Pub Date : 2024-11-05DOI: 10.1038/s44172-024-00305-1
Zhe Xu, Aaron M. Dollar
Due to the nature of their implementation, nearly all low-level fabrication processes produce solidly filled structures. However, lattice structures are significantly stronger for the same amount of material, resulting in structures that are much lighter and more materially efficient. Here we propose an approach for fabricating lattice structures that echoes 3D printing techniques. In it, a modular chain of specially designed links is “extruded” onto a substrate to produce various lattices configurations depending on the chosen assembly algorithm, ranging from rigid regular lattices with nodal connectivity of 12, octet-truss, to significantly less dense configurations. Compared to conventional additive manufacturing methods, our approach allows for efficient use of nearly any material or combination of materials to construct lattices with programmed arrangements. We experimentally demonstrate that a 3x3x2 lattice structure (287 total links) is fabricated in 27 minutes via a modified robotic arm and can support approximately 1000 N in compression testing. Extrusion-based 3D printing, in which a filament of material is extruded through a nozzle has been widely adopted. Here, Zhe Xu and Aaron Dollar report an approach for fabricating lattice structures in which a modular chain of specially designed links is “extruded” onto a substrate allowing for construction of multiscale structures that are efficient in weight and varied in composition.
由于其实施的性质，几乎所有的低级制造工艺都会产生固体填充结构。然而，对于相同数量的材料来说，晶格结构的强度要高得多，因此结构更轻，材料利用率更高。在这里，我们提出了一种与 3D 打印技术相呼应的制造晶格结构的方法。在这种方法中，由专门设计的链接组成的模块链被 "挤压 "到基底上，根据所选的组装算法，产生各种晶格配置，从节点连通性为 12 的刚性规则晶格、八阶桁架，到密度明显较低的配置，不一而足。与传统的增材制造方法相比，我们的方法可以高效地使用几乎任何材料或材料组合来构建具有编程排列的晶格。我们在实验中证明，通过改进的机械臂，在 27 分钟内就能制造出 3x3x2 网格结构（共 287 个链接），并能在压缩测试中支持约 1000 N 的压力。挤出式三维打印是一种通过喷嘴挤出材料丝的技术，已被广泛采用。徐哲和亚伦-多拉尔（Aaron Dollar）在此报告了一种制造晶格结构的方法，这种方法是将特殊设计的模块链 "挤压 "到基底上，从而制造出重量轻、成分多样的多尺度结构。
{"title":"Chain-based lattice printing for efficient robotically-assembled structures","authors":"Zhe Xu, Aaron M. Dollar","doi":"10.1038/s44172-024-00305-1","DOIUrl":"10.1038/s44172-024-00305-1","url":null,"abstract":"Due to the nature of their implementation, nearly all low-level fabrication processes produce solidly filled structures. However, lattice structures are significantly stronger for the same amount of material, resulting in structures that are much lighter and more materially efficient. Here we propose an approach for fabricating lattice structures that echoes 3D printing techniques. In it, a modular chain of specially designed links is “extruded” onto a substrate to produce various lattices configurations depending on the chosen assembly algorithm, ranging from rigid regular lattices with nodal connectivity of 12, octet-truss, to significantly less dense configurations. Compared to conventional additive manufacturing methods, our approach allows for efficient use of nearly any material or combination of materials to construct lattices with programmed arrangements. We experimentally demonstrate that a 3x3x2 lattice structure (287 total links) is fabricated in 27 minutes via a modified robotic arm and can support approximately 1000 N in compression testing. Extrusion-based 3D printing, in which a filament of material is extruded through a nozzle has been widely adopted. Here, Zhe Xu and Aaron Dollar report an approach for fabricating lattice structures in which a modular chain of specially designed links is “extruded” onto a substrate allowing for construction of multiscale structures that are efficient in weight and varied in composition.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00305-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1038/s44172-024-00302-4
Pawel Mieszczanek, Peter Corke, Courosh Mehanian, Paul D. Dalton, Dietmar W. Hutmacher
Melt electrowriting (MEW) is an emerging high-resolution 3D printing technology used in biomedical engineering, regenerative medicine, and soft robotics. Its transition from academia to industry faces challenges such as slow experimentation, low printing throughput, poor reproducibility, and user-dependent operation, largely due to the nonlinear and multiparametric nature of the MEW process. To address these challenges, we applied computer vision and machine learning to monitor and analyze the process in real-time through imaging of the MEW jet between the nozzle-collector gap. To collect data for training we developed an automated data collection methodology that eases the experimental time from days to hours. A feedforward neural network, working in concert with optimization methods and a feedback loop, is used to develop closed-loop control ensuring reproducibility of the printed parts. We demonstrate that machine learning allows streamlining the MEW operation via closed-loop control of the highly nonlinear 3D printing technology. Pawel Mieszczanek and colleagues design a machine learning-based approach to improve 3D printing processes based on melt electrowriting. They present a closed-loop control framework that is based on data-driven models and enables them to monitor the melt electrowriting operations in real time in order to improve reproducibility.
