the parameters ${phi }_2, M,$ and $Br,$ while demonstrating a decreasing tendency of function of the parameter $alpha $. The Bejan number profile has a positive correlation with the parameter $alpha $ but shows a negative correlation with the variables ${phi }_2, M,$ and $Br$.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140223497","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}

{"title":"GPU parallel computation strategy for electrothermal coupling problems using improved assembly-free FEM","authors":"Shaowen Wu, Youyuan Wang, Jinhong Hou, Ruixiao Meng","doi":"10.1093/jcde/qwae024","DOIUrl":"https://doi.org/10.1093/jcde/qwae024","url":null,"abstract":"\u0000 The analysis of electrothermal coupling problems finds extensive application in engineering. However, for large-scale electrothermal coupling problems, the time cost and storage requirements for solving them using the Finite Element Method (FEM) are substantial. We optimise the finite element electrothermal coupling computation from two aspects: computational speed and storage usage. Based on the assembly-free FEM, we explore the symmetry of element matrices to reduce storage for second-order tetrahedral elements and propose a GPU parallel algorithm to improve computational speed. At the same time, we allocate the parallel parts of an electrothermal coupling problem to two GPUs to improve the speed further. In addition, for the three types of boundary conditions in electrothermal coupling problems, we design parallel application methods suitable for assembly-free FEM. Finally, we compare our strategy with methods from other literature through the numerical experiment. Our method reduces the element matrices’ storage by 45%. Compared with the solution process using the element level method and degree of freedom(DoF) level method, our strategy achieves average acceleration ratios of 5.83 and 1.38, respectively.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238038","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}

{"title":"Combining Active Learning and Self-Paced Learning for Cost-Effective Process Design Intents Extraction of Process Data","authors":"Huang Rui, Zhu Shuyi, Huang Bo","doi":"10.1093/jcde/qwae027","DOIUrl":"https://doi.org/10.1093/jcde/qwae027","url":null,"abstract":"\u0000 With the widespread use of computer-aided technologies like CAD/CAM/CAPP in the product manufacturing process, a large amount of process data is constantly generated, and data-driven process planning has shown promising potentials for effectively reusing the process knowledge. However, a lot of labeled data are needed to train a deep learning model for effectively extracting the embedded knowledge and experiences within these process data, and the labeling of process data is quite expensive and time-consuming. This paper proposes a cost-effective process design intents extraction approach for process data by combining active learning (AL) and self-paced learning (SPL). First, the process design intents inference model based on Bi-LSTM is generated by using a few pre-labeled samples. Then, the prediction uncertainty of each unlabeled sample is calculated by using a Bayesian neural network, which can assist in the identification of high confidence samples in SPL and low confidence samples in AL. Finally, the low confidence samples with manual-labels and the high confidence samples with pseudo-labels are incorporated into the training data for retraining the process design intents inference model iteratively until the model attains optimal performance. The experiments demonstrate that our approach can substantially decrease the number of labeled samples required for model training, and the design intents in the process data could be inferred effectively with dynamically undated training data.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140243645","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}

{"title":"A Dual Population Collaborative Harmony Search Algorithm with Adaptive Population Size for the System Reliability-Redundancy Allocation Problems","authors":"H. Ouyang, Siqi Liang, Steven Li, Ziyu Zhou, Zhi-hui Zhan","doi":"10.1093/jcde/qwae026","DOIUrl":"https://doi.org/10.1093/jcde/qwae026","url":null,"abstract":"\u0000 Aiming at the problem that the diversity of the current double population algorithm with dynamic population size reduction cannot be guaranteed in real time in iteration and is easy to fall into local optimum, this study presents a dual population collaborative harmony search algorithm with adaptive population size (DPCHS). Firstly, we propose a dual population algorithm framework for improving the algorithm global search capability. Within this framework, the guidance selection strategy and information interaction mechanism are integrated to strengthen the competition and cooperation among populations, and achieving a good balance between exploration and exploitation. A population state assessment method is designed to monitor population changes in real-time for enhancing population real-time self-regulation. Additionally, population size adjustment approach is designed to adopted to effectively streamline population resources and improve population quality. Comprehensive experiment results demonstrate that DPCHS effectively addresses system reliability-redundancy allocation problems with superior performance and robust convergence compared to other HS variants and algorithms from different categories.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140245018","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}

