bluuebunny/crossref_metadata_2025 huggingface dataset:
Measurement of electromagnetic pulse in laser acceleration enhanced by near-critical density targets
Authors: He Q. Y. , Yan W. , Liu Z. P. , Yan Y. , Fang Y. L. , Li C. T. , Wu Q. F. , Song T. , Li T. S. , Lin C. , Ma W. J. , Yan X. Q.
Published: October 2024

High-power laser interactions with solid targets create an abundance of high-energy charged particles, resulting in the generation of intense electromagnetic pulses (EMPs), which are strongly pertinent to the target parameters. In this study, the characteristics of EMPs generated by relativistic femtosecond laser irradiation of double-layer targets composed of near-critical density carbon nanotube foams (CNFs) and an aluminum (Al) foil are investigated. The results demonstrate that the CNF double-layer targets accelerate proton energy by over 1.6 times compared to a single-layer Al plane target, thereby indirectly amplifying the EMP amplitude by over 3.6 times. The findings are beneficial to gaining insight into EMPs induced by femtosecond laser coupling with near-critical density targets and open a new avenue to achieve tunable EMPs by managing the material and structure of the target to optimize the coupling efficiency between the laser and solid targets.
Quantifying multifractal anisotropy in two dimensional objects
Authors: Rak Rafał , Drożdż Stanisław , Kwapień Jarosław , Oświęcimka Paweł
Published: October 2024

An efficient method of exploring the effects of anisotropy in the fractal properties of 2D surfaces and images is proposed. It can be viewed as a direction-sensitive generalization of the multifractal detrended fluctuation analysis into 2D. It is tested on synthetic structures to ensure its effectiveness, with results indicating consistency. The interdisciplinary potential of this method in describing real surfaces and images is demonstrated, revealing previously unknown directional multifractality in data sets from the Martian surface and the Crab Nebula. The multifractal characteristics of Jackson Pollock’s paintings are also analyzed. The results point to their evolution over the time of creation of these works.
Active control of transonic airfoil flutter using synthetic jets through deep reinforcement learning
Authors: Gong Tianchi , Wang Yan , Zhao Xiang
Published: October 2024

This paper presents a novel framework for the active control of transonic airfoil flutter using synthetic jets through deep reinforcement learning (DRL). The research, conducted in a wide range of Mach numbers and flutter velocities, involves an elastically mounted airfoil with two degrees of freedom of pitching and plunging oscillations, subjected to transonic flow conditions at varying Mach numbers. Synthetic jets with zero-mass flux are strategically placed on the airfoil's upper and lower surfaces. This fluid–structure interaction (FSI) problem is treated as the learning environment and is addressed by using the arbitrary Lagrangian–Eulerian lattice Boltzmann flux solver (ALE-LBFS) coupled with a structural solver on dynamic meshes. DRL strategies with proximal policy optimization agents are introduced and trained, based on the velocities probed around the airfoil and the dynamic responses of the structure. The results demonstrate that the pitching and plunging motions of the airfoil in the limited cycle oscillation (LCO) can be effectively alleviated across an extended range of Mach numbers and critical flutter velocities beyond the initial training conditions for control onset. Furthermore, the aerodynamic performance of the airfoil is also enhanced, with an increase in lift coefficient and a reduction in drag coefficient. Even in previously unseen environments with higher flutter velocities, the present strategy is achievable satisfactory control results, including an extended flutter boundary and a reduction in the transonic dip phenomenon. This work underscores the potential of DRL in addressing complex flow control challenges and highlights its potential to expedite the application of DRL in transonic flutter control for aeronautical applications.
A generalized mathematical model for the damped free motion of a liquid column in a vertical U-tube
Authors: Kannaiyan Ashok , R Thundil Karuppa Raj , Sarno Luca , Urbanowicz Kamil , Martino Riccardo
Published: October 2024

A one-dimensional mathematical model is developed to analyze the unsteady characteristics of arbitrary damped free motion, denoted as Z(τ), in a rigid, constant-diameter vertical column of a U-tube filled with an incompressible Newtonian liquid, where τ represents time. This model utilizes a simplified unsteady momentum equation derived from the Navier–Stokes equations in a circular coordinate system. Moreover, it incorporates assumptions about the periodicity of arbitrary damped free oscillations and employs the Fourier series representation to characterize the damped free motion. The combination of assumptions made for periodicity, the simplified momentum equation, and the Fourier series representation makes the current mathematical model unique and novel compared to prevailing models in the literature. In this model, the governing partial differential equation contains two dependent variables: Z(τ) is the known variable, as one can measure from experiments, and the instantaneous velocity uz is the unknown variable. Fitting the experimental data into the Fourier series provides the Fourier coefficients associated with the specific experiment. The Laplace transform method is used to determine the analytical solution for uz corresponding to the known Z(τ). The analytical expressions for instantaneous flow characteristics of practical importance, including area-averaged velocity, wall shear stress, and acceleration/deceleration, are deduced from uz. The analytical solutions presented are valid for generalized unsteady motions, including underdamped oscillations with varying amplitudes and periods, underdamped oscillations with varying amplitudes and constant period, and overdamped motion that does not exhibit a single oscillation. The findings from the present model offer insights for formulating a new friction model.
Exploring electronic resonances in pyridine: Insights from orbital stabilization techniques
Authors: Pyla Maneesh , Matsika Spiridoula
Published: October 2024

