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Thin-film lithium niobate (TFLN) based optical microresonators offer large nonlinear coefficient d_33 and high light-wave confinement, allowing highly efficient second-order optical nonlinear frequency conversion. Here, we achieved ultra-efficiency second harmonic generation (SHG) from high-Q polygon modes by maximizing the utilization of the highest nonlinear coefficient d_33 in a monocrystalline X-cut TFLN microdisk resonator for the first time. The polygon modes are designed and formed with two parallel sides perpendicular to the optical axis of the lithium niobate crystal by introducing weak perturbations into the microdisk of a tapered fiber, which maximizes the utilization of d_33. The polygon modes exhibit ultrahigh intrinsic Q factors of ~3.86X10(7), due to the fact that polygon modes are located far from the relatively rough sidewall of the microdisk. Moreover, the pump and second harmonic polygon modes share high modal overlap factor of ~80%. Consequently, SHG from cavity polygon modes with absolute conversion efficiency as high as 48.08% was realized at an on-chip pump level of only 4.599 mW without fine domain structures, surpassing the best results (23% and 30%) reported in other two domain-inversion-free phase matching schemes and even approaching the record (52%) in PPLN microresonators., Comment: 17 pages, 4 figures

The presence of dispersed-phase droplets can result in a notable increase in the system's drag. However, our understanding of the mechanism underlying this phenomenon remains limited. In this study, we use three-dimensional direct numerical simulations with a modified multi-marker volume-of-fluid method to investigate liquid-liquid two-phase turbulence in a Taylor-Couette geometry. The dispersed phase has the same density and viscosity as the continuous phase. The Reynolds number $Re\equiv r_i\omega_i d/\nu$ is fixed at 5200, the volume fraction of the dispersed phase is up to $40\%$, and the Weber number $We\equiv \rho u^2_\tau d/\sigma$ is around 8. It is found that the increase in the system's drag originates from the contribution of interfacial tension. Specifically, droplets experience significant deformation and stretching in the streamwise direction due to shear near the inner cylinder. Consequently, the rear end of the droplets lags behind the fore head. This causes opposing interfacial tension effects on the fore head and rear end of the droplets. For the fore head of the droplets, the effect of interfacial tension appears to act against the flow direction. For the rear end, the effect appears to act in the flow direction. The increase in the system's drag is primarily attributed to the effect of interfacial tension on the fore head of the droplets which leads to the hindering effect of the droplets on the surrounding continuous phase. This hindering effect disrupts the formation of high-speed streaks, favoring the formation of low-speed ones, which are generally associated with higher viscous stress and drag of the system. This study provides new insights into the mechanism of drag enhancement reported in our previous experiments.

In gas evolving electrolysis, bubbles grow at electrodes due to a diffusive influx from oversaturation generated locally in the electrolyte by the electrode reaction. When considering electrodes of micrometer-size resembling catalytic islands, bubbles are found to approach dynamic equilibrium states at which they neither grow nor shrink. Such equilibrium states are found at low oversaturation for both, pinning and expanding wetting regimes of the bubbles and are based on the balance of local influx near the bubble foot and global outflux. Unlike the stability of pinned nano-bubbles studied earlier, the Laplace pressure plays a minor role only. We extend the analytical solution of Zhang & Lohse (2023) by taking into account the non-uniform distribution of dissolved gas around the bubble obtained from direct numerical simulation. This allows us to identify the parameter regions of bubble growth, dissolution and dynamic equilibrium as well as to analyze the stability of the equilibrium states. Finally, we draw conclusions on how to possibly enhance the efficiency of electrolysis., Comment: 16 pages, 13 figures

