bluuebunny/crossref_metadata_2025 huggingface dataset:
What's hindering the deployment of energy storage devices in distributed photovoltaic systems: An evolutionary game analysis based on system dynamics
Authors: Chen Zhiyuan , Wang Tieli , Wang Feng
Published: August 2024

This paper investigates the obstacles hindering the deployment of energy storage (ES) in distributed photovoltaic (DPV) systems by constructing a tripartite evolutionary game model involving energy storage investors (ESIs), distributed photovoltaic plants (DPPs), and energy consumers (ECs). Utilizing system dynamics (SD), this study systematically analyzes how parameter changes impact the deployment of DPV-ES systems. The findings reveal several critical impediments: (1) Lack of Incentives for DPPs. Under basic parameters, DPPs show no incentive to adopt positive deployment strategies due to high costs without corresponding returns. (2) Parameter Influence. Factors such as weak electricity supply capacity for ECs, high accident risk charges, and land rent may hinder ESIs or DPPs from adopting positive deployment strategies. However, stakeholders' initial willingness, fines for ECs, profit distribution ratios, and annual investment costs of ES systems do not alter participant strategies. (3) Cooperative Relationships and Economic Factors. Higher unit electricity prices and accident risk charges associated with DPV-ES plants increase electricity costs for ECs, potentially hindering the deployment of ES systems and leading to a circular game among stakeholders without ESS deployment. This study provides valuable insights for government and industry stakeholders to identify and eliminate barriers to the integrated development of DPV and ES systems, thereby promoting more effective deployment strategies.
Nonparametric distributions of tensor-valued Lorentzian diffusion spectra for model-free data inversion in multidimensional diffusion MRI
Authors: Narvaez Omar , Yon Maxime , Jiang Hong , Bernin Diana , Forssell-Aronsson Eva , Sierra Alejandra , Topgaard Daniel
Published: August 2024

Magnetic resonance imaging (MRI) is the method of choice for noninvasive studies of micrometer-scale structures in biological tissues via their effects on the time- and frequency-dependent (restricted) and anisotropic self-diffusion of water. While new designs of time-dependent magnetic field gradient waveforms have enabled disambiguation between different aspects of translational motion that are convolved in traditional MRI methods relying on single pairs of field gradient pulses, data analysis for complex heterogeneous materials remains a challenge. Here, we propose and demonstrate nonparametric distributions of tensor-valued Lorentzian diffusion spectra, or “D(ω) distributions,” as a general representation with sufficient flexibility to describe the MRI signal response from a wide range of model systems and biological tissues investigated with modulated gradient waveforms separating and correlating the effects of restricted and anisotropic diffusion.
Velocity field reconstruction of mixing flow in T-junctions based on particle image database using deep generative models
Authors: Yin Yuzhuo , Jiang Yuang , Lin Mei , Wang Qiuwang
Published: August 2024

Flow field data obtained by particle image velocimetry (PIV) could include isolated large damaged areas that are caused by the refractive index, light transmittance, and tracking capability of particles. The traditional deep learning reconstruction methods of PIV fluid data are all based on the velocity field database, and these methods could not achieve satisfactory results for large flow field missing areas. We propose a new reconstruction method of fluid data using PIV particle images. Since PIV particle images are the source of PIV velocity field data, particle images include more complete underlying information than velocity field data. We study the application of PIV experimental particle database in the reconstruction of flow field data using deep generative networks (GAN). To verify the inpainting effect of velocity field using PIV particle images, we design two semantic inpainting methods based on two GAN models with PIV particle image database and PIV fluid velocity database, respectively. Then, the qualitative and quantitative inpainting results of two PIV databases are compared on different metrics. For the reconstruction of velocity field, the mean relative error of using the particle image database could achieve a 52% reduction compared to a velocity database. For the reconstruction of vorticity field, the maximal and mean relative errors can reduce by 50% when using the particle image database. The maximum inpainting errors of two database inputs are both mainly concentrated on the turbulence vortex area, which means the reconstruction of complex non-Gaussian distribution of turbulence vortex is a problem for semantic inpainting of the experimental data.
LibERI—A portable and performant multi-GPU accelerated library for electron repulsion integrals via OpenMP offloading and standard language parallelism
Authors: Alkan Melisa , Pham Buu Q. , Del Angel Cruz Daniel , Hammond Jeff R. , Barnes Taylor A. , Gordon Mark S.
Published: August 2024