Electron attachment to pyridine results in electronic resonances, metastable states that can decay through electronic or nuclear degrees of freedom. This study uses orbital stabilization techniques combined with bound electronic structure methods, based on equation of motion coupled cluster or multi-reference methods, to calculate positions and widths of electronic resonances in pyridine that exist below 10 eV. We report four 2B1 and four 2A2 resonances, including one 2B1 not previously reported experimentally and two 2A2 resonances not reported at all in the literature. The two lower energy resonances are one-particle shape resonances, while the remaining are mixed or primarily core-excited resonances. Multi-reference perturbation theory provides the best description of these resonances, especially when their character is mixed. We describe the character of these resonances qualitatively and calculate Dyson orbitals, which provide information about their decay channels.
Double-suction pump impeller optimization and performance analysis based on nonlinear programming quadratic Lagrangian algorithm
Authors: Wang Jiaqiong , Chen Hongxu , Zhou Ling , Zhang Ruijie , Jiang Guotao
Published: October 2024

To reduce the energy consumption of the double-suction pump, this study optimizes the impeller of the pump based on the Nonlinear Programming Quadratic Lagrangian (NLPQL) algorithm in OptiSLang. Taking the weighted efficiency as the optimization objective, 28 design parameters of the impeller are selected as the input variables, 300 groups of sample schemes are generated through the Advanced Latin Hypercubic Sampling method, and an automatic numerical simulation platform is setup to optimize the impeller under different working conditions. A metamodel of optimal prognosis is built by OptiSLang based on sample data and system response, seven variables with high sensitivity are screened out, and the NLPQL algorithm is adopted to carry out the optimization design to obtain the optimized model. The optimized model is, then, verified by the numerical simulation, and it is analyzed and compared with the original model from external characteristic curves and internal flow mechanism. The results show that: the match between the optimized design variables and the optimization objective is good; the weighted efficiency of the optimized pump is increased by 4.3%, the high efficiency zone is obviously enlarged and the shaft power is significantly decreased; the flow lines in the optimized impeller are obviously improved, the vortex is eliminated, and the velocity distribution is more uniform; the low pressure zone at the inlet of the vane is obviously reduced.
Hydrodynamic response characteristics of curved shape trash intercepting net under wave–current combined conditions
Authors: Ren Xiudi , Du Weiming , Qiao Dongsheng , Liang Ning , Tang Guoqiang , Lu Lin , Ou Jinping
Published: October 2024

Under the influence of waves and ocean currents, the mesh panels of trash nets at nuclear power plants exhibit a curved shape. The combined action of waves and currents creates a Doppler effect that significantly influences the hydrodynamic response of these nets. This paper used OpenFOAM to establish a porous media macroscopic computational fluid dynamics model for analyzing the forces on curved trash nets. Wave–current combined conditions were simulated by introducing a uniform current in the direction of wave propagation. Forces on the nets under wave–current combined conditions were analyzed and compared with forces under separate uniform current and wave conditions. The results indicate that wave run-up on the nets is more significant under wave–current combined conditions than under wave action alone. The dimensionless horizontal force decreases more rapidly with increasing wave steepness (kA) and wave number (kd) under combined conditions than with linear superposition. Specifically, the rate of increase in dimensionless horizontal force with current speed, at the same blockage ratio, is about twice that of linear superposition. Additionally, the tension in the main cable of the nets is 17% higher under combined conditions than with linear superposition.
Quantized embedding approaches for collective strong coupling—Connecting ab initio and macroscopic QED to simple models in polaritonics
Authors: Lindel Frieder , Lentrodt Dominik , Buhmann Stefan Yoshi , Schäfer Christian
Published: October 2024

Collective light–matter interactions have been used to control chemistry and energy transfer, yet accessible approaches that combine ab initio methodology with large many-body quantum optical systems are missing due to the fast increase in computational cost for explicit simulations. We introduce an accessible ab initio quantum embedding concept for many-body quantum optical systems that allows us to treat the collective coupling of molecular many-body systems effectively in the spirit of macroscopic quantum electrodynamics while keeping the rigor of ab initio quantum chemistry for the molecular structure. Our approach fully includes the quantum fluctuations of the polaritonic field and yet remains much simpler and more intuitive than complex embedding approaches such as dynamical mean-field theory. We illustrate the underlying assumptions by comparison to the Tavis–Cummings model. The intuitive application of the quantized embedding approach and its transparent limitations offer a practical framework for the field of ab initio polaritonic chemistry to describe collective effects in realistic molecular ensembles.
A novel and effective method for characterizing time series correlations based on martingale difference correlation
Authors: Li Ang , Shang Du , Shang Pengjian
Published: October 2024

