{"results":[{"id":"ss_b19c5c56cf324f377bde187c4cdc986fdaaaf4fe","title":"Engineering Design","authors":[{"name":"F. Engelmann"},{"name":"Alois Breiing"},{"name":"T. Gutowski"}],"abstract":"","source":"Semantic Scholar","year":2019,"language":"en","subjects":null,"doi":"10.1007/978-3-7643-8140-0_100","url":"https://www.semanticscholar.org/paper/b19c5c56cf324f377bde187c4cdc986fdaaaf4fe","is_open_access":true,"citations":1069,"published_at":"","score":93},{"id":"ss_2096fc288ef32942b52602fc671a8fc1bca5c001","title":"Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems","authors":[{"name":"S. Mirjalili"},{"name":"A. Gandomi"},{"name":"Seyedeh Zahra Mirjalili"},{"name":"Shahrzad Saremi"},{"name":"Hossam Faris"},{"name":"S. Mirjalili"}],"abstract":"","source":"Semantic Scholar","year":2017,"language":"en","subjects":["Computer Science","Engineering"],"doi":"10.1016/j.advengsoft.2017.07.002","url":"https://www.semanticscholar.org/paper/2096fc288ef32942b52602fc671a8fc1bca5c001","is_open_access":true,"citations":4344,"published_at":"","score":91},{"id":"ss_615f4d4edaa7b889f590c8c782370614b510bbea","title":"Engineering Design via Surrogate Modelling - A Practical Guide","authors":[{"name":"A. Forrester"},{"name":"A. Sóbester"},{"name":"A. Keane"}],"abstract":"","source":"Semantic Scholar","year":2008,"language":"en","subjects":["Computer Science","Mathematics"],"doi":"10.1002/9780470770801","url":"https://www.semanticscholar.org/paper/615f4d4edaa7b889f590c8c782370614b510bbea","is_open_access":true,"citations":2947,"published_at":"","score":82},{"id":"ss_a41b359f085a266951c41185b8e6ed3e4a9f22a3","title":"Engineering Design: A Systematic Approach","authors":[{"name":"G. Pahl"},{"name":"W. Beitz"}],"abstract":"","source":"Semantic Scholar","year":1984,"language":"en","subjects":["Engineering"],"doi":"10.1049/sqj.1963.0055","url":"https://www.semanticscholar.org/paper/a41b359f085a266951c41185b8e6ed3e4a9f22a3","pdf_url":"https://link.springer.com/content/pdf/bfm:978-1-84628-319-2/1","is_open_access":true,"citations":5183,"published_at":"","score":80},{"id":"ss_a82418b7e8714755dd2c29081019fadbac7598a4","title":"Genetic algorithms and engineering design","authors":[{"name":"M. Gen"},{"name":"Runwei Cheng"}],"abstract":"","source":"Semantic Scholar","year":1997,"language":"en","subjects":["Computer Science"],"doi":"10.1002/9780470172254","url":"https://www.semanticscholar.org/paper/a82418b7e8714755dd2c29081019fadbac7598a4","pdf_url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780470172254.fmatter","is_open_access":true,"citations":2847,"published_at":"","score":80},{"id":"ss_008777caf23c9afaef51d4b6dd866e70eac0dedd","title":"Engineering design thinking, teaching, and learning","authors":[{"name":"C. Dym"},{"name":"A. Agogino"},{"name":"O. Eris"},{"name":"D. Frey"},{"name":"L. Leifer"}],"abstract":"","source":"Semantic Scholar","year":2005,"language":"en","subjects":["Engineering"],"doi":"10.1002/j.2168-9830.2005.tb00832.x","url":"https://www.semanticscholar.org/paper/008777caf23c9afaef51d4b6dd866e70eac0dedd","is_open_access":true,"citations":3063,"published_at":"","score":80},{"id":"ss_d6b5edf6a1b9f80a0cb6e6cb178815a27a09dffa","title":"Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems","authors":[{"name":"Malik Braik"}],"abstract":"Abstract This paper presents a novel meta-heuristic algorithm named Chameleon Swarm Algorithm (CSA) for solving global numerical optimization problems. The base inspiration for CSA is the dynamic behavior of chameleons when navigating and hunting for food sources on trees, deserts and near swamps. This algorithm mathematically models and implements the behavioral steps of chameleons in their search for food, including their behavior in rotating their eyes to a nearly 360°scope of vision to locate prey and grab prey using their sticky tongues that launch at high speed. These foraging mechanisms practiced by chameleons eventually lead to feasible solutions when applied to address optimization problems. The stability of the proposed algorithm was assessed on sixty-seven benchmark test functions and the performance was examined using several evaluation measures. These test functions involve unimodal, multimodal, hybrid and composition functions with different levels of complexity. An extensive comparative study was conducted to demonstrate the efficacy of CSA over other meta-heuristic algorithms in terms of optimization accuracy. The applicability of the proposed algorithm in reliably addressing real-world problems was demonstrated in