A portable and performant graphics processing unit (GPU)-accelerated library for electron repulsion integral (ERI) evaluation, named LibERI, has been developed and implemented via directive-based (e.g., OpenMP and OpenACC) and standard language parallelism (e.g., Fortran DO CONCURRENT). Offloaded ERIs consist of integrals over low and high contraction s, p, and d functions using the rotated-axis and Rys quadrature methods. GPU codes are factorized based on previous developments [Pham et al., J. Chem. Theory Comput. 19(8), 2213–2221 (2023)] with two layers of integral screening and quartet presorting. In this work, the density screening is moved to the GPU to enhance the computational efficacy for large molecular systems. The L-shells in the Pople basis set are also separated into pure S and P shells to increase the ERI homogeneity and reduce atomic operations and the memory footprint. LibERI is compatible with any quantum chemistry drivers supporting the MolSSI Driver Interface. Benchmark calculations of LibERI interfaced with the GAMESS software package were carried out on various GPU architectures and molecular systems. The results show that the LibERI performance is comparable to other state-of-the-art GPU-accelerated codes (e.g., TeraChem and GMSHPC) and, in some cases, outperforms conventionally developed ERI CUDA kernels (e.g., QUICK) while fully maintaining portability.
Processing calibration data of low-temperature thermometer based on clustering algorithm
Authors: Liao Yi , Zhang Yu , Zha Kuifan , Liu Xuming , Pan Changzhao
Published: August 2024

The scarcity of cryogenic thermometers often stems from their high cost and lengthy lead times for calibration. Establishing an in-lab temperature calibration system is necessary to quickly make use of uncalibrated sensors or self-made sensors. This paper introduces a straightforward and high-accuracy thermometer calibration system. By employing copper screws as thermal links between the sensor platform and the cryogen-free refrigerator, temperature oscillation on the sensor platform is suppressed to a few millikelvins. In addition, this paper presents a data processing model based on clustering algorithms. These algorithms sort and group data based on distance, which is similar to human visual judgment of data. This paper discusses the parameter optimization process of the clustering algorithm to interpret the automated data process. The cryogenic temperature sensors calibrated by this system exhibited high accuracy, with relative errors of less than 1% compared to standard thermometers. Moreover, automatically processing calibration data from two uncalibrated thermometers takes just over 10 min, highlighting the effectiveness of this calibration system.
Photoluminescent cooling with incoherent light
Authors: Ghonge Sushrut , Kuno Masaru , Jankó Boldizsár
Published: August 2024

Optical refrigeration using anti-Stokes photoluminescence is now well established, especially for rare-earth-doped solids where cooling to cryogenic temperatures has recently been achieved. The cooling efficiency of optical refrigeration is constrained by the requirement that the increase in the entropy of the photon field must be greater than the decrease in the entropy of the sample. Laser radiation has been used in all demonstrated cases of optical refrigeration with the intention of minimizing the entropy of the absorbed photons. Here, we show that as long as the incident radiation is unidirectional, the loss of coherence does not significantly affect the cooling efficiency. Using a general formulation of radiation entropy as the von Neumann entropy of the photon field, we show how the cooling efficiency depends on the properties of the light source, such as wavelength, coherence, and directionality. Our results suggest that the laws of thermodynamics permit optical cooling of materials with incoherent sources, such as light emitting diodes and filtered sunlight, almost as efficiently as with lasers. Our findings have significant and immediate implications for design of compact all-solid-state devices cooled via optical refrigeration.
Modeling of gas non-isothermal desorption-diffusion in deformed coal and application in lost gas calculation
Authors: Wei Chengmin , Li Chengwu , Ye Qiusheng , Li Mingjie , Hao Min , Li Xing
Published: August 2024

Coalbed methane (CBM) content is a crucial parameter for CBM exploitation and disaster prevention, with accurate gas loss calculation being essential for improving measurement precision. To investigate the physical response to gas loss and quantify the impact and interrelation of various factors, this study developed a comprehensive model incorporating the effects of gas diffusion, temperature, and pressure effects on porosity and coal deformation. By utilizing the OpenFOAM platform and C++ for numerical solution development and establishing an experimental system for gas loss measurement, the model's reliability was confirmed. The new model demonstrates an 8.56% average error, representing a significant improvement over traditional methods, with error reductions of 26.59% and 18.31% compared to the square root of time and power function methods, respectively. The hierarchy of gas loss is determined to be gas flow > gas-coal deformation coupling > gas-temperature coupling > gas-coal deformation-temperature coupling. Average diffusion contributions are identified as 41.43% from surface diffusion, 30.71% from Knudsen diffusion, and 27.86% from viscous flow. The factors affecting gas loss, in decreasing order of impact, are adsorption equilibrium pressure, temperature, porosity, moisture, and adsorption constants. These findings provide theoretical support for enhancing gas content measurement accuracy and techniques, significantly benefiting CBM extraction and mine safety.
Compactification of determinant expansions via transcorrelation
Authors: Ammar Abdallah , Scemama Anthony , Loos Pierre-François , Giner Emmanuel
Published: August 2024