{"title":"Data-driven multivariate regression-based anomaly detection and recovery of unmanned aerial vehicle flight data","authors":"Lei Yang, Shaobo Li, Chuanjiang Li, Caichao Zhu","doi":"10.1093/jcde/qwae023","DOIUrl":"https://doi.org/10.1093/jcde/qwae023","url":null,"abstract":"\u0000 Flight data anomaly detection is crucial to ensuring the safe operation of unmanned aerial vehicles (UAVs) and has been extensively studied. However, the accurate modeling and analysis of flight data is challenging due to the influence of random noise. Meanwhile, existing methods are often inadequate in parameter selection and feature extraction when dealing with large-scale and high-dimensional flight data. This paper proposes a data-driven multivariate regression-based framework considering spatio-temporal correlation for UAV flight data anomaly detection and recovery, which integrates the techniques of correlation analysis (CA), one-dimensional convolutional neural network and long short-term memory (1D CNN-LSTM), and error filtering (EF), named CA-1DCL-EF. Specifically, correlation analysis is first performed on original UAV flight data to select parameters with correlation to reduce the model input and avoid the negative impact of irrelevant parameters on the model. Next, a regression model based on 1D CNN-LSTM is designed to fully extract the spatio-temporal features of UAV flight data and realize parameter mapping. Then, to overcome the effect of random noise, a filtering technique is introduced to smooth the errors to improve the anomaly detection performance. Finally, two common anomaly types are injected into real UAV flight datasets to verify the effectiveness of the proposed method.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140248023","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}

{"title":"A many-objective evolutionary algorithm based on learning assessment and mapping guidance of historical superior information","authors":"Jinlian Xiong, Gang Liu, Zhigang Gao, Chong Zhou, Peng Hu, Qian Bao","doi":"10.1093/jcde/qwae022","DOIUrl":"https://doi.org/10.1093/jcde/qwae022","url":null,"abstract":"\u0000 Multi-objective optimization algorithms have shown effectiveness on problems with two or three objectives. As the number of objectives increases, the proportion of non-dominated solutions increases rapidly, resulting in insufficient selection pressure. Nevertheless, insufficient selection pressure usually leads to the loss of convergence, too intense selection pressure often results in a lack of diversity. Hence, balancing the convergence and diversity remains a challenging problem in many-objective optimization problems. To remedy this issue, a many-objective evolutionary algorithm based on learning assessment and mapping guidance of historical superior information, referred to here as MaOEA-LAMG, is presented. In the proposed algorithm, an effective learning assessment strategy according to historical superior information based on an elite archive updated by indicator ${I}_{varepsilon + }$ is proposed, which can estimate the shape of the Pareto front and lay the foundation for subsequent fitness and acute angle-based similarity calculations. From this foundation, to balance the convergence and diversity dynamically, a mapping guidance strategy based on the historical superior information is designed, which contains clustering, associating, and proportional selection. The performance of the proposed algorithm is validated and compared with ten state-of-the-art algorithms on 24 test instances with various Pareto fronts and real-world water resource planning problem. The empirical studies substantiate the efficacy of the results with competitive performance.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140252194","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}

{"title":"Revolutionizing latent heat storage: boosting discharge performance with innovative undulated PCM container shapes in vertical shell-and-tube systems","authors":"Hakim S. Sultan, H. Mohammed, Nirmalendu Biswas, Hussein Togun, Raed Khalid Ibrahem, Jasim M. Mahdi, W. Yaïci, Amir Keshmiri, P. Talebizadehsardari","doi":"10.1093/jcde/qwae020","DOIUrl":"https://doi.org/10.1093/jcde/qwae020","url":null,"abstract":"\u0000 This paper examines the impact of various parameters, including frames, zigzag number, and enclosure shape, on the solidification process and thermal energy storage rate of phase change materials (PCM). The study also assesses the effects of the flow rate of the heat transfer fluid as well as changing the materials of the PCM between RT35 and RT35HC. In addition, the study compares the framed vs unframed systems and, subsequently, the best case was tested with various zigzag pitch numbers before changing the zigzag shape structure to arc and reversed arc. The findings are examined by contrasting the different scenarios' liquid fractions, temperature distributions, solidification rates, and heat storage rates. The results show that the framed geometry is 66% faster to reach the target temperature compared to the unframed geometry and employing a zigzag enclosure in a PCM can significantly improve its solidification time and heat recovery rate. As the number of pitches in the zigzag enclosure increases, the improvement rate decreases but still improves the solidification time and heat recovery rate. The reverse arc-shaped structure has the best performance compared with the other undulated surfaces. For the system with RT35HC, the discharge time is 55% higher compared to that of the system with RT35, while the discharge rate is 8.2% higher for the former during the first 3000s of the discharging process.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140429542","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}