solving five constrained and computationally expensive engineering design problems. The overall results of CSA show that it offered a favorable global or near global solution and better performance compared to other meta-heuristics.","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Computer Science"],"doi":"10.1016/J.ESWA.2021.114685","url":"https://www.semanticscholar.org/paper/d6b5edf6a1b9f80a0cb6e6cb178815a27a09dffa","is_open_access":true,"citations":431,"published_at":"","score":77.93},{"id":"ss_90c4081fd752df2bb4a6cabce728d116472d457d","title":"Deep Generative Models in Engineering Design: A Review","authors":[{"name":"Lyle Regenwetter"},{"name":"A. Nobari"},{"name":"Faez Ahmed"}],"abstract":"Automated design synthesis has the potential to revolutionize the modern engineering design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to design engineering may enable such automated design synthesis and is a research subject of great importance. We present a review and analysis of Deep Generative Learning models in engineering design. Deep Generative Models (DGMs) typically leverage deep networks to learn from an input dataset and synthesize new designs. Recently, DGMs such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), feedforward Neural Networks (NNs) and certain Deep Reinforcement Learning (DRL) frameworks have shown promising results in design applications like structural optimization, materials design, and shape synthesis. The prevalence of DGMs in Engineering Design has skyrocketed since 2016. Anticipating continued growth, we conduct a review of recent advances with the hope of benefitting researchers interested in DGMs for design. We structure our review as an exposition of the algorithms, datasets, representation methods, and applications commonly used in the current literature. In particular, we discuss key works that have introduced new techniques and methods in DGMs, successfully applied DGMs to a design-related domain, or directly supported development of DGMs through datasets or auxiliary methods. We further identify key challenges and limitations currently seen in DGMs across design fields, such as design creativity, handling constraints and objectives, and modeling both form and functional performance simultaneously. In our discussion we identify possible solution pathways as key areas on which to target future work.","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Computer Science","Mathematics"],"doi":"10.1115/1.4053859","url":"https://www.semanticscholar.org/paper/90c4081fd752df2bb4a6cabce728d116472d457d","pdf_url":"https://arxiv.org/pdf/2110.10863","is_open_access":true,"citations":294,"published_at":"","score":73.82},{"id":"ss_cd38b58edf6690459767097aca745a3806824236","title":"Review of artificial intelligence applications in engineering design perspective","authors":[{"name":"N. Yüksel"},{"name":"H. R. Börklü"},{"name":"H. K. Sezer"},{"name":"O. Canyurt"}],"abstract":"","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Computer Science"],"doi":"10.1016/j.engappai.2022.105697","url":"https://www.semanticscholar.org/paper/cd38b58edf6690459767097aca745a3806824236","is_open_access":true,"citations":176,"published_at":"","score":72.28},{"id":"ss_32b1b64eb56252c8db4eddfb348bf88aceb12721","title":"Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results","authors":[{"name":"L. Abualigah"},{"name":"M. A. Elaziz"},{"name":"Ahmad M. Khasawneh"},{"name":"Mohammad Alshinwan"},{"name":"R. Ibrahim"},{"name":"M. A. Al-qaness"},{"name":"Seyedali Mirjalili"},{"name":"Putra Sumari"},{"name":"Amir H. Gandomi"}],"abstract":"","source":"Semantic Scholar","year":2022,"language":"en","subjects":["Computer Science"],"doi":"10.1007/s00521-021-06747-4","url":"https://www.semanticscholar.org/paper/32b1b64eb56252c8db4eddfb348bf88aceb12721","is_open_access":true,"citations":182,"published_at":"","score":71.46000000000001},{"id":"ss_e453b518beeeb5327d6d4c903da5171bebb59d44","title":"Engineering Design Optimization","authors":[{"name":"J. Martins"},{"name":"A. Ning"}],"abstract":"Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 400 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice. Accompanied online by a solutions manual for instructors and source code for problems, this is ideal for a one- or two-semester graduate course on optimization in aerospace, civil, mechanical, electrical, and chemical engineering departments.","source":"Semantic