Pub Date : 2024-11-29DOI: 10.1038/s44172-024-00327-9
Michael Qian Vergnolle, Eastman Z Y Wu, Yanan Sui, Qian Wang
Rapid and precise forecasting of dynamical systems is critical to ensuring safe aerospace missions. Previous forecasting research has primarily concentrated on global trend analysis using full-scale inputs. However, time series arising from real-world applications such as aerospace propulsion, exhibit a distinct dynamical periodicity over a limited timeframe. Here we develop a deep learning model, TimeWaves, to capture both global trends and local variations, through 3D spectrum-oriented interval extraction from an integrated viewpoint of biological perceptions. Specifically, a shared parameter fusion algorithm is employed to effectively integrate Fourier and Wavelet analyses, providing full and sliced 1D sequences to form 2D tensors that can be seamlessly processed by parameter-efficient inception blocks. Additionally, a dual-way learning workflow using TwinBlock, inspired by the cooperative behavior of visual cells, is implemented to enhance perception of dynamical multi-scale features at a reduced computational cost. TimeWaves demonstrates reliable and robust performance in predicting rocket combustion instability, a key challenge in the aerospace industry.
{"title":"Bio-inspired multi-dimensional deep fusion learning for predicting dynamical aerospace propulsion systems.","authors":"Michael Qian Vergnolle, Eastman Z Y Wu, Yanan Sui, Qian Wang","doi":"10.1038/s44172-024-00327-9","DOIUrl":"https://doi.org/10.1038/s44172-024-00327-9","url":null,"abstract":"<p><p>Rapid and precise forecasting of dynamical systems is critical to ensuring safe aerospace missions. Previous forecasting research has primarily concentrated on global trend analysis using full-scale inputs. However, time series arising from real-world applications such as aerospace propulsion, exhibit a distinct dynamical periodicity over a limited timeframe. Here we develop a deep learning model, TimeWaves, to capture both global trends and local variations, through 3D spectrum-oriented interval extraction from an integrated viewpoint of biological perceptions. Specifically, a shared parameter fusion algorithm is employed to effectively integrate Fourier and Wavelet analyses, providing full and sliced 1D sequences to form 2D tensors that can be seamlessly processed by parameter-efficient inception blocks. Additionally, a dual-way learning workflow using TwinBlock, inspired by the cooperative behavior of visual cells, is implemented to enhance perception of dynamical multi-scale features at a reduced computational cost. TimeWaves demonstrates reliable and robust performance in predicting rocket combustion instability, a key challenge in the aerospace industry.</p>","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":"3 1","pages":"179"},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-27DOI: 10.1038/s44172-024-00298-x
Elham H. Fini, Mohammadjavad Kazemi, Lily Poulikakos, Georgy Lazorenko, Vajiheh Akbarzade, Anthony Lamanna, Peter Lammers
As waste production increases and resources become limited, sewage sludge presents a valuable resource with potential beyond traditional land use and incineration. This review emphasizes exploring innovative non-fertilizer applications of sewage sludges and advocates for viewing wastewater treatment plants as sources of valuable feedstock and carbon sequestration. Innovative uses include integrating sewage sludge into construction materials such as asphalt pavements, geopolymer, cementitious composites, and masonry blocks. These methods not only immobilize heavy metals and mitigate environmental hazards but also support carbon sequestration, contrasting with incineration and land application methods that release carbon into the atmosphere. The review also addresses emerging technologies like bio-adhesives, bio-binders for asphalt, hydrogels, bioplastics, and corrosion inhibitors. It highlights the recovery of valuable materials from sewage sludge, including phosphorus, oils, metals, cellulose, and polyhydroxyalkanoates as well as enzyme production. By focusing on these non-fertilizer applications, this review presents a compelling case for re-envisioning wastewater treatment plants as sources of valuable feedstock and carbon sequestration, supporting global efforts to manage waste effectively and enhance sustainability. Ellie Fini and co-authors investigate innovative and often overlooked methods for managing sewage sludge. Their work emphasizes how these alternative approaches can effectively reduce environmental hazards, such as heavy metal contamination and carbon emissions, while also fostering the development of value-added products.