{"title":"Industrial defective chips detection using deep convolutional neural network with inverse feature matching mechanism","authors":"Waseem Ullah, Samee Ullah Khan, Min Je Kim, Altaf Hussain, Muhammad Munsif, Mi Young Lee, Daeho Seo, Sung Wook Baik","doi":"10.1093/jcde/qwae019","DOIUrl":"https://doi.org/10.1093/jcde/qwae019","url":null,"abstract":"\u0000 The growing demand for high-quality industrial products has led to a significant emphasis on image anomaly detection (AD). Anomaly detection in industrial goods presents a formidable research challenge that demands the application of sophisticated techniques to identify and address deviations from the expected norm accurately. Manufacturers increasingly recognize the significance of employing intelligent systems to detect flaws and defects in product parts. However, industrial settings pose several challenges: diverse categories, limited abnormal samples, and vagueness. Hence, there is a growing demand for advanced image anomaly detection techniques within industrial product manufacturing. In this paper, an intelligent industrial defective chips detection framework is proposed which mainly consists of three core components. First, the convolutional features of the efficient backbone model is effectively utilized to balance the computational complexity and performance of industrial resource-constrained devices. Secondly, a novel inverse feature matching followed by masking method is proposed to enhance the explanability that localizes the abnormal regions of the abnormal chips. Finally, to evaluate our proposed method a comprehensive ablation study is conducted, where different machine learning and deep learning algorithms are analyzed to claim the superiority of our method. Furthermore, to help the research community, a benchmark dataset is collected from real-world industry manufacturing for defective chip detection. The empirical results from the dataset demonstrate the strength and effectiveness of the proposed model compared to the other models.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140448739","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}

{"title":"Generative AI and Building Design: Early Photorealistic Render Visualization of Façades using Local Identity-trained Models","authors":"Hayoung Jo, Jin-Kook Lee, Yong-Cheol Lee, Seungyeon Choo","doi":"10.1093/jcde/qwae017","DOIUrl":"https://doi.org/10.1093/jcde/qwae017","url":null,"abstract":"\u0000 This paper elucidates an approach that utilizes generative AI to develop alternative architectural design options based on local identity. The advancement of AI technologies has increasingly piqued the interest of the AEC-FM (architecture, engineering, construction and facility management) industry. Notably, the topic of ‘visualization’ has gained prominence as a means for enhancing communication related to a project, especially in the early phases of design. This study aims to enhance the ease of obtaining design images during initial phases of design by drawing from multiple texts and images. It develops an additional training model to generate various design alternatives that resonate with the identity of the locale through the application of generative AI to the façade design of buildings. The identity of a locality in cities and regions is the capacity for the cities and regions to be identified and recognized as a specific area. Among the various visual elements of urban and regional landscapes, the front face of buildings may play a significant role in people's aesthetic perception and overall impression of the local environment. The research proposes an approach that transcends the conventional employment of three-dimensional modeling and rendering tools by readily deriving design alternatives that consider this local identity in commercial building remodeling. This approach allows for financial and temporal efficiency in the design communication phase of the initial architectural design process. The implementation and utilization of the proposed approach's supplementary training model in this study proceeds as follows: 1) image data are collected from the target area using open-source street-view resources and preprocessed for conversion to a trainable format; 2) textual data are prepared for pairing with preprocessed image data; 3) additional training and outcome testing are performed using varied text prompts and images; 4) the ability to generate building façade images that reflect the identity of the collected locale by using the additional trained model is determined, as evidenced by the findings of the proposed application method study. This enables the generation of design alternatives that integrate regional styles and diverse design requirements for buildings. The training model implemented in this study can be leveraged through weight adjustments and prompt engineering to generate a greater number of design reference images, among other diverse approaches.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139961349","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}

{"title":"The effect of gear-manufacturing quality on the mechanical and thermal responses of a polymer-gear pair","authors":"D. Zorko, Peitang Wei, Nikola Vukašinović","doi":"10.1093/jcde/qwae010","DOIUrl":"https://doi.org/10.1093/jcde/qwae010","url":null,"abstract":"\u0000 Gear-manufacturing quality affects the load sharing between the meshing gears as well as the load distribution along the width of the tooth. This study aims to investigate the effect of gear-manufacturing quality on the mechanical and thermal states of polymer-gear pairs and consequently on their lifetime. The deviations of the geometric quality parameters, i.e., the lead profile and pitch, were found to have a substantial effect on the stress (root and flank) state of the gear. The effect of the lead deviation was found to be most pronounced for the quality grades Q12 to Q10, where depending on the load, a 30–80% stress reduction was observed when improving the gear quality from Q12 to Q10. Improving the quality from Q10 to Q8 did not lead to a substantial improvement in the load distribution and the observed stress reduction was in range of 5–20%. Similar trends were found for the pitch deviation, where again the most pronounced stress reduction was seen when improving the quality grade from Q12 to Q10. The study reveals where the most effective changes, leading to an increased gear-life, can be achieved. Improving the gear quality grade from Q12 to Q11 proved to have a much more substantial effect than improving the gear quality from Q9 to Q8. Considering that improving the gear quality from Q12 to Q11 or even Q10 can be achieved by a proper tool design and corrective iterations with the right process parameters, while improving the quality from Q9 to Q8 is by far more challenging. A novel methodology is proposed to assess the effect of the gear's quality on the generation of heat and the resulting operational temperature. The proposed methodology enables more accurate prediction of the gear pair's operating temperature.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139597476","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}