Scholar","year":2021,"language":"en","subjects":null,"doi":"10.1017/9781108980647","url":"https://www.semanticscholar.org/paper/e453b518beeeb5327d6d4c903da5171bebb59d44","is_open_access":true,"citations":213,"published_at":"","score":71.39},{"id":"ss_0406b398b8f79bfe22b4814c7df9941b9c978560","title":"Analyzing the potential of Virtual Reality for engineering design review","authors":[{"name":"Josef Wolfartsberger"}],"abstract":"Abstract Virtual Reality (VR) technology still needs to evolve, but as the pace of innovations accelerates, systems allow for more novel modes of visualization and interaction to support engineering design reviews. Currently, the classic design review process is often performed on a PC with the support of CAD software packages. However, CAD on a screen cannot always meet all the requirements in regard to the functional and ergonomic validations of complex 3D models. In this paper, the development and evaluation of a VR-based tool to support engineering design review is described. “VRSmart” visualizes CAD data and allows for an intuitive interaction. In a preliminary user study, the tool was checked for its usability and user experience. VRSmart was then evaluated in a real industrial environment and tested in an authentic design review. The results indicate that a VR-supported design review allows users to see slightly more faults in a 3D model than in a CAD software-based approach on a PC screen. Furthermore, VR reduces the risk of exclusion of certain professional groups from the design review process. In addition, the intuitive interaction with the VR system allowed for a much faster entry into the design review. In summary, VR will not replace the traditional design review process on screen, but it provides a useful addition to engineering companies.","source":"Semantic Scholar","year":2019,"language":"en","subjects":["Computer Science"],"doi":"10.1016/J.AUTCON.2019.03.018","url":"https://www.semanticscholar.org/paper/0406b398b8f79bfe22b4814c7df9941b9c978560","is_open_access":true,"citations":254,"published_at":"","score":70.62},{"id":"ss_be6b093d69fb97cf1dc20843c16badd6f877e677","title":"Effects of infusing the engineering design process into STEM project-based learning to develop preservice technology teachers’ engineering design thinking","authors":[{"name":"Kuen-Yi Lin"},{"name":"Ying-Tien Wu"},{"name":"Yi-Ting Hsu"},{"name":"P. Williams"}],"abstract":"Background This study focuses on probing preservice technology teachers’ cognitive structures and how they construct engineering design in technology-learning activities and explores the effects of infusing an engineering design process into science, technology, engineering, and mathematics (STEM) project-based learning to develop preservice technology teachers’ cognitive structures for engineering design thinking. Results The study employed a quasi-experimental design, and twenty-eight preservice technology teachers participated in the teaching experiment. The flow-map method and metalistening technique were utilized to enable preservice technology teachers to create flow maps of engineering design, and a chi-square test was employed to analyze the data. The results suggest that (1) applying the engineering design process to STEM project-based learning is beneficial for developing preservice technology teachers’ schema of design thinking, especially with respect to clarifying the problem, generating ideas, modeling, and feasibility analysis, and (2) it is important to encourage teachers to further explore the systematic concepts of engineering design thinking and expand their abilities by merging the engineering design process into STEM project-based learning. Conclusions The findings of this study provide initial evidence on the effects of infusing the engineering design process into STEM project-based learning to develop preservice technology teachers’ engineering design thinking. However, further work should focus on exploring how to overcome the weaknesses of preservice technology teachers’ engineering design thinking by adding a few elements of engineering design thinking pedagogy, e.g., designing learning activities that are relevant to real life.","source":"Semantic Scholar","year":2021,"language":"en","subjects":null,"doi":"10.1186/s40594-020-00258-9","url":"https://www.semanticscholar.org/paper/be6b093d69fb97cf1dc20843c16badd6f877e677","pdf_url":"https://stemeducationjournal.springeropen.com/track/pdf/10.1186/s40594-020-00258-9","is_open_access":true,"citations":186,"published_at":"","score":70.58},{"id":"ss_569e323c84bcc01fc6562ea2c7084c7a2a00e703","title":"Multi-modal