The distributed telescope array offers promise for conducting large-sky-area, high-frequency time domain surveys. Multiple telescopes can be deployed at each observation site, so intra-site observation task scheduling is crucial for enhancing observation efficiency and quality. Efficient use of observable time and rapid response to special situations are critical to maximize scientific discovery in time domain surveys. Besides, the competing scientific priorities, time-varying observation conditions, and capabilities of observation equipment, lead to a vast search space of the scheduling. So with the increasing number of telescopes and observation fields, balancing computational time with solution quality in observation scheduling poses a significant challenge. Informed by the seminal contributions of earlier studies on a multilevel scheduling model and global scheduler for time domain telescope array, this study is devoted to further exploring the site scheduler. Formulating the observation scheduling of multiple telescopes at the site as a cooperative decision-making problem, this paper proposes GRRIS, a real-time intra-site observation scheduling scheme for telescope array using graph and reinforcement learning. It employs a graph neural network to learn node features that can embed the spatial structure of the observation scheduling. An algorithm based on multi-agent reinforcement learning is designed to efficiently learn the optimum allocation policy of telescope agents to field nodes. Through numerical simulations with real-world scenarios, GRRIS can achieve up to a 22% solution improvement over the most competitive scheme. It offers better scalability and sub-second decision speed, meeting the needs of observation scheduling control for future distributed telescope arrays., Comment: Astronomical Journal

Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, prompting researchers to explore their potential for use in recommendation systems. Initial attempts have leveraged the exceptional capabilities of LLMs, such as rich knowledge and strong generalization through In-context Learning, which involves phrasing the recommendation task as prompts. Nevertheless, the performance of LLMs in recommendation tasks remains suboptimal due to a substantial disparity between the training tasks for LLMs and recommendation tasks and inadequate recommendation data during pre-training. This paper introduces RLRF4Rec, a novel framework integrating Reinforcement Learning from Recsys Feedback for Enhanced Recommendation Reranking(RLRF4Rec) with LLMs to address these challenges. Specifically, We first have the LLM generate inferred user preferences based on user interaction history, which is then used to augment traditional ID-based sequence recommendation models. Subsequently, we trained a reward model based on knowledge augmentation recommendation models to evaluate the quality of the reasoning knowledge from LLM. We then select the best and worst responses from the N samples to construct a dataset for LLM tuning. Finally, we design a structure alignment strategy with Direct Preference Optimization(DPO). We validate the effectiveness of RLRF4Rec through extensive experiments, demonstrating significant improvements in recommendation re-ranking metrics compared to baselines. This demonstrates that our approach significantly improves the capability of LLMs to respond to instructions within recommender systems.

We conducted a series of pore-scale numerical simulations on convective flow in porous media, with a fixed Schmidt number of 400 and a wide range of Rayleigh numbers. The porous media are modeled using regularly arranged square obstacles in a Rayleigh-B\'enard (RB) system. As the Rayleigh number increases, the flow transitions from a Darcy-type regime to an RB-type regime, with the corresponding $Sh-Ra_D$ relationship shifting from sublinear scaling to the classical 0.3 scaling of RB convection. Here, $Sh$ and $Ra_D$ represent the Sherwood number and Rayleigh-Darcy number, respectively. For different porosities, the transition begins at approximately $Ra_D = 4000$, at which point the characteristic horizontal scale of the flow field is comparable to the size of a single obstacle unit. When the thickness of the concentration boundary layer is less than about one-sixth of the pore spacing, the flow finally enters the RB regime. In the Darcy regime, the scaling exponent of $Sh$ and $Ra_D$ decreases as porosity increases. Based on the Grossman-Lohse theory (J. Fluid Mech., vol. 407, 2000; Phys. Rev. Lett., vol. 86, 2001), we provide an explanation for the scaling laws in each regime and highlight the significant impact of mechanical dispersion effects within the boundary layer. Our findings provide some new insights into the validity range of the Darcy model.

