
doi:10.1007/s4054402207093 
Tribological behaviour of Ti3C2Tx nanosheets: Substratedependent tribochemical reactions 
20230801 
Friction 
22237704 
40544 
10.1007/s4054402207093 
1522 , 8 , 11 
http://dx.doi.org/10.1007/s4054402207093 
Springer , ©2022 The author(s) 
AbstractMXenes, a newly emerging class of layered two dimensional (2D) materials, are promising solid lubricants due to their 2D structure consisting of weaklybonded layers with a low shear strength and ability to form beneficial tribolayers. This work aims at evaluating for the first time MXenes lubrication performance and tribofilm formation ability on different metallic substrates (mirrorlapped Fe and Cu discs). After depositing MXenes via ethanol (1 wt%) on the substrates, pronounced differences in the resulting substratedependent frictional evolution are observed. While MXenes are capable to reduce friction for both substrates after the full evaporation of ethanol, MXenes lubricating effect on Cu is longlasting, with a 35fold increased lifetime compared to Fe. Raman spectra acquired in the weartracks of the substrates and counterbodies reveal notable differences in the frictioninduced chemical changes depending on the substrate material. In case of Fe, the progressive failure of MXenes lubrication generates different Fe oxides on both the substrate and the ball, resulting in continuously increasing friction and a poor lubrication effect. For Cu, sliding induces the formation of a Ti3C2based tribofilm on both rubbing surfaces, enabling a longlasting lubricating effect. This work boosts further experimental and theoretical work on MXenes involved tribochemical processes. 

Rota, Alberto
Bellina, Nicolas
Wang, Bo
Rosenkranz, Andreas

doi:10.1007/s10231022012975 
Concentration phenomenon of semiclassical states to reaction–diffusion systems 
20230801 
Annali di Matematica Pura ed Applicata (1923 ) 
16181891 
10231 
10.1007/s10231022012975 
1679 , 4 , 202 
http://dx.doi.org/10.1007/s10231022012975 
Springer , ©2022 Fondazione Annali di Matematica Pura ed Applicata and SpringerVerlag GmbH Germany, part of Springer Nature 
AbstractIn this paper, we consider concentration phenomenon of semiclassical states to the following 2Mcomponent reaction–diffusion system in $$\mathbb {R}\times \mathbb {R}^N$$R×RN, $$\begin{aligned} \left\{ \begin{aligned} \partial _t u&=\varepsilon ^2 \Delta _x uuV(x)v + \partial _v H(u, v),\\ \partial _t v&=\varepsilon ^2 \Delta _x v+v + V(x)u  \partial _u H(u, v), \end{aligned} \right. \end{aligned}$$∂tu=ε2ΔxuuV(x)v+∂vH(u,v),∂tv=ε2Δxv+v+V(x)u∂uH(u,v),where $$M \ge 1$$M≥1, $$N \ge 1$$N≥1, $$\varepsilon >0$$ε>0 is a small parameter, $$V \in C^1(\mathbb {R}^N, \, \mathbb {R})$$V∈C1(RN,R), $$H \in C^1(\mathbb {R}^M \times \mathbb {R}^M, \, \mathbb {R})$$H∈C1(RM×RM,R) and $$(u, v): \mathbb {R}\times \mathbb {R}^N \rightarrow \mathbb {R}^M \times \mathbb {R}^M$$(u,v):R×RN→RM×RM. It is proved that there exist semiclassical states concentrating around the local minimum points of V under mild assumptions. The approach is variational, which is mainly based upon a new linkingtype argument, iterative techniques and interior estimates for nonlinear parabolic equations. 

Gou, Tianxiang
Zhang, Zhitao

doi:10.1007/s10231023013043 
Vaisman manifolds and transversally Kähler–Einstein metrics 
20230801 
Annali di Matematica Pura ed Applicata (1923 ) 
16181891 
10231 
10.1007/s10231023013043 
1855 , 4 , 202 
http://dx.doi.org/10.1007/s10231023013043 
Springer , ©2023 Fondazione Annali di Matematica Pura ed Applicata and SpringerVerlag GmbH Germany, part of Springer Nature 
AbstractWe use the transverse Kähler–Ricci flow on the canonical foliation of a closed Vaisman manifold to deform the Vaisman metric into another Vaisman metric with a transverse Kähler–Einstein structure. We also study the main features of such a manifold. Among other results, using techniques from the theory of parabolic equations, we obtain a direct proof for the shorttime existence of the solution for transverse Kähler–Ricci flow on Vaisman manifolds, recovering in a particular setting a result of Bedulli et al. (J Geom Anal 28:697–725, 2018), but without employing the Molino structure theorem. Moreover, we investigate Einstein–Weyl structures in the setting of Vaisman manifolds and find their relationship with quasiEinstein metrics. Some examples are also provided to illustrate the main results. 