Although selected configuration interaction (SCI) algorithms can tackle much larger Hilbert spaces than the conventional full CI method, the scaling of their computational cost with respect to the system size remains inherently exponential. In addition, inaccuracies in describing the correlation hole at small interelectronic distances lead to the slow convergence of the electronic energy relative to the size of the one-electron basis set. To alleviate these effects, we show that the non-Hermitian, transcorrelated (TC) version of SCI significantly compactifies the determinant space, allowing us to reach a given accuracy with a much smaller number of determinants. Furthermore, we note a significant acceleration in the convergence of the TC-SCI energy as the basis set size increases. The extent of this compression and the energy convergence rate are closely linked to the accuracy of the correlation factor used for the similarity transformation of the Coulombic Hamiltonian. Our systematic investigation of small molecular systems in increasingly large basis sets illustrates the magnitude of these effects.
Investigation of the impact of splitter rotation speed on mode transition characteristics of an over-under turbine-based combined cycle inlet
Authors: Chen Liang , Zhang Yue , Xu Yi-Xuan , Tan Hui-Jun , Xue Hong-Chao , Wang Zi-Yun
Published: August 2024

This paper presents the findings of a wind tunnel experiment aimed at investigating the mode transition process of a two-dimensional over-under turbine-based combined cycle inlet at an incoming flow Mach number of 2.9. The study utilized high-speed schlieren and dynamic pressure acquisition systems to examine the evolution process of the shock-dominated flow structure of the high-speed duct during the mode transition process. Additionally, the impact of mode transition speed on the unstart/restart characteristics of the high-speed duct was analyzed. The results indicate that, during the forward mode transition process, the increasing captured airflow of the high-speed duct leads to a higher number of shock reflections and the shock train moves forward in the duct, ultimately resulting in unstart. The unstarting flow field exhibits a small oscillation characteristic dominated by the separation bubble located at the entrance. However, evident hysteresis characteristics were observed in the restart process during the reverse transition. Furthermore, a higher mode transition speed delays the unstart and restart of the high-speed duct, consequently increasing the hysteresis interval. Theoretical analysis suggests that a larger mode transition speed leads to lower mass accumulation efficiency in the high-speed duct, thereby slowing the pressure response and causing the shock train to lag forward, resulting in delayed unstart. The delay in the restart process is attributed to the relative slip motion of the separation bubble with the upper surface of the splitter, in addition to its forced motion.
Bubble collapse patterns recognition and flow field prediction based on machine learning
Authors: Chen Hao , Ren Shao-Fei , Li Shi-Min , Zhang Shuai , Zhang Guo-Fei
Published: August 2024

A machine learning method is proposed to predict the collapse patterns and flow field state of underwater explosion bubbles subjected to the vertical sidewall and free surface, which can overcome the limitations of high costs of traditional experimental tests and long computation times of numerical simulations. The dataset was obtained by the boundary element method, including the cases of the bubble with different buoyancy parameters at different distances from the free surface and vertical sidewall. Due to the strong geometric nonlinearity of the bubble influenced by boundary, three classification models are adopted to identify the collapse patterns of bubbles, which are support vector machines, K nearest neighbor, and decision tree. Meanwhile, an ensemble learning (EL) model based on the three classification models is adopted to enhance the prediction accuracy. Furthermore, three regression models, which are deep neural network (DNN), extreme learning machine (ELM), and random forest (RF), were adopted and compared to predict flow field information around the bubble. The results show that EL exhibits better robustness to the distribution and proportion of samples when identifying collapse patterns. Meanwhile, compared with ELM and RF, DNN demonstrates stronger performance in capturing nonlinear relationships, especially in regions where the bubble curvature changes abruptly. Moreover, a learning rate decay strategy is proposed to effectively suppress the phenomenon of loss oscillation in the training process of DNN based on adaptive activation functions.
Stochastic approach to analysis of vortex dynamic and turbulence in superfluid
Authors: Avramenko A. A. , Dmitrenko N. P. , Tyrinov A. I. , Kobzar A. S.
Published: August 2024

The article is devoted to the study of the stability of a single vortex filament and a vortex ring in superfluid helium He II during the rotation of a cylindrical vessel. The formalism of the Fokker–Planck equation was used to solve this problem. As a result of the study, it was revealed that the main parameter that affects the stability of a vortex filament or its instability is the Rossby number (Ro). For numbers Ro > 1, when the normal velocity component prevails, the state of the vortex filament is stable. For numbers Ro < 1, when the superfluid component exerts its influence more, the vortex filament loses its stability. An increase in temperature also contributes to the development of instability in the vortex filament. A study of the radius of the vortex ring torus showed that its decrease entails a decrease in the radius of the vortex ring itself, at which it is stable.
Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference
Authors: MacDonald M. J. , Hammel B. A. , Bachmann B. , Bitter M. , Efthimion P. , Gaffney J. A. , Gao L. , Hammel B. D. , Hill K. W. , Kraus B. F. , MacPhee A. G. , Peterson L. , Schneider M. B. , Scott H. A. , Thorn D. B. , Yeamans C. B.
Published: August 2024