随着废物产量的增加和资源的有限，污水污泥成为一种宝贵的资源，其潜力已超越了传统的土地利用和焚烧。本综述强调探索污水污泥非肥料化的创新应用，提倡将污水处理厂视为宝贵的原料和碳固存来源。创新用途包括将污水污泥融入沥青路面、土工聚合物、水泥基复合材料和砌块等建筑材料中。这些方法不仅能固定重金属、减轻环境危害，还能支持碳固存，与将碳释放到大气中的焚烧和土地应用方法形成鲜明对比。综述还讨论了生物粘合剂、沥青生物粘合剂、水凝胶、生物塑料和缓蚀剂等新兴技术。报告重点介绍了从污水污泥中回收有价值的材料，包括磷、油、金属、纤维素、聚羟基烷酸酯以及酶的生产。通过重点介绍这些非肥料应用，这篇综述提出了一个令人信服的理由，即重新认识污水处理厂，将其作为有价值的原料和固碳来源，支持全球为有效管理废物和提高可持续性所做的努力。Ellie Fini 和合著者研究了创新的、经常被忽视的污水污泥管理方法。他们的工作强调了这些替代方法如何有效减少重金属污染和碳排放等环境危害，同时促进增值产品的开发。
{"title":"Perspectives on innovative non-fertilizer applications of sewage sludge for mitigating environmental and health hazards","authors":"Elham H. Fini, Mohammadjavad Kazemi, Lily Poulikakos, Georgy Lazorenko, Vajiheh Akbarzade, Anthony Lamanna, Peter Lammers","doi":"10.1038/s44172-024-00298-x","DOIUrl":"10.1038/s44172-024-00298-x","url":null,"abstract":"As waste production increases and resources become limited, sewage sludge presents a valuable resource with potential beyond traditional land use and incineration. This review emphasizes exploring innovative non-fertilizer applications of sewage sludges and advocates for viewing wastewater treatment plants as sources of valuable feedstock and carbon sequestration. Innovative uses include integrating sewage sludge into construction materials such as asphalt pavements, geopolymer, cementitious composites, and masonry blocks. These methods not only immobilize heavy metals and mitigate environmental hazards but also support carbon sequestration, contrasting with incineration and land application methods that release carbon into the atmosphere. The review also addresses emerging technologies like bio-adhesives, bio-binders for asphalt, hydrogels, bioplastics, and corrosion inhibitors. It highlights the recovery of valuable materials from sewage sludge, including phosphorus, oils, metals, cellulose, and polyhydroxyalkanoates as well as enzyme production. By focusing on these non-fertilizer applications, this review presents a compelling case for re-envisioning wastewater treatment plants as sources of valuable feedstock and carbon sequestration, supporting global efforts to manage waste effectively and enhance sustainability. Ellie Fini and co-authors investigate innovative and often overlooked methods for managing sewage sludge. Their work emphasizes how these alternative approaches can effectively reduce environmental hazards, such as heavy metal contamination and carbon emissions, while also fostering the development of value-added products.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00298-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737652","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 : 2024-11-25DOI: 10.1038/s44172-024-00319-9
Meitham Amereh, Shahla Shojaei, Amir Seyfoori, Tavia Walsh, Prashant Dogra, Vittorio Cristini, Ben Nadler, Mohsen Akbari
Non-physiological levels of oxygen and nutrients within the tumors result in heterogeneous cell populations that exhibit distinct necrotic, hypoxic, and proliferative zones. Among these zonal cellular properties, metabolic rates strongly affect the overall growth and invasion of tumors. Here, we report on a hybrid discrete-continuum (HDC) mathematical framework that uses metabolic data from a biomimetic two-dimensional (2D) in-vitro cancer model to predict three-dimensional (3D) behaviour of in-vitro human glioblastoma (hGB). The mathematical model integrates modules of continuum, discrete, and neurons. Results indicated that the HDC model is capable of quantitatively predicting growth, invasion length, and the asymmetric finger-type invasion pattern in in-vitro hGB tumors. Additionally, the model could predict the reduction in invasion length of hGB tumoroids in response to temozolomide (TMZ). This model has the potential to incorporate additional modules, including immune cells and signaling pathways governing cancer/immune cell interactions, and can be used to investigate targeted therapies. Meitham Amereh and colleagues report a hybrid discrete-continuum model to predict the cancerous growth, invasion, and treatment response of glioblastoma tumours. Their in-silico model uses metabolic data from a biomimetic two-dimensional in-vitro cancer model to predict three-dimensional behaviour of in-vitro human glioblastoma.