Slesar, Vladimir
Vîlcu, GabrielEduard

doi:10.1007/s10231023013052 
The fundamental equations of a pseudoFinsler submersion 
20230801 
Annali di Matematica Pura ed Applicata (1923 ) 
16181891 
10231 
10.1007/s10231023013052 
1877 , 4 , 202 
http://dx.doi.org/10.1007/s10231023013052 
Springer , ©2023 The Author(s) 
AbstractThe main result in this paper is the generalisation of the fundamental equations of a Riemannian submersion presented in the 1966 article by O’Neill (Michigan Math J 13:459–469, 1966) to the context of pseudoFinsler submersions. In the meantime, we also explore some basic properties of the O’Neill fundamental tensors of the submersion and study Finsler submersions with totally geodesic fibers.


Huber, Matthieu
Javaloyes, Miguel Angel

doi:10.1007/s10231023013061 
Integrating Nijenhuis structures 
20230801 
Annali di Matematica Pura ed Applicata (1923 ) 
16181891 
10231 
10.1007/s10231023013061 
1907 , 4 , 202 
http://dx.doi.org/10.1007/s10231023013061 
Springer , ©2023 The Author(s) 
AbstractA Nijenhuis operator on a manifold is a (1, 1) tensor whose Nijenhuis torsion vanishes. A Nijenhuis operator $${\mathcal {N}}$$N determines a Lie algebroid that knows everything about $${\mathcal {N}}$$N. In this sense, a Nijenhuis operator is an infinitesimal object. In this paper, we identify its global counterpart. Namely, we characterize Lie groupoids integrating the Lie algebroid of a Nijenhuis operator. We illustrate our integration result in various examples, including that of a linear Nijenhuis operator on a vector space or, which is equivalent, a preLie algebra structure. 

Pugliese, Fabrizio
Sparano, Giovanni
Vitagliano, Luca

doi:10.1007/s1023102201291x 
On a Runge theorem over $${\mathbb {R}}_3$$R3 
20230801 
Annali di Matematica Pura ed Applicata (1923 ) 
16181891 
10231 
10.1007/s1023102201291x 
1531 , 4 , 202 
http://dx.doi.org/10.1007/s1023102201291x 
Springer , ©2022 Fondazione Annali di Matematica Pura ed Applicata and SpringerVerlag GmbH Germany, part of Springer Nature 
AbstractIn this paper, we investigate a topological characterization of the Runge theorem in the Clifford algebra $$ {\mathbb {R}}_3$$R3 via the description of the homology groups of axially symmetric open subsets of the quadratic cone in $${\mathbb {R}}_3$$R3.


Bisi, Cinzia
Martino, Antonino
Winkelmann, Jörg

doi:10.1007/s10462022103298 
Physical laws meet machine intelligence: current developments and future directions 
20230701 
Artificial Intelligence Review 
15737462 
10462 
10.1007/s10462022103298 
6947 , 7 , 56 
http://dx.doi.org/10.1007/s10462022103298 
Springer , ©2022 The Author(s), under exclusive licence to Springer Nature B.V. 
AbstractThe advent of technology including big data has allowed machine learning technology to strengthen its place in solving different science and engineering complex problems. Conventional deep machine learning algorithms work as a black box while dealing with various complex physicsdriven problems. This problem can be reduced by integrating the physical laws with machine learning algorithms to ensure the developed models are complied with the physics and are potentially more explainable. This physicsinformed machine learning (PIML) approach allows the integration of physical laws in the form of PDEs into the loss function of the neural network, hence, constraining the training of the complex problems based on both the physical, experimental, and mathematical boundaries. This, hence, allows the development of a more general predictive model for different science, engineering, and optimization tasks. Considering such advancements in the machine learning domain, this review presents the systematic progress in the development of integrating physics into the neural networks and recent applications in solving various forward and inverse problems in science and engineering. This paper can serve as a reference for the researchers, developers, and users to get all information they need before developing, implementing, and deploying AI models and smart systems that are equipped with the PIML methodology. It highlights the benefits and points out its limitations and recommendations for further development. The review also compares the traditional datadriven machine learning and PIML approach in dealing with the physics of complex problems. In general, the PIML has been found to provide consistent results with the exact solutions and physical nature of the system. However, similar to other AI system development, a more robust and complex AI algorithm requires more computational power which is also the case in PIML development and implementation. It should be noted that different terminologies such as physicsinformed neural networks (PINN), scienceinformed neural networks, physicsinspired neural networks, and physicsconstrained neural networks have been used in the literature that describes the very similar concept of integrating physical laws with machine intelligence. For consistency, we use the PIML term throughout this paper which covers all listed terminologies in this regard. 