Machine Learning in Engineering Design: A Review and Future Directions","authors":[{"name":"Binyang Song"},{"name":"Ruilin Zhou"},{"name":"Faez Ahmed"}],"abstract":"In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of multiple data modalities has the potential to reshape various applications. This paper presents a comprehensive overview of the current state, advancements, and challenges of MMML within the sphere of engineering design. The review begins with a deep dive into five fundamental concepts of MMML:multi-modal information representation, fusion, alignment, translation, and co-learning. Following this, we explore the cutting-edge applications of MMML, placing a particular emphasis on tasks pertinent to engineering design, such as cross-modal synthesis, multi-modal prediction, and cross-modal information retrieval. Through this comprehensive overview, we highlight the inherent challenges in adopting MMML in engineering design, and proffer potential directions for future research. To spur on the continued evolution of MMML in engineering design, we advocate for concentrated efforts to construct extensive multi-modal design datasets, develop effective data-driven MMML techniques tailored to design applications, and enhance the scalability and interpretability of MMML models. MMML models, as the next generation of intelligent design tools, hold a promising future to impact how products are designed.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Computer Science"],"doi":"10.48550/arXiv.2302.10909","url":"https://www.semanticscholar.org/paper/569e323c84bcc01fc6562ea2c7084c7a2a00e703","pdf_url":"https://arxiv.org/pdf/2302.10909","is_open_access":true,"citations":71,"published_at":"","score":69.13},{"id":"ss_7ac74d14734a55d207d95663b636217af14961a8","title":"From concept to manufacturing: evaluating vision-language models for engineering design","authors":[{"name":"Cyril Picard"},{"name":"Kristen M. Edwards"},{"name":"Anna C. Doris"},{"name":"Brandon Man"},{"name":"Giorgio Giannone"},{"name":"Md Ferdous Alam"},{"name":"Faez Ahmed"}],"abstract":"Engineering design is undergoing a transformative shift with the advent of AI, marking a new era in how we approach product, system, and service planning. Large language models have demonstrated impressive capabilities in enabling this shift. Yet, with text as their only input modality, they cannot leverage the large body of visual artifacts that engineers have used for centuries and are accustomed to. This gap is addressed with the release of multimodal vision-language models (VLMs), such as GPT-4V, enabling AI to impact many more types of tasks. Our work presents a comprehensive evaluation of VLMs across a spectrum of engineering design tasks, categorized into four main areas: Conceptual Design, System-Level and Detailed Design, Manufacturing and Inspection, and Engineering Education Tasks. Specifically in this paper, we assess the capabilities of two VLMs, GPT-4V and LLaVA 1.6 34B, in design tasks such as sketch similarity analysis, CAD generation, topology optimization, manufacturability assessment, and engineering textbook problems. Through this structured evaluation, we not only explore VLMs’ proficiency in handling complex design challenges but also identify their limitations in complex engineering design applications. Our research establishes a foundation for future assessments of vision language models. It also contributes a set of benchmark testing datasets, with more than 1000 queries, for ongoing advancements and applications in this field.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Computer Science"],"doi":"10.1007/s10462-025-11290-y","url":"https://www.semanticscholar.org/paper/7ac74d14734a55d207d95663b636217af14961a8","is_open_access":true,"citations":70,"published_at":"","score":69.1},{"id":"doaj_10.1016/j.eng.2024.10.021","title":"LearningEMS: A Unified Framework and Open-Source Benchmark for Learning-Based Energy Management of Electric Vehicles","authors":[{"name":"Yong Wang"},{"name":"Hongwen He"},{"name":"Yuankai Wu"},{"name":"Pei Wang"},{"name":"Haoyu Wang"},{"name":"Renzong Lian"},{"name":"Jingda Wu"},{"name":"Qin Li"},{"name":"Xiangfei Meng"},{"name":"Yingjuan Tang"},{"name":"Fengchun Sun"},{"name":"Amir Khajepour"}],"abstract":"An effective energy management strategy (EMS) is essential to optimize the energy efficiency of electric vehicles (EVs). With the advent of advanced machine learning techniques, the focus on developing sophisticated EMS for EVs is