{"title":"Insights from a multiscale framework on metabolic rate variation driving glioblastoma multiforme growth and invasion","authors":"Meitham Amereh, Shahla Shojaei, Amir Seyfoori, Tavia Walsh, Prashant Dogra, Vittorio Cristini, Ben Nadler, Mohsen Akbari","doi":"10.1038/s44172-024-00319-9","DOIUrl":"10.1038/s44172-024-00319-9","url":null,"abstract":"Non-physiological levels of oxygen and nutrients within the tumors result in heterogeneous cell populations that exhibit distinct necrotic, hypoxic, and proliferative zones. Among these zonal cellular properties, metabolic rates strongly affect the overall growth and invasion of tumors. Here, we report on a hybrid discrete-continuum (HDC) mathematical framework that uses metabolic data from a biomimetic two-dimensional (2D) in-vitro cancer model to predict three-dimensional (3D) behaviour of in-vitro human glioblastoma (hGB). The mathematical model integrates modules of continuum, discrete, and neurons. Results indicated that the HDC model is capable of quantitatively predicting growth, invasion length, and the asymmetric finger-type invasion pattern in in-vitro hGB tumors. Additionally, the model could predict the reduction in invasion length of hGB tumoroids in response to temozolomide (TMZ). This model has the potential to incorporate additional modules, including immune cells and signaling pathways governing cancer/immune cell interactions, and can be used to investigate targeted therapies. Meitham Amereh and colleagues report a hybrid discrete-continuum model to predict the cancerous growth, invasion, and treatment response of glioblastoma tumours. Their in-silico model uses metabolic data from a biomimetic two-dimensional in-vitro cancer model to predict three-dimensional behaviour of in-vitro human glioblastoma.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00319-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716632","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 : 2024-11-25DOI: 10.1038/s44172-024-00313-1
Kenji Shimazoe, Donghwan Kim, Moh Hamdan, Yosuke Kobayashi, Kei Kamada, Masao Yoshino, Yasuhiro Shoji, Kyohei Sakamoto, Fabio Acerbi, Alberto Gola
X-ray photon-counting computed tomography (PCCT) has garnered considerable interest owing to its low-dose administration, high-quality imaging, and material decomposition characteristics. Current commercial PCCT systems employ compound semiconductor photon-counting X-ray detectors, which offer good energy resolution. However, the choice of materials is limited, and cadmium telluride or cadmium zinc telluride is mostly used. Although indirect radiation detectors can be used as alternatives to compound semiconductor detectors, implementing fine-pitch segmentation in such detectors is challenging. Here we designed an indirect fine-pitch X-ray photon-counting detector by combining miniaturized silicon photomultiplier arrays and fast scintillation crystals, with a pixel size of 250 µm, for future indirect PCCT. The fabricated array detector has the potential to discriminate photon energies with a 27% resolution at 122 keV, 296 µm spatial resolution, and charge-sharing inhibition ability. Kenji Shimazoe and co-authors present an indirect fine-pitch X-ray photon-counting detector by combining silicon photomultiplier arrays and fast scintillation crystals. The detector is capable of detecting the photons and differentiating them by the energy level.