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. We present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.
Edge scanning reflectometry for density profile measurement on the SPARC tokamak
Authors: Lin Y. , Nikolaeva V. , Hachmeister D. , Kowalski E. , Reinke M. L.
Published: August 2024

Edge scanning reflectometry (ESRL) on the SPARC tokamak aims to measure the electron density profile from the far scrape-off layer to the top of the typical H-mode pedestal and provide real-time data for plasma control. ESRL uses a standard frequency-modulated continuous wave technique from 18 to 90 GHz. By implementing both the O-mode and left-hand-cutoff X-mode, it covers densities from ∼4 × 1018 to ∼4 × 1020 m−3 at B0 ∼12 T. A voltage-controlled oscillator acts as the frequency sweep source. Phase-locked dielectric resonator oscillators and bandpass filters generate base signals ∼9–15 GHz. The signals are then frequency multiplied and amplified to reach the K (18–26 GHz), Ka (26–40 GHz), U (40–60 GHz), and E (60–90 GHz) bands. Multi-band signals are combined via the quasi-optical technique. ESRL plans to use oversized waveguides (∼20 m one-way) and a bi-static arrangement to minimize signal losses and distortions while allowing system flexibility. A COMSOL Multiphysics RF model in 2D has been set up to simulate the reflectometry process and help decide the layout of the horn antennas. Engineering analyses of the key parts of the system have been carried out in support of its preliminary design.
Measurement equipment and result analysis of B–H curve of current transformer core material under mixing frequency
Authors: Zhang Jun , Yin Xiaodong , Wang Quan , Peng Chuning , Chen Haibin , Lu Bing , Zhao Qiancheng , Tong Tao , Ding Can
Published: August 2024

To examine the effect of mixed-frequency excitation conditions on the hysteresis and loss characteristics of ferromagnetic materials, this paper shows how to construct magnetic property test equipment for electrical steel using Epstein’s square circle. Experiments were carried out to assess the hysteresis and loss properties of oriented silicon steel wafers under mixed-frequency sinusoidal operating conditions with different occupancy ratios. It used heterodyne extraction to investigate the effect of different occupancy ratios of industry and magnetization frequency on the hysteresis and loss characteristics of oriented silicon steel sheets.
Evolution of gas permeability and porosity of sandstone under stress loading and unloading conditions
Authors: Wang Meng , Gao Xiyi , Xu Jiwei , Li Xuekai , Liu Rentai , Li Mengtian , Chen Bing
Published: August 2024

In-depth study of the permeability of rock mass in high-pressure gas environment is of great significance for the site selection and construction of compressed air energy storage underground storage. The purpose of this study is to investigate the effects of multiple stress loading and unloading on the gas permeability and porosity of sandstone. The steady-state flow and porosity under stress loading and unloading conditions were measured. Based on the confining pressure sensitivity coefficient and permeability recovery rate, the permeability change characteristics of the sample before and after stress loading and unloading were analyzed. According to the test results, the empirical relationship between sandstone permeability and porosity with confining pressure is derived. The evolution law of permeability and porosity under repeated cyclic loading and unloading was analyzed and compared with the existing research results. The results show that high-pressure gas promotes pore expansion or dilation, with porosity increasing by about 27.3% at 10 MPa compared to 2 MPa. During 55 cycles of loading and unloading, there is little noticeable change in permeability with respect to confining pressure starting from the 22nd cycle. Repeated loading leads to a decrease in the sensitivity of permeability and porosity to changes in stress. Permeability is more sensitive to changes in stress than porosity. In the experimental stress range, the relationship between permeability and porosity under high air pressure was expressed by power function equation. This study provides a theoretical basis for the site selection and sealing design of gas storage caverns.
Model-based deep reinforcement learning for active control of flow around a circular cylinder using action-informed episode-based neural ordinary differential equations
Authors: Mao Yiqian , Zhong Shan , Yin Hujun
Published: August 2024