{"title":"Towards industry-ready additive manufacturing: AI-enabled closed-loop control for 3D melt electrowriting","authors":"Pawel Mieszczanek, Peter Corke, Courosh Mehanian, Paul D. Dalton, Dietmar W. Hutmacher","doi":"10.1038/s44172-024-00302-4","DOIUrl":"10.1038/s44172-024-00302-4","url":null,"abstract":"Melt electrowriting (MEW) is an emerging high-resolution 3D printing technology used in biomedical engineering, regenerative medicine, and soft robotics. Its transition from academia to industry faces challenges such as slow experimentation, low printing throughput, poor reproducibility, and user-dependent operation, largely due to the nonlinear and multiparametric nature of the MEW process. To address these challenges, we applied computer vision and machine learning to monitor and analyze the process in real-time through imaging of the MEW jet between the nozzle-collector gap. To collect data for training we developed an automated data collection methodology that eases the experimental time from days to hours. A feedforward neural network, working in concert with optimization methods and a feedback loop, is used to develop closed-loop control ensuring reproducibility of the printed parts. We demonstrate that machine learning allows streamlining the MEW operation via closed-loop control of the highly nonlinear 3D printing technology. Pawel Mieszczanek and colleagues design a machine learning-based approach to improve 3D printing processes based on melt electrowriting. They present a closed-loop control framework that is based on data-driven models and enables them to monitor the melt electrowriting operations in real time in order to improve reproducibility.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00302-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With global conflicts on the rise, it is crucial for transdisciplinary researchers to become more actively involved in leveraging technology to promote peace. After years of experience and discussions with multiple stakeholders, we have identified specific aspects that require greater attention from science, technology, engineering and mathematics scientists. To address these, we propose a practical framework designed to better integrate professionals from technical disciplines into the field of PeaceTech. This framework is built on four key principles: adopting a common language, prioritizing needs-driven innovation, engaging with diverse stakeholders, and adhering to the do-no-harm principle. With global conflicts on the rise, it is crucial for researchers in science, engineering, and technology to engage more actively in promoting peace. Here, Mariazel Maqueda-López et al. propose a practical framework focusing on common language, needs-driven innovation, stakeholder collaboration, and minimizing harm.
{"title":"Science and technology: a framework for peace","authors":"Mariazel Maqueda López, Sheena Kennedy, Solomzi Makohliso, Yves Daccord, Klaus Schönenberger","doi":"10.1038/s44172-024-00310-4","DOIUrl":"10.1038/s44172-024-00310-4","url":null,"abstract":"With global conflicts on the rise, it is crucial for transdisciplinary researchers to become more actively involved in leveraging technology to promote peace. After years of experience and discussions with multiple stakeholders, we have identified specific aspects that require greater attention from science, technology, engineering and mathematics scientists. To address these, we propose a practical framework designed to better integrate professionals from technical disciplines into the field of PeaceTech. This framework is built on four key principles: adopting a common language, prioritizing needs-driven innovation, engaging with diverse stakeholders, and adhering to the do-no-harm principle. With global conflicts on the rise, it is crucial for researchers in science, engineering, and technology to engage more actively in promoting peace. Here, Mariazel Maqueda-López et al. propose a practical framework focusing on common language, needs-driven innovation, stakeholder collaboration, and minimizing harm.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00310-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-04DOI: 10.1038/s44172-024-00301-5
Mehedi Hasan, Charles Nicholls, Keegan Pitre, Boris Spokoinyi, Trevor Hall
Phase noise reduces target sensitivity in radar and increases bit error rate in telecommunications systems. Optoelectronic oscillators are known for using optical fibre technology to realise the large delay required to attain superior phase noise performance compared to conventional microwave source technology. However, the long fibre is vulnerable to environmentally induced phase perturbations, while conventional phase shifters have insufficient range to compensate for the phase drift over the operational temperature range without the use of a temperature-controlled enclosure. Here we introduce a vector modulator controlled by a Stuart-Landau integrator, as a solution to non-efficient tuning for the phase shift. The concept is verified by simulation and experimentally demonstrated using an optoelectronic oscillator phase-locked to a system reference. Phase lock is maintained over four free spectral ranges equivalent to a tuning phase range of 1440° when the optoelectronic oscillator is cycled over a 10 °C to 85 °C temperature range. These demonstrations highlight the practical potential of our continuous tuning method. Mehedi Hasan and colleagues present a technique for mitigating the frequency drift in an optoelectronic oscillator. They demonstrate the performance and practicality of their approach over a wide temperature range.