Muther, Temoor
Dahaghi, Amirmasoud Kalantari
Syed, Fahad Iqbal
Pham, Vuong

doi:10.1007/s00466023023285 
Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials 
20230701 
Computational Mechanics 
14320924 
466 
10.1007/s00466023023285 
221 , 1 , 72 
http://dx.doi.org/10.1007/s00466023023285 
Springer , ©2023 The Author(s), under exclusive licence to SpringerVerlag GmbH Germany, part of Springer Nature 
AbstractPhononic metamaterials have the capability to manipulate the propagation of mechanical waves. The traditional finite element (FE) analysisbased methods for predicting phonon dispersion curves are computationally expensive for structure optimization that may require thousands of design evaluations, especially when applied to highresolution metamaterial models with a large number of elements. To address this issue, this paper presents two physicsembedded deep convolutional neural networks to predict the phonon dispersion curves of 2D metamaterials: (1) a transfer learningbased convolutional neural network (TLCNN) and (2) a physicsguided convolutional neural network (PGCNN). The physics knowledge is embedded into the two proposed models by modifying the loss function of the convolutional neural network (CNN). A comparative study among CNN, TLCNN and PGCNN is conducted to understand the relative merits. The effectiveness of the physicsembedded methods is evaluated by comparing the predicted normalized eigenfrequencies with those obtained by direct numerical simulations (DNS) using FE simulation. Furthermore, a comparison of the computational costs, which include computing time and memory usage, is presented among DNS, CNN, TLCNN and PGCNN. It is demonstrated that the proposed TLCNN and PGCNN have the potential to improve prediction accuracy with a limited amount of input data. However, the computational costs of the “offline” model training are still significant. Among the three methods, PGCNN shows the best prediction accuracies on both the training and test sets. 

Wang, Zihan
Xian, Weikang
Li, Ying
Xu, Hongyi

doi:10.1007/s003760222141z 
Black Carbon Size in Snow of Chinese Altai Mountain in Central Asia 
20230701 
Advances in Atmospheric Sciences 
18619533 
376 
10.1007/s003760222141z 
1199 , 7 , 40 
http://dx.doi.org/10.1007/s003760222141z 
Springer , ©2023 Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and SpringerVerlag GmbH Germany, part of Springer Nature 
AbstractBlack carbon (BC) in snow plays an important role to accelerate snow melting. However, current studies mostly focused on BC concentrations, few on their size distributions in snow which affected BC’s effect on albedo changes. Here we presented refractory BC (rBC) concentrations and size distributions in snow collected from Chinese Altai Mountains in Central Asia from November 2016 to April 2017. The results revealed that the average rBC concentrations were 5.77 and 2.82 ng g−1 for the surface snow and subsurface snow, which were relatively higher in the melting season (April) than that in winter (November–January). The mass median volumeequivalent diameter of rBC size in surface snow was approximately at 120–150 nm, which was typically smaller than that in the atmosphere (about 200 nm for urban atmosphere). However, there existed no specific mass median volumeequivalent diameter of BC size for subsurface snow in winter. While during the melting season, the median mass size of rBC in subsurface snow was similar to that in surface snow. Backward trajectories indicated that anthropogenic sourced BC dominated rBC in snow (70%–85%). This study will promote our understanding on BC size distributions in snow, and highlight the possible impact of BC size on climate effect.摘 要雪冰中的黑碳对加速积雪消融具有重要作用。然而,目前的研究大多集中于黑碳浓度的影响,很少关注积雪中黑碳粒径的分布,这显著影响黑碳对反照率变化的评估。鉴于此,我们于2016年11月至2017年4月在北疆阿勒泰地区开展了积雪中难熔黑碳浓度和粒径分布的研究。结果表明,表雪和次表层雪的黑碳平均浓度分别为5.77和2.82 ng g−1,且在融雪季节(4月)相对冬季(11月至1月)偏高。表雪中黑碳粒径的质量中值直径约为120−150 nm,小于大气中黑碳颗粒的直径(城市大气中约为200 nm)。然而,冬季次表层雪中黑碳粒径的质量中值直径不明显,而在融雪季节,次表层雪中黑碳的中值粒径与表层雪中相似。结合火点资料的后向轨迹表明,北疆阿勒泰地区积雪中人为源黑碳占主导(70%−85%)。该研究将进一步促进我们对雪中黑碳粒径分布的理解,并为评估黑碳粒径大小对气候效应的可能影响提供基础数据。 