To date, applications of deep reinforcement learning (DRL) to active flow control (AFC) have been largely achieved via model-free DRL wherein the agent's policy is optimized through direct interactions with the actual physical system represented by computational fluid dynamics solvers. However, high computational demands and tendency of numerical divergence can significantly compromise the effectiveness of model-free DRL as the Reynolds number increases. A model-based DRL paradigm, which utilizes neural ordinary differential equations (NODE) to develop an environment model through integration with dimensionality reduction, offers a promising way forward to overcome this problem. This study presents an inaugural application of NODE model-based DRL to control the vortex shedding process from a two-dimensional circular cylinder using two synthetic jet actuators at a freestream Reynolds number of 100. An action-informed episode-based NODE (AENODE) method is developed to overcome the error cascading effect caused by recursive predictions in the existing studies, which typically adopt a single-step prediction NODE (denoted as the time step-based NODE (TNODE) in this paper). Both the AENODE and TNODE methods are employed in this study, and they are amalgamated with three distinct feature extraction approaches, expert-placed velocity sensors, proper orthogonal decomposition, and autoencoders, to construct six low-dimensional dynamical models (LDMs) of the DRL environment. It is found that AENODE resulted in over 90% fewer prediction errors at the end of an episode than TNODE with all LDMs via effectively mitigating the accumulation of long-term prediction errors associated with the recursive use of TNODE, leading to a more robust convergence in training the agents throughout repeated runs. Furthermore, the model-based DRL with either AENODE or TNODE is capable of identifying very similar control strategies to that obtained by the model-free DRL. The AENODE agents achieved 66.2%–72.4% of the rewards obtained by the model-free DRL, whereas the TNODE agents attained merely 43.4%–54.7%, indicating that AENODE provides a more accurate modeling of environment dynamics in DRL. It is also shown that completing a model-based DRL task using either TNODE or AENODE utilized only 10% of the data size requiring either 14% or 33% of the total wall-clock time required by the model-free DRL, and the actual time required for training the agents within the environment model was less than 1% of that required by the model-free DRL. Therefore, the AENODE method developed in this work not only enables a significant saving in computational costs but also outperforms the TNODE method in training convergence and reward. It represents a novel low-dimensional dynamical modeling method tailored for model-based DRL, which would enable the DRL-aided AFC to be applied to more complex flow scenarios occurring at high Reynolds numbers.
Quantum neural network approach to Markovian dissipative dynamics of many-body open quantum systems
Authors: Long Cun , Cao Long , Ge Liwei , Li Qun-Xiang , Yan YiJing , Xu Rui-Xue , Wang Yao , Zheng Xiao
Published: August 2024

Numerous variational methods have been proposed for solving quantum many-body systems, but they often face exponentially increasing computational complexity as the Hilbert space dimension grows. To address this, we introduce a novel approach using quantum neural networks to simulate the dissipative dynamics of many-body open quantum systems. This method combines neural-network quantum state representation with the time-dependent variational principle, both implemented via quantum algorithms. This results in accurate open quantum dynamics described by the Lindblad quantum master equation, exemplified by the spin-boson and transverse field Ising models. Our approach avoids the computational expense of classical algorithms and demonstrates the potential advantages of quantum computing for many-body simulations. To reduce measurement errors, we introduce a projection reset procedure, which could benefit other quantum simulations. In addition, our approach can be extended to simulate non-Markovian quantum dynamics.
Thousand foci coherent anti-Stokes Raman scattering microscopy
Authors: Gudavičius Dominykas , Kontenis Lukas , Langbein Wolfgang
Published: August 2024

We demonstrate coherent anti-Stokes Raman scattering (CARS) microscopy with 1089 foci, enabled by a high repetition rate amplified oscillator and an optical parametric amplifier. We employ a camera as a multichannel detector to acquire and separate the signals from the foci, rather than using the camera image itself. This allows us to retain the insensitivity of the imaging to scattering afforded by the non-linear excitation point-spread function, which is the hallmark of point-scanning techniques. We show frame rates of 0.3 Hz for a megapixel CARS image, limited by the camera used. The laser source and corresponding CARS signal allows for at least 1000 times higher speed, and using faster cameras would allow acquiring at that speed, opening a perspective to megapixel CARS imaging with a frame rate of more than 100 Hz.
On the horizontal dynamic performance of standing wave-type near-field ultrasonic levitation
Authors: Liu Yuanyuan , Zhao Zilong , Sun Xiaodong , Geng Lin
Published: August 2024

Near-field ultrasonic levitation (NFUL) technology is increasingly attracting attention for its advantages of non-contact nature, compactness, and environmental friendliness. Nevertheless, the development of NFUL is hindered by challenges such as carrying capacity and stability. To date, most studies have focused on the static stability of NFUL, primarily through analysis of the restoring force. However, there remains a significant gap in the literature regarding the motion prediction of levitated objects, which is the focus of this paper. A numerical model coupling the levitated object and the squeeze film is established, and then, the Reynolds equation considering the motion parameters of the levitator is derived. Since the misalignment and inclination of the levitator are concurrent cases, its inclination needs to be considered in the film thickness expression. Subsequently, due to the introduction of an imaginary levitator with a groove, the eight-point discrete method is applied to solve the discontinuous film thickness problem. Thereupon, the pressure profile is obtained by determining the inclination angle of the levitator using the spline interpolation. The motion trajectory and frequency of the levitator are estimated utilizing the time-marching method and corroborated through experimental measurements. Both numerical and experimental results indicate that the motion frequency initially increases sharply with rising the preset eccentricity, before gradually diminishing. Additionally, higher motion frequencies are observed at larger amplitudes of the vibrator and lower weights of the levitator. Comparatively, the motion frequency of a levitator under a flexible vibrator is also found to be higher than that under a rigid vibrator.
A novel hyper-reduction framework featuring direct projection without an approximation process
Authors: Wang Hongjiang , Jiang Genghui , Wang Weizhe , Liu Yingzheng
Published: August 2024