X 射线光子计数计算机断层扫描（PCCT）因其低剂量给药、高质量成像和材料分解特性而备受关注。目前的商用 PCCT 系统采用化合物半导体光子计数 X 射线探测器，具有良好的能量分辨率。不过，可供选择的材料有限，大多使用碲化镉或碲化镉锌。虽然间接辐射探测器可作为化合物半导体探测器的替代品，但在这类探测器中实现细间距分割具有挑战性。在此，我们结合微型硅光电倍增管阵列和快速闪烁晶体，设计了一种间接细间距 X 射线光子计数探测器，像素尺寸为 250 微米，可用于未来的间接 PCCT。所制造的阵列探测器具有分辨光子能量的潜力，在 122 千伏时分辨率为 27%，空间分辨率为 296 微米，并具有电荷共享抑制能力。
{"title":"An energy-resolving photon-counting X-ray detector for computed tomography combining silicon-photomultiplier arrays and scintillation crystals","authors":"Kenji Shimazoe, Donghwan Kim, Moh Hamdan, Yosuke Kobayashi, Kei Kamada, Masao Yoshino, Yasuhiro Shoji, Kyohei Sakamoto, Fabio Acerbi, Alberto Gola","doi":"10.1038/s44172-024-00313-1","DOIUrl":"10.1038/s44172-024-00313-1","url":null,"abstract":"X-ray photon-counting computed tomography (PCCT) has garnered considerable interest owing to its low-dose administration, high-quality imaging, and material decomposition characteristics. Current commercial PCCT systems employ compound semiconductor photon-counting X-ray detectors, which offer good energy resolution. However, the choice of materials is limited, and cadmium telluride or cadmium zinc telluride is mostly used. Although indirect radiation detectors can be used as alternatives to compound semiconductor detectors, implementing fine-pitch segmentation in such detectors is challenging. Here we designed an indirect fine-pitch X-ray photon-counting detector by combining miniaturized silicon photomultiplier arrays and fast scintillation crystals, with a pixel size of 250 µm, for future indirect PCCT. The fabricated array detector has the potential to discriminate photon energies with a 27% resolution at 122 keV, 296 µm spatial resolution, and charge-sharing inhibition ability. Kenji Shimazoe and co-authors present an indirect fine-pitch X-ray photon-counting detector by combining silicon photomultiplier arrays and fast scintillation crystals. The detector is capable of detecting the photons and differentiating them by the energy level.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00313-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717947","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}
Lithium–sulfur (Li–S) rechargeable batteries have been expected to be lightweight energy storage devices with the highest gravimetric energy density at the single-cell level reaching up to 695 Wh kg(cell)−1, having also an ultralow rate of 0.005 C only in the first discharge. Sulfurized polyacrylonitrile (SPAN) is one of the sulfur-based active materials, which allows more freedom in the Li–S cell design because it shows no undesirable reactions with electrolyte solutions. Here we present an original Li–S pouch cell construction, ADEKA’s Lithium–Sulfur/Pouch Cell (ALIS-PC). It is an ultra-lightweight rechargeable battery cell, which is designed by combining the SPAN cathode and effective ten technologies involving chemical engineering. As a result, the highest gravimetric energy densities of 713 and 761 Wh kg(cell)−1 after some charge-and-discharge cycles, which were based on the total mass of all cell components, were achieved with successful operating at 0.1 and 0.05C-rates, respectively, significantly exceeding those of commercial lithium-ion and next-generation rechargeable batteries in development. Kenji Kakiage and colleagues report an ultra-lightweight Li-S pouch cell with a gravimetric energy density of 761 Wh/kg. They use sulfurized polyacrylonitrile as a cathode active material, combining ten technologies for rechargeable batteries.
{"title":"Ultra-lightweight rechargeable battery with enhanced gravimetric energy densities >750 Wh kg−1 in lithium–sulfur pouch cell","authors":"Kenji Kakiage, Toru Yano, Hiroki Uehara, Masaki Kakiage","doi":"10.1038/s44172-024-00321-1","DOIUrl":"10.1038/s44172-024-00321-1","url":null,"abstract":"Lithium–sulfur (Li–S) rechargeable batteries have been expected to be lightweight energy storage devices with the highest gravimetric energy density at the single-cell level reaching up to 695 Wh kg(cell)−1, having also an ultralow rate of 0.005 C only in the first discharge. Sulfurized polyacrylonitrile (SPAN) is one of the sulfur-based active materials, which allows more freedom in the Li–S cell design because it shows no undesirable reactions with electrolyte solutions. Here we present an original Li–S pouch cell construction, ADEKA’s Lithium–Sulfur/Pouch Cell (ALIS-PC). It is an ultra-lightweight rechargeable battery cell, which is designed by combining the SPAN cathode and effective ten technologies involving chemical engineering. As a result, the highest gravimetric energy densities of 713 and 761 Wh kg(cell)−1 after some charge-and-discharge cycles, which were based on the total mass of all cell components, were achieved with successful operating at 0.1 and 0.05C-rates, respectively, significantly exceeding those of commercial lithium-ion and next-generation rechargeable batteries in development. Kenji Kakiage and colleagues report an ultra-lightweight Li-S pouch cell with a gravimetric energy density of 761 Wh/kg. They use sulfurized polyacrylonitrile as a cathode active material, combining ten technologies for rechargeable batteries.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44172-024-00321-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717165","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}