相位噪声会降低雷达的目标灵敏度，增加电信系统的误码率。与传统的微波源技术相比，光电振荡器使用光纤技术实现了所需的大延迟，从而获得了优异的相位噪声性能。然而，长光纤容易受到环境引起的相位扰动的影响，而传统的移相器在不使用温控外壳的情况下，没有足够的范围来补偿工作温度范围内的相位漂移。在此，我们介绍一种由斯图尔特-朗道积分器控制的矢量调制器，作为相移非高效调谐的解决方案。我们使用与系统基准锁相的光电振荡器对这一概念进行了模拟验证和实验演示。当光电振荡器在 10 °C 至 85 °C 的温度范围内循环时，相位锁定可在四个自由光谱范围内保持，相当于 1440° 的调谐相位范围。这些演示凸显了我们的连续调谐方法的实用潜力。Mehedi Hasan 及其同事介绍了一种减轻光电振荡器频率漂移的技术。他们展示了其方法在宽温度范围内的性能和实用性。
{"title":"Delay drift compensation of an optoelectronic oscillator over a large temperature range through continuous tuning","authors":"Mehedi Hasan, Charles Nicholls, Keegan Pitre, Boris Spokoinyi, Trevor Hall","doi":"10.1038/s44172-024-00301-5","DOIUrl":"10.1038/s44172-024-00301-5","url":null,"abstract":"Phase noise reduces target sensitivity in radar and increases bit error rate in telecommunications systems. Optoelectronic oscillators are known for using optical fibre technology to realise the large delay required to attain superior phase noise performance compared to conventional microwave source technology. However, the long fibre is vulnerable to environmentally induced phase perturbations, while conventional phase shifters have insufficient range to compensate for the phase drift over the operational temperature range without the use of a temperature-controlled enclosure. Here we introduce a vector modulator controlled by a Stuart-Landau integrator, as a solution to non-efficient tuning for the phase shift. The concept is verified by simulation and experimentally demonstrated using an optoelectronic oscillator phase-locked to a system reference. Phase lock is maintained over four free spectral ranges equivalent to a tuning phase range of 1440° when the optoelectronic oscillator is cycled over a 10 °C to 85 °C temperature range. These demonstrations highlight the practical potential of our continuous tuning method. Mehedi Hasan and colleagues present a technique for mitigating the frequency drift in an optoelectronic oscillator. They demonstrate the performance and practicality of their approach over a wide temperature range.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00301-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142574263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-03DOI: 10.1038/s44172-024-00306-0
Maximilian Lechner, Anna Kollenda, Konrad Bendzuck, Julian K. Burmeister, Kashfia Mahin, Josef Keilhofer, Lukas Kemmer, Maximilian J. Blaschke, Gunther Friedl, Ruediger Daub, Arno Kwade
Battery production cost models are critical for evaluating the cost competitiveness of different cell geometries, chemistries, and production processes. To address this need, we present a detailed bottom-up approach for calculating the full cost, marginal cost, and levelized cost of various battery production methods. Our approach ensures comparability across research fields and industries, reflecting capital and imputed interest costs. We showcase the model with case studies of a prismatic PHEV2 hardcase cell and a cylindrical 4680 cell in four different chemistries. Our publicly available browser-based modular tool incorporates up-to-date parameters derived from literature and expert interviews. This work enables researchers to quickly assess the production cost implications of new battery production processes and technologies, ultimately advancing the goal of reducing the cost of electrified mobility. Battery production cost models are critical for evaluating cost competitiveness but frequently lack transparency and standardization. A bottom-up approach for calculating the full cost, marginal cost, and levelized cost of various battery production methods is proposed, enriched by a browser-based modular user tool.