Zhang, Yulan
Kang, Shichang
Gao, Tanguang
Sprenger, Michael
Zhang, Wei
Wang, Zhaoqing

doi:10.1007/s1119202304739w 
Finally! How time lapse in Nobel Prize reception affects emotionality in the Nobel Prize banquet speeches 
20230701 
Scientometrics 
15882861 
11192 
10.1007/s1119202304739w 
4089 , 7 , 128 
http://dx.doi.org/10.1007/s1119202304739w 
Springer , ©2023 The Author(s) 
AbstractWe have a limited understanding of the role emotions play in academia, as exploring emotions consistently and comparably is challenging due to the powerful influence of contextual factors. However, we have identified an interesting setting to empirically investigate the emotional response in academia by examining Nobel Prize winners. Scientists who aspire to earn a Nobel Prize are under pressure from their environment if they have not yet received the Prize. While there are various indicators that suggest the Nobel Prize is forthcoming, the question of “when” weighs heavily on the minds of leading candidates. Consequently, waiting for the Nobel Prize is emotionally taxing. We therefore hypothesize that Nobel laureates who have experienced a prolonged wait for the award would feel a stronger sense of relief upon receiving it. We are interested in measuring their level of emotionality after receiving the Nobel Prize by analyzing their banquet speeches using linguistic content analysis. Banquet speeches provide a consistent and controlled setting to compare emotionality across scientists and over time, as we can measure the same responses to the same recognition under the same circumstances. We expect that waiting longer for the Nobel Prize will increase the positive emotionality of Nobelists’ speeches. The waiting time is determined by calculating the years since the Nobel Prizewinning work was performed. By conducting this timelapse study, we find a robust positive correlation between waiting time and positive emotions in Nobel Prize banquet speeches. We conclude that scientists who waited longer for the Nobel Prize reported higher levels of positive emotions during their speeches. 

Aranzales, Iván
Chan, Ho Fai
Torgler, Benno

doi:10.1007/s00466023023374 
Special issue of computational mechanics on machine learning theories, modeling, and applications to computational materials science, additive manufacturing, mechanics of materials, design and optimization 
20230701 
Computational Mechanics 
14320924 
466 
10.1007/s00466023023374 
1 , 1 , 72 
http://dx.doi.org/10.1007/s00466023023374 
Springer , ©2023 The Author(s), under exclusive licence to SpringerVerlag GmbH Germany, part of Springer Nature 