Spent LiNixCoyMnzO2 (x + y + z = 1) and polyethylene terephthalate are major solid wastes due to the growing Li-ion battery market and widespread plastic usage. Here we propose a synergistic pyrolysis strategy to recover valuable metals by thermally treating LiNi1/3Co1/3Mn1/3O2 and polyethylene terephthalate. With polyethylene terephthalate assistance, LiNi1/3Co1/3Mn1/3O2 decomposes at 400 °C, and fully converts to Li2CO3, MnO, and Ni-Co alloy at 550 °C within 30 min, using a 1.0:0.3 mass ratio of LiNi1/3Co1/3Mn1/3O2 to polyethylene terephthalate. Furthermore, density functional theory calculations confirm the preference for O-Li bonding. Surface adsorption and free radical/gaseous reduction reactions explain the role of polyethylene terephthalate in promoting lattice destruction. The complete decomposition facilitates efficient post-treatment, achieving over 99% recovery of Li, Ni, Co, and Mn via water washing. Regenerated LiNi1/3Co1/3Mn1/3O2 was synthesized by using recovered Li- and transition metal-containing products as feedstocks. This study provided a chemical-free, energy-saving, and scalable recovery strategy while addressing polyethylene terephthalate waste minimization. Zhe Meng and co-authors demonstrate the feasibility of synergetic pyrolysis of lithium-ion battery cathode materials with PET plastic for recovering Li and transition metals. They demonstrate a high recovery ratio and energy efficiency.
由于锂离子电池市场的不断增长和塑料的广泛使用，废镍钴锰酸锂（x + y + z = 1）和聚对苯二甲酸乙二醇酯成为主要的固体废物。在此，我们提出了一种协同热解策略，通过热处理 LiNi1/3Co1/3Mn1/3O2 和聚对苯二甲酸乙二醇酯来回收有价金属。在聚对苯二甲酸乙二醇酯的辅助下，镍1/3钴1/3锰1/3O2锂在400 ℃分解，并在30分钟内完全转化为2CO3锂、氧化锰和镍钴合金，镍1/3钴1/3锰1/3O2锂与聚对苯二甲酸乙二醇酯的质量比为1.0:0.3。此外，密度泛函理论计算证实了 O-Li 键的偏好。表面吸附和自由基/气体还原反应解释了聚对苯二甲酸乙二酯在促进晶格破坏方面的作用。完全分解有助于进行高效的后处理，通过水洗，锂、镍、钴和锰的回收率超过 99%。以回收的含锂和过渡金属的产品为原料，合成了再生的 LiNi1/3Co1/3Mn1/3O2 。这项研究提供了一种无化学品、节能和可扩展的回收策略，同时还解决了聚对苯二甲酸乙二醇酯废物最小化的问题。
{"title":"Synergetic pyrolysis of lithium-ion battery cathodes with polyethylene terephthalate for efficient metal recovery and battery regeneration","authors":"Zhe Meng, Jinchuan Dai, Xiao-Ying Lu, Kehua Wu, Yonghong Deng, Jun Wang, Kaimin Shih, Yuanyuan Tang","doi":"10.1038/s44172-024-00317-x","DOIUrl":"10.1038/s44172-024-00317-x","url":null,"abstract":"Spent LiNixCoyMnzO2 (x + y + z = 1) and polyethylene terephthalate are major solid wastes due to the growing Li-ion battery market and widespread plastic usage. Here we propose a synergistic pyrolysis strategy to recover valuable metals by thermally treating LiNi1/3Co1/3Mn1/3O2 and polyethylene terephthalate. With polyethylene terephthalate assistance, LiNi1/3Co1/3Mn1/3O2 decomposes at 400 °C, and fully converts to Li2CO3, MnO, and Ni-Co alloy at 550 °C within 30 min, using a 1.0:0.3 mass ratio of LiNi1/3Co1/3Mn1/3O2 to polyethylene terephthalate. Furthermore, density functional theory calculations confirm the preference for O-Li bonding. Surface adsorption and free radical/gaseous reduction reactions explain the role of polyethylene terephthalate in promoting lattice destruction. The complete decomposition facilitates efficient post-treatment, achieving over 99% recovery of Li, Ni, Co, and Mn via water washing. Regenerated LiNi1/3Co1/3Mn1/3O2 was synthesized by using recovered Li- and transition metal-containing products as feedstocks. This study provided a chemical-free, energy-saving, and scalable recovery strategy while addressing polyethylene terephthalate waste minimization. Zhe Meng and co-authors demonstrate the feasibility of synergetic pyrolysis of lithium-ion battery cathode materials with PET plastic for recovering Li and transition metals. They demonstrate a high recovery ratio and energy efficiency.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11585579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696068","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 : 2024-11-20DOI: 10.1038/s44172-024-00322-0
Jonathan Tran, Kai Fukami, Kenta Inada, Daisuke Umehara, Yoshimichi Ono, Kenta Ogawa, Kunihiko Taira