Analysis of correlation between time series is an essential step for complex system studies and dynamical characteristics extractions. Martingale difference correlation (MDC) theory is mainly concerned with the correlation of conditional mean values between response variables and predictor variables. It is the generalization and deepening of the Pearson correlation coefficient, Spearman correlation coefficient, Kendall correlation coefficient, and other statistics. In this paper, on the basis of phase space reconstruction, the generalized dependence index (GDI) is proposed by using MDC and martingale difference divergence matrix theories, which can measure the degree of dependence between time series more effectively. Moreover, motivated by the theoretical framework of the refined distance correlation method, the corresponding dependence measure (DE) is employed in this paper to construct the DE-GDI plane, so as to comprehensively and intuitively distinguish different types of data and deeply explore the operating mechanism behind the relevant time series and complex systems. According to the performances tested by the different simulated and real-world data, our proposed method performs relatively reasonably and reliably in dependence measuring and data distinguishing. The proposal of this complex data clustering method can not only recognize the features of complex systems but also distinguish them effectively so as to acquire more relevant detailed information.
Sloshing in a tank with elastic side walls and a membrane cover
Authors: Sun Shi Yan , Wu G. X.
Published: October 2024

The coupled problem of liquid sloshing in a tank with a membrane cover and elastic side walls is investigated. The velocity potential theory is used for the flow and the linear elastic theory for the cover and side walls. For the former, the vertical mode expansion is used, in which all the roots of the dispersion relationship are first found. The expansion automatically satisfies the governing equation, the tank bottom, and the tank cover conditions. The deflections of the side walls are expanded into a cosine series together with a four term polynomial, which is vital for the procedure to succeed. These expansions are then matched through the dynamic and kinematic equations of the plates, and the problem is completed by imposing the edge conditions of the plate and membrane. Through the combined matrix equation, the natural frequencies of the system are obtained. In addition, the nature of the dispersion relationship of the membrane is analyzed. Explicit solutions are obtained for some special cases, and the link with the free surface sloshing is established. Extensive numerical results are provided, and their physics is analyzed.
Comparison of a visiting subspecialist ophthalmology service to Royal Darwin Hospital with interstate transfers: costs and clinical outcomes of treatment
Authors: Lam Danny , Moore Madelaine , Cunich Michelle , Lake Stewart , Ho I-Van , McCluskey Peter , Mahendrarajah Tharmalingam
Published: October 2024

Objective This study aimed to compare the costs and clinical outcomes of treating patients with retinal diseases requiring surgery managed locally in Darwin through a visiting subspecialist ophthalmology service or transferred to interstate tertiary eye centres. Methods A retrospective analysis of a case series of 70 consecutive patients presenting to the Royal Darwin Hospital for vitreo-retinal surgery during the calendar years 2018 and 2019 was performed. Results Twenty-two of the 29 patients in the transfer group had a retinal detachment and five vitreous haemorrhage. The 41 patients managed in Darwin had a range of diagnoses. Surgical success, complications and costs were similar. Conclusion There were excellent clinical outcomes and a minimal insignificant difference in costs.
Collective phase transitions in confined fish schools
Authors: Huang Chenchen , Ling Feng , Kanso Eva
Published: October 2024

The collective patterns that emerge in schooling fish are often analyzed using models of self-propelled particles in unbounded domains. However, while schooling fish in both field and laboratory settings interact with domain boundaries, these effects are typically ignored. Here, we propose a model that incorporates geometric confinement, by accounting for both flow and wall interactions, into existing data-driven behavioral rules. We show that new collective phases emerge where the school of fish “follows the tank wall” or “double mills.” Importantly, confinement induces repeated switching between two collective states, schooling and milling. We describe the group dynamics probabilistically, uncovering bistable collective states along with unintuitive bifurcations driving phase transitions. Our findings support the hypothesis that collective transitions in fish schools could occur spontaneously, with no adjustment at the individual level, and opens venues to control and engineer emergent collective patterns in biological and synthetic systems that operate far from equilibrium.
The more proficient the learners, the less sophisticated their L2 vocabulary?
Authors: Bottini Raffaella , Le Foll Elen
Published: October 2024

Mean-frequency scores of lexical sophistication are used to evaluate written and spoken language production. They
are calculated using word frequencies extracted from a reference corpus. Using mixed-effects regression models, we analyse the
strength of the relationship between L2 proficiency and mean-frequency scores in spoken and written texts using reference corpora
representing different modes and registers. We control for task and topic effects. We observe that mean-frequency measures of
lexical sophistication are considerably more influenced by the mode and register of the reference corpus used to calculate these
scores than by language users’ proficiency level. Advanced language users produce more frequent vocabulary, typical of the target
register, in both spoken monologues and written essays. These results provide evidence in favour of a conceptual and
terminological shift from lexical sophistication to register appropriateness (as suggested by
Durrant & Brenchley, 2019) to refer to the construct captured by mean-frequency
scores of vocabulary use.
“The twain is crossing”
Authors: Adam Naomi , Ghintuială Paula
Published: October 2024