Liu, Wing Kam
Bessa, Miguel A.
Chinesta, Francisco
Li, Shaofan
Trask, Nathaniel

doi:10.1007/s0037602222259 
Impact of Revised Trigger and Closure of the DoublePlume Convective Parameterization on Precipitation Simulations over East Asia 
20230701 
Advances in Atmospheric Sciences 
18619533 
376 
10.1007/s0037602222259 
1225 , 7 , 40 
http://dx.doi.org/10.1007/s0037602222259 
Springer , ©2023 Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and SpringerVerlag GmbH Germany, part of Springer Nature 
AbstractA doubleplume convective parameterization scheme is revised to improve the precipitation simulation of a global model (GlobaltoRegional Integrated Forecast System; GRIST). The improvement is achieved by considering the effects of largescale dynamic processes on the trigger of deep convection. The closure, based on dynamic CAPE, is improved accordingly to allow other processes to consume CAPE under the more restricted convective trigger condition. The revised convective parameterization is evaluated with a variableresolution model setup (110−35 km, refined over East Asia). The Atmospheric Model Intercomparison Project (AMIP) simulations demonstrate that the revised convective parameterization substantially delays the daytime precipitation peaks over most land areas, leading to an improved simulated diurnal cycle, evidenced by delayed and less frequent afternoon precipitation. Meanwhile, changes to the threshold of the trigger function yield a small impact on the diurnal amplitude of precipitation because of the consistent setting of dCAPEbased trigger and closure. The simulated mean precipitation remains reasonable, with some improvements evident along the southern slopes of the Tibetan Plateau. The revised scheme increases convective precipitation at the lower levels of the windward slope and reduces the largescale precipitation over the upper slope, ultimately shifting the rainfall peak southward, which is in better agreement with the observations.摘 要本研究在双羽对流参数化方案的触发条件中考虑了大尺度动力过程的影响,从而提高了全球区域一体化预测系统(GRIST)对降水过程的模拟能力。文中还针对不同的触发条件,对基于动态对流有效位能(CAPE)的闭合假设进行了改进,使其它他物理过程在更严格的对流触发条件下消耗CAPE。并基于加密东亚地区的变分辨率(110–35 km)AMIP模拟,评估了改进方案对降水模拟的影响。结果表明,改进方案推迟了陆地日降水峰值的发生时间,减少了午后降水频率,使模拟降水日循环得以改善。同时,得益于相匹配的动态闭合假设,触发阈值变化对降水变化强度的影响很小。此外,降水平均态也比较合理,青藏高原南坡的降水分布亦有改进。迎风低坡的对流降水量增加,从而减少了水汽向高坡的输送、削弱了高坡上虚假的大尺度降水量,使降水峰值南移,模拟结果更接近观测。 

Li, Xiaohan
Zhang, Yi
Lin, Yanluan
Peng, Xindong
Zhou, Baiquan
Zhai, Panmao
Li, Jian

doi:10.1007/s003760222195y 
Phosphorus Limitation on Carbon Sequestration in China under RCP8.5 
20230701 
Advances in Atmospheric Sciences 
18619533 
376 
10.1007/s003760222195y 
1187 , 7 , 40 
http://dx.doi.org/10.1007/s003760222195y 
Springer , ©2023 Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and SpringerVerlag GmbH Germany, part of Springer Nature 
AbstractCurrently, there is a lack of understanding regarding carbon (C) sequestration in China arising as a result of phosphorus (P) limitation. In this study, a global land surface model (CABLE) was used to investigate the response of C uptake to P limitation after 1901. In China, P limitation resulted in reduced net primary production (NPP), heterotrophic respiration, and net ecosystem production (NEP) in both the 2030s and the 2060s. The reductions in NEP in the period 2061–70 varied from 0.32 Pg C yr−1 in China to 5.50 Pg C yr−1 at the global scale, translating to a decrease of 15.0% for China and 7.6% globally in the period 2061–70, relative to the changes including C and nitrogen cycles. These ranges reflect variations in the magnitude of P limitation on C uptake (or storage) at the regional and global scales. Both in China and at the global scale, these differences can be attributed to differences in soil nutrient controls on C uptake, or positive feedback between NPP and soil decomposition rates, or both. Our results highlight the strong ability of P limitation to influence the pattern, response, and magnitude of C uptake under future conditions (2030s–2060s), which may help to clarify the potential influence of P limitation when projecting C uptake in China.摘 要目前,磷限制对中国和全球的碳汇和碳存储影响强度与差异缺乏系统地研究。本研究基于全球陆面模式CABLE,分析了1901年后磷限制对中国未来碳汇和碳存储变化的影响。在中国,磷限制引起了2030s和2060s的净初级生产力(NPP)、异养呼吸(HR)和净生态系统生产力(NEP)减少。相对于没有考虑磷循环过程,2061至2070年,中国与全球净初级生产力分别减少了0.32Pg C yr1(或者15.0%)和5.50Pg C yr1(或者7.6%)。这表明磷限制对中国碳汇的限制强度明显高于全球水平。土壤养分对碳吸收的影响差异、NPP和土壤分解速率之间的正反馈均会改变磷限制影响的强度。我们的研究强调了未来情景下磷限制对中国碳吸收有重要的影响。考虑磷限制的影响将有助于改善模式对中国碳汇评估与预估的能力。 