The transition to electric vehicles is driving a fundamental shift in the automobile design process. Changes in constraints afforded by the absence of a combustion engine create new opportunities for modifying vehicle geometries. Current approaches to optimizing vehicle aerodynamics require a vast amount of computational studies and physical experiments, which are expensive when performing parameter sweeps over conceivable geometric configurations, suggesting the need for more efficient surrogate models to assist analysis. Here we analyze a dataset of industry-quality automobile geometries with their associated aerodynamic performance obtained from experimentally validated, high-fidelity large-eddy simulations. We show that a relationship between these geometries and their respective aerodynamics can be extracted in a low-dimensional manner by leveraging a nonlinear autoencoder which is simultaneously trained to estimate the drag coefficient from the latent variables. We perform aerodynamic design optimization of vehicle designs by making use of the learned aerodynamic relationship in the low-order space obtained by the model. We demonstrate that the aerodynamic trends for the geometries produced from the optimization process show agreement with validation simulations. The findings of this work demonstrate the application of data-driven approaches to the analysis and design of vehicles in a production environment. Jonathan Tran and colleagues use aerodynamics-guided machine learning for the shape optimization of electric cars. Their approach saves computational time for high complexity engineering tasks, e.g., computational fluid dynamics-based design optimization.
{"title":"Aerodynamics-guided machine learning for design optimization of electric vehicles","authors":"Jonathan Tran, Kai Fukami, Kenta Inada, Daisuke Umehara, Yoshimichi Ono, Kenta Ogawa, Kunihiko Taira","doi":"10.1038/s44172-024-00322-0","DOIUrl":"10.1038/s44172-024-00322-0","url":null,"abstract":"The transition to electric vehicles is driving a fundamental shift in the automobile design process. Changes in constraints afforded by the absence of a combustion engine create new opportunities for modifying vehicle geometries. Current approaches to optimizing vehicle aerodynamics require a vast amount of computational studies and physical experiments, which are expensive when performing parameter sweeps over conceivable geometric configurations, suggesting the need for more efficient surrogate models to assist analysis. Here we analyze a dataset of industry-quality automobile geometries with their associated aerodynamic performance obtained from experimentally validated, high-fidelity large-eddy simulations. We show that a relationship between these geometries and their respective aerodynamics can be extracted in a low-dimensional manner by leveraging a nonlinear autoencoder which is simultaneously trained to estimate the drag coefficient from the latent variables. We perform aerodynamic design optimization of vehicle designs by making use of the learned aerodynamic relationship in the low-order space obtained by the model. We demonstrate that the aerodynamic trends for the geometries produced from the optimization process show agreement with validation simulations. The findings of this work demonstrate the application of data-driven approaches to the analysis and design of vehicles in a production environment. Jonathan Tran and colleagues use aerodynamics-guided machine learning for the shape optimization of electric cars. Their approach saves computational time for high complexity engineering tasks, e.g., computational fluid dynamics-based design optimization.","PeriodicalId":72644,"journal":{"name":"Communications engineering","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683482","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 : 2024-11-19DOI: 10.1038/s44172-024-00299-w
Friedrich von Bülow, Felix Heinrich, William Arthur Paxton
Operational data of lithium-ion batteries from battery electric vehicles can be logged and used to model lithium-ion battery aging, i.e., the state of health. Here, we discuss future State of Health definitions, the use of data from battery production beyond production, the logging & aggregation of operational data and challenges of the State of Health in automotive applications. Our suggestions could improve data transfer efficiency and data storage costs. Operational data of lithium-ion batteries from battery electric vehicles can be logged and used to model lithium-ion battery aging, i.e., the state of health. Here, we discuss future State of Health definitions, the use of data from battery production beyond production, the logging & aggregation of operational data and challenges of the State of Health in automotive applications. Our suggestions could improve data transfer efficiency and data storage costs.