Objective: To explore and compare the performance of ChatGPT and other state-of-the-art LLMs on domain-specific NER tasks covering different entity types and domains in TCM against COVID-19 literature. Methods: We established a dataset of 389 articles on TCM against COVID-19, and manually annotated 48 of them with 6 types of entities belonging to 3 domains as the ground truth, against which the NER performance of LLMs can be assessed. We then performed NER tasks for the 6 entity types using ChatGPT (GPT-3.5 and GPT-4) and 4 state-of-the-art BERT-based question-answering (QA) models (RoBERTa, MiniLM, PubMedBERT and SciBERT) without prior training on the specific task. A domain fine-tuned model (GSAP-NER) was also applied for a comprehensive comparison. Results: The overall performance of LLMs varied significantly in exact match and fuzzy match. In the fuzzy match, ChatGPT surpassed BERT-based QA models in 5 out of 6 tasks, while in exact match, BERT-based QA models outperformed ChatGPT in 5 out of 6 tasks but with a smaller F-1 difference. GPT-4 showed a significant advantage over other models in fuzzy match, especially on the entity type of TCM formula and the Chinese patent drug (TFD) and ingredient (IG). Although GPT-4 outperformed BERT-based models on entity type of herb, target, and research method, none of the F-1 scores exceeded 0.5. GSAP-NER, outperformed GPT-4 in terms of F-1 by a slight margin on RM. ChatGPT achieved considerably higher recalls than precisions, particularly in the fuzzy match. Conclusions: The NER performance of LLMs is highly dependent on the entity type, and their performance varies across application scenarios. ChatGPT could be a good choice for scenarios where high recall is favored. However, for knowledge acquisition in rigorous scenarios, neither ChatGPT nor BERT-based QA models are off-the-shelf tools for professional practitioners., Comment: 22 pages with 2 figures

Infrastructure sensors installed at elevated positions offer a broader perception range and encounter fewer occlusions. Integrating both infrastructure and ego-vehicle data through V2X communication, known as vehicle-infrastructure cooperation, has shown considerable advantages in enhancing perception capabilities and addressing corner cases encountered in single-vehicle autonomous driving. However, cooperative perception still faces numerous challenges, including limited communication bandwidth and practical communication interruptions. In this paper, we propose CTCE, a novel framework for cooperative 3D object detection. This framework transmits queries with temporal contexts enhancement, effectively balancing transmission efficiency and performance to accommodate real-world communication conditions. Additionally, we propose a temporal-guided fusion module to further improve performance. The roadside temporal enhancement and vehicle-side spatial-temporal fusion together constitute a multi-level temporal contexts integration mechanism, fully leveraging temporal information to enhance performance. Furthermore, a motion-aware reconstruction module is introduced to recover lost roadside queries due to communication interruptions. Experimental results on V2X-Seq and V2X-Sim datasets demonstrate that CTCE outperforms the baseline QUEST, achieving improvements of 3.8% and 1.3% in mAP, respectively. Experiments under communication interruption conditions validate CTCE's robustness to communication interruptions., Comment: Accepted by IEEE ITSC 2024

Solidification of droplets is of great importance to various technological applications, drawing considerable attention from scientists aiming to unravel the fundamental physical mechanisms. In the case of multicomponent droplets undergoing solidification, the emergence of concentration gradients may trigger significant interfacial flows that dominate the freezing dynamics. Here, we experimentally investigate the fascinating snow-globe freezing dynamics of supercooled ethanol-water droplets. We reveal that these unique freezing dynamics are driven by solidification-induced solutal Marangoni flow within the droplets. We quantitatively characterize the concentration-dependent migration and growth dynamics of ice particles, tightly connecting them to the solutal Marangoni effect and the associated convective heat transfer. Moreover, we show that the final wrapping state of droplets can be modulated by the concentration of ethanol. Our findings may pave the way for novel insights into the physicochemical hydrodynamics of multicomponent liquids undergoing phase transitions., Comment: 5 pages, 4 figures