Peng, Jing
Dan, Li
Tang, Xiba

doi:10.1007/s0037602222491 
Characteristics of PM2.5 and Its Reactive Oxygen Species in Heating Energy Transition and Estimation of Its Impact on the Environment and Health in China—A Case Study in the Fenwei Plain 
20230701 
Advances in Atmospheric Sciences 
18619533 
376 
10.1007/s0037602222491 
1175 , 7 , 40 
http://dx.doi.org/10.1007/s0037602222491 
Springer , ©2023 Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and SpringerVerlag GmbH Germany, part of Springer Nature 
AbstractTo reduce the adverse effects of traditional domestic solid fuel, the central government began implementing a clean heating policy in northern China in 2017. Clean coal is an alternative lowcost fuel for rural households at the present stage. In this study, 18 households that used lump coal, biomass, and clean coal as the main fuel were selected to evaluate the benefits of clean heating transformation in Tongchuan, an energy city in the Fenwei Plain, China. Both indoor and personal exposure (PE) samples of fine particulate matter (PM2.5) were synchronically collected. Compared with the lump coal and biomass groups, the indoor PM2.5 concentration in the clean coal group is 43.6% and 20.0% lower, respectively, while the values are 16.8% and 21.3% lower, respectively, in the personal exposure samples. PM2.5bound elements Cd, Ni, Zn, and Mn strongly correlated with reactive oxygen species (ROS) levels in all fuel groups, indicating that transition metals are the principal components to generate oxidative stress. Using a reliable estimation method, it is predicted that after the substitution of clean coal as a household fuel, the allcause, cardiovascular, and respiratory disease that causes female deaths per year could be reduced by 16, 6, and 3, respectively, in the lump coal group, and 22, 8, and 3, respectively, in the biomass group. Even though the promotion of clean coal has led to impressive environmental and health benefits, the efficiencies are still limited. More environmentalfriendly energy sources must be promoted in the rural regions of China.摘 要为减少农村居民使用传统固体燃料取暖产生的不利环境与健康影响,我国于2017年在北方进行清洁取暖试点推广。本研究中,我们选择汾渭平原地区典型的使用块煤、生物质和清洁型煤为主要冬季取暖燃料的家庭进行室内和个人暴露PM2.5样品的采集与问卷调查,以定量评估该地区采用清洁型煤取暖带来的环境与健康收益。结果表明,清洁型煤组的室内PM2.5浓度与块煤组和生物质组相比分别降低了43.6%和20.0%,人体暴露PM2.5浓度分别降低了16.8%和21.3%。清洁型煤组的活性氧簇(ROS)活性均值在室内与块煤组和生物质组相比分别降低了40.6%和10.2%。PM2.5中的元素如镉、镍、锌和锰与ROS在三个燃料组均显示出显著的正相关性,表明过渡金属对PM2.5的氧化应激有重要影响。结合流行病学获得的关中地区PM2.5暴露反应曲线与健康影响函数,计算出将清洁型煤替代传统固体燃料取暖后,使用块煤家庭由于全病因、心血管疾病和呼吸系统疾病导致的女性死亡人数分别约减少16、6和3人,使用生物质的女性死亡人数分别减少约22、8和3人。虽然清洁型煤的推广能够带来较为显著的环境和健康收益,但其效果仍然有限。因此还须在我国农村地区不断推广更多种、更清洁的环境友好型能源。 

Wang, Zexuan
Xu, Hongmei
Feng, Rong
Gu, Yunxuan
Sun, Jian
Liu, Suixin
Zhang, Ningning
Li, Dan
Wang, Tao
Qu, Linli
Ho, Steven Sai Hang
Shen, Zhenxing
Cao, Junji

doi:10.1007/s00466023023249 
Efficient multiscale modeling of heterogeneous materials using deep neural networks 
20230701 
Computational Mechanics 
14320924 
466 
10.1007/s00466023023249 
155 , 1 , 72 
http://dx.doi.org/10.1007/s00466023023249 
Springer , ©2023 The Author(s) 
AbstractMaterial modeling using modern numerical methods accelerates the design process and reduces the costs of developing new products. However, for multiscale modeling of heterogeneous materials, the wellestablished homogenization techniques remain computationally expensive for high accuracy levels. In this contribution, a machine learning approach, convolutional neural networks (CNNs), is proposed as a computationally efficient solution method that is capable of providing a high level of accuracy. In this work, the dataset used for the training process, as well as the numerical tests, consists of artificial/real microstructural images (“input”). Whereas, the output is the homogenized stress of a given representative volume element $$\mathcal {RVE}$$RVE. The model performance is demonstrated by means of examples and compared with traditional homogenization methods. As the examples illustrate, high accuracy in predicting the homogenized stresses, along with a significant reduction in the computation time, were achieved using the developed CNN model. 