Existing methodologies for the hyper projection-based reduced order model (HPROM) fall into two categories: the approximate-then-project and the project-then-approximate approaches. Both involves approximation and projection procedures. This paper introduces a novel hyper-reduction framework, the direct hyper projection-based reduced order model (DHPROM), which exclusively relies on the projection process without the need for the online approximation of Jacobian matrices, and nonlinear residual vectors. During the offline phase, DHPROM avoids the need for gathering the Jacobian matrices and nonlinear residual vectors associated with solving discrete partial differential equations (PDEs) at each iteration, nor for applying dimensionality reduction preprocessing techniques such as proper orthogonal decomposition, the discrete empirical interpolation method, and energy-conserving sampling and weighting to the collected dataset. These characteristics of the offline and online phases contribute to DHPROM's superior speed and accuracy compared to the HPROM. In terms of model applicability, various types of projection-based reduced order models arising from different choices of the left reduced order basis (ROB) Ψ can be derived, which are correspondingly expressed in the DHPROM with the left ROB Ψ̂. The generalizability of DHPROM is demonstrated through tests on the classic turbulent flow over periodic hills with moderately extrapolated parameters. The relative L2 norm error remains at the order of 10−3, indicating good performance. Finally, it is noteworthy that the DHPROM is applicable to any physical problems necessitating the numerical solution of PDEs.
Study of oxygen transport in glassy polymers on a nanometer length scale utilizing the kinetic Monte Carlo simulations
Authors: Syutkin V. M. , Vyazovkin V. L. , Grebenkin S.
Published: August 2024

Diffusion-controlled deactivation of excited phenanthrene and oxidation of triplet aryl-nitrene by molecular oxygen were used to determine the energetics of oxygen jump rates in the set of glassy polymers: poly(methyl methacrylate), poly(n-butyl methacrylate), polycarbonate, polystyrene, and polysulfone. To interpret experimental results, a simple model based on the transition state theory of diffusion jump has been used. The kinetic Monte Carlo simulations of phenanthrene deactivation and nitrene oxidation were carried out in a cubic lattice that modeled a polymer matrix. The bonds of the lattice were assigned to be activation barriers for the diffusion jumps of oxygen molecules from one site of the lattice to another. The standard deviation, σ‡, and spatial correlation length, rc, of the free energy of diffusion jump have been determined. It is shown that the spatial correlation of oxygen jump rates on a nanometer scale and the entropic nature of the dynamic heterogeneity are common features of all the studied polymers.
Magneto-Rayleigh–Taylor instability and feedthrough in a resistive liquid-metal liner of finite thickness
Authors: Makaremi-Esfarjani Paria , Higgins Andrew J.
Published: August 2024

The effect of magnetic tension and diffusion on the perturbation growth of a liquid-metal liner subjected to magneto-Rayleigh–Taylor (MRT) instability is investigated. An initially magnetic-field-free liquid-metal slab of finite thickness is surrounded by two lower-density regions. Within the lower region, a constant axial magnetic field of arbitrary magnitude is applied. The numerical examination of the MRT instability growth, initiated by a seeded perturbation parallel to the magnetic field at the liner's unstable interface, is performed for both perfectly conductive and resistive liners. To this end, a novel level set-based two-phase incompressible solver for ideal/resistive magnetohydrodynamic (MHD) flows within the finite-difference framework is introduced. Utilizing the implemented numerical toolkit, the impact of different Alfvén numbers and magnetic Reynolds numbers on the MRT growth rate and feedthrough at the upper interface of the liner is studied. Accounting for the finite resistivity of the liner results in an increase in the MRT growth and feedthrough compared to the ideal MHD case. The results indicate that magnetic diffusion primarily affects the MRT growth rate for higher wavenumbers, while for smaller wavenumbers, the effect of finite resistivity is only observed over a longer duration of instability development. We further demonstrate that decreasing the Alfvén number results in faster emergence of the magnetic diffusion effect on the MRT growth rate. It is also observed that a greater electrical conductivity jump across the liner results in an increased perturbation growth. Finally, the impact of surface tension on MRT instability growth for both ideal and resistive MHD cases is studied across different wavenumbers, specifically for Bond numbers related to fusion applications.
A model for electroencephalogram emotion recognition: Residual block-gated recurrent unit with attention mechanism
Authors: Wang Yujie , Zhang Xiu , Zhang Xin , Sun Baiwei , Xu Bingyue
Published: August 2024