We investigate the coupling effects of the two-phase interface, viscosity ratio, and density ratio of the dispersed phase to the continuous phase on the flow statistics in two-phase Taylor-Couette turbulence at a system Reynolds number of 6000 and a system Weber number of 10 using interface-resolved three-dimensional direct numerical simulations with the volume-of-fluid method. Our study focuses on four different scenarios: neutral droplets, low-viscosity droplets, light droplets, and low-viscosity light droplets. We find that neutral droplets and low-viscosity droplets primarily contribute to drag enhancement through the two-phase interface, while light droplets reduce the system's drag by explicitly reducing Reynolds stress due to the density dependence of Reynolds stress. Additionally, low-viscosity light droplets contribute to greater drag reduction by further reducing momentum transport near the inner cylinder and implicitly reducing Reynolds stress. While interfacial tension enhances turbulent kinetic energy (TKE) transport, drag enhancement is not strongly correlated with TKE transport for both neutral droplets and low-viscosity droplets. Light droplets primarily reduce the production term by diminishing Reynolds stress, whereas the density contrast between the phases boosts TKE transport near the inner wall. Therefore, the reduction in the dissipation rate is predominantly attributed to decreased turbulence production, causing drag reduction. For low-viscosity light droplets, the production term diminishes further, primarily due to their greater reduction in Reynolds stress, while reduced viscosity weakens the density difference's contribution to TKE transport near the inner cylinder, resulting in a more pronounced reduction in the dissipation rate and consequently stronger drag reduction. Our findings provide new insights into the turbulence modulation in two-phase flow.

The radar signal processing algorithm is one of the core components in through-wall radar human detection technology. Traditional algorithms (e.g., DFT and matched filtering) struggle to adaptively handle low signal-to-noise ratio echo signals in challenging and dynamic real-world through-wall application environments, which becomes a major bottleneck in the system. In this paper, we introduce an end-to-end through-wall radar human detection network (TWP-CNN), which takes raw radar Analog-to-Digital Converter (ADC) signals without any preprocessing as input. We replace the conventional radar signal processing flow with the proposed DFT-based adaptive feature extraction (DAFE) module. This module employs learnable parameterized 3D complex convolution layers to extract superior feature representations from ADC signals, which is beyond the limitation of traditional preprocessing methods. Additionally, by embedding phase information from radar data within the network and employing multi-task learning, a more accurate detection is achieved. Finally, due to the absence of through-wall radar datasets containing raw ADC data, we gathered a realistic through-wall (RTW) dataset using our in-house developed through-wall radar system. We trained and validated our proposed method on this dataset to confirm its effectiveness and superiority in real through-wall detection scenarios., Comment: 11pages,13figures

Understanding the solidification dynamics of impacted water droplets is fundamental and crucial for applications, especially with the presence of frost and salt. Here, we experimentally investigate the spreading and freezing dynamics of saline droplets upon impact on a cold, frosty surface. Our findings demonstrate that the frost and salt can lead to an extended spreading of impacted droplets under specific conditions. In addition to the well-known 1/2 inertial spreading scaling law for droplets impact on a cold substrate, we observe a distinct transition to a 1/10 scaling law dominated by the capillary-viscous relation at low impacting velocities. We formulate the onset criterion for this extended spreading regime and derive a scaling dependence that captures the droplet's arrested diameter over various supercooling temperatures, by incorporating the effect of impact inertia, partial-wetting behavior and salinity. Based on the analysis, a unified model is proposed for predicting the droplet arrested diameter over a wide range of impact velocity and salinity., Comment: 10 pages, 10 figures

The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i.e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials. One of the reasons is the inadequacy of Cross-drug Response Evaluation (CRE) during the late stages of drug R&D. Although in-silico CRE models bring a promising solution, existing methodologies are restricted to early stages of drug R&D, such as target and cell-line levels, offering limited improvement to clinical success rates. Herein, we introduce DeepCRE, a pioneering AI model designed to predict CRE effectively in the late stages of drug R&D. DeepCRE outperforms the existing best models by achieving an average performance improvement of 17.7% in patient-level CRE, and a 5-fold increase in indication-level CRE, facilitating more accurate personalized treatment predictions and better pharmaceutical value assessment for indications, respectively. Furthermore, DeepCRE has identified a set of six drug candidates that show significantly greater effectiveness than a comparator set of two approved drugs in 5/8 colorectal cancer organoids. This demonstrates the capability of DeepCRE to systematically uncover a spectrum of drug candidates with enhanced therapeutic effects, highlighting its potential to transform drug R&D.