Aldakheel, Fadi
Elsayed, Elsayed S.
Zohdi, Tarek I.
Wriggers, Peter

doi:10.1007/s00466023022899 
Physicsinformed machinelearning model of temperature evolution under solid phase processes 
20230701 
Computational Mechanics 
14320924 
466 
10.1007/s00466023022899 
125 , 1 , 72 
http://dx.doi.org/10.1007/s00466023022899 
Springer , ©2023 © Battelle Memorial Institute 
AbstractWe model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermomechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting microstructural scale mechanisms of heat generation to macroscale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the microstructural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental dataset. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations. 

King, Ethan
Li, Yulan
Hu, Shenyang
Machorro, Eric

doi:10.1007/s003760222208x 
Fluorescence Properties and Chemical Composition of Fine Particles in the Background Atmosphere of North China 
20230701 
Advances in Atmospheric Sciences 
18619533 
376 
10.1007/s003760222208x 
1159 , 7 , 40 
http://dx.doi.org/10.1007/s003760222208x 
Springer , ©2023 Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press and SpringerVerlag GmbH Germany, part of Springer Nature 
AbstractTo understand the aerosol characteristics in a regional background environment, fineparticle (PM2.5, n = 228) samples were collected over a oneyear period at the Shangdianzi (SDZ) station, which is a Global Atmospheric Watch regional background station in North China. The chemical and optical characteristics of PM2.5 were analyzed, including organic carbon, elemental carbon, watersoluble organic carbon, watersoluble inorganic ions, and fluorescent components of watersoluble organic matter. The source factors of major aerosol components are apportioned, and the sources of the fluorescent chromophores are further analyzed. The major chemical components of PM2.5 at SDZ were NO3−, organic matter, SO42−, and NH4+. Annually, watersoluble organic carbon contributed 48% ± 15% to the total organic carbon. Secondary formation (52%) and fossil fuel combustion (63%) are the largest sources of watersoluble organic matter and waterinsoluble organic matter, respectively. In addition, three humiclike and one proteinlike matter were identified via parallel factor analysis for excitation—emission matrices. The fluorescence intensities of the components were highest in winter and lowest in summer, indicating the main impact of burning sources. This study contributes to understanding the chemical and optical characteristics of ambient aerosols in the background atmosphere.摘 要地处北京市远郊区的上甸子区域大气本底站气溶胶的化学组分浓度,能很好地代表京津冀地区大气区域本底的背景浓度,同时也反映北京城区和周边地区污染物传输的影响。本文在上甸子站采集了四个季节共228个PM2.5样品,通过分析其化学组分和荧光性质,包括有机碳、元素碳、水溶性有机碳、水溶性无机离子以及水溶性有机物的荧光组分,并利用气团后向轨迹和正矩阵因数分解法解析来源,为进一步研究京津冀地区气溶胶本底浓度变化,特别是人类活动如何影响背景地区气溶胶形成提供观测依据。结果表明,上甸子PM2.5的化学组分与城市气溶胶组成基本一致,主要为NO3、有机物、SO42和NH4+。其中,SO42在夏季浓度最高,其他组分浓度是秋季最高。水溶性有机碳占总有机碳的48%±15%。大气二次生成(52%)和化石燃料燃烧(63%)分别是水溶性有机物和水不溶性有机物的最大来源。运用平行因子分析法解析三维荧光光谱确定三种类腐殖酸和一种类蛋白荧光组分,荧光强度在冬季最强、夏季最弱。荧光强度的季节变化和荧光指数表明,水溶性荧光物质主要是燃烧源和二次生成的贡献,少量来源于微生物源。与冬季相比,夏季大气荧光物质更倾向于高度腐殖化或高度芳香化。研究发现,即使在上甸子这样的空气洁净地区,化石燃料燃烧和生物质燃烧等人为排放源对气溶胶的贡献也非常显著。 