The storyworld presented in the 2023 fantasy-comedy film Barbie is predicated upon a simple
ontological binary: there is the Real World, in which humans reside, and Barbie Land, populated by plastic dolls, and it is
roundly accepted that “never the twain shall cross”. However, throughout the film this divide is problematised, as the two domains
impinge upon one another so consistently that Barbie herself recognises that “[t]he twain is crossing”. In this article, we explore the complex ontological structure underlying the Barbie film. A
multimodal stylistic approach is adopted, and combined with insights from possible worlds theory, in order to investigate the
linguistic, structural and visual features which present permeable and liminal spaces and occasion incidences of ontolepsis.
Ultimately, the analysis serves to illuminate Barbie’s playful approach to multiple, interwoven, and overlapping
ontological levels — be they in Barbie Land, the Real World, or even our own real world.
Applying Cognitive Linguistics to elucidate the meanings of the particles IN/OUT and UP/DOWN in L2
classrooms
Authors: Piquer-Píriz Ana M. , Martín-Gilete Marta
Published: October 2024

Cognitive Linguistics offers valuable insights for second language instruction, particularly in enhancing
motivated polysemy to facilitate vocabulary acquisition. This paper reports on a longitudinal, classroom-based study and analyses
the design, implementation, and assessment of activities inspired by Cognitive Linguistics, elucidating the polysemous meanings of
the particles IN/OUT and UP/DOWN. The study involved an experimental group comprising 81 Spanish secondary school students and a
control group of 26 students. Following a pre-test/post-test design, participants completed two tests (a gap-fill particle test
and a lexical depth test) designed to measure students’ command of polysemous meanings. Despite positive teacher feedback and a
perceived increase in awareness of polysemy, the experimental learners showed no statistically significant improvement in the test
results. We conclude that a comprehensive approach is required to evaluate learning outcomes, encompassing pedagogical experience,
classroom-based research factors, and effective assessment measures.
CCM.G-K2.2023: CIPM key comparison of absolute gravimeters
Authors: Newell David , Ullrich Christian , Butta Hubert , de Sousa Mauro Andrade , Quaresma Daniel , Green Richard , Parks Harold , Liard Jacques , Wang Qiyu , Feng Jinyang , Hsieh Wen-Chi , Kostelecký Jakub , Pálinkás Vojtech , Bilker-Koivula Mirjam , Näränen Jyri , Rothleitner Christian , Konrad Johannes , Telada Souichi , Kayukawa Yohei , Choi In-Mook , Chung Jin-Wan , Esparza-Ramirez Alfredo , Aljuwayr Ahmed , Alotaibi Homood , Mametja Thapelo , Kirbaş Cafer , Seifert Frank
Published: October 2024

Main text

The Key Comparison of Absolute Gravimeters, CCM.G-K2.2023, was conducted at the Table Mountain Geophysical Observatory (TMGO) near Boulder, Colorado between 21 August and 29 September, 2023. Dr David Newell of the National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, served as the Pilot Laboratory representative, while Dr Derek van Westrum of the National Oceanic and Atmospheric Administration National Geodetic Survey (NOAA-NGS), Boulder, Colorado, served as the site host and coordinator. The comparison was organized in accordance with the International Committee for Weights and Measures (CIPM) document CIPM MRA-G-11 of the Consultative Committee for Mass and Related Quantities (CCM) and is registered with the CIPM as CCM.G-K2.2023. Fifteen National Metrology Institutes (NMIs) or Designated Institutes (DIs) participated, with a total of 30 absolute gravimeters from 27 institutes, including 2 atom gravimeters. Since all the contributions of absolute gravity data were consistent within the 95% confidence level of applied consistency tests, no data were omitted in the final analysis. The standard deviation of the gravimeters' degrees of equivalence (DoEs) for CCM.G-K2.2023 is less than 1.3 µGal, indicating excellent agreement among the gravimeters.

In this report, the gal (symbol: Gal) is a non-SI unit of acceleration employed in geodesy and geophysics to express acceleration due to gravity, where 1 Gal = 1 cm s-2 (1 µGal = 10-8 m s-2).

To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.

The final report has been peer-reviewed and approved for publication by the CCM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
The pseudospectrum and transient of Kaluza–Klein black holes in Einstein–Gauss–Bonnet gravity
Authors: Chen Jia-Ning , Wu Liang-Bi , Guo Zong-Kuan
Published: October 2024

The spectrum and dynamical instability, as well as the transient effect of the tensor perturbation for the so-called Maeda–Dadhich black hole, a type of Kaluza–Klein black hole, in Einstein–Gauss–Bonnet gravity have been investigated in framework of pseudospectrum. We cast the problem of solving quasinormal modes (QNMs) in AdS-like spacetime as the linear evolution problem of the non-normal operator in null slicing by using ingoing Eddington–Finkelstein coordinates. In terms of spectrum instability, based on the generalized eigenvalue problem, the QNM spectrum and ε-pseudospectrum has been studied, while the open structure of ε-pseudospectrum caused by the non-normality of operator indicates the spectrum instability. In terms of dynamical instability, we introduce the concept of the distance to dynamical instability, which plays a crucial role in bridging the spectrum instability and the dynamical instability. We calculate such distance, named the complex stability radius, as parameters vary. Finally, we show the behavior of the energy norm of the evolution operator, which can be roughly reflected by the three kinds of abscissas in context of pseudospectrum, and find the transient growth of the energy norm of the evolution operator.
Solution-processed lead halide perovskite films for application in perovskite solar cells: from coating methods to growth dynamics
Authors: Xiao Yuhuan , Zhou Conghua
Published: October 2024