increasing. Here, we introduce LearningEMS: a unified framework and open-source benchmark designed to facilitate rapid development and assessment of EMS. LearningEMS is distinguished by its ability to support a variety of EV configurations, including hybrid EVs, fuel cell EVs, and plug-in EVs, offering a general platform for the development of EMS. The framework enables detailed comparisons of several EMS algorithms, encompassing imitation learning, deep reinforcement learning (RL), offline RL, model predictive control, and dynamic programming. We rigorously evaluated these algorithms across multiple perspectives: energy efficiency, consistency, adaptability, and practicability. Furthermore, we discuss state, reward, and action settings for RL in EV energy management, introduce a policy extraction and reconstruction method for learning-based EMS deployment, and conduct hardware-in-the-loop experiments. In summary, we offer a unified and comprehensive framework that comes with three distinct EV platforms, over 10  000 km of EMS policy data set, ten state-of-the-art algorithms, and over 160 benchmark tasks, along with three learning libraries. Its flexible design allows easy expansion for additional tasks and applications. The open-source algorithms, models, data sets, and deployment processes foster additional research and innovation in EV and broader engineering domains.","source":"DOAJ","year":2025,"language":"","subjects":["Engineering (General). Civil engineering (General)"],"doi":"10.1016/j.eng.2024.10.021","url":"http://www.sciencedirect.com/science/article/pii/S2095809924007136","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.21608/jesaun.2024.326709.1374","title":"Evaluation of the Applications of using Global free Digital Elevation Models and GNSS-RTK data for Agricultural purposes in Egypt using Machine Learning","authors":[{"name":"Ashraf abdallah"},{"name":"Bara\u0026#039; Al-MISTAREHI"},{"name":"Amir SHTAYAT"}],"abstract":"Agriculture is a vital component of Egypt's economy; therefore, using Digital Elevation Models (DEMs) in agricultural planning in Egypt has significant benefits regarding water management, site appropriateness assessment, flood risk mitigation, and infrastructure construction. It is also essential for planners to make more informed decisions, optimize resource allocation, and support sustainable farming practices. This research paper investigates the accuracy of obtaining DEM data from four free global models (STRM30, ALOS30, COP30, and TanDEM-X90). The global DEM data has been compared to an actual GNSS-RTK DEM data surveyed onsite for two agricultural block areas in Aswan, the southern Government of Egypt. The two blocks are a part of a national project. For Block I and II, the RMSE of the Model STRM30 was 2.92 m and 3.59 m, respectively, indicating a poorer solution. Regarding accuracy, the ALOS30 model ranks third, reporting an RMSE of 2.58 m for block II and 3.30 m for block I. COP30 has an RMSE value of 1.06 m for blocks I and II and.91 m overall. TanDEM-X90 is the most accurate model in this investigation; block I provided an RMSE of 0.90 m with an SD of 0.58 m (SD95% = 0.38 m). After removing the anomalies, the model's stated RMSE for block II was 0.34 m, with an SD value of 0.62 m and 1.03 m. According to the classification using machine learning algorithms, with an accuracy of 84.7% for block I and 85% for block II, TanDEM-X90 is the best solution.","source":"DOAJ","year":2025,"language":"","subjects":["Engineering (General). Civil engineering (General)"],"doi":"10.21608/jesaun.2024.326709.1374","url":"https://jesaun.journals.ekb.eg/article_393447_9546e355affffebb2c5da930aaeba9a0.pdf","pdf_url":"https://jesaun.journals.ekb.eg/article_393447_9546e355affffebb2c5da930aaeba9a0.pdf","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.1186/s13617-025-00153-4","title":"Physical, psychological and behavioural responses of aircraft occupants to volcanic emissions","authors":[{"name":"C. J. Horwell"},{"name":"S. Ravenhall"},{"name":"R. Clarkson"},{"name":"M. Edmonds"},{"name":"G. J. Rubin"},{"name":"C. Witham"},{"name":"R. Howell"}],"abstract":"Abstract Volcanic eruptions produce plumes of ash, gas and aerosols that present a risk to aviation at all standard flight levels. Here, we investigate atmospheric dispersal of volcanic emissions, whether and how they infiltrate aircraft, and whether ground-level public health exposure thresholds can be related to the pressurised cabin environment. We then review the limited evidence