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Pub Date : 2024-11-16DOI: 10.1038/s44172-024-00316-y
Ciril Samuel Prasad, Henry O. Everitt, Gururaj V. Naik
It is not currently possible for an infrared camera to see through a hot window. The window’s own blinding thermal emission prevents objects on the other side from being imaged. Here, we demonstrate a path to overcoming this challenge by coating a hot window with an asymmetrically emitting infrared metasurface whose specially engineered imaginary index of refraction produces an asymmetric spatial distribution of absorption losses in its constituent nanoscale resonators. Operating at 873 K, this metasurface-coated window suppresses thermal emission towards the camera while being sufficiently transparent for thermal imaging, doubling the thermal imaging contrast when compared to a control window at the same temperature It is not currently possible for an infrared camera to see through a hot window. Now Ciril Samuel Prasad and colleagues report a metasurface-coated window which suppresses thermal emission towards an IR camera while being sufficiently transparent for thermal imaging.
目前，红外热像仪无法看穿热窗。热窗自身刺眼的热辐射使另一侧的物体无法成像。在这里，我们展示了一种克服这一难题的方法，即在热窗口上镀上一层非对称发射红外线的元表面，这种元表面经过专门设计，其假想折射率会在其组成的纳米级谐振器中产生非对称的吸收损耗空间分布。在 873 K 温度下工作时，这种元表面涂层窗口可抑制向照相机的热辐射，同时具有足够的热成像透明度，与相同温度下的对照窗口相比，热成像对比度提高了一倍。
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Pub Date : 2024-11-15DOI: 10.1038/s44172-024-00323-z
Rolf Behling, Christopher Hulme, Gavin Poludniowski, Panagiotis Tolias, Mats Danielsson
The spatiotemporal resolution of diagnostic X-ray images is limited by the erosion and rupture of conventional stationary and rotating anodes of X-ray tubes from extreme density of input power and thermal cycling of the anode material. Conversely, detector technology has developed rapidly. Finer detector pixels demand improved output from brilliant keV-type X-ray sources with smaller X-ray focal spots than today and would be available to improve the efficacy of medical imaging. In addition, novel cancer therapy demands for greatly improved output from X-ray sources. However, since its advent in 1929, the technology of high-output compact X-ray tubes has relied upon focused electrons hitting a spinning rigid rotating anode; a technology that, despite of substantial investment in material technology, has become the primary bottleneck of further improvement. In the current study, an alternative target concept employing a stream of fast discrete metallic microparticles that intersect with the electron beam is explored by simulations that cover the most critical uncertainties. The concept is expected to have far-reaching impact in diagnostic imaging, radiation cancer therapy and non-destructive testing. We outline technical implementations that may become the basis of future X-ray source developments based on the suggested paradigm shift. Rolf Behling and colleagues propose a new X-ray source concept to improve the resolution of X-ray computed tomography and non-destructive testing and the efficacy of radiation cancer therapy by replacing the rotary anode with a fast stream of microparticles in the electron beam.
传统 X 射线管的固定和旋转阳极会因输入功率密度过大和阳极材料的热循环而受到侵蚀和破裂，从而限制了诊断 X 射线图像的时空分辨率。相反，探测器技术却发展迅速。更精细的探测器像素要求高亮千伏 X 射线源的输出功率比现在更高，X 射线焦斑比现在更小，这将提高医学成像的效果。此外，新型癌症疗法也要求大幅提高 X 射线源的输出。然而，自 1929 年问世以来，高输出紧凑型 X 射线管技术一直依赖于聚焦电子撞击旋转的刚性阳极；尽管在材料技术方面进行了大量投资，但这项技术已成为进一步改进的主要瓶颈。在当前的研究中，我们通过涵盖最关键的不确定性的模拟，探索了采用快速离散金属微粒流与电子束相交的替代靶概念。预计这一概念将在诊断成像、癌症放射治疗和无损检测领域产生深远影响。我们概述了技术实现方法，这些方法可能会成为未来基于建议的范式转变开发 X 射线源的基础。
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