Evaporation of multicomponent droplets is important in a wide range of applications, albeit complex, and requires a careful investigation. We experimentally and numerically investigate the evaporation characteristics of spherical, ethanol-water droplets with different initial concentration ratios in the acoustic levitation field. Imaging techniques and infrared thermometry are used for acquiring volume and surface temperature variations of droplets, reflecting their mass and heat transfer characteristics. Numerical simulations are conducted using modified parameters based on a theoretical model to consider the effect of the acoustic field. The calculation results show good agreement with the experimental data. The concentration and temperature distribution within the droplet is further investigated based on the numerical results., Comment: 14 pages, 6 figures

We perform the linear stability analysis and direct numerical simulations to study the effect of radius ratio on the instability and flow characteristics of the sheared annular centrifugal Rayleigh-B\'enard convection (ACRBC), where the cold inner cylinder and the hot outer cylinder rotate with a small angular velocity difference. With the shear enhancement, the thermal convection is suppressed and finally gets stable for different radius ratios $\eta\in[0.2, 0.95]$. Considering the inhomogeneous distribution of shear stresses in the base flow, a new global Richardson number $Ri_g$ is defined and the marginal-state curves for different radius ratios are successfully unified in the parameter domain of $Ri_g$ and the Rayleigh number $Ra$. The results are consistent with the marginal-state curve of the wall-sheared classical RBC in the streamwise direction, demonstrating that the basic stabilization mechanisms are identical. Moreover, systems with small radius ratios exhibit greater geometric asymmetry. On the one hand, this results in a smaller equivalent aspect ratio for the system, accommodating fewer convection roll pairs. Fewer roll pairs are more likely to cause a transition in the flow structure during shear enhancement. On the other hand, the shear distribution is more inhomogeneous, allowing for an outward shift of the convection region and the elevation of bulk temperature under strong shear.

Photonic integrated circuits based on erbium doped thin film lithium niobate on insulator has attracted broad interests with insofar various waveguide amplifiers and microlasers demonstrated. Wideband operation facilitated by the broadband absorption and emission of erbium ions necessitates the functional integration of wavelength filter and multiplexer on the same chip. Here a low-loss wavelength division multiplexer at the resonant pumping and emission wavelengths (~1480 nm and 1530~1560 nm) of erbium ions based on angled multimode interferometer, is realized in the erbium doped thin film lithium niobate on insulator fabricated by the photolithography assisted chemomechanical etching technique. The minimum on-chip insertion losses of the fabricated device are <0.7 dB for both wavelength ranges, and a 3-dB bandwidth of >20 nm is measured at the telecom C-band. Besides, direct visualization of the multimode interference pattern by the visible upconversion fluorescence of erbium ions compares well with the simulated light propagation in the multimode interferometer. Spectral tuning of the wavelength division multiplexer by structural design is also demonstrated and discussed., Comment: 11 pages, 5 figures

This work presents the first fabricated electrophysiology-optogenetic closed-loop bidirectional brain-machine interface (CL-BBMI) system-on-chip (SoC) with electrical neural signal recording, on-chip sleep staging and optogenetic stimulation. The first multiplexer with static assignment based table lookup solution (MUXnet) for multiplier-free NN processor was proposed. A state-of-the-art average accuracy of 82.4% was achieved with an energy consumption of only 0.2$\mu$J/class in sleep staging task., Comment: 2 pages, 6 figures. Accepted by IEEE Custom Integrated Circuits Conference (CICC) 2024. The codes for the MUXnet (constructing neural networks using multiplexers instead of multipliers) will be open-sourced after the Journal version of this work is accepted

This paper studies the distributed optimization problem under the influence of heavy-tailed gradient noises. Here, a heavy-tailed noise means that the noise does not necessarily satisfy the bounded variance assumption. Instead, it satisfies a more general assumption. The commonly-used bounded variance assumption is a special case of the considered noise assumption. A typical example of this kind of noise is a Pareto distribution noise with tail index within (1,2], which has infinite variance. Despite that there has been several distributed optimization algorithms proposed for the heavy-tailed noise scenario, these algorithms need a centralized server in the network which collects the information of all clients. Different from these algorithms, this paper considers that there is no centralized server and the agents can only exchange information with neighbors in a communication graph. A distributed method combining gradient clipping and distributed stochastic subgradient projection is proposed. It is proven that when the gradient descent step-size and the gradient clipping step-size meet certain conditions, the state of each agent converges to the optimal solution of the distributed optimization problem with probability 1. The simulation results validate the algorithm.