After 15 years of development, perovskite solar cells have achieved a certified efficiency of >26%, offering one of the most reliable solutions for green energy. This progress should be ascribed to the excellent optoelectronic properties of metal halide perovskite film, for example, the excellent extinction behavior and the long diffusion length of charge carriers. Basically, these properties rely on the crystallization quality. In recent years, many methods have been developed to grow uniform and well-crystallized perovskite films, such as one-step and two-step solution-based methods, and evaporation methods. These methods help to grow films with areas ranging from several to hundreds of square centimeters. For each method, regulating the growth dynamics of perovskites has remained as one of the main tasks. Here, in this review, we discuss the coarsening dynamics of lead halide perovskites when using the solution-based methods, such as one-step or two-step spincoating, and bladecoating. Strategies such as component engineering, solvent engineering and additive engineering are briefly introduced and discussed. Finally, some perspectives are given.
Effect of interlayer twisting angle on electronic and optical properties of graphene/MoS2 heterostructure
Authors: Wu Hongping , Chen Li , Yang Maoyou , Meng Yan , Zhao Le , Shi Jiakuo , Zhang Duo , Hou Weimin , Su Shuo
Published: October 2024

Based on the first-principles calculation, the electronic and optical properties of the graphene/MoS2 heterostructure at different twisting angles are studied. The interface contact type changes from N-Schottky contact to Ohmic contact with the interlayer twisting angle of 40.90°, which is accompanied by the interfacial charge transfer from graphene to MoS2, and the increase of the contribution of Mo–d
xy
, Mo–d
x

2−y2 orbitals in the conduction band and S–p
z
, Mo–s, Mo–p
z
and Mo–d
z

2 orbitals in the valence band. Interestingly, the absorption coefficient, reflectivity and refractive index are improved in the infrared region when the twisting angle is 40.90°. In the visible light range, the absorption coefficient increases, while the refractive index decreases, and the reflectivity at 2.8 eV increases. In the ultraviolet region, the absorption coefficient reaches 1.2 × 106 cm−1 at 11.6 eV with a twisting angle of 30°. The results provide an effective way to apply materials in the photoelectric field.
Influence of local Dzyaloshinskii–Moriya interaction on spin waves in symmetrical heavy metal/ferromagnet/heavy metal multilayer
Authors: Yang Zhi , Liu Changjiang , Yang Xiaofei , Zhang Yue
Published: October 2024

In a heavy metal/ferromagnet/heavy metal multilayer with inversion symmetry, the total interlayer Dzyaloshinskii–Moriya interaction (iDMI) is expected to be absent while the local iDMI constants for the top and bottom FM atomic layers can still be nonzero. This study investigates the influence of local iDMI on spin waves in this structure, in which the FM medium consists of two atomic layers with opposite local iDMI at the bottom HM/FM interface and the top FM/HM interface. Theoretical analysis and simulations are conducted to examine the spin-wave dynamics and propagation in the symmetrical sandwich structure. The results show that the local iDMI decreases the characteristic frequency of spin waves, indicating a regulation of spin wave propagation by iDMI and shows the asymmetry of spin-wave propagation between the two FM atomic layers due to iDMI. This work paves the way for deep exploration of spin waves in such structures and offers valuable insights for subtle magnetic interactions in other symmetrical multilayers.
Naturalization and Evaluation of Synthetic Random Stripes Pattern for Digital Image Correlation
Authors: Ye Meitu , Guo Min , Xie Meilin , Tian Guangyuan , Shi Heng , Tian Yan
Published: October 2024

The morphology of speckle patterns commonly used in the digital image correlation (DIC) community can be categorized into artificially generated free shapes and computer-synthesized circular dots. Due to the non-repeatability of manually sprayed patterns, researchers tend to favor code-controlled dot patterns, which exist in two forms: ‘white dots on a black background’ and ‘black dots on a white background’. However, from the perspective of biological evolution, these two dot patterns can be hybridized ‘in silico’ using the Turing model to create an intermediate form—the ‘striped-like’ pattern. This hybridization significantly improves species identification and environmental adaptability. Although there have been reports on producing stripe patterns based on the kernel-based Turing model, there is no specific speckle pattern generation specification or comprehensive evaluation research for DIC applications. This paper naturalizes a novel striped speckle pattern and a corresponding generation approach. The pattern quality was assessed and compared with circular dots and hand-sprayed speckles. The stripes pattern outperformed the other two forms regarding Mean Intensity Gradient, q-factor, systematic bias, and random error. Sub-pixel displacement simulation and actual translation test results demonstrate that the proposed striped pattern offers higher precision and robustness in displacement and static strain measurements. Therefore, this striped pattern provides a preferred alternative for DIC technicians.
Optimization of optical systems for background oriented schlieren
Authors: Zhou Tanbo , Gaskins Jonathan , Poggie Jonathan , Bane Sally P M
Published: October 2024