{"title":"Cost modeling for the GWh-scale production of modern lithium-ion battery cells","authors":"Maximilian Lechner, Anna Kollenda, Konrad Bendzuck, Julian K. Burmeister, Kashfia Mahin, Josef Keilhofer, Lukas Kemmer, Maximilian J. Blaschke, Gunther Friedl, Ruediger Daub, Arno Kwade","doi":"10.1038/s44172-024-00306-0","DOIUrl":"10.1038/s44172-024-00306-0","url":null,"abstract":"Battery production cost models are critical for evaluating the cost competitiveness of different cell geometries, chemistries, and production processes. To address this need, we present a detailed bottom-up approach for calculating the full cost, marginal cost, and levelized cost of various battery production methods. Our approach ensures comparability across research fields and industries, reflecting capital and imputed interest costs. We showcase the model with case studies of a prismatic PHEV2 hardcase cell and a cylindrical 4680 cell in four different chemistries. Our publicly available browser-based modular tool incorporates up-to-date parameters derived from literature and expert interviews. This work enables researchers to quickly assess the production cost implications of new battery production processes and technologies, ultimately advancing the goal of reducing the cost of electrified mobility. Battery production cost models are critical for evaluating cost competitiveness but frequently lack transparency and standardization. A bottom-up approach for calculating the full cost, marginal cost, and levelized cost of various battery production methods is proposed, enriched by a browser-based modular user tool.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00306-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-02DOI: 10.1038/s44172-024-00309-x
Zhengdong Hu, Yuanbo Li, Chong Han
Terahertz communications are envisioned as a promising technology for the sixth generation and beyond wireless systems, which can support wireless links with Terabits-per-second (Tbps) data rates. As the foundation of designing terahertz communications, channel modeling and characterization are crucial to scrutinize the potential of this spectrum. However, current channel modeling in the terahertz band heavily relies on time-consuming and costly measurements. Here, we propose a transfer learning enabled transformer based generative adversarial network to mitigate this problem in terahertz channel modeling. Specifically, as a fundamental building block, a generative adversarial network is exploited to generate channel parameters. To improve the accuracy, a transformer structure with a self-attention mechanism is incorporated in generative adversarial network. Still incurring errors compared with ground-truth measurement, a transfer learning is designed to solve the mismatch between the formulated network and measurement. The proposed method can achieve high accuracy in channel modeling, while requiring only rather limited amount of measurement, which is a promising complement of current channel modeling techniques. Zhengdong Hu and colleagues propose Transfer learning and Transformers in a Generative Adversarial Network for channel modelling in the Terahertz band. They reduce the required number of measurements while maintaining the accuracy.