Superconducting materials exhibit unique physical properties and hold great scientific value and vast industrial application prospects. However, due to limitations such as critical temperature (TC) and critical current density (JC), the large-scale application of superconducting materials remains challenging. Chemical doping has been a commonly used method to enhance the superconductivity of B(P)SCCO. However, satisfactory enhancement results have been difficult to achieve. In this study, we introduced green light GaN p-n junction particles as inhomogeneous phases into B(P)SCCO polycrystalline particles to form a smart meta-superconductors (SMSCs) structure. Based on the electroluminescence properties of the p-n junction, the Cooper pairs were stimulated and strengthened to enhance the superconductivity of B(P)SCCO. Experimental results demonstrate that the introduction of inhomogeneous phases can indeed enhance the critical temperature TC, critical current density JC, and complete diamagnetism (Meissner effect) of B(P)SCCO superconductors. Moreover, When the particle size of raw material of B(P)SCCO is reduced from 30{\mu}m to 5{\mu}m, the grain size of the sintered samples also decreases, and the optimal doping concentration of the inhomogeneous phases increases from 0.15 wt.% to 0.2 wt.%, further improving the enhancement of superconductivity., Comment: 17 pages,7 figures

Understanding the dynamics of material objects advected by turbulent flows is a long standing question in fluid dynamics. In this perspective article we focus on the characterization of the statistical properties of non-interacting finite-sized massive spherical particles advected by a vigorous turbulent flow. We study the fluctuations and temporal correlations of particle accelerations and explore their behaviours with respect to the particle size and the particle mass density by means of fully-resolved numerical simulations. We observe that the measured trends can not be interpreted as the simple multiplicative combination of the two dominant effects: the spatial filtering of fluid accelerations and the added-mass-adjusted fluid-to-particle density ratio. We argue that other hydrodynamical forces or effects, e.g. preferential flow sampling, have still a significant role even at the largest particle sizes, which rich here the integral scale of turbulence., Comment: 7 pages, 4 figures

Direct numerical simulations are performed to explore the effects of rotating direction of the vertical asymmetric rough wall on the transport properties of Taylor-Couette (TC) flow up to a Taylor number of $\textit{Ta} = 2.39 \times 10^7$. It is shown that compared to the smooth wall, the rough wall with vertical asymmetric strips can enhance the dimensionless torque \textit{Nu}$_\omega$, and more importantly, at high \textit{Ta} clockwise rotation of the inner rough wall (the fluid is sheared by the steeper slope side of the strips) results in a significantly bigger torque enhancement as compared to the counter-clockwise rotation (the fluid is sheared by the smaller slope side of the strips) due to the larger convective contribution to the angular velocity flux, although the rotating direction has a negligible effect on the torque at low \textit{Ta}. The larger torque enhancement caused by the clockwise rotation of vertical asymmetric rough wall at high \textit{Ta} is then explained by the stronger coupling between the rough wall and the bulk due to the larger biased azimuthal velocity towards the rough wall at the mid-gap of TC system, the increased intensity of turbulence manifesting by larger Reynolds stress and thinner boundary layer, and the more significant contribution of the pressure force on the surface of rough wall to the torque., Comment: 17 pages,11 figures

The presence of a dispersed phase can significantly modulate the drag in turbulent systems. We derived a conserved quantity that characterizes the radial transport of azimuthal momentum in the fluid-fluid two-phase Taylor-Couette turbulence. This quantity consists of contributions from advection, diffusion, and two-phase interface, which are closely related to density, viscosity, and interfacial tension, respectively. We found that the presence of the two-phase interface consistently produces a positive contribution to the momentum transport and leads to drag enhancement, while decreasing the density and viscosity ratios of the dispersed phase to the continuous phase reduces the contribution of local advection and diffusion terms to the momentum transport, respectively, resulting in drag reduction. Therefore, we concluded that the decreased density ratio and the decreased viscosity ratio work together to compete with the presence of two-phase interface for achieving drag modulation in fluid-fluid two-phase turbulence.