for physical and mental health, and behavioural impacts, resulting from volcanic emissions entering aircraft. Serious health risks are considered low for healthy individuals, but respiratory irritation is likely for a high exposure scenario to sulfur dioxide (SO2). Asthmatics are particularly sensitive to SO2, with even relatively low, short exposures, potentially resulting in severe respiratory impacts. Negative group behaviours are not expected but individual distress is possible. Communicating this evidence to the aviation industry may result in more informed decision-making on flightpath alterations and triggering of emergency protocols, both before and during volcanic emission encounters.","source":"DOAJ","year":2025,"language":"","subjects":["Environmental protection","Disasters and engineering"],"doi":"10.1186/s13617-025-00153-4","url":"https://doi.org/10.1186/s13617-025-00153-4","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.3390/math13091454","title":"DAF-UNet: Deformable U-Net with Atrous-Convolution Feature Pyramid for Retinal Vessel Segmentation","authors":[{"name":"Yongchao Duan"},{"name":"Rui Yang"},{"name":"Ming Zhao"},{"name":"Mingrui Qi"},{"name":"Sheng-Lung Peng"}],"abstract":"Segmentation of retinal vessels from fundus images is critical for diagnosing diseases such as diabetes and hypertension. However, the inherent challenges posed by the complex geometries of vessels and the highly imbalanced distribution of thick versus thin vessel pixels demand innovative solutions for robust feature extraction. In this paper, we introduce DAF-UNet, a novel architecture that integrates advanced modules to address these challenges. Specifically, our method leverages a pre-trained deformable convolution (DC) module within the encoder to dynamically adjust the sampling positions of the convolution kernel, thereby adapting the receptive field to capture irregular vessel morphologies more effectively than traditional convolutional approaches. At the network’s bottleneck, an enhanced atrous spatial pyramid pooling (ASPP) module is employed to extract and fuse rich, multi-scale contextual information, significantly improving the model’s capacity to delineate vessels of varying calibers. Furthermore, we propose a hybrid loss function that combines pixel-level and segment-level losses to robustly address the segmentation inconsistencies caused by the disparity in vessel thickness. Experimental evaluations on the DRIVE and CHASE_DB1 datasets demonstrated that DAF-UNet achieved a global accuracy of 0.9572/0.9632 and a Dice score of 0.8298/0.8227, respectively, outperforming state-of-the-art methods. These results underscore the efficacy of our approach in precisely capturing fine vascular details and complex boundaries, marking a significant advancement in retinal vessel segmentation.","source":"DOAJ","year":2025,"language":"","subjects":["Mathematics"],"doi":"10.3390/math13091454","url":"https://www.mdpi.com/2227-7390/13/9/1454","is_open_access":true,"published_at":"","score":69},{"id":"ss_c9b786eb7c5f0d82d6020c6f3559b7d17d4cb45c","title":"Engineering design of the CFETR machine","authors":[{"name":"Yuntao Song"},{"name":"Jiangang Li"},{"name":"Yuanxi Wan"},{"name":"Yong Liu"},{"name":"Xiaolin Wang"},{"name":"B. Wan"},{"name":"Peng Fu"},{"name":"P. Weng"},{"name":"Songtao Wu"},{"name":"X. Duan"},{"name":"Qing Yang"},{"name":"K. Feng"},{"name":"Qiang Li"},{"name":"Ming-Jiang Ye"},{"name":"G. Zhuang"},{"name":"Yunfeng Liang"},{"name":"Xiang Gao"},{"name":"Chang-hong Chen"},{"name":"Heyi Wang"},{"name":"G. Zheng"},{"name":"Yuhong Xu"},{"name":"Tianlin Qian"},{"name":"V. Chan"},{"name":"B. Xiao"},{"name":"K. Lu"},{"name":"Jinxing Zheng"},{"name":"Mingxuan Lu"},{"name":"Dequan Liu"},{"name":"Jian Liu"},{"name":"Yu Wu"},{"name":"Xufeng Liu"},{"name":"Yi Shi"},{"name":"B. Hou"},{"name":"Chen Liu"},{"name":"J. Ge"},{"name":"C. Zhou"},{"name":"Hong Ran"},{"name":"Qijie Wang"},{"name":"Xiaoyu Wang"},{"name":"Songlin Liu"},{"name":"S. Liu"},{"name":"D. Yao"},{"name":"Yong Cheng"},{"name":"Liqun Hu"},{"name":"Chundong Hu"},{"name":"Fukun Liu"},{"name":"Gen Chen"}],"abstract":"","source":"Semantic Scholar","year":2022,"language":"en","subjects":null,"doi":"10.1016/j.fusengdes.2022.113247","url":"https://www.semanticscholar.org/paper/c9b786eb7c5f0d82d6020c6f3559b7d17d4cb45c","is_open_access":true,"citations":86,"published_at":"","score":68.58}],"total":23388202,"page":1,"page_size":20,"sources":["DOAJ","arXiv","CrossRef","Semantic Scholar"],"query":"Engineering design"}