{"title":"Transfer learning enabled transformer-based generative adversarial networks for modeling and generating terahertz channels","authors":"Zhengdong Hu, Yuanbo Li, Chong Han","doi":"10.1038/s44172-024-00309-x","DOIUrl":"10.1038/s44172-024-00309-x","url":null,"abstract":"Terahertz communications are envisioned as a promising technology for the sixth generation and beyond wireless systems, which can support wireless links with Terabits-per-second (Tbps) data rates. As the foundation of designing terahertz communications, channel modeling and characterization are crucial to scrutinize the potential of this spectrum. However, current channel modeling in the terahertz band heavily relies on time-consuming and costly measurements. Here, we propose a transfer learning enabled transformer based generative adversarial network to mitigate this problem in terahertz channel modeling. Specifically, as a fundamental building block, a generative adversarial network is exploited to generate channel parameters. To improve the accuracy, a transformer structure with a self-attention mechanism is incorporated in generative adversarial network. Still incurring errors compared with ground-truth measurement, a transfer learning is designed to solve the mismatch between the formulated network and measurement. The proposed method can achieve high accuracy in channel modeling, while requiring only rather limited amount of measurement, which is a promising complement of current channel modeling techniques. Zhengdong Hu and colleagues propose Transfer learning and Transformers in a Generative Adversarial Network for channel modelling in the Terahertz band. They reduce the required number of measurements while maintaining the accuracy.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00309-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-02DOI: 10.1038/s44172-024-00300-6
Song Deng, Longxiang Zhang, Dong Yue
Timely and precise security risk evaluation is essential for optimal operational planning, threat detection, and the reliable operation of smart grid. The smart grid can integrate extensive high-dimensional operational data. However, conventional risk assessment techniques often struggle with managing such data volumes. Moreover, many methods use centralized evaluation, potentially neglecting privacy issues. Additionally, Power grid operators are often reluctant to share sensitive risk-related data due to privacy concerns. Here we introduce a data-driven and privacy-preserving risk assessment method that safeguards Power grid operators’ data privacy by integrating deep learning and secure encryption in a federated learning framework. The method involves: (1) developing a two-tier risk indicator system and an expanded dataset; (2) using a deep convolutional neural network -based model to analyze the relationship between system variables and risk levels; and (3) creating a secure, federated risk assessment protocol with homomorphic encryption to protect model parameters during training. Experiments on IEEE 14-bus and IEEE 118-bus systems show that our approach ensures high assessment accuracy and data privacy. Song Deng and colleagues present a data-driven and privacy preserving risk assessment approach to protect the data privacy of all power grid operators. They demonstrate the feasibility of their method in experiments with IEEE 14-bus and 118-bus systems.
{"title":"Data-driven and privacy-preserving risk assessment method based on federated learning for smart grids","authors":"Song Deng, Longxiang Zhang, Dong Yue","doi":"10.1038/s44172-024-00300-6","DOIUrl":"10.1038/s44172-024-00300-6","url":null,"abstract":"Timely and precise security risk evaluation is essential for optimal operational planning, threat detection, and the reliable operation of smart grid. The smart grid can integrate extensive high-dimensional operational data. However, conventional risk assessment techniques often struggle with managing such data volumes. Moreover, many methods use centralized evaluation, potentially neglecting privacy issues. Additionally, Power grid operators are often reluctant to share sensitive risk-related data due to privacy concerns. Here we introduce a data-driven and privacy-preserving risk assessment method that safeguards Power grid operators’ data privacy by integrating deep learning and secure encryption in a federated learning framework. The method involves: (1) developing a two-tier risk indicator system and an expanded dataset; (2) using a deep convolutional neural network -based model to analyze the relationship between system variables and risk levels; and (3) creating a secure, federated risk assessment protocol with homomorphic encryption to protect model parameters during training. Experiments on IEEE 14-bus and IEEE 118-bus systems show that our approach ensures high assessment accuracy and data privacy. Song Deng and colleagues present a data-driven and privacy preserving risk assessment approach to protect the data privacy of all power grid operators. They demonstrate the feasibility of their method in experiments with IEEE 14-bus and 118-bus systems.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00300-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1038/s44172-024-00303-3
Weiwei He, Jinzhao Li, Xuan Kong, Lu Deng
Physics-informed neural network has emerged as a promising approach for solving partial differential equations. However, it is still a challenge for the computation of structural mechanics problems since it involves solving higher-order partial differential equations as the governing equations are fourth-order nonlinear equations. Here we develop a multi-level physics-informed neural network framework where an aggregation model is developed by combining multiple neural networks, with each one involving only first-order or second-order partial differential equations representing different physics information such as geometrical, constitutive, and equilibrium relations of the structure. The proposed framework demonstrates a remarkable advancement over the classical neural networks in terms of the accuracy and computation time. The proposed method holds the potential to become a promising paradigm for structural mechanics computation and facilitate the intelligent computation of digital twin systems. Weiwei He and colleagues implement a multi-level physicsinformed neural network to solve partial differential equations, a key problem for efficient structure analysis. Their results improve the accuracy and computation time for solving these problems.