Background oriented schlieren (BOS) is a non-intrusive optical method for measuring density gradients in a fluid flow based on variations of the local refractive index. The type of BOS optical system used, i.e. entocentric vs. telecentric, and the system design determine the accuracy and quality of the measurement. This work aims to optimize both types of optical systems to minimize the error for measurements of high-speed compressible turbulent boundary layers. Claims of the advantages offered by telecentric optical systems over entocentric systems are investigated, as well as the out-of-focus effects for types of systems. Numerical ray tracing simulations are performed using density fields from large eddy simulations (LES) of a Mach 2 turbulent boundary layer to generate synthetic but realistic BOS images. The results show that telecentric systems have lower overall error and less sensitivity. Contrary to recommendations by early BOS work, the best accuracy is achieved when density gradient object is placed outside of the depth of field of the optical system, for both entocentric and telecentric systems.
Dynamic range extension method for sinusoidal phase modulating interferometer based on in-phase component signal reconstruction
Authors: Zhang Qiyuan , Zhang Lieshan , Xing Zhaoqiang , Fang Wenjun , Xia Dunzhu
Published: October 2024

The continuous increase of the amplitude of the measured vibration will cause spectrum aliasing in the interference signal of the sinusoidal phase modulating interferometer (SPMI), thereby constraining its measurement dynamic range (DR). To address this issue, a DR extension method of SPMI based on the reconstruction of in-phase component signal is proposed. On the basis of conventional SPMI, an additional reference signal is introduced for estimating the phase modulation depth (PMD) and the major to minor axis ratio R
e of the Lissajous ellipse formed by orthogonal interference signal pair and a phase demodulation algorithm named as phase generated carrier (PGC)- in-phase component signal reconstruction (ICR)- differential-and-cross-multiplying algorithm (DCM) which integrates PGC-DCM algorithm and the ICR is proposed. Initially, the PMD is regulated to approximately 1.8412 rad, ensuring that the quadrature component signal can be accurately extracted from the mixed signal of the interference and carrier signals, even in the presence of spectrum aliasing in the original interference signal. Then, the undistorted quadrature component signal is used to reconstruct an in-phase component signal with the estimated value of R
e. Finally, the reconstructed in-phase component signal and quadrature component signal are normalized, and a DCM approach is applied to obtain the measured vibration. A SPMI measurement system based on Michelson interferometer structure is constructed to experimentally validate the proposed method. The experimental results demonstrate that the proposed method can effectively extend the DR of the SPMI system in the presence of spectrum aliasing. The DR of the system is increased from 97.52 dB @ 55 Hz to 102.6 dB @ 55 Hz, with the maximum measurable amplitude increasing from 7728.75 nm @ 55 Hz to 13 705.16 nm @ 55 Hz.
Remaining useful life prediction of turbofan engines based on dual attention mechanism guided parallel CNN-LSTM
Authors: Han Baokun , Yin Peiwen , Zhang Zongzhen , Wang Jinrui , Bao Huaiqian , Song Lijin , Liu Xinwei , Ma Hao , Wang Dawei
Published: October 2024

Remaining useful life (RUL) prediction is an valuable research task for predictive health management of aero engines, which is crucial to enhance the safety, dependability and economy of the motor. Accurate prediction of remaining engine service life is an important means for the effectively monitoring of engine operating conditions. The forecasting accuracy of turbofan RUL is inadequate with the traditional single-parameter, single-stage mode. To boost the precision of aero-engine RUL anticipation, a novel mode is recommended, based on a parallel convolutional neural network (CNN) with a long and short-term memory (LSTM) neural network and a dual attention mechanism, named PCLD. The degradation information directly from time series sensor data. The advantages of CNN networks and LSTM networks in feature mining and time series processing, respectively, are employed to process time series data, which is conducive to preventing the loss of important element in the data. At last, the experimental results on the aero-engine performance recession dataset C-MAPSS demonstrate that the method outperforms the currently popular models with better robustness and higher prediction accuracy.
Applications of machine learning in real-time control systems: a review
Authors: Zhao Xiaoning , Sun Yougang , Li Yanmin , Jia Ning , Xu Junqi
Published: October 2024