Catastrophic phase inversion, the sudden breakdown of a dense emulsion, occurs when the dispersed majority phase irreversibly exchanges role with the continuous minority phase. This common process has been extensively studied over the past decades and yet its fundamental physical mechanism has remained largely unexplored. Here we experimentally and numerically study the dynamics of catastrophic phase inversion as it occurs when the volume fraction of the dispersed phase exceeds a critical volume fraction (typically around 92% in experiments). Our data accurately quantify the abrupt change of both the global torque and average droplet size at approaching and across the phase inversion point, exhibiting strong hysteresis. Most importantly, we reveal that the fluctuations in the global torque diverge as a power-law while approaching the critical volume-fraction and we connect their growth to the formation of highly heterogeneous spatial droplet structures. The present finding, unveiling the tight connection between fluctuations in dynamic heterogeneity and the critical divergence of torque fluctuation, paves the way to a quantitative description of catastrophic phase inversion as an out-of-equilibrium critical-like phenomena.

Wind-wave interactions impose wind forcing on wave surface and wave effects on turbulent wind structures, which essentially influences the wind-wave loading on structures. Existing research treats the wind and wave loading separately and ignores their interactions. The present study aims to characterize the turbulent airflow over wave surfaces and analyze the coupled wind-wave loading on offshore wind turbines. A high-fidelity two-phase model is developed to simulate highly turbulent wind-wave fields based on the open-source program OpenFOAM. A numerical case study is conducted to simulate extreme wind-wave conditions, where coupled wind-wave fields are applied. The intensity of wave induced turbulences and the height of wave influenced regions are affected by the wind velocity and wave heights. Higher wind velocities induce greater turbulence, which can be increased by over 100%. Then the combined wind-wave loading on offshore wind turbines is simulated under operational and extreme conditions. Under operational conditions, the wind-wave coupling effect on the combined loading is minimal. However, under extreme conditions, the coupled wind-wave fields lead to an increase in the average aerodynamic loading and a significant amplification of the fluctuation in the aerodynamic loading. Specifically, the maximum bending moment Fy at both the tower bottom and the monopile bottom experiences an increase of around 6%. The standard deviation of the shear force at the tower bottom increases by up to 45%. Also, the standard deviation of the bending moment at the tower bottom increases by approximately 27%. This study reveals the importance of considering the wind-wave coupling effect under extreme conditions, which provides valuable insights into the planning and design of offshore wind turbines., Comment: arXiv admin note: substantial text overlap with arXiv:2206.02085

Recent extreme tropical cyclones caused extensive damages to critical civil infrastructure globally. To better capture the unique hurricane wind characteristics, a large eddy simulation (LES) Hurricane Boundary Layer (HBL) model is developed by considering the variation of meso-scale kinematic and thermodynamic conditions. An asymmetric model is adopted to obtain the gradient wind velocity using the National Hurricane Center data. The meso-scale thermal conditions are obtained by extracting the hourly air temperature and relative humidity profiles from generated proxy soundings. Measurements recorded at the Aransas County airport during Hurricane Harvey and that at the City of Naples during Irma are used to validate the developed LES model. Research results show that the simulated 10-minute average wind speed and direction are consistent with the observations. The developed model can well predict the high wind turbulences, which are around 20% in Hurricane Harvey and 26% in Hurricane Irma. The 3-s gust wind speeds reach 62.4 m/s at 10-m elevation during Hurricane Harvey and 53.5 m/s at 15-m elevation during Hurricane Irma, close to the field observed data of 61.3 m/s and 54.2 m/s, respectively. The simulated 3-s gust factors are close to the observation except at some moments with significant variations because of the poorly understood physical phenomena. The simulated wind spectrum in longitudinal and lateral directions agrees well with the observed results. In summary, the developed LES-based HBL model can capture the main characteristics of hurricane structure and turbulence characteristics and is applicable for modeling civil infrastructure exposed to hurricanes.