{"title":"Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics","authors":"Weiwei He, Jinzhao Li, Xuan Kong, Lu Deng","doi":"10.1038/s44172-024-00303-3","DOIUrl":"10.1038/s44172-024-00303-3","url":null,"abstract":"Physics-informed neural network has emerged as a promising approach for solving partial differential equations. However, it is still a challenge for the computation of structural mechanics problems since it involves solving higher-order partial differential equations as the governing equations are fourth-order nonlinear equations. Here we develop a multi-level physics-informed neural network framework where an aggregation model is developed by combining multiple neural networks, with each one involving only first-order or second-order partial differential equations representing different physics information such as geometrical, constitutive, and equilibrium relations of the structure. The proposed framework demonstrates a remarkable advancement over the classical neural networks in terms of the accuracy and computation time. The proposed method holds the potential to become a promising paradigm for structural mechanics computation and facilitate the intelligent computation of digital twin systems. Weiwei He and colleagues implement a multi-level physicsinformed neural network to solve partial differential equations, a key problem for efficient structure analysis. Their results improve the accuracy and computation time for solving these problems.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00303-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1038/s44172-024-00296-z
Ignacio R. Bartol, Martin S. Graffigna Palomba, Mauricio E. Tano, Shaheen A. Dewji
The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction. Shaheen Dewji and colleagues introduce a hybrid-automated computational framework for modelling particles in the human respiratory tract (HRT) with variable geometries. Their method produces patient specific particle deposition profiles that highlights how geometrical characteristics can vary aerosol deposition within the HRT and radiation exposure between patients.
{"title":"Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries","authors":"Ignacio R. Bartol, Martin S. Graffigna Palomba, Mauricio E. Tano, Shaheen A. Dewji","doi":"10.1038/s44172-024-00296-z","DOIUrl":"10.1038/s44172-024-00296-z","url":null,"abstract":"The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction. Shaheen Dewji and colleagues introduce a hybrid-automated computational framework for modelling particles in the human respiratory tract (HRT) with variable geometries. Their method produces patient specific particle deposition profiles that highlights how geometrical characteristics can vary aerosol deposition within the HRT and radiation exposure between patients.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00296-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1038/s44172-024-00294-1
Catherine Jiayi Cai, Hui Huang, Hongliang Ren
Magnetically actuated miniature origami crawlers are capable of robust locomotion in confined environments but are limited to passive functionalities. Here, we propose a bistable origami crawler that can shape-morph to access two separate regimes of folding degrees of freedom that are separated by an energy barrier. Using the modified bistable V-fold origami crease pattern as the fundamental unit of the crawler, we incorporated internal permanent magnets to enable untethered shape-morphing. By modulating the orientation of the external magnetic field, the crawler can reconfigure between an undeployed locomotion state and a deployed load-bearing state. In the undeployed state, the crawler can deform to enable out-of-plane crawling for robust bi-directional locomotion and navigation in confined environments based on friction anisotropy. In the deployed state, the crawler can execute microneedle insertion in confined environments. Through this work, we demonstrated the advantage of incorporating bistability into origami mechanisms to expand their capabilities in space-constraint applications. Catherine Jiayi Cai, Hui Huang and Hongliang Ren design a magnetically actuated bistable origami crawler that can transition between a locomotion and function mode, where each mode can be operated independently. The origami crawler is designed for use in confined spaces such as the gastrointestinal tract.
磁力驱动的微型折纸爬行器能够在密闭环境中稳健运动，但仅限于被动功能。在这里，我们提出了一种双稳态折纸爬行器，它可以进行形状变形，以获得被能量屏障隔开的两个独立的折叠自由度。我们使用改进的双稳态 V 型折纸折痕图案作为爬行器的基本单元，并在其中加入内部永久磁铁，以实现无束缚的形状变换。通过调节外部磁场的方向，爬行器可以在未展开的运动状态和展开的承重状态之间重新配置。在未部署状态下，爬行器可以变形以实现平面外爬行，从而在基于摩擦各向异性的密闭环境中实现稳健的双向运动和导航。在展开状态下，爬行器可以在密闭环境中执行微针插入。通过这项工作，我们展示了将双稳态性融入折纸机械装置的优势，从而扩展了它们在空间受限应用中的能力。
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