Real-time control systems (RTCSs) have become an indispensable part of modern industry, finding widespread applications in fields such as robotics, intelligent manufacturing and transportation. However, these systems face significant challenges, including complex nonlinear dynamics, uncertainties and various constraints. These challenges result in weakened disturbance rejection and reduced adaptability, which make it difficult to meet increasingly stringent performance requirements. In fact, RTCSs generate a large amount of data, which presents an important opportunity to enhance control effectiveness. Machine learning, with its efficiency in extracting valuable information from big data, holds significant potential for applications in RTCSs. Exploring the applications of machine learning in RTCSs is of great importance for guiding scientific research and industrial production. This paper first analyzes the challenges currently faced by RTCSs, elucidating the motivation for integrating machine learning into these systems. Subsequently, it discusses the applications of machine learning in RTCSs from various aspects, including system identification, controller design and optimization, fault diagnosis and tolerance, and perception. The research indicates that data-driven machine learning methods exhibit significant advantages in addressing the multivariable coupling characteristics of complex nonlinear systems, as well as the uncertainties arising from environmental disturbances and faults, thereby effectively enhancing the system’s flexibility and robustness. However, compared to traditional methods, the applications of machine learning also faces issues such as poor model interpretability, high computational requirements leading to insufficient real-time performance, and a strong dependency on high-quality data. This paper discusses these challenges and proposes potential future research directions.
Modeling of an inductive displacement sensor based on 1DCNN-LSTM-AT
Authors: Li Jinlin , Yang Xinyue , Zhong Shuncong , Liang Wei , Guo Qiaoying
Published: October 2024

The input-output relationship of inductive displacement sensors is typically nonlinear, which demands numerous computational resources for precise calculation. Therefore, the traditional analytical methods for the inductive displacement sensors cannot meet the real-time high-precision measurement requirements. In response to the aforementioned issues, this paper proposes an optimized Long Short-Term Memory (LSTM) neural networks based on one-dimensional convolutional neural networks (1DCNN) to modeling the input-output relationship of the inductive displacement sensor. First, the measurement principle of the inductive displacement sensors was analyzed, and the analytical model of the sensor output was derived. And the influences of the key parameters of sensors on the relationship between the induced voltage and the displacement were studied. Then, the 1DCNN-LSTM-AT network for modelling the input-output relationship of the inductive displacement sensor was studied. The spatial features of historical induced electromotive force (EMF) data generated by the induction coil were initially extracted using the 1DCNN network. And these spatial features were then utilized as input to the LSTM neural network to capture the temporal features of the historical induced EMF data. Subsequently, the spatiotemporal features of the induced EMF data were fed into the regression prediction layer to compute the displacement measurement results corresponding to the current input. Moreover, the attention mechanism was used for the 1DCNN-LSTM model to enhance the prediction accuracy and stability of the model. Finally, the experimental results demonstrate that the proposed 1DCNN-LSTM-AT model achieves an average absolute percentage error of 3.1%, significantly outperforming traditional models such as LSTM (29.3%), CNN (4.7%), and ANN (4.3%). This paper provides a new method for modelling the nonlinear relationships of the inductive displacement sensor, and presents a fresh perspective for research in the data processing of nonlinear sensors.
A Novel Demodulator Design for Signal Processing of Inductive Oil Debris Sensors
Authors: Wang Guan , Wu Xianwei , Qian Zhenghua , Liu Dianzi , Li Peng , Kuznetsova Iren
Published: October 2024

Metal debris from bearing wear occurred in the lubrication system of aircraft engines represents valuable information, which can be utilized to assess the current engine condition and further enable the life-cycle prediction. The demodulation of debris signals consists of two parts: mixing and filtering. In traditional methods, the mixing process is complex to adjust and prone to failure, while the filtering employs the fixed low-pass filtering, which significantly affects the demodulation performance when the debris flow rate changes. To address these issues, this paper proposes a novel demodulation model to enhance the demodulation performance and increase the signal-to-noise ratio of chip signals. Firstly, the circuit innovatively adopts a self-frequency mixing method to avoid the complex phase adjustment required by traditional reference-frequency mixing methods for the improved stability of the circuit. Following that, a digital potentiometer is employed to implement the flow rate filtering, enabling precise control over the filter type and range, thus mitigating signal distortion caused by changes in flow rate and increasing the signal-to-noise ratio. Throughout extensive experiments and data analysis, the self-frequency mixing method improves the signal-to-noise ratio by 0.72 dB and reduces the signal standard deviation by 59.12% as compared to the reference-frequency mixing method. Considering the debris flow rate in the range of 0.2 to 0.5 m/s, the flow rate filtering method improves the signal-to-noise ratio by 19.71 dB and reduces the amplitude standard deviation by 66.18%, as compared to results by the traditional fixed low-pass filtering method. Results demonstrate that the aforementioned circuit design effectively enhances the signal amplitude and stability of inductive oil debris sensors in the wear assessment of mechanical systems and provides useful insights into the development of advanced sensors used in the field of structural health monitoring.
Segmentation of tunnel water leakage based on a lightweight DeepLabV3+ model
Authors: Wang Dandan , Hou Gongyu , Chen Qinhuang , Li Weiyi , Fu Huanhuan , Sun Xiaorong , Yu Xunan
Published: October 2024