Electroencephalogram (EEG) signals, serving as a tool to objectively reflect real emotional states, hold a crucial position in emotion recognition research. In recent years, deep learning approaches have been widely applied in emotion recognition research, and the results have demonstrated their effectiveness in this field. Nevertheless, the challenge remains in selecting effective features, ensuring their retention as the network depth increases, and preventing the loss of crucial information. In order to address the issues, a novel emotion recognition method is proposed, which is named Res-CRANN. In the proposed method, the raw EEG signals are transformed into four dimensional spatial-frequency-temporal information, which can provide a more enriched and complex feature representation. First, the residual block is incorporated into the convolutional layers to extract spatial and frequency domain information. Subsequently, gated recurrent unit (GRU) is employed to capture temporal information from the convolutional neural network outputs. Following GRU, attention mechanisms are applied to enhance awareness of key information and diminish interference from irrelevant details. By reducing attention to irrelevant or noisy temporal steps, it ultimately improves the accuracy and robustness of the classification process. The Res-CRANN method exhibits excellent performance on the DEAP dataset, with an accuracy of 96.63% for valence and 96.87% for arousal, confirming its effectiveness.
Real-space energy decomposition analysis method for qualitative and quantitative interpretations
Authors: Zhang Yueyang , Xiong Xuewei , Wu Wei , Su Peifeng
Published: August 2024

In the work, a real-space energy decomposition analysis method, called DM-EDA(RS), is introduced based on our recently developed DM-EDA method [Zhang et al., J. Chem. Phys. 160, 174101 (2024)]. The EDA terms in DM-EDA(RS), including electrostatic, exchange, repulsion, polarization, and correlation, are expressed as the summations of grid-based energy density in real-space. This method is able to interpret intermolecular interactions in a unified qualitative and quantitative way. DM-EDA(RS) results provide not only comprehensive explanations for intermolecular interactions but also insights for sub-region interactions involving different functional groups.
Topologically protected sound enhancement
Authors: Wen Xueyun , Gu Zhongming , Zhu Jie
Published: August 2024

The pursuit of robust and enhanced acoustic sensing has garnered significant attention across multiple fields of acoustic engineering. Here, we propose a feasible approach to realizing sound energy enhancement along the interface of two topologically distinct materials in an acoustic topological waveguide (ATW) with a gradient gap width. By adjusting the width of the topological bandgap, the sound energy can be localized within a desired region. Benefiting from topological protections, the confinement process is immune to certain types of defects. Our ATW is constructed using two types of C3-symmetric sonic crystals through topology optimization to extremely enlarge the width of the bandgap. Both numerical and experimental results confirm the robust edge states and enhanced energy confinement, even in the presence of non-spin-mixing defects. This work represents an advancement in the design of acoustic functional devices and may inspire potential applications in areas such as acoustic imaging, energy harvesting, and communication systems.
The effect of chemical doping on the lithiation processes of the crystalline Si anode ‒ A first-principles study
Authors: Chiang Han-Hsin , Pan Li-Yi , Kuo Chin-Lung
Published: August 2024

We employed first-principles calculations to investigate the effect of chemical doping on the lithiation kinetics and dynamic properties of the c–Si anode. Our ab initio molecular dynamics simulations reveal that phosphorous/arsenic doping can greatly enhance the lithiation kinetics of c–Si, whereas boron doping is unable to produce such an improvement. Our calculations also show that boron doping could enhance Li insertion into c–Si, but phosphorous/arsenic doping tends to increase the insertion energy of Li ions. Although the migration energy barriers of Li ions may slightly increase (decrease) in the boron-(phosphorus-/arsenic-)doped c–Si, these changes were only effective within the range of the nearest-neighbor distance from dopants. Furthermore, it was found that the phosphorus-/arsenic-doped Si can be more ductile and can more easily undergo plastic deformation upon lithiation, while the c–Si matrix becomes more brittle and stiffer when doped with boron. Our simulation results also demonstrate that phosphorous- and arsenic-doping can effectively speed up the Li-induced structural amorphization of c–Si while boron doping appears to severely slow it down. These findings unambiguously indicate that the induced mechanical softening of the c–Si bond network can be the primary factor that leads to the enhanced lithiation kinetics in the n-type doped c–Si anodes.
Unsteady measurement in a transonic axial compressor with optimized axial slot casing treatment
Authors: Fan Zhonggang , Xu Xiaobin , Liu Yang , Ba Dun , Zhang Min , Du Juan
Published: August 2024

Casing treatment has been widely adopted to enhance the operating range of compressors by extending the stall margin. In this article, a high-precision experimental investigation was conducted in a transonic compressor. Unsteady pressure is measured in the casing wall using a cluster of time-resolved transducers with axial and circumferential spatial resolution. The experimental data show that the optimized axial slot casing treatment improves the stall margin without peak efficiency penalty for all measured speedlines. A comprehensive flow field analysis indicates that the surge boundary of transonic compressor is sensitive to the position of shock wave and tip leakage flow. After the application of optimized axial slot casing treatment, the flow structure can be significantly modified, which specifically manifests as both the mitigation in shock wave detachment and redistribution of tip leakage flow trajectory toward the trailing edge of the blade. Furthermore, power spectral density analysis is conducted to examine the unsteady effect. The amplitude of characteristic frequency band induced by the unsteady fluctuation of tip leakage flow and shock wave is considerably suppressed, which can also be regard as the stability enhancement mechanism of casing treatment.
Flowing plasma rearrangement in the presence of static perturbing fields
Authors: Rubin T. , Ochs I. E. , Fisch N. J.
Published: August 2024