Li, Ping
Yue, Siyao
Yang, Xiaoyang
Liu, Di
Zhang, Qiang
Hu, Wei
Hou, Shengjie
Zhao, Wanyu
Ren, Hong
Li, Gang
Gao, Yuanguan
Deng, Junjun
Xie, Qiaorong
Sun, Yele
Wang, Zifa
Fu, Pingqing

doi:10.1007/s405440220658x 
Application of physicsinformed neural network in the analysis of hydrodynamic lubrication 
20230701 
Friction 
22237704 
40544 
10.1007/s405440220658x 
1253 , 7 , 11 
http://dx.doi.org/10.1007/s405440220658x 
Springer , ©2022 The author(s) 
AbstractThe last decade has witnessed a surge of interest in artificial neural network in many different areas of scientific research. Despite the rapid expansion in the application of neural networks, few efforts have been carried out to introduce such a powerful tool into lubrication studies. Thus, this work aims to apply the physicsinformed neural network (PINN) to the hydrodynamic lubrication analysis. The 2D Reynolds equation is solved. The PINN is a meshless method and does not require big data for network training compared with classical methods. Our results are consistent with those obtained by experiments and the finite element method. Hence, we envision that the PINN method will have great application potential in lubrication and bearing research. 

Zhao, Yang
Guo, Liang
Wong, Patrick Pat Lam

doi:10.1007/s00723023015449 
Clustering into Three Groups on a Quantum Processor of Five Spins S = 1, Controlled by Pulses of Resonant RF Fields 
20230701 
Applied Magnetic Resonance 
16137507 
723 
10.1007/s00723023015449 
661 , 7 , 54 
http://dx.doi.org/10.1007/s00723023015449 
Springer , ©2023 The Author(s), under exclusive licence to SpringerVerlag GmbH Austria, part of Springer Nature 
AbstractWe consider a quantum processor based on five qutrits represented by spins S = 1, which is driven by radio frequency (RF) pulses selective in transitions between adjacent levels. Numerical simulation of the implementation of the quantumadiabatic clustering algorithm was performed on the example of partitioning a set of six points into three groups. We find the amplitudes and durations of rectangular RF pulses, as well as the durations of free evolution intervals in the control pulse sequence, which made it possible to engineer a timedependent effective Hamiltonian in the discretetime approximation. Also we studied the dependence of the implementation fidelity on the parameters. We took quadrupole nuclei as qutrits, but the results obtained will be useful for controlling quantum processors based on qutrits represented by other systems. 

Pichkovskiy, I. S.
Zobov, V. E.

doi:10.1007/s0046602302293z 
HiDeNNFEM: a seamless machine learning approach to nonlinear finite element analysis 
20230701 
Computational Mechanics 
14320924 
466 
10.1007/s0046602302293z 
173 , 1 , 72 
http://dx.doi.org/10.1007/s0046602302293z 
Springer , ©2023 The Author(s), under exclusive licence to SpringerVerlag GmbH Germany, part of Springer Nature 
AbstractThe hierarchical deeplearning neural network (HiDeNN) (Zhang et al. Computational Mechanics, 67:207–230) provides a systematic approach to constructing numerical approximations that can be incorporated into a wide variety of Partial differential equations (PDE) and/or Ordinary differential equations (ODE) solvers. This paper presents a framework of the nonlinear finite element based on HiDeNN approximation (nonlinear HiDeNNFEM). This is enabled by three basic building blocks employing structured deep neural networks: (1) A partial derivative operator block that performs the differentiation of the shape functions with respect to the element coordinates, (2) An radaptivity block that improves the local and global convergence properties and (3) A materials derivative block that evaluates the material derivatives of the shape function. While these building blocks can be applied to any element, specific implementations are presented in 1D and 2D to illustrate the application of the deep learning neural network. Twostep optimization schemes are further developed to allow for the capabilities of radaptivity and easy integration with any existing FE solver. Numerical examples of 2D and 3D demonstrate that the proposed nonlinear HiDeNNFEM with radaptivity provides much higher accuracy than regular FEM. It also significantly reduces element distortion and suppresses the hourglass mode. 

Liu, Yingjian
Park, Chanwook
Lu, Ye
Mojumder, Satyajit
Liu, Wing Kam
Qian, Dong