The accurate and efficient detection of water leakage with complex backgrounds is crucial for the safety of metro operations. A lightweight segmentation method for metro tunnel water leakage based on transfer learning is proposed. Firstly, this is based on the Deeplabv3+ model and adopts MobileNetv3-Large as the backbone feature extraction network, which significantly reduces the network parameters and improves the detection speed; secondly, it incorporates the efficient channel attention mechanism, which enables the model to adaptively adjust the weights of the channel features and capture the inter-channel relationships in the image, which significantly improves the model’s ability for feature extraction ability; furthermore, for the problem of severe imbalance between positive and negative samples in the dataset, the recognition accuracy of complex samples is increased by optimizing the loss function; finally, the training method of transfer learning is utilized to solve the problem of scarcity of water leakage dataset, and to improve the model’s accuracy and generalization ability. The results show that the model has more significant detection accuracy and segmentation speed advantages than today’s mainstream semantic segmentation model. With strong generalization ability in complex environments (e.g. low illumination and multiple obstructions), model can be used for intelligent operation and maintenance in metro tunnel projects.
Adaptive hybrid prediction model for adapting to data distribution shifts in machining quality prediction
Authors: Li Feng , Yang Xu , Gao Jie , Du Jinsong , Wang Wei
Published: October 2024

The accuracy of data-driven intelligent prediction for machining quality relies on the training samples. However, in actual applications, the continuous operation of machining equipment leads to gradual distribution shifts between the process data and the training samples for modeling. The shifts result in a degradation in the performance of predictive model, previous studies have often overlooked this issue. To tackle with the intricate problem, this research proposes a real-time model optimization approach. Firstly, a method for detecting machining data distribution shifts based on the two-sample Kolmogorov–Smirnov test is proposed. Then, an adaptive hybrid prediction model (AHPM) capable of real-time optimization is developed. This model consists of a deep neural network (DNN) and a broad learning system (BLS). DNN plays a primary role in prediction within the hybrid model with excellent generalization capability. BLS quickly completes optimization prior to DNN with its unique parameter update mechanism to compensate for prediction loss. Experimental results indicate that AHPM achieves the shortest optimization time while maintaining high accuracy, with post-optimization error reduction rates for mean squared error, mean absolute error, and mean absolute percentage error all exceeding 10%. In the test of application to actual machining cases, accuracy improved by 8.88% compared to traditional methods without optimization.
MOF-derived three-dimensional porous dodecahedral structured bimetallic Mn/Co-C-N composite for high-performance durable oxygen reduction reaction electrocatalysts
Authors: Qiao Yelin , Guo Yuanyuan , Zhao Yueqi , Chang Caiyun , Wang Shuo , Zhang Xiaoting , Gao Faming , Chen Rongna , Hou Li
Published: October 2024

Investigating high-efficiency oxygen reduction reaction (ORR) catalysts is one of the most effective methods for addressing the sluggish kinetics at the fuel cell cathode. Bimetallic three-dimensional porous materials have garnered significant attention due to their diverse structures, large specific surface area and synergistic catalytic effects. Herein, we synthesized a bimetallic three-dimensional porous dodecahedral structure, Mn/Co-C-N, derived from MOF using a straightforward approach. Experimental reults confirm that the strategic incorporation of Mn enhances the electrocatalytic activity for ORR. Meanwhile, the synergistic effects of Mn and Co, as well as the advantages of the dodecahedral structure for expediting electron transfer, all contribute to the exceptional ORR performance. Arc testing in an alkaline electrolyte reveals that the initial potential (E
onset) and the half-wave potential (E
1/2) are 0.89 V and 0.80 V, closely approximating those of commercial Pt/C (20 wt%). Following 10 000 stability test cycles, the half-wave potential exhibits a mere 8 mV change, confirming its remarkable stability.
Exploring nonlinear correlations among transition metal nanocluster properties using deep learning: a comparative analysis with LOO-CV method and cosine similarity
Authors: Nasiri Mahd Zahra , Kokabi Alireza , Fallahzadeh Maryam , Naghibi Zohreh
Published: October 2024

A novel approach is introduced for the rapid and accurate correlation analysis of nonlinear properties in Transition Metal (TM) clusters utilizing the Deep Leave-One-Out Cross-Validation technique. This investigation demonstrates that the Deep Neural Network (DNN)-based approach offers a more efficient predictive method for various properties of fourth-row TM nanoclusters compared to conventional Density Functional Theory methods, which are computationally intensive and time-consuming. The feature space, also known as descriptors, is established based on a broad spectrum of electronic and physical characteristics. Leveraging the similarities among these clusters, the DNN-based model is employed to explore the correlations among TM cluster properties. The proposed method, in conjunction with cosine similarity, achieves remarkable accuracy up to 10−9 for predicting total energy, lowest vibrational mode, binding energy, and HOMO-LUMO energy gap of TM2, TM3, and TM4 nanoclusters. By analyzing correlation errors, the most closely coupled TM clusters are identified. Notably, Mn and Ni clusters exhibit the highest and lowest levels of energy coupling with other TMs, respectively. Generally, energy prediction for TM2, TM3, and TM4 clusters exhibit similar trends, while an alternating behavior is observed for vibrational modes and binding energies. Furthermore, Ti, V, and Co demonstrate the highest binding energy correlations with TM2, TM3, and TM4 sets, respectively. Regarding energy gap predictions, Ni exhibits the strongest correlation in the smallest TM2 clusters, while Cr shows the highest dependence in TM3 and TM4 sets. Lastly, Zn displays the largest error in HOMO-LUMO energy gap across all sets, indicating distinctive independent energy gap characteristics.