Charged particles interacting with electromagnetic waves have a portion of their energy tied up in wave-driven oscillations. When these waves are localized to the exhaust of linear magnetic confinement systems, this ponderomotive effect can be utilized to enhance particle confinement. The same effect can be derived for particles moving via an E×B drift into a region of a static perturbation to the electromagnetic fields which has a large wave vector component in the direction of the motion. In this work, we use a simplified slab model to self-consistently solve for the electromagnetic fields within the fluid flowing plasma of a static flute-like (k∥=0) perturbation and evaluate the resulting ponderomotive potential. We find that two types of perturbations can exist within the flowing plasma, which are an O wave and an X wave in the frame moving with the fluid. In the case of tenuous plasma, these perturbations are magnetostatic or electrostatic multipole-analog perpendicular to the guiding magnetic field in the lab frame, respectfully. For denser plasmas, the O wave-like perturbation is screened at the electron skin depth scale, and the X wave-like perturbation is a combination of a similar perpendicular electric perturbation and parallel magnetic perturbation. The ponderomotive potential generated in the X wave-like case is gyrofrequency-dependent and can be used as either potential barriers or potential wells, depending on the direction of the flow velocity.
Molecular donor–acceptor linked systems as models for examining their interactions in excited states
Authors: Imahori Hiroshi , Akiyama Midori
Published: August 2024

Molecular donor–acceptor (D–A) linked systems have attracted significant attention due to their potential to address D–A interactions in excited states. In these systems, it is crucial to understand the interplay between electrons and spin behaviors, atomic nucleus movements (including vibration, rotation, fluctuation, and transfer), and collective motion (electron–phonon coupling) over time. Through intentional manipulation of locally excited, charge-transfer excited, and charge-separated states, along with modulation of dynamic effects (enhancement or restraint), we expect to unlock the full potential of D–A systems for photofunctions in electronics, energy, healthcare, and functional materials. In this perspective, we present our recent examples of D–A linked systems and related ones that address the aforementioned issues as part of our “Dynamic Exciton” research project in Japan.
Exchange–correlation potential built on the derivative discontinuity of electron density
Authors: Huang Chen
Published: August 2024

Electronic structures are fully determined by the exchange–correlation (XC) potential. In this work, we develop a new method to construct reliable XC potentials by properly mixing the exact exchange and the local density approximation potentials in real space. The spatially dependent mixing parameter is derived based on the derivative discontinuity of electron density and is first-principle. We derived the equations for solving the mixing parameter and proposed an approximation to simplify these equations. Based on this approximation, this new method gives reasonable predictions for the ionization energies, fundamental gaps, and singlet–triplet energy differences for various molecular systems. The impact of the approximation on the constructed XC potentials is examined, and it is found that the quality of the XC potentials can be further improved by removing the approximation. This work demonstrates that the derivative discontinuity of electron density is a promising constraint for constructing high-quality XC potentials.
Reconstruction of flow structure in a scramjet combustor using a multi-level connected shifted-window transformer
Authors: Wu Fan , Meng Liang , Tian Ye , Le Jialing , Guo Mingming
Published: August 2024

Stable combustion is desirable for efficient operation of scramjet engines at high flight Mach numbers, and being able to reconstruct the flow-field wave patterns in stable combustion facilitates proactive evaluation of engine operating conditions. Proposed here is a multi-level connected shifted-window transformer (MCSwinT) model for reconstructing the flow-field wave patterns of stable combustion in a supersonic combustor. A combustion feature conversion block is used to convert high-dimensional and low-dimensional combustion features; a deep pressure feature extraction block is used to extract the flow-field wave patterns, and MCSwinT blocks enable multi-level fusion, thereby extracting the high-dimensional combustion features of the flow-field wave system. A dynamic loss function unifies spatial content loss and feature space loss, leading to enhanced reconstruction results. Separately, data on the stable combustion process of a hydrogen-fueled scramjet engine were collected in a direct-connect supersonic pulse combustion wind tunnel, and these data are used to validate the robustness and generalization capability of MCSwinT. The experimental results show that the flow-field wave patterns of stable combustion are reconstructed successfully using MCSwinT of different scales. Compared to other models, MCSwinT exhibits lower model complexity while achieving performance improvements of 7% and 17% in peak signal-to-noise ratio and structural similarity index, respectively. Additionally, the high generalization ability of the proposed model is validated in a sparsity experiment. This model effectively reconstructs the flow-field wave patterns of stable combustion, providing a crucial foundation for